{"id":1042,"date":"2024-10-29T12:00:00","date_gmt":"2024-10-29T12:00:00","guid":{"rendered":"https:\/\/forecastingresearch.org\/?post_type=research&#038;p=1042"},"modified":"2026-04-28T14:07:39","modified_gmt":"2026-04-28T14:07:39","slug":"nuclear-risk","status":"publish","type":"research","link":"https:\/\/forecastingresearch.org\/research\/nuclear-risk","title":{"rendered":"Can Humanity Achieve a Century of Nuclear Peace?"},"content":{"rendered":"\n<h2 class=\"wp-block-heading\" id=\"abstract\">Abstract<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">While the world has avoided large-scale nuclear war, questions remain\nabout the role of chance versus policy choices in preventing such\nevents. This study systematically assesses expert beliefs about the\nprobability of a nuclear catastrophe by 2045, the centenary of the\nbombings of Hiroshima and Nagasaki. We define a nuclear catastrophe as\nan event where nuclear weapons cause the death of at least 10 million\npeople. Through a combination of expert interviews and surveys, 110\ndomain experts and 41 expert forecasters (\u201csuperforecasters\u201d) predicted\nthe likelihood of nuclear conflict, explained the mechanisms underlying\ntheir predictions, and forecasted the impact of specific tractable\npolicies on the likelihood of nuclear catastrophe. Experts assigned a\nmedian 5% probability of a nuclear catastrophe by 2045, while\nsuperforecasters put the probability at 1%. Factors contributing to\nhigher risk estimates included ongoing geopolitical tensions, the\nproliferation of nuclear weapons, and technological vulnerabilities.\nLower risk estimates highlighted the continued effectiveness of nuclear\ndeterrence. Although Russia and NATO was the adversarial domain thought\nmost likely to cause a nuclear catastrophe, experts believe that risks\nare dispersed roughly uniformly across regional conflict theaters\n(Russia and NATO, China and the USA, the Korean Peninsula, India and\nPakistan, and Israel and Iran). Participants believe that the\nimplementation of a bundle of six tractable policies, including the\nestablishment of a crisis communications network and the implementation\nof failsafe reviews, would together halve the risk of a nuclear\ncatastrophe.<\/p>\n\n\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a class=\"btn orange\" href=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">View the full PDF report <svg width=\"7\" height=\"9\" viewBox=\"0 0 7 9\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n  <path d=\"M0.000156283 8.60806L4.22416 4.33606V4.24006L0.000156283 6.10352e-05H1.80816L6.06416 4.28806L1.80816 8.60806H0.000156283Z\" fill=\"#102B23\"\/>\n<\/svg>\n<svg width=\"8\" height=\"10\" viewBox=\"0 0 8 10\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n  <path d=\"M0.601719 8.85794L4.82572 4.58594V4.48994L0.601719 0.249939H2.40972L6.66572 4.53794L2.40972 8.85794H0.601719Z\" fill=\"#102B23\"\/>\n<\/svg><\/a><\/div>\n<\/div>\n\n\n\n<div class=\"wp-block-group is-vertical is-layout-flex wp-container-core-group-is-layout-4fc3f8e1 wp-block-group-is-layout-flex\">\n<details class=\"wp-block-details is-layout-flow wp-block-details-is-layout-flow\"><summary>Acknowledgments<\/summary>\n<p class=\"wp-block-paragraph\">This research would not have been possible without the generous support of Open Philanthropy.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">We thank the following people, who kindly served as expert interviewees: James Acton, Catherine Dill, Robert Einhorn, Peter Hayes, Feroz Khan, Frank O\u2019Donnell, David Santoro, Manpreet Sethi, Sir Graham Stacey, Dmitry Stefanovich, Tatsujiro Suzuki, Jon Wolfsthal, and Tong Zhao. Their expertise helped to inform the direction of the project, but we note that the content of the project and this report don\u2019t necessarily reflect their views. <\/p>\n\n\n\n<p class=\"wp-block-paragraph\">We also thank Peter Scoblic for helpful feedback on survey questions. We greatly appreciate the assistance of Josh Rosenberg, Kayla Gamin, Sam Glover, Harrison Durland, Nikitas Angeletos Chrysaitis, Catherine Wu, Amory Bennett, Kaitlyn Coffee, Coralie Consigny, Rhiannon Britt, and Rebecca Ceppas de Castro throughout the project. <\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Lastly, we extend our gratitude to our research participants for their invaluable contributions.<\/p>\n<\/details>\n<\/div>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"executive-summary\">Executive Summary<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">This study is the largest systematic survey of subject matter experts\non the risk posed by nuclear weapons. In addition to experts, we\nsurveyed forecasters with a strong track record of accuracy\n(\u201csuperforecasters\u201d). The study summarizes responses on two\ncomplementary surveys taken a month apart: the first survey focused on\nrisk pathways and the second on policy responses.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"participants\">Participants<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">A total of 151 participants (110 experts and 41 superforecasters)\ncompleted the full first survey, and 148 participants completed both\nsurveys (109 experts and 39 superforecasters). Most respondents engaged\ndeeply with the questions. The median expert or superforecaster\nparticipant reported spending nine hours completing the two surveys and\nwrote around 4,200 words to explain their forecasts.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"key-results\">Key results<\/h3>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"forecasts-of-nuclear-catastrophe-by-2045\">Forecasts of nuclear\ncatastrophe by 2045<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">We asked participants to estimate the probability that, before 2045,\none or more incidents involving nuclear weapons will cause the death of\nat least 10 million people. The median expert forecast was 5% and the\nmedian superforecaster response was 1%. The median forecast from a\nsample of the US public was 10% (see Figure 1). Respondents thought that\na nuclear conflict between Russia and NATO was the adversarial domain\nmost likely to be the cause of a nuclear catastrophe of this scale;\nhowever risk was dispersed relatively evenly among all adversarial\ndomains we asked about, which also included China and the USA, the\nKorean Peninsula, India and Pakistan, and Israel and Iran.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Participants who were more concerned about nuclear risk often\nmentioned ongoing military conflicts between nuclear powers, the\nproliferation of nuclear weapons, the development of new military\ntechnologies, and the weakening of international arms control\nagreements. Participants less concerned about nuclear risk emphasized\nthe long-standing effectiveness of deterrence, improvements in safety\nmechanisms, and the assumption that most nuclear states will act\nrationally.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/forecastingresearch.org\/wp-content\/uploads\/2026\/03\/working-paper_2024-10-29_nuclear-risk_fig-01.png\" alt=\"Figure 1: Distribution of forecasts of the probability of nuclear catastrophe by 2045. We jitter the data points horizontally to allow for better visualization of the distribution of forecasts. Horizontal variation within each group serves no other empirical purpose. The y-axis uses a logarithmic scale to informatively show variation in forecasts in the 0\u201310% range.\"\/><figcaption class=\"wp-element-caption\"><strong>Figure 1:<\/strong> Distribution of forecasts of the probability of nuclear catastrophe by 2045. We jitter the data points horizontally to allow for better visualization of the distribution of forecasts. Horizontal variation within each group serves no other empirical purpose. The y-axis uses a logarithmic scale to informatively show variation in forecasts in the 0\u201310% range.<\/figcaption><\/figure>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"factors-influencing-risk\">Factors influencing risk<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">Participants saw conflict between adversarial countries and new\nactors acquiring nuclear weapons as the major drivers of nuclear risk.\nWe discuss a large set of events in this study, but to summarize three\nkey events:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li>The median participant in this study reported that violent\nconflict between Russia and NATO would triple the risk of a nuclear\ncatastrophe. Both experts and superforecasters reported a low\nprobability of this event occurring: median forecasts of 5% and 1.8%,\nrespectively;<\/li>\n\n\n\n<li>Participants were more concerned about the likelihood of a\nChinese invasion of Taiwan, with the median expert estimating a 25%\nchance of this occurring by 2030. The median expert reported that this\nevent would roughly double their forecast of the risk of nuclear\ncatastrophe;<\/li>\n\n\n\n<li>The median expert also put a 25% chance of Iran acquiring nuclear\nweapons by 2030. Should this event occur, their forecast of the risk of\ncatastrophe would increase by 50%.<\/li>\n<\/ol>\n\n\n\n<p class=\"wp-block-paragraph\">Many of the events we asked about did not influence the median\nrespondent\u2019s forecast of the risk of nuclear catastrophe. These events\ninclude: summits between adversarial countries, a nuclear weapons test\nby North Korea, ballistic missile submarines becoming more detectable,\nand an accidental non-test detonation of a nuclear weapon.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"effects-of-policies\">Effects of policies<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">We asked participants about their beliefs on the effectiveness of\nseveral policy options aimed at reducing the risk of a nuclear\ncatastrophe. The distribution of relative risk scores for the six\npolicies we included are shown in Figure 2. We also asked participants\nto rank the policies by how much they would like to see each policy\nimplemented and how much they would support funding aimed at\nimplementing the policies.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/forecastingresearch.org\/wp-content\/uploads\/2026\/03\/working-paper_2024-10-29_nuclear-risk_fig-02.png\" alt=\"Figure 2: Violin plots showing distribution of relative risk associated with each policy, and all six of these policies implemented together. The relative risk is the relative change in probability of nuclear catastrophe conditional on policy implementation. The group median is shown in text. The thicker bar within each violin shows the interquartile range (25th to 75th percentile forecasts), and the thin line shows the range of forecasts minus outliers.\"\/><figcaption class=\"wp-element-caption\"><strong>Figure 2:<\/strong> Violin plots showing distribution of relative risk associated with each policy, and all six of these policies implemented together. The relative risk is the relative change in probability of nuclear catastrophe conditional on policy implementation. The group median is shown in text. The thicker bar within each violin shows the interquartile range (25<sup>th<\/sup> to 75<sup>th<\/sup> percentile forecasts), and the thin line shows the range of forecasts minus outliers.<sup data-fn=\"0e558f0c-0266-4b03-8306-a59806414564\" class=\"fn\"><a href=\"#0e558f0c-0266-4b03-8306-a59806414564\" id=\"0e558f0c-0266-4b03-8306-a59806414564-link\">1<\/a><\/sup><\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Two policies emerged as clear favorites: a crisis communications network<sup data-fn=\"b5cb55a3-a6f5-4e1b-b9fd-41ae9c0feaaa\" class=\"fn\"><a href=\"#b5cb55a3-a6f5-4e1b-b9fd-41ae9c0feaaa\" id=\"b5cb55a3-a6f5-4e1b-b9fd-41ae9c0feaaa-link\">2<\/a><\/sup> and nuclear-armed states implementing failsafe reviews.<sup data-fn=\"c6f13b70-38ab-4909-b351-5ff855b35099\" class=\"fn\"><a href=\"#c6f13b70-38ab-4909-b351-5ff855b35099\" id=\"c6f13b70-38ab-4909-b351-5ff855b35099-link\">3<\/a><\/sup> The median expert thought that a crisis communications network would reduce the risk of a nuclear catastrophe by 25%, and failsafe reviews would reduce it by 20%. The superforecasters were less optimistic about the effects of the policies, with median relative risk of 15% and 10%, respectively. We also asked experts to say how their forecast of nuclear catastrophe by 2045 would change if all six policies we described were implemented. The median expert thought that the combined bundle of policies would halve the risk of a nuclear catastrophe and the median superforecaster thought it would reduce risk by 42%.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"probability-of-policy-implementation\">Probability of policy\nimplementation<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">We asked participants to forecast the probability that each policy\nwould be implemented (on a time frame consistent with a decision to\nimplement the policy within the next three years). The median expert\nforecasted a 15% probability of implementation for the crisis\ncommunications network and a 15% probability of implementation for the\nfailsafe reviews policy. Superforecasters estimated probabilities of 10%\nand 7%, respectively. Both groups thought that funding could improve the\nprobability of policy implementation. Conditional on $500 million of\nfunding being put towards the goal of having the policy implemented, the\nmedian expert forecasted a 25% chance that a crisis communications\nnetwork would be established and a 30% chance of the failsafe reviews\npolicy being implemented. The median superforecaster\u2019s forecast rose to\n18% and 10%, respectively.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"limitations-and-next-steps\"><strong>Limitations and next\nsteps<\/strong><\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">While this study is the largest and most comprehensive survey of\nnuclear experts\u2019 beliefs about nuclear risk, there are some important\nlimitations that we hope to address in follow-up work. First, most\nparticipants were based either in the USA or Western Europe. The number\nof expert participants from South Asia was similar to the number from\nthe USA, but there were very few participants from East Asia, Eastern\nEurope, or the Middle East. Although this was the largest survey of its\nkind, the sample size was still relatively small, limiting the\nstatistical inferences we can make from the results. The lists of\npolicies we included may not have represented the full range of\nviewpoints on reducing nuclear weapons risk.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Despite these limitations, this study provides important insight into\nviews on the risk of nuclear war. It clarifies experts\u2019 views on how the\nworld could best mitigate those risks and maintain nuclear peace until\nand beyond the centennial anniversary of the first and so far only use\nof nuclear weapons in warfare. We believe there is a role for\nquantitative forecasts to build upon this work, improving our\nunderstanding of beliefs about nuclear weapons use and how the\nassociated risks change over time.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"background\">1. Background<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Since 1945, the world has lived under the threat of nuclear weapons.\nSo far, we have managed to avoid the disaster that would be nuclear war.\nHave we been lucky? Or was the probability of nuclear war always low?\nHow confident should we be that humanity will make it to the\nhundred-year anniversary of the bombings of Hiroshima and Nagasaki\nwithout any further nuclear catastrophes?<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">These questions are difficult to answer. However, understanding views\non the magnitude and nature of this risk can inform decisions about how\nto best prioritize resources to improve humanity\u2019s prospects.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The goal of this study was to understand current views about the potential causes of a nuclear catastrophe. We systematically collected the views of subject matter experts, highly accurate forecasters (\u201csuperforecasters\u201d),<sup data-fn=\"758e9bb7-1fcd-4147-97ed-463e43451c07\" class=\"fn\"><a href=\"#758e9bb7-1fcd-4147-97ed-463e43451c07\" id=\"758e9bb7-1fcd-4147-97ed-463e43451c07-link\">4<\/a><\/sup> and members of the public on the threat of nuclear weapons. Specifically, we asked participants to forecast the probability that we will survive a century without another nuclear catastrophe, and the effects of events and policies that may alter this outlook. The result is the largest survey ever conducted of policy experts\u2019 views on the magnitude of risks posed by nuclear weapons. A total of 110 experts collectively spent over 1,300 hours and wrote 520,000 words developing forecasts and explaining their reasoning.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"previous-forecasts-of-nuclear-weapons-risks\">1.1 Previous\nforecasts of nuclear weapons risks<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">While our study is the largest survey collecting forecasts from\nnuclear weapons policy experts, it is not the first. Over the past three\ndecades, there have been at least four other surveys asking subject\nmatter experts to make predictions about nuclear weapons risk:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>As described in <em>Expert Political Judgment<\/em> (Tetlock 2005), 1988 and 1997 surveys asked non-proliferation experts and non-experts<sup data-fn=\"45752514-46e5-48b1-9fd3-6f7088fedcf4\" class=\"fn\"><a href=\"#45752514-46e5-48b1-9fd3-6f7088fedcf4\" id=\"45752514-46e5-48b1-9fd3-6f7088fedcf4-link\">5<\/a><\/sup> about the probability of nuclear war in 10, 25, 50, and 100 years.<sup data-fn=\"c8e4b196-3698-4204-911b-001d3bf6c52f\" class=\"fn\"><a href=\"#c8e4b196-3698-4204-911b-001d3bf6c52f\" id=\"c8e4b196-3698-4204-911b-001d3bf6c52f-link\">6<\/a><\/sup> Among experts, the median participant forecasted a 29% chance of nuclear war occurring in the next 50 years, while the median non-expert forecasted a 30% probability.<\/li>\n\n\n\n<li>In 2005, the Lugar Survey asked 79 non-proliferation and national security experts to predict the probability of a nuclear attack in the next five or 10 years.<sup data-fn=\"a7e8e02f-375c-42e9-a2aa-f4ab2d49adb9\" class=\"fn\"><a href=\"#a7e8e02f-375c-42e9-a2aa-f4ab2d49adb9\" id=\"a7e8e02f-375c-42e9-a2aa-f4ab2d49adb9-link\">7<\/a><\/sup> The median forecast was 10% for the next five years and 20% for the next 10 years.<\/li>\n\n\n\n<li>A decade later, in 2015, a study by the Project for Study of the 21st Century (PS21) polled 50 national security experts on the probability of a major nuclear conflict in the next 25 years that causes more fatalities than World War II.<sup data-fn=\"60dd9d0a-e48a-44c5-8657-d5fd82954159\" class=\"fn\"><a href=\"#60dd9d0a-e48a-44c5-8657-d5fd82954159\" id=\"60dd9d0a-e48a-44c5-8657-d5fd82954159-link\">8<\/a><\/sup> The median forecast was 5%.<\/li>\n\n\n\n<li>More recently, in 2022, Karger et al. ran a project called the Existential Risk Persuasion Tournament (<a href=\"https:\/\/forecastingresearch.org\/research\/existential-risk-persuasion-tournament\" type=\"research\" id=\"876\" target=\"_blank\" rel=\"noreferrer noopener\">XPT<\/a>), where people with expertise in catastrophic risks (including nuclear weapons risk) and superforecasters forecasted the risk of various catastrophes and related events.<sup data-fn=\"aab97946-4875-4de3-aa06-7b31c520e260\" class=\"fn\"><a href=\"#aab97946-4875-4de3-aa06-7b31c520e260\" id=\"aab97946-4875-4de3-aa06-7b31c520e260-link\">9<\/a><\/sup> This included a question on the probability that nuclear weapons reduce the human population by at least 10% by 2030, 2050, and 2100. The median forecast for this outcome by 2030 was 1% for experts and 0.5% for superforecasters.<sup data-fn=\"604b594e-b78f-4a24-bc60-74eee3ab6d6c\" class=\"fn\"><a href=\"#604b594e-b78f-4a24-bc60-74eee3ab6d6c\" id=\"604b594e-b78f-4a24-bc60-74eee3ab6d6c-link\">10<\/a><\/sup><\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Public surveys conducted in the USA, Russia, and UK show how concerned citizens are about nuclear risk. According to online surveys by Statista and YouGov, the proportion of US adults who saw a nuclear war as \u201cvery likely\u201d or \u201cfairly likely\u201d within the next 10 years nearly doubled from February 28, 2022,<sup data-fn=\"8c4963a1-4c22-4c9b-8b77-9b10a3ee2a39\" class=\"fn\"><a href=\"#8c4963a1-4c22-4c9b-8b77-9b10a3ee2a39\" id=\"8c4963a1-4c22-4c9b-8b77-9b10a3ee2a39-link\">11<\/a><\/sup> to February 2024, rising from 34% to 67%.<sup data-fn=\"dcc54637-bd3f-4634-b836-75867518b3eb\" class=\"fn\"><a href=\"#dcc54637-bd3f-4634-b836-75867518b3eb\" id=\"dcc54637-bd3f-4634-b836-75867518b3eb-link\">12<\/a><\/sup> A survey conducted in 2023 asked Russian citizens whether they thought there is a threat of military conflict involving nuclear weapons in the world today, to which 71% answered \u201cthere is\u201d and 20% answered \u201cthere is not.\u201d<sup data-fn=\"e20baa58-8279-4da2-8319-351c40d6b416\" class=\"fn\"><a href=\"#e20baa58-8279-4da2-8319-351c40d6b416\" id=\"e20baa58-8279-4da2-8319-351c40d6b416-link\">13<\/a><\/sup> Results from these and other relevant surveys are provided in <a href=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=81\" id=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=81\" target=\"_blank\" rel=\"noreferrer noopener\">Appendix 1<\/a>.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><div class=\"table-wrapper\"><table class=\"has-fixed-layout\"><tbody><tr><td><strong>Event<\/strong><\/td><td><strong>Probability (median)<\/strong><\/td><td><strong>Annualized<\/strong><\/td><td><strong>Estimator info\/Estimator number<\/strong><\/td><td><strong>Date\/Retrieval date<\/strong><sup data-fn=\"e02fc319-5841-41cf-8d24-40b326b3945e\" class=\"fn\"><a href=\"#e02fc319-5841-41cf-8d24-40b326b3945e\" id=\"e02fc319-5841-41cf-8d24-40b326b3945e-link\">14<\/a><\/sup><\/td><td><strong>Source<\/strong><\/td><\/tr><tr><td rowspan=\"2\">Nuclear weapons will be used in combat by 2047<sup data-fn=\"d5bd4ed7-715e-48cb-8400-985fd7281cd8\" class=\"fn\"><a href=\"#d5bd4ed7-715e-48cb-8400-985fd7281cd8\" id=\"d5bd4ed7-715e-48cb-8400-985fd7281cd8-link\">15<\/a><\/sup><\/td><td>29%<\/td><td>0.68%<\/td><td>11 nuclear experts<\/td><td rowspan=\"2\">1997<\/td><td rowspan=\"2\"><a href=\"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/ffo2.157\"><em><u>Expert\nPolitical Judgment studies<\/u><\/em><\/a><\/td><\/tr><tr><td>30%<\/td><td>0.71%<\/td><td>23 non-experts<\/td><\/tr><tr><td>Nuclear attack occurs in the next 10 years<\/td><td>20%<\/td><td>2.2%<\/td><td>79 non-proliferation and national\nsecurity experts<\/td><td>2005<\/td><td><a href=\"https:\/\/irp.fas.org\/threat\/lugar_survey.pdf\"><u>The Lugar\nSurvey<\/u><\/a><\/td><\/tr><tr><td>Major nuclear conflict causes more fatalities than WWII\n(~80,000,000) by 2030<\/td><td>6.8%<\/td><td>0.45%<\/td><td>50 national security experts<\/td><td>2015<\/td><td><a href=\"https:\/\/projects21.org\/2015\/11\/12\/ps21-survey-experts-see-increased-risk-of-nuclear-war\/\"><u>PS21\nGreat Power Conflict Report<\/u><\/a><\/td><\/tr><tr><td rowspan=\"2\">Nuclear weapons reduce the human population by at least\n10% by the end of (2030, 2050, 2100)<\/td><td><p>1%<\/p>\n<p>(2030), 3.4% (2050),<\/p>\n<p>8%<\/p>\n<p>(2100)<\/p><\/td><td><p>0.11% (2030) 0.12% (2050)<\/p>\n<p>0.11% (2100)<\/p><\/td><td>12 domain experts<\/td><td rowspan=\"2\">Jun &#8211; Oct 2022<\/td><td rowspan=\"2\">Existential Risk Persuasion Tournament (<a href=\"https:\/\/forecastingresearch.org\/research\/xpt\" id=\"876\"><a href=\"https:\/\/forecastingresearch.org\/research\/existential-risk-persuasion-tournament\" type=\"research\" id=\"876\" target=\"_blank\" rel=\"noreferrer noopener\">XPT<\/a><\/a>)<\/td><\/tr><tr><td><p>0.5% (2030), 1.825% (2050),<\/p>\n<p>4%<\/p>\n<p>(2100)<\/p><\/td><td><p>0.056% (2030)<\/p>\n<p>0.063% (2050)<\/p>\n<p>0.051% (2100)<\/p><\/td><td>88 superforecasters<\/td><\/tr><\/tbody><\/table><\/div><figcaption class=\"wp-element-caption\"><strong>Table 1:<\/strong> Existing surveys of experts eliciting forecasts of nuclear risk.<\/figcaption><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"why-develop-quantitative-forecasts\">1.2 Why develop quantitative\nforecasts?<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Although these studies provide some evidence that nuclear experts\nhave engaged with quantitative predictions, the community has been\ncautious in assigning probabilities to unlikely but catastrophic events.\nThis is understandable given the high levels of uncertainty and the lack\nof historical precedents for using nuclear weapons since 1945. However,\nwe believe that efforts to quantify these uncertain risks can serve\nimportant functions.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">First, quantification can enable better understanding of viewpoints on a topic. This partly comes from providing clarity in expression. Famously, the US Joint Chiefs of Staff assessed the probability of success in the 1961 Bay of Pigs invasion at 30%. When advising President Kennedy, they communicated this as \u201ca fair chance\u201d of success. It was later reported that the president assumed this indicated favorable odds of success.<sup data-fn=\"3db2aa6e-57c4-44e0-b1c4-ad3c922a12a9\" class=\"fn\"><a href=\"#3db2aa6e-57c4-44e0-b1c4-ad3c922a12a9\" id=\"3db2aa6e-57c4-44e0-b1c4-ad3c922a12a9-link\">16<\/a><\/sup> More recently, a 2018 survey found wide variation in how people interpret qualitative probability terminology. For example, the quantitative probability readers assigned to the term \u201ca real possibility\u201d ranged from 20% to 80%.<sup data-fn=\"7b32080a-5826-40e8-93e8-2d985b5c1d02\" class=\"fn\"><a href=\"#7b32080a-5826-40e8-93e8-2d985b5c1d02\" id=\"7b32080a-5826-40e8-93e8-2d985b5c1d02-link\">17<\/a><\/sup> If nothing else, quantification ensures that people are speaking the same language when they share their views.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Developing more precise forecasts helps clarify areas of agreement, disagreement, and uncertainty, and assists in the comparison of potential threats and potential courses of action. As Bertrand Russell put it, being precise helps us realize and identify what is vague.<sup data-fn=\"6b75f296-e069-42f0-8c2d-d2ac026eb77c\" class=\"fn\"><a href=\"#6b75f296-e069-42f0-8c2d-d2ac026eb77c\" id=\"6b75f296-e069-42f0-8c2d-d2ac026eb77c-link\">18<\/a><\/sup> The process of developing quantitative forecasts can prompt a person to think more carefully through their mental models of the world and critically analyze their assumptions. Some empirical research also suggests that greater precision can result in more accurate forecasts. A 2018 study found that precise numeric forecasts became less accurate when they were coarsened into forecast ranges that were more akin to qualitative statements of probability.<sup data-fn=\"ad362f26-fa6a-44b4-bffc-6adbc13a12dd\" class=\"fn\"><a href=\"#ad362f26-fa6a-44b4-bffc-6adbc13a12dd\" id=\"ad362f26-fa6a-44b4-bffc-6adbc13a12dd-link\">19<\/a><\/sup><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A potential drawback of quantification is that it can imply greater\ncertainty than is warranted, as most people associate numbers with more\nconcrete predictions. Therefore, when communicating numeric forecasts,\nexperts should clarify their degree of confidence in the results and how\nthey developed the estimates. However, without clear metrics,\ndiscussions around nuclear risks can become clouded by ambiguity or\nshaped by dominant perspectives. Assigning probabilities not only\nenhances clarity but also enables policymakers to prioritize\neffectively, allowing them to focus on the most urgent threats and align\nactions with evidence-based insights. By translating abstract concerns\ninto measurable probabilities, engaging in informed, rational\nprioritization amid a chaotic and noisy political environment becomes\neasier.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"judgmental-forecasting\">1.3 Judgmental forecasting<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Assessing the probability of highly uncertain events, like the use of\nnuclear weapons, is challenging. Computational models that are used for\nprediction in other domains (such as climate change and epidemiology)\ncannot as readily capture the geopolitical and human factors that impact\nnuclear risk. Given these limitations in traditional forecasting\ntechniques, judgmental forecasting emerges as a possible\nalternative.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Judgmental forecasting, relying on individuals making considered predictions, has shown promise at producing more accurate forecasts in domains where other methods have failed. Aggregating predictions from the forecasters with the best track-records has been effective in accurately predicting complex geopolitical events, economic trends, and technological developments that have eluded traditional forecasting models.<sup data-fn=\"ca439d97-db4b-4c13-b741-4c7f4f02de74\" class=\"fn\"><a href=\"#ca439d97-db4b-4c13-b741-4c7f4f02de74\" id=\"ca439d97-db4b-4c13-b741-4c7f4f02de74-link\">20<\/a><\/sup> This approach&#8217;s effectiveness was demonstrated in the Intelligence Advanced Research Projects Activity&#8217;s (IARPA) Aggregative Contingent Estimation (ACE) program from 2011 to 2015. A series of geopolitical forecasting tournaments, the Good Judgment Project, used judgmental forecasting techniques to consistently outperform competitors in predicting complex events ranging from pandemics to political leadership changes.<sup data-fn=\"c3431a5f-d200-4c78-901c-300e6dbad1e7\" class=\"fn\"><a href=\"#c3431a5f-d200-4c78-901c-300e6dbad1e7\" id=\"c3431a5f-d200-4c78-901c-300e6dbad1e7-link\">21<\/a><\/sup><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">That said, the application of judgmental forecasting to catastrophic risks suffers from two salient limitations. First, empirical evidence for sustained accuracy over extended time horizons is limited. Much of the evidence for the ability of select groups of forecasters to consistently outperform chance has primarily focused on forecasts with time horizons of one to six months.<sup data-fn=\"ade75780-9b56-4138-8584-78ad76b4a001\" class=\"fn\"><a href=\"#ade75780-9b56-4138-8584-78ad76b4a001\" id=\"ade75780-9b56-4138-8584-78ad76b4a001-link\">22<\/a><\/sup> The longer-term forecasting that <em>has<\/em> been empirically studied has generally involved questions that are easier to predict due to large amounts of relevant data, such as forecasting medium-term GDP or defense spending. Second, previous efforts to forecast catastrophic events have resulted in a wide range of predictions\u2014including some that differ by several orders of magnitude\u2014underscoring the substantial uncertainty inherent in making these sorts of predictions.<sup data-fn=\"e8ec8ee0-8a6a-4db8-89f6-c57bdd9dd861\" class=\"fn\"><a href=\"#e8ec8ee0-8a6a-4db8-89f6-c57bdd9dd861\" id=\"e8ec8ee0-8a6a-4db8-89f6-c57bdd9dd861-link\">23<\/a><\/sup><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Despite these limitations, we believe that judgmental forecasting may\nbe a useful tool in assessing the probability of highly important, but\nhighly uncertain events.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"methods\">2. Methods<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The overarching aim of this project was to characterize views on the\nprobability of a large-scale nuclear weapons disaster in the coming\ndecades. We focused on understanding views about the likelihood that\nhumanity makes it through a full century of nuclear weapons without\nrepeat use of such weaponry. Our primary question was:<\/p>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p class=\"wp-block-paragraph\"><em>What is the probability that by 2045, one or more incidents\ninvolving nuclear weapons will cause the death of more than 10 million\nhumans, within a 5-year time period?<\/em><\/p>\n<\/blockquote>\n\n\n\n<p class=\"wp-block-paragraph\">Throughout this report, we use the term <em>nuclear catastrophe<\/em>\nto refer to the outcome specified in this question: one or more\nincidents involving nuclear weapons causing the death of more than 10\nmillion humans within a five-year period.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">We chose to focus this study on a very large-scale nuclear event for two main reasons. First, what makes nuclear weapons, of all deadly weapons, particularly horrific is their potential to cause death and destruction on a massive scale within minutes. All weapons can cause harm, but nuclear weapons are perhaps unique in their ability to cause such a catastrophic event so quickly. We wanted to focus analysis on this feature of nuclear weapons, which is a key reason why so much attention is given to nuclear weapons, relative to other weapons. Second, it seems there is a relative lack of discussion of how to prevent worst-case scenarios for nuclear weapons, compared to how to prevent any use of nuclear weapons.<sup data-fn=\"20c227aa-b640-45e0-9e72-dc9f5bf15327\" class=\"fn\"><a href=\"#20c227aa-b640-45e0-9e72-dc9f5bf15327\" id=\"20c227aa-b640-45e0-9e72-dc9f5bf15327-link\">24<\/a><\/sup> It seems possible that the strategies and interventions that are most effective at reducing the risk of any nuclear weapon use may be different from those aimed at reducing the risk of large-scale harms from nuclear weapons (although there is likely overlap). We therefore sought to address this relatively neglected aspect of the risk of nuclear weapons.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This project had three main components:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Interviews with a small number of highly experienced nuclear\nweapons policy experts<\/li>\n\n\n\n<li>A survey asking about risk pathways for a nuclear\ncatastrophe<\/li>\n\n\n\n<li>A survey asking about policies that aim to mitigate the risk of a\nnuclear catastrophe<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">The interviews focused on identifying potential ideologically charged cruxes\u2014that is, questions whose answers would influence views on the risk of nuclear catastrophe and where there is likely to be disagreement among experts. We used the results to develop the surveys. For a detailed discussion of the interviews and the process of developing the surveys, please see <a href=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=82\" id=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=82\" target=\"_blank\" rel=\"noreferrer noopener\">Appendix 2<\/a>.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"survey-content\">2.1 Survey content<\/h3>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"survey-1\">2.1.1 Survey 1<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">The first survey focused on risk pathways to a nuclear catastrophe.\nIt also asked for forecasts of the probability of nuclear catastrophe by\n2045, participants\u2019 beliefs about the probability of five adversarial\ndomains causing a nuclear catastrophe, if one were to occur, and\nparticipants\u2019 beliefs about four aspects of nuclear weapons policy: the\nstrength of nuclear deterrence, the likelihood of nuclear escalation\nfollowing a first strike, the merits of aiming for total disarmament,\nand the proliferation risk of nuclear energy.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The main body of the survey consisted of forecasting questions that resolve in 2030. These questions asked about the probability of certain events happening by 2030. These events were intended to capture ideologically charged cruxes: events with the potential to sway clashing camps\u2019 forecasts of nuclear war. Examples of questions are shown in Box 1, and the full list of questions is provided in <a href=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=85\" id=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=85\" target=\"_blank\" rel=\"noreferrer noopener\">Appendix 3<\/a>. Descriptions of questions linked to information sheets, which linked to information that we thought forecasters would likely seek out to inform their forecasts.<\/p>\n\n\n\n<div class=\"wp-block-group\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h4 class=\"wp-block-heading\">Box 1: Examples of 2030 Crux Questions<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>What is the probability of a [x] non-test detonation of a nuclear weapon occurring before the 1st of January, 2030?\n<ul class=\"wp-block-list\">\n<li>a) Accidental<\/li>\n\n\n\n<li>b) Inadvertent<\/li>\n\n\n\n<li>c) Deliberate<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li>What is the probability that [x] conducts a nuclear weapons test or comes into possession of nuclear weapons before the 1st of January 2030?\n<ul class=\"wp-block-list\">\n<li>a) Iran<\/li>\n\n\n\n<li>b) Any state other than Iran, that is not currently believed to have nuclear weapons<\/li>\n\n\n\n<li>c) A non-state actor<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li>What is the probability that, by the 1st of January 2030, the US will have formally announced its intention to withdraw from NATO?<\/li>\n\n\n\n<li>What is the probability that, by January 1st 2030, there will have been more than 500 deaths in militarized conflict between [adversarial domain] in one calendar year?<\/li>\n<\/ul>\n<\/div><\/div>\n\n\n\n<p class=\"wp-block-paragraph\">Some of the questions were general, but some related to specific\ncountries or adversarial domains. Participants were asked to choose one\nof four adversarial domains according to their expertise.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The four domains were:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>China and the USA<\/li>\n\n\n\n<li>India and Pakistan<\/li>\n\n\n\n<li>Korean Peninsula<\/li>\n\n\n\n<li>Russia and NATO<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Participants were then randomly assigned a second domain. Due to the\nhigh number of participants choosing the Russia and NATO domain, we\naltered the survey settings soon after the survey began so that this\ndomain would not be randomly assigned. Every participant answered\nquestions on 14 general topics, plus questions on one additional topic\nper domain. The exact number of questions answered depended on chosen\nand allocated domains.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">For each question, participants gave a forecast of the probability\nthat the question would resolve positively (i.e. that the event would\noccur), and then provided a forecast of the probability of a nuclear\nweapons catastrophe conditional on the question resolving positively,\nand the probability conditional on the question resolving\nnegatively.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"survey-2\">2.1.2 Survey 2<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">The second survey focused on policy responses to nuclear weapons risk. Using the results from interviews, the first survey, and policy suggestions published by organizations working on nuclear issues,<sup data-fn=\"ffeb8537-c72d-4014-95d8-be11f18d0e7c\" class=\"fn\"><a href=\"#ffeb8537-c72d-4014-95d8-be11f18d0e7c\" id=\"ffeb8537-c72d-4014-95d8-be11f18d0e7c-link\">25<\/a><\/sup> we developed a list of potential policies to ask about. Out of this list, policies for the second survey were selected with input from analysts from the Open Nuclear Network and other external advisors. Policy selection was based on the policies\u2019 potential to influence nuclear catastrophic risk, their interest to the nuclear weapons policy community, their practicability, and likelihood of implementation.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">As with the first survey, participants were asked to choose an adversarial domain according to their expertise. Participants were not allocated an additional domain for the second survey. We investigated views on 23 different policies, including six general policies (i.e., not specific to any adversarial domain) and some domain-specific policies. For most domains, we included three domain-specific policies. As we anticipated a greater number of respondents electing to answer questions on the Russia and NATO domain, we included eight policies specific to this domain, although each participant only provided answers for three of these eight policies. These policies are listed briefly in Box 2 and described fully in <a href=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=142\" id=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=85\" target=\"_blank\" rel=\"noreferrer noopener\">Appendix 4<\/a>.<\/p>\n\n\n\n<div class=\"wp-block-group\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h4 class=\"wp-block-heading\"><strong>Box 2: Policies included in Survey 2<\/strong><\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li><span style=\"font-size: revert;color: initial\">General (answered by all participants)<\/span>\n<ul class=\"wp-block-list\">\n<li><span style=\"color: initial\">All nuclear-armed states sign and ratify the Comprehensive Test Ban Treaty<\/span><\/li>\n\n\n\n<li><span style=\"color: initial\">All nuclear-armed states conduct a failsafe review<\/span><\/li>\n\n\n\n<li><span style=\"color: initial\">A secure multilateral crisis communications network is established with all nuclear-armed states participating<\/span><\/li>\n\n\n\n<li><span style=\"color: initial\">A Fissile Material Cut-off Treaty is signed by all of the P5 countries and India and Pakistan<\/span><\/li>\n\n\n\n<li><span style=\"color: initial\">The USA removes the President of the United States&#8217; sole authority to authorize the use of nuclear weapons<\/span><\/li>\n\n\n\n<li><span style=\"color: initial\">P5 states 1) jointly develop a risk assessment framework for the use of AI models in nuclear command, control and communication systems, and 2) agree to a moratorium on the use of high-risk AI models in NC3 systems<\/span><\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><span style=\"color: initial\">China and the USA domain <\/span>\n<ul class=\"wp-block-list\">\n<li><span style=\"color: initial\">The USA implements a no-first-use policy<\/span><\/li>\n\n\n\n<li><span style=\"color: initial\">The USA and China sign a missile launch notification agreement<\/span><\/li>\n\n\n\n<li><span style=\"color: initial\">China and the USA establish regular, high-level nuclear dialogue<\/span><\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><span style=\"color: initial\">India and Pakistan domain <\/span>\n<ul class=\"wp-block-list\">\n<li><span style=\"color: initial\">India and Pakistan formalize their low-alert status and agree to maintain their ground-based nuclear weapons in a de-mated state<\/span><\/li>\n\n\n\n<li><span style=\"color: initial\">India and Pakistan establish a mechanism to conduct regular exchanges of information on nuclear and military matters<\/span><\/li>\n\n\n\n<li><span style=\"color: initial\">An India-Pakistan Nuclear Risk Reduction Center has been established<\/span><\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><span style=\"color: initial\">Korean Peninsula domain <\/span>\n<ul class=\"wp-block-list\">\n<li><span style=\"color: initial\">The USA declares that it will not conduct left of launch attacks on North Korean nuclear command, control and communications systems<\/span><\/li>\n\n\n\n<li><span style=\"color: initial\">The United States establishes a liaison office in Pyongyang, North Korea, to facilitate communication, diplomacy, and engagement with the North Korean government<\/span><\/li>\n\n\n\n<li><span style=\"color: initial\">The USA and North Korea establish Track 1.5 diplomacy to facilitate regular dialogue and cooperation<\/span><\/li>\n<\/ul>\n<\/li>\n\n\n\n<li><span style=\"color: initial\">Russia and NATO domain<\/span>\n<ul class=\"wp-block-list\">\n<li><span style=\"color: initial\">Russia and the USA sign an arms control treaty succeeding New START<\/span><\/li>\n\n\n\n<li><span style=\"color: initial\">Russia and the USA agree on limits or bans for intermediate-range missiles<\/span><\/li>\n\n\n\n<li><span style=\"color: initial\">The USA eliminates its launch-on-warning posture<\/span><\/li>\n\n\n\n<li><span style=\"color: initial\">Russia eliminates its launch-on-warning posture<\/span><\/li>\n\n\n\n<li><span style=\"color: initial\">The USA decreases the role of nuclear weapons with a yield of less than 50 kt in its nuclear posture<\/span><\/li>\n\n\n\n<li><span style=\"color: initial\">Russia decreases the role of nuclear weapons with a yield of less than 50 kt in its nuclear posture<\/span><\/li>\n\n\n\n<li><span style=\"color: initial\">The USA increases the role of nuclear weapons with a yield of less than 50 kt in its nuclear posture<\/span><\/li>\n\n\n\n<li><span style=\"color: initial\">Russia increases the role of nuclear weapons with a yield of less than 50 kt in its nuclear posture<\/span><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<\/div><\/div>\n\n\n\n<p class=\"wp-block-paragraph\">We asked participants to say whether (and how) the implementation of\nthese policies would influence their forecast of the probability of\nnuclear catastrophe. We asked participants to only consider the causal\neffects of the policy, rather than what such a policy being implemented\nwould say about the state of the world. For example, someone might\nreduce their forecast of nuclear catastrophe for two reasons if they\nknew an arms control agreement between the USA and Russia would be\nimplemented: i) the limitations of the agreement might reduce the number\nof weapons available to use, or ii) the fact that the USA and Russia\nreached an agreement might signal improved relations between the\ncountries. We asked participants to only include the first\nconsideration, not the second.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">In addition to forecasting the effect of the policies on the\nprobability of nuclear catastrophe by 2045, we also asked participants\nto forecast the probability that, within the next three years, action\nwould be taken to implement the policy. We asked participants to give an\nunconditional forecast, and to give their forecast of this probability\nconditional on a nonprofit team being given $500 million dedicated to\ngetting the policy implemented.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Participants were asked to rank the policies according to how much\nthey would like them to be implemented and how much they would like $500\nmillion of funding to go to attempts to have the policy be implemented.\nWe also asked about other effects (other than effects on the probability\nof a nuclear catastrophe) of the policies, including other positive and\nnegative effects.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"reciprocal-scoring\">2.1.3 Reciprocal scoring<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">Because most of the questions participants were asked to forecast in this study won\u2019t resolve for many years, we included some questions to give an earlier indication of participant accuracy. This included some questions that will resolve in 2026 (see <a href=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=85\" id=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=85\" target=\"_blank\" rel=\"noreferrer noopener\">Appendix 3<\/a>), and some questions that asked participants to predict the forecasts of other participants. Specifically, we asked all participants to predict what the median expert in the study would forecast for the probability of nuclear catastrophe in 2045, and to predict the median expert\u2019s forecast on five of the crux questions that will resolve in 2030.<sup data-fn=\"019937a7-6537-49a3-b2d2-45c55bece462\" class=\"fn\"><a href=\"#019937a7-6537-49a3-b2d2-45c55bece462\" id=\"019937a7-6537-49a3-b2d2-45c55bece462-link\">26<\/a><\/sup> We used these results to generate \u201creciprocal scores\u201d for each participant. Forecasts elicited this way can be as accurate as forecasts incentivized using comparisons to the truth.<sup data-fn=\"089e2614-bb58-4ada-8324-090737ec8534\" class=\"fn\"><a href=\"#089e2614-bb58-4ada-8324-090737ec8534\" id=\"089e2614-bb58-4ada-8324-090737ec8534-link\">27<\/a><\/sup><\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"recruitment\">2.2 Recruitment<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">The two main participant groups for the survey were people with\nexpertise relevant to nuclear weapons policy (we use the term\n<em>experts<\/em> for this group), and people with a strong track record\nof accurate forecasting.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">We recruited subject matter experts through three channels: advertisement via relevant professional organizations,<sup data-fn=\"c0960c9f-6ab5-47ed-b9df-3bc33fe0e4b9\" class=\"fn\"><a href=\"#c0960c9f-6ab5-47ed-b9df-3bc33fe0e4b9\" id=\"c0960c9f-6ab5-47ed-b9df-3bc33fe0e4b9-link\">28<\/a><\/sup> review of staff pages of websites of relevant organizations and author lists of relevant reports,<sup data-fn=\"f5fb56b4-48f0-464f-bb93-5305547cd093\" class=\"fn\"><a href=\"#f5fb56b4-48f0-464f-bb93-5305547cd093\" id=\"f5fb56b4-48f0-464f-bb93-5305547cd093-link\">29<\/a><\/sup> and snowball sampling that asked prospective participants to nominate other people who may be appropriate participants. In an effort to capture viewpoint diversity, we asked people to nominate two people who they thought would largely agree with their views on nuclear risks and two people who they thought would largely disagree with them.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Ultimately, we emailed 514 subject matter experts (who we thought\nmight meet our requirements) directly about the survey, and likely\nreached more through the general approaches to advertisement described\nabove.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">To ensure that our sample of subject matter experts reflected the\npopulation that would generally be considered \u201cexpert,\u201d we required\nexpert participants to have a minimum of five years of experience\nrelevant to nuclear weapons policy (or two years and a relevant graduate\ndegree). To ensure that our subject matter experts met this bar, we\ninvited interested experts to register their interest in participating\nthrough completing a form that asked for details of their relevant\neducation and professional experience. 239 people registered their\ninterest in the study. Of these, 171 met the required level of\nexperience and were invited to complete the surveys. 110 experts\ncompleted the first survey and all but one of these completed the\nsecond.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">We recruited accurate forecasters by directly inviting so-called \u201csuperforecasters\u201d\u2014people who have been shown to be highly accurate forecasters and outperformed experts and intelligence analysts in large-scale forecasting tournaments held by the Good Judgment Project and subsequent forecasting exercises run by Good Judgment, Inc.<sup data-fn=\"11446d22-b021-4ec9-a89c-f66fd1840fff\" class=\"fn\"><a href=\"#11446d22-b021-4ec9-a89c-f66fd1840fff\" id=\"11446d22-b021-4ec9-a89c-f66fd1840fff-link\">30<\/a><\/sup> A total of 55 superforecasters initially expressed interest in the study and were invited to participate. Of these, 41 completed the first survey, and 39 of these completed the second.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">We also recruited members of the public to complete a shortened\nversion of the surveys. These were previous participants in studies run\nby the Forecasting Research Institute, recruited via Facebook ads\ntargeting people interested in global news, geopolitics, and other\ntopics. We report some key findings from this public survey for the\npurpose of comparison with the responses from the expert and\nsuperforecaster participants. However, this report focuses on the\nresults of the expert and superforecaster surveys.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"participant-compensation\">2.3 Participant compensation<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">We provided an honorarium of $250 to expert and superforecaster\nparticipants who completed both surveys to compensate them for their\ntime. Participants who chose to spend more time on the surveys were paid\nan additional $50\/hour for self-reported hours of work above five hours,\nup to a maximum of 10 additional hours. Participants who spent more than\n10 hours on the first survey were awarded an additional $100 for\ncompleting the second survey. We incentivized high quality engagement in\nthe survey by offering additional monetary prizes, which will be awarded\non the basis of the quality of text rationales given for forecasts and\naccuracy on some forecasting questions. Prior to the awarding of these\nadditional prizes, participants received an average of $525 to\ncompensate them for the time spent completing the two surveys.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">For the shortened survey of the public, we recruited people who had\nbeen participants in previous studies run by the Forecasting Research\nInstitute by email invitation. Members of the public received a payment\nof $20 for each of the two surveys they completed.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"survey-engagement\">2.4 Survey engagement<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Participants completed Survey 1 between March and May 2024 and Survey\n2 between June and August 2024. There was generally a high level of\nengagement with the surveys. The median expert or superforecaster\nparticipant reported spending nine hours completing the two surveys.\nSuperforecasters spent a longer time on the surveys than did experts.\nThe median superforecaster spent approximately 13 hours on the surveys\nand the median expert spent around 7.5 hours. Superforecasters also\nwrote more words in their rationales (a median of roughly 4,900,\ncompared to an expert median of roughly 3,800). Collectively, expert and\nsuperforecaster participants wrote over 747,000 words in their\nrationales.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"participants-1\">3. Participants<\/h2>\n\n\n\n<div class=\"wp-block-group\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h4 class=\"wp-block-heading\" id=\"box-key-points\">Key points<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>151 participants (110 experts and 41 superforecasters) completed\nthe first survey and 148 (109 experts and 39 superforecasters) completed\nthe second.<\/li>\n\n\n\n<li>Experts largely worked in think tanks and academia, and had a\nmedian of nine years of relevant experience.<\/li>\n\n\n\n<li>Participants were from 37 different countries, although around a\nquarter of experts and half of superforecasters were born in the\nUSA.<\/li>\n\n\n\n<li>36% of experts think that nuclear deterrence is robust, 33% think\nit is fragile, and 30% think it is fragile at present but could be\nrobust in the future.<\/li>\n\n\n\n<li>56% of experts think that nuclear escalation is very likely\nfollowing a nuclear first strike, 16% think that escalation can be\nprevented, and 27% are very uncertain about whether escalation would\noccur.<\/li>\n<\/ul>\n<\/div><\/div>\n\n\n\n<p class=\"wp-block-paragraph\">A total of 151 participants completed the full first survey. This\nincluded 110 expert participants and 41 superforecaster participants. Of\nthese, three participants (one expert and two superforecasters) did not\ncomplete the second survey, so a total of 148 participants completed\nboth surveys (109 experts and 39 superforecasters). Here we present\ndetails of the 151 participants who completed at least the first\nsurvey.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"demographics\">3.1 Demographics<\/h3>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"age-and-gender\">3.1.1 Age and gender<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">The age and gender breakdown of both participant groups are shown in Figure 3. The majority of participants identified as male, with 69% of expert participants and 93% of superforecasters identifying as male.<sup data-fn=\"26b45041-471d-4824-9a22-0399db4b0191\" class=\"fn\"><a href=\"#26b45041-471d-4824-9a22-0399db4b0191\" id=\"26b45041-471d-4824-9a22-0399db4b0191-link\">31<\/a><\/sup> Proportionally, the expert group was younger than the superforecasters, with 20\u201334 years old being the most common age category for experts, accounting for 40% of expert participants. The most common age category for superforecasters was 45\u201354 years old, which accounted for 29% of these participants.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/forecastingresearch.org\/wp-content\/uploads\/2026\/03\/working-paper_2024-10-29_nuclear-risk_fig-03.png\" alt=\"Figure 3: Age and gender distribution of expert and superforecaster participants.\"\/><figcaption class=\"wp-element-caption\"><strong>Figure 3:<\/strong> Age and gender distribution of expert and superforecaster participants.<\/figcaption><\/figure>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"geographic-region\">3.1.2 Geographic region<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">The USA was the most common country of birth of participants. This was particularly true for the superforecaster group, 49% of whom were born in the USA. The USA was still the most common country of birth for experts, but it accounted for only 25% of the participants. The next most common country of birth was Pakistan, where 15% of expert participants were born. Figure 4 shows the most common countries of birth for expert participants. For more detail on the country of origin and country of residence of participants, please see <a href=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=163\" id=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=85\" target=\"_blank\" rel=\"noreferrer noopener\">Appendix 5<\/a>.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/forecastingresearch.org\/wp-content\/uploads\/2026\/03\/working-paper_2024-10-29_nuclear-risk_fig-04.png\" alt=\"Figure 4: Count of experts born in the most common countries of birth for expert participants.\"\/><figcaption class=\"wp-element-caption\"><strong>Figure 4:<\/strong> Count of experts born in the most common countries of birth for expert participants.<\/figcaption><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"expertise\">3.2 Expertise<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Expert participants were required to have a minimum of five years of\nexperience working in a field relevant to nuclear weapons policy, or to\nhave two years of relevant work experience as well as a relevant\ngraduate degree (master\u2019s or doctorate). The distribution of years of\nexperience is shown in Figure 5.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/forecastingresearch.org\/wp-content\/uploads\/2026\/03\/working-paper_2024-10-29_nuclear-risk_fig-05.png\" alt=\"Figure 5: Distribution of years of experience of experts.\"\/><figcaption class=\"wp-element-caption\"><strong>Figure 5:<\/strong> Distribution of years of experience of experts.<\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">We asked experts to list the organizations they were affiliated with.\nWe then classified these organizations into several types. Figure 6\nshows the distribution of these organizational affiliations. Academic\ninstitutions and think tanks were the most common type of affiliation.\nMany experts were affiliated with more than one type of\norganization.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">We also asked experts about postgraduate education relevant to\nnuclear weapons policy. The majority of expert participants (86%) had a\nrelevant postgraduate degree (master\u2019s or PhD). The most common field of\nstudy was international relations, with 36 experts holding at least one\ngraduate degree in this field. This was followed by security studies and\npolitical science. Many experts combined two or more of these fields of\nstudy.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/forecastingresearch.org\/wp-content\/uploads\/2026\/03\/working-paper_2024-10-29_nuclear-risk_fig-06.png\" alt=\"Figure 6: Type of organizational affiliations and fields of study of experts.\"\/><figcaption class=\"wp-element-caption\"><strong>Figure 6:<\/strong> Type of organizational affiliations and fields of study of experts.<\/figcaption><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"beliefs-about-contentious-issues\">3.3 Beliefs about contentious\nissues<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">We asked participants about four issues in order to capture important\nideological differences about nuclear weapons policy:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>The fragility \/ robustness of nuclear deterrence<\/li>\n\n\n\n<li>The likelihood that a nuclear strike would be met with nuclear\nretaliation<\/li>\n\n\n\n<li>The proliferation risk posed by nuclear energy<\/li>\n\n\n\n<li>The desirability of complete nuclear disarmament<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">For each of these issues we asked participants to rank three statements representing different viewpoints on the issue. Two of these statements were intended to represent two opposing views and one was intended as a \u201cmiddle ground\u201d between the opposing views. As an example, the statements for the \u201cnuclear deterrence\u201d issue are shown in Box 3, and the full list of statements is available in <a href=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=166\" id=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=85\" target=\"_blank\" rel=\"noreferrer noopener\">Appendix 6<\/a>.<\/p>\n\n\n\n<div class=\"wp-block-group\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h4 class=\"wp-block-heading\">Box 3: Statements for assessing views on nuclear deterrence<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Opposing view 1: Nuclear deterrence is inherently fragile (easily shattered by human irrationality and chance events\u2014so not a reliable safeguard against nuclear war).<\/li>\n\n\n\n<li>Opposing view 2: Nuclear deterrence can be robust with clear communications, tight command and control, and mutually assured destruction.<\/li>\n\n\n\n<li>Middle-ground view: Nuclear deterrence could be effective, but the current state of global communication, command and control systems, and weapon deployment are easily fallible, and so deterrence is not a safe system at present.<\/li>\n<\/ul>\n<\/div><\/div>\n\n\n\n<p class=\"wp-block-paragraph\">Figure 7 shows the distribution of experts and superforecasters who selected each of the statements as most representative of their views. There were no statistically significant differences between the proportions of the two groups choosing each statement (see <a href=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=163\" id=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=85\" target=\"_blank\" rel=\"noreferrer noopener\">Appendix 5<\/a> for details).<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/forecastingresearch.org\/wp-content\/uploads\/2026\/03\/working-paper_2024-10-29_nuclear-risk_fig-07.png\" alt=\"Figure 7: Proportion of respondents selecting each statement as the closest match (of the three) to their own view on four nuclear weapons policy issues: views on the nuclear deterrence, views on nuclear escalation risk, views on the goal of total disarmament, views on the proliferation risk of nuclear energy programs. See Appendix 6 (view PDF) for full issue statements.\"\/><figcaption class=\"wp-element-caption\"><strong>Figure 7:<\/strong> Proportion of respondents selecting each statement as the closest match (of the three) to their own view on four nuclear weapons policy issues: views on the nuclear deterrence, views on nuclear escalation risk, views on the goal of total disarmament, views on the proliferation risk of nuclear energy programs. See <a href=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=166\" id=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=85\" target=\"_blank\" rel=\"noreferrer noopener\">Appendix 6<\/a> for full issue statements.<\/figcaption><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"forecasts-of-nuclear-catastrophe-risk\">4. Forecasts of nuclear\ncatastrophe risk<\/h2>\n\n\n\n<div class=\"wp-block-group\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h4 class=\"wp-block-heading\" id=\"box-key-points-1\">Key points<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>The median expert forecast for the probability of a nuclear\nweapons incident killing more than 10 million people before 2045 was 5%.\nThe median superforecaster\u2019s forecast was 1%, and the median member of\nthe public\u2019s forecast was 10%.<\/li>\n\n\n\n<li>Conditional on a nuclear weapons catastrophe occurring by 2045,\non average experts forecast a 26% probability that Russia and NATO would\nbe the cause, roughly 20% for both the Korean Peninsula and India and\nPakistan, and roughly 13% for both Israel and Iran, and China and the\nUSA.<\/li>\n\n\n\n<li>Violent conflict and new actors acquiring nuclear weapons were\nthe events associated with the highest increase in risk.<\/li>\n\n\n\n<li>For many participants many of the events wouldn\u2019t influence risk\nincluding: an accidental non-test detonation, no-first use policies,\nsummits between adversarial countries, and more.<\/li>\n<\/ul>\n<\/div><\/div>\n\n\n\n<p class=\"wp-block-paragraph\">Here, we present key findings from the forecasting components of the surveys. For most questions we present the median response from the expert and superforecaster participants. This represents the mid-point of the group responses; half the group\u2019s responses are higher than this value and half are lower than this value.<sup data-fn=\"3484acb4-565d-4e85-8ee4-a427963b3770\" class=\"fn\"><a href=\"#3484acb4-565d-4e85-8ee4-a427963b3770\" id=\"3484acb4-565d-4e85-8ee4-a427963b3770-link\">32<\/a><\/sup><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">There are many ways to aggregate forecasts, but we choose the median because it is straightforward to calculate, transparent, robust to extreme outlying observations, and easier to understand than most other methods. Also, reassuringly, in previous work we have found that it is never the highest nor the lowest of several aggregation methods that were considered.<sup data-fn=\"151cbe3e-71d5-4c1b-b612-39a074dbb49e\" class=\"fn\"><a href=\"#151cbe3e-71d5-4c1b-b612-39a074dbb49e\" id=\"151cbe3e-71d5-4c1b-b612-39a074dbb49e-link\">33<\/a><\/sup><\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"probability-of-nuclear-catastrophe\">4.1 Probability of nuclear\ncatastrophe<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Participants were asked to answer the following question:<\/p>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p class=\"wp-block-paragraph\"><em>\u201cWhat is the probability that by 2045, one or more incidents\ninvolving nuclear weapons will cause the death of more than 10 million\nhumans, within a five-year time period?\u201d<\/em><\/p>\n<\/blockquote>\n\n\n\n<p class=\"wp-block-paragraph\">The median expert forecast was 5% (IQR: 1\u201318.5%) and the median\nsuperforecaster response was 1% (IQR: 0.15\u20132.3%). There was substantial\nvariation in forecasts within both groups, although this was more\npronounced for experts, where the standard deviation was 18.4%, compared\nto 5.3% for superforecasters. The median forecast from our survey of the\npublic was 10%, with a standard deviation of 24.9%.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Table 2 and Figure 8 summarize the responses to this question from\nexperts, superforecasters and members of the public. Figure 9 shows the\ndistribution of responses from experts and superforecasters. Figure 10\nshows the proportion of expert and superforecaster responses that fall\nwithin different ranges.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><div class=\"table-wrapper\"><table class=\"has-fixed-layout\"><tbody><tr><td><strong>Group<\/strong><\/td><td><strong>Number of respondents<\/strong><\/td><td><strong>Median forecast<\/strong><\/td><td><strong>Interquartile range (IQR)<\/strong><\/td><td><strong>Standard Deviation (SD)<\/strong><\/td><\/tr><tr><td>Experts<\/td><td>110<\/td><td>5%<\/td><td>1-18.5%<\/td><td>18.4%<\/td><\/tr><tr><td>Superforecasters<\/td><td>41<\/td><td>1%<\/td><td>0.15-2.3%<\/td><td>5.3%<\/td><\/tr><tr><td>The public<\/td><td>401<\/td><td>10%<\/td><td>1-35%<\/td><td>24.9%<\/td><\/tr><\/tbody><\/table><\/div><figcaption class=\"wp-element-caption\"><strong>Table 2:<\/strong> Summary of forecasts on the probability of nuclear catastrophe from experts, superforecasters, and the public.<\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/forecastingresearch.org\/wp-content\/uploads\/2026\/03\/working-paper_2024-10-29_nuclear-risk_fig-01.png\" alt=\"Figure 8: Plots show forecasts of the probability of nuclear catastrophe by 2045. The median forecast is provided in text. The boxes show the 25th\u201375th percentile forecasts, and the lines the range of forecasts minus outliers. We jitter the data points horizontally to allow for better visualization of the distribution of forecasts. Horizontal variation within each group serves no other empirical purpose. The y-axis uses a logarithmic scale to informatively show variation in forecasts in the 0\u201310% range.\"\/><figcaption class=\"wp-element-caption\"><strong>Figure 8:<\/strong> Plots show forecasts of the probability of nuclear catastrophe by 2045. The median forecast is provided in text. The boxes show the 25<sup>th<\/sup>\u201375<sup>th<\/sup> percentile forecasts, and the lines the range of forecasts minus outliers. We jitter the data points horizontally to allow for better visualization of the distribution of forecasts. Horizontal variation within each group serves no other empirical purpose. The y-axis uses a logarithmic scale to informatively show variation in forecasts in the 0\u201310% range.<\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/forecastingresearch.org\/wp-content\/uploads\/2026\/03\/working-paper_2024-10-29_nuclear-risk_fig-09.png\" alt=\"Figure 9: Density plot showing the distribution of forecasts of the probability of nuclear catastrophe for expert and superforecaster participants. The dashed line shows the median forecast for each group. The x-axis uses a logarithmic scale to informatively show variation in forecasts in the 0\u201310% range.\"\/><figcaption class=\"wp-element-caption\"><strong>Figure 9:<\/strong> Density plot showing the distribution of forecasts of the probability of nuclear catastrophe for expert and superforecaster participants. The dashed line shows the median forecast for each group. The x-axis uses a logarithmic scale to informatively show variation in forecasts in the 0\u201310% range.<\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/forecastingresearch.org\/wp-content\/uploads\/2026\/03\/working-paper_2024-10-29_nuclear-risk_fig-10.png\" alt=\"Figure 10: Plot shows the proportion of respondents whose forecasts of the probability of nuclear catastrophe by 2045 fall into each of the ranges.\"\/><figcaption class=\"wp-element-caption\"><strong>Figure 10:<\/strong> Plot shows the proportion of respondents whose forecasts of the probability of nuclear catastrophe by 2045 fall into each of the ranges.<\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Participants were also asked to provide a rationale for their\nforecasts. Respondents who forecasted a higher probability for nuclear\ncatastrophe pointed to increased tensions and ongoing conflicts between\nnuclear powers, especially between Russia and NATO, China and the USA,\nand India and Pakistan. Many suggested that nuclear weapons\nproliferation, new military technologies, and weakening of international\narms control agreements heighten risk. These rationales also expressed\nconcerns that disinformation and misunderstanding could lead to\nescalation.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Rationales for lower probability estimates emphasized that there has been no use of nuclear weapons since 1945. They also argued that the doctrine of mutually assured destruction disincentivizes using nuclear weapons even in times of conflict, and that most decision makers are rational actors who wish to avoid catastrophic outcomes. According to some participants, a death toll of 10 million would require an extensive nuclear exchange where major cities are targeted, which they deemed unlikely. They also cited improvements in safety mechanisms, which reduce the likelihood of inadvertent and accidental use. A more detailed summary of the arguments provided for different ranges of forecasts is provided in <a href=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=167\" id=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=85\" target=\"_blank\" rel=\"noreferrer noopener\">Appendix 7<\/a>.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"risk-from-specific-adversarial-domains\">4.2 Risk from specific\nadversarial domains<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Participants were asked which of the adversarial domains would be most likely to have been the primary cause of a nuclear catastrophe (that killed more than 10 million people), if such a catastrophe were to occur before 2045. They were asked to allocate probabilities among five adversarial domains\u2014Russia and NATO, China and the USA, the Korean Peninsula, India and Pakistan, and Israel and Iran\u2014and an \u201cOther\u201d category. The average probability allocated to each domain is shown in Figure 11.<sup data-fn=\"e6bb5c35-0dca-43d8-81f5-036ee994b8e2\" class=\"fn\"><a href=\"#e6bb5c35-0dca-43d8-81f5-036ee994b8e2\" id=\"e6bb5c35-0dca-43d8-81f5-036ee994b8e2-link\">34<\/a><\/sup><\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/forecastingresearch.org\/wp-content\/uploads\/2026\/03\/working-paper_2024-10-29_nuclear-risk_fig-11.png\" alt=\"Figure 11: Plot shows the average probability placed on domains being the primary cause of a nuclear catastrophe by 2045.\"\/><figcaption class=\"wp-element-caption\"><strong>Figure 11:<\/strong> Plot shows the average probability placed on domains being the primary cause of a nuclear catastrophe by 2045.<\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">We tested whether expert participants were more likely to forecast a\nhigher probability that their chosen domain would be the primary cause\nof catastrophe. The results are shown in Table 3. Although there was a\ntrend to give higher forecasts for three of the domains (all except\nIndia and Pakistan) when experts chose that domain, the only\nstatistically significant difference was for experts choosing the Korean\nPeninsula domain.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><div class=\"table-wrapper\"><table class=\"has-fixed-layout\"><tbody><tr><td><strong>Adversarial domain<\/strong><\/td><td><strong>Median forecast from experts choosing domain<\/strong><\/td><td><strong>Median forecast from experts <u>not<\/u> choosing\ndomain<\/strong><\/td><td><strong>P-value from Mann-Whitney U test*<\/strong><\/td><\/tr><tr><td>China and the USA<\/td><td>15% (n=12)<\/td><td>10% (n=98)<\/td><td>0.95<\/td><\/tr><tr><td>Russia and NATO<\/td><td>25% (n=56)<\/td><td>20% (n=54)<\/td><td>0.55<\/td><\/tr><tr><td>India and Pakistan<\/td><td>15% (n=27)<\/td><td>15% (n=83)<\/td><td>1<\/td><\/tr><tr><td>Korean Peninsula<\/td><td>28% (n=15)<\/td><td>20% (n=95)<\/td><td>0.01<\/td><\/tr><\/tbody><\/table><\/div><figcaption class=\"wp-element-caption\"><strong>Table 3:<\/strong> Views of the probability of domain being most likely cause of catastrophe disaggregated by whether experts chose the domain. *This test was performed with a Bonferroni correction.<\/figcaption><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"risk-pathways\">4.3 Risk pathways<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">We asked participants questions about the probability of various events occurring by 2030. These questions were intended to represent potential ideological cruxes, by which we mean questions whose answer would influence participants\u2019 assessment of the risk of nuclear catastrophe by 2045. For each question, participants were asked for their forecast on the likelihood of the event occurring and to describe how their forecast of the probability of nuclear catastrophe by 2045 would change conditional on the event occurring and conditional on the event not occurring.<sup data-fn=\"ca6ff985-c41c-4231-a5f1-bd56d5d38d60\" class=\"fn\"><a href=\"#ca6ff985-c41c-4231-a5f1-bd56d5d38d60\" id=\"ca6ff985-c41c-4231-a5f1-bd56d5d38d60-link\">35<\/a><\/sup> The full list of questions is available in <a href=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=85\" id=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=85\" target=\"_blank\" rel=\"noreferrer noopener\">Appendix 3<\/a>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">In response to these questions, some participants gave forecasts that were incoherent. For example, if a participant\u2019s forecast of catastrophe conditional on an event occurring is lower than their unconditional forecast of catastrophe, then their forecast of catastrophe conditional on the event <strong>not<\/strong> occurring cannot also be lower than their unconditional forecast of catastrophe. (Similarly, the forecast of catastrophe conditional on the event occurring and conditional on the event <strong>not<\/strong> occurring cannot both be higher than the unconditional forecast of catastrophe). When respondents gave forecasts that were incoherent in this way, or contradicted their written rationales, we dropped these responses from the analysis. For this reason, the number of respondents varies between the questions. Fewer than 5% of forecasts analyzed in this report were dropped from the dataset due to incoherence. While 50 of the 151 respondents had at least one of their forecasts dropped, this is not surprising given the length of the survey, which required that participants submit forecasts and rationales for a median of 9 hours. For more detail on how we managed incoherent responses, please see <a href=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=173\" id=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=85\" target=\"_blank\" rel=\"noreferrer noopener\">Appendix 8<\/a>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Perhaps unsurprisingly, deliberate and inadvertent non-test nuclear\nweapons detonations were associated with a large increase in risk of\nnuclear catastrophe, as were violent conflicts between nuclear-armed\nstates and horizontal proliferation of nuclear weapons to new actors.\nHere we discuss how forecasts of nuclear catastrophe by 2045 change\nconditional on these and other events that might influence risk.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"accidental-inadvertent-and-deliberate-non-test-detonation\">4.3.1\nAccidental, inadvertent, and deliberate non-test detonation<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">To understand views on different risk pathways, we asked participants how their forecast of nuclear catastrophe would change if they knew that an accidental, inadvertent, or deliberate non-test nuclear weapon detonation occurred by 2030. For these questions, we took our definition of the different types of non-test detonation from Barrett, Baum and Hostetler (2012).<sup data-fn=\"be7abff7-058e-417d-8fa4-f381cb8708b9\" class=\"fn\"><a href=\"#be7abff7-058e-417d-8fa4-f381cb8708b9\" id=\"be7abff7-058e-417d-8fa4-f381cb8708b9-link\">36<\/a><\/sup> An accidental detonation is one where \u201csystem safeguards or procedures to maintain control over nuclear weapons fail in such a way that a nuclear weapon \u2026 explodes without direction from leaders.\u201d An inadvertent detonation is one in which the attacking group \u201cmistakenly concludes that it is under attack and launches nuclear weapons in what it believes is a counterattack.\u201d A deliberate detonation is one in which \u201cthe attacking nation decides to attack based on accurate information about the state of affairs.\u201d Figure 12 shows how forecasts on the probability of catastrophe change conditional on each type of detonation occurring by 2030.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/forecastingresearch.org\/wp-content\/uploads\/2026\/03\/working-paper_2024-10-29_nuclear-risk_fig-12.png\" alt=\"Figure 12: Violin plots showing distribution of forecasts of the probability of nuclear catastrophe, unconditional and conditional on different types of non-test nuclear detonations occurring before 2030. The group median is shown in text. The thicker bar within each violin shows the interquartile range (25th to 75th percentile forecasts), and the thin line shows the range of forecasts minus outliers.\"\/><figcaption class=\"wp-element-caption\"><strong>Figure 12:<\/strong> Violin plots showing distribution of forecasts of the probability of nuclear catastrophe, unconditional and conditional on different types of non-test nuclear detonations occurring before 2030. The group median is shown in text. The thicker bar within each violin shows the interquartile range (25<sup>th<\/sup> to 75<sup>th<\/sup> percentile forecasts), and the thin line shows the range of forecasts minus outliers.<\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Deliberate and inadvertent non-test detonations were associated with\na large increase in forecasts of catastrophe. The median expert would\nincrease their forecast of catastrophe by four times, conditional on a\ndeliberate non-test detonation occurring. The median superforecaster\nwould increase theirs by 6.7 times. Conditional on an inadvertent\ndetonation, the median expert and the median superforecaster would\ntriple their forecast. Figures 13 to 15 show how participants\u2019 forecasts\nof nuclear catastrophe change conditional on a deliberate non-test\ndetonation occurring by 2030.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/forecastingresearch.org\/wp-content\/uploads\/2026\/03\/working-paper_2024-10-29_nuclear-risk_fig-13.png\" alt=\"Figure 13: Plot shows how individual respondents\u2019 forecasts of nuclear catastrophe would change conditional on a deliberate non-test detonation before 2030. The blue and orange dots show the baseline forecasts, and the tips of the arrows show the forecast conditional on the event. Yellow dots indicate forecasts that would not change conditional on the event. The y-axis uses a logarithmic scale to informatively show variation in forecasts in the 0\u201310% range.\"\/><figcaption class=\"wp-element-caption\"><strong>Figure 13:<\/strong> Plot shows how individual respondents\u2019 forecasts of nuclear catastrophe would change conditional on a deliberate non-test detonation before 2030. The blue and orange dots show the baseline forecasts, and the tips of the arrows show the forecast conditional on the event. Yellow dots indicate forecasts that would not change conditional on the event. The y-axis uses a logarithmic scale to informatively show variation in forecasts in the 0\u201310% range.<\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-table\"><div class=\"table-wrapper\"><table class=\"has-fixed-layout\"><tbody><tr><td rowspan=\"2\"><strong>Non-test detonation event by 2030<\/strong><\/td><td colspan=\"3\"><strong>Expert<\/strong><br>Median (IQR)<\/td><td colspan=\"3\"><strong>Superforecaster<\/strong><br>Median (IQR)<\/td><\/tr><tr><td><strong>N*<\/strong><\/td><td><strong>Relative risk<\/strong><\/td><td><strong>Probability of occurring<\/strong><\/td><td><strong>N*<\/strong><\/td><td><strong>Relative risk<\/strong><\/td><td><strong>Probability of occurring<\/strong><\/td><\/tr><tr><td>Deliberate<\/td><td>87<\/td><td>4x<br>(1.5x \u2013 31.3x)<\/td><td>1.0%<br>(0.1% \u2013 10%)<\/td><td>37<\/td><td>6.7x<br>(1.65x \u2013 16.7x)<\/td><td>0.5%<br>(0.1% \u2013 2%)<\/td><\/tr><tr><td>Inadvertent<\/td><td>88<\/td><td>3x<br>(1.3x \u2013 18.5x)<\/td><td>1.3%<br>(0.1% \u2013 10%)<\/td><td>37<\/td><td>3x<br>(1.5x \u2013 10x)<\/td><td>0.1%<br>(0.01% \u2013 0.5%)<\/td><\/tr><tr><td>Accidental<\/td><td>87<\/td><td>1x<br>(1x \u2013 2x)<\/td><td>1.0%<br>(0.01% \u2013 10%)<\/td><td>37<\/td><td>1x<br>(1x \u2013 1.1x)<\/td><td>0.05%<br>(0.01% \u2013 0.3%)<\/td><\/tr><\/tbody><\/table><\/div><figcaption class=\"wp-element-caption\"><strong>Table 4:<\/strong> Relative risk of nuclear catastrophe conditional on different types of non-test nuclear detonations occurring before 2030 and the probability of this occurring. The median and interquartile ranges for experts and superforecasters are shown. <br>*N is the number of responses for relative risk. 110 experts and 39 superforecasters provided forecasts on the probability of the events occurring.<\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/forecastingresearch.org\/wp-content\/uploads\/2026\/03\/working-paper_2024-10-29_nuclear-risk_fig-14.png\" alt=\"Figure 14: Plot shows the proportion of respondents who would increase, decrease, or not change their forecast of nuclear catastrophe, if they knew that different types of non-test detonations would occur by 2030.\"\/><figcaption class=\"wp-element-caption\"><strong>Figure 14:<\/strong> Plot shows the proportion of respondents who would increase, decrease, or not change their forecast of nuclear catastrophe, if they knew that different types of non-test detonations would occur by 2030.<\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/forecastingresearch.org\/wp-content\/uploads\/2026\/03\/working-paper_2024-10-29_nuclear-risk_fig-15.png\" alt=\"Figure 15: Plot shows the proportion of respondents whose relative risk (relative change in risk of nuclear catastrophe) for a deliberate non-test detonation falls into each of the ranges.\"\/><figcaption class=\"wp-element-caption\"><strong>Figure 15:<\/strong> Plot shows the proportion of respondents whose relative risk (relative change in risk of nuclear catastrophe) for a deliberate non-test detonation falls into each of the ranges.<\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Perhaps more surprisingly, participants were split on the effects of an accidental non-test detonation on the risk of nuclear catastrophe. Figures 16 and 17 show how forecasts would change if this were to occur. Around 60% of superforecasters and 49% of experts would increase their probability of a nuclear catastrophe, but roughly 15% of participants would decrease their forecast. In rationales, those who decreased their forecast suggested that an accidental nuclear detonation could serve as a wake-up call that prompts greater action to reduce nuclear weapons risk. (For more detail on arguments, see <a href=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=175\" id=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=85\" target=\"_blank\" rel=\"noreferrer noopener\">Appendix 9<\/a>.)<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/forecastingresearch.org\/wp-content\/uploads\/2026\/03\/working-paper_2024-10-29_nuclear-risk_fig-16.png\" alt=\"Figure 16: Plot shows how individual respondents\u2019 forecasts of nuclear catastrophe would change conditional on an accidental non-test detonation before 2030. The blue and orange dots show the baseline forecasts, and the tips of the arrows show the forecast conditional on the event. Yellow dots indicate forecasts that would not change conditional on the event. The y-axis uses a logarithmic scale to informatively show variation in forecasts in the 0\u201310% range.\"\/><figcaption class=\"wp-element-caption\"><strong>Figure 16:<\/strong> Plot shows how individual respondents\u2019 forecasts of nuclear catastrophe would change conditional on an accidental non-test detonation before 2030. The blue and orange dots show the baseline forecasts, and the tips of the arrows show the forecast conditional on the event. Yellow dots indicate forecasts that would not change conditional on the event. The y-axis uses a logarithmic scale to informatively show variation in forecasts in the 0\u201310% range.<\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/forecastingresearch.org\/wp-content\/uploads\/2026\/03\/working-paper_2024-10-29_nuclear-risk_fig-17.png\" alt=\"Figure 17: Plot shows the proportion of respondents whose relative risk (relative change in risk of nuclear catastrophe) for an accidental non-test detonation falls into each of the ranges.\"\/><figcaption class=\"wp-element-caption\"><strong>Figure 17:<\/strong> Plot shows the proportion of respondents whose relative risk (relative change in risk of nuclear catastrophe) for an accidental non-test detonation falls into each of the ranges.<\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Most participants thought the probability of any type of non-test\nnuclear detonation before 2030 was quite low. The median expert\nforecasted a 1% chance of a deliberate non-test detonation occurring and\na 1.3% chance of an inadvertent non-test detonation occurring. The\nmedian superforecaster thought these events were even less likely,\nforecasting 0.5% and 0.1% probabilities for deliberate and inadvertent\nnon-test detonations occurring, respectively. Superforecasters also\nthought the probability of an accidental non-test detonation was very\nlow, with a median probability of 0.05%. The median expert put the\nprobability of an accidental detonation at 1%. Table 4 summarizes these\nresults and views on the relative change in risk conditional on\ndifferent types of detonations.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"section\">4.3.2 Key factors influencing risk: conflict and horizontal proliferation<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">The other events driving higher forecasts of nuclear catastrophe\neither involved conflict with nuclear-armed countries or the spread of\nnuclear weapons to new actors (horizontal proliferation).<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">In particular, participants believed conflict between Russia and NATO\nwould increase risk. We asked about both conflict between Russia and the\nUSA and conflict between Russia and a NATO member other than the USA.\nConditional on conflict between Russia and the USA, the median\nparticipant of both groups would roughly triple their forecast of the\nrisk of nuclear catastrophe. The median expert forecast a 5% probability\nof such conflict occurring before 2030, and the median superforecaster\n1.8%. Both groups thought that conflict between Russia and a NATO member\nother than the USA was more likely. The median expert forecast a 10%\nchance of this happening before 2030, and the median superforecaster a\n5.5% chance. The median expert thought this would triple the risk of\nnuclear catastrophe by 2045, and the median superforecaster that it\nwould roughly double it.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Participants thought the probability of a Chinese invasion of Taiwan\nwas more likely than other types of conflict, with the median expert\nforecasting a 25% probability of this happening before 2030, and the\nmedian superforecaster 19%. A Chinese invasion of Taiwan was also\nassociated with a substantial increase in the probability of nuclear\ncatastrophe, increasing by roughly 2.3 times for the median expert and\nroughly doubling for the median superforecaster.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The median expert also thought there was a 20% chance of violent\nconflict between India and Pakistan before 2030, which they thought\nwould increase the risk by around 40%. Superforecasters were more\nskeptical of both the probability of this event occurring (median of\n6.5%) and its importance for nuclear risk (with a median relative risk\nof 1, indicating no change in risk).<\/p>\n\n\n\n<figure class=\"wp-block-table\"><div class=\"table-wrapper\"><table><tbody><tr><td rowspan=\"2\"><strong>Event by 2030<\/strong><\/td><td colspan=\"3\"><strong>Expert<\/strong><br>Median (IQR)<\/td><td colspan=\"3\"><strong>Superforecaster<\/strong><br>Median (IQR)<\/td><\/tr><tr><td><strong>N<\/strong>*<\/td><td><strong>Relative risk<\/strong><\/td><td><strong>Probability of occurring<\/strong><\/td><td><strong>N<\/strong><\/td><td><strong>Relative risk<\/strong><\/td><td><strong>Probability of occurring<\/strong><\/td><\/tr><tr><td>500 militarized deaths between Russia and the USA<\/td><td>50<\/td><td>3.1x<br>(1.5\u201311.8x)<\/td><td>5%<br>(1\u201310%)<\/td><td>36<\/td><td>2.8x<br>(1.5\u201312.5x)<\/td><td>1.8%<br>(0.6\u20135%)<\/td><\/tr><tr><td>500 militarized deaths between Russia and a different NATO country<\/td><td>50<\/td><td>3x<br>(1.3\u20137.5x)<\/td><td>10%<br>(2.8\u201332.5%)<\/td><td>36<\/td><td>1.9x<br>(1.2\u20133.2x)<\/td><td>5.5%<br>(1.2\u201315%)<\/td><\/tr><tr><td>China invades Taiwan<\/td><td>36<\/td><td>2.3x<br>(1\u20135.5x)<\/td><td>25%<br>(10\u201345%)<\/td><td>11<\/td><td>1.9x<br>(1.3\u20133.6x)<\/td><td>19%<br>(4\u201331.5%)<\/td><\/tr><tr><td>500 militarized deaths between North Korea and USA<\/td><td>48<\/td><td>1.7x<br>(1\u20135x)<\/td><td>4%<br>(1\u201312.5%)<\/td><td>15<\/td><td>2x<br>(1.4\u20139x)<\/td><td>2%<br>(1\u20134%)<\/td><\/tr><tr><td>500 militarized deaths between China and the USA<\/td><td>36<\/td><td>1.8x<br>(1\u20135x)<\/td><td>10%<br>(1.3\u201330%)<\/td><td>11<\/td><td>2x<br>(1.2\u20133.4x)<\/td><td>6%<br>(2.8\u201314%)<\/td><\/tr><tr><td>500 militarized deaths between North Korea and South Korea<\/td><td>49<\/td><td>1.6x<br>(1\u20135x)<\/td><td>8%<br>(4.5\u201321.3%)<\/td><td>15<\/td><td>1.4x<br>(1.1\u20133x)<\/td><td>3.3%<br>(2\u20135.3%)<\/td><\/tr><tr><td>500 militarized deaths between India and Pakistan<\/td><td>42<\/td><td>1.4x<br>(1\u20133.5x)<\/td><td>20%<br>(5\u201350%)<\/td><td>14<\/td><td>1x<br>(1\u20131.1x)<\/td><td>6.5%<br>(3.5\u201322.8%)<\/td><\/tr><\/tbody><\/table><\/div><figcaption class=\"wp-element-caption\"><strong>Table 5:<\/strong> Relative risk of nuclear catastrophe conditional on different types of violent conflict occurring before 2030 and probability of event occurring by 2030.<br>*N is the number of responses for relative risk. 110 experts provided forecasts on the probability of the events occurring.<\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">The median expert thought that non-state actors acquiring nuclear weapons would double the probability of nuclear catastrophe. The median expert forecast a 1% probability of this occurring by 2030, and the median superforecaster a 0.3% probability. Both groups thought it more likely that Iran would acquire a nuclear weapon (with median forecasts of 25% and 30% for experts and superforecasters, respectively). The median expert\u2019s forecast of nuclear catastrophe would increase by roughly 50% if this were to occur, and the median superforecaster\u2019s by approximately 20%. For a more detailed discussion of the rationales participants gave for their responses to these crux questions, please see <a href=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=191\" id=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=85\" target=\"_blank\" rel=\"noreferrer noopener\">Appendix 10<\/a>.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><div class=\"table-wrapper\"><table><tbody><tr><td rowspan=\"2\"><strong>Event by 2030<\/strong><\/td><td colspan=\"3\"><strong>Expert<\/strong><br><span style=\"font-size: revert;font-family: inherit;font-weight: inherit;color: initial\">Median (IQR)<\/span><\/td><td colspan=\"3\"><strong>Superforecaster<\/strong><br><span style=\"font-size: revert;font-family: inherit;font-weight: inherit;color: initial\">Median (IQR)<\/span><\/td><\/tr><tr><td>N*<\/td><td>Relative risk<\/td><td>Probability of occurring<\/td><td>N<\/td><td>Relative risk<\/td><td>Probability of occurring<\/td><\/tr><tr><td>A non-state actor acquires nuclear weapons<\/td><td>88<\/td><td>2x <p>(1.2\u201310x)<\/p><\/td><td>1% <p>(0.002\u20135%)<\/p><\/td><td>39<\/td><td>1.8x <p>(1\u20135x)<\/p><\/td><td>0.3% <p>(0.1\u20131.4%)<\/p><\/td><\/tr><tr><td>Iran acquires nuclear weapons<\/td><td>88<\/td><td>1.5x <p>(1.1\u20133x)<\/p><\/td><td>25% <p>(15\u201350%)<\/p><\/td><td>39<\/td><td>1.2x <p>(1.1\u20131.5x)<\/p><\/td><td>30% <p>(10\u2013 50%)<\/p><\/td><\/tr><tr><td>Any state other than Iran acquires nuclear weapons<\/td><td>89<\/td><td>1.3x <p>(1\u20132.4x)<\/p><\/td><td>1% <p>(0.3\u201310%)<\/p><\/td><td>39<\/td><td>1.2x <p>(1.1\u20131.7x)<\/p><\/td><td>5% <p>(1\u201310%)<\/p><\/td><\/tr><\/tbody><\/table><\/div><figcaption class=\"wp-element-caption\"><strong>Table 6:<\/strong> Relative risk of nuclear catastrophe conditional on different actors acquiring nuclear weapons before 2030 and probability of this occurring. The median and interquartile ranges for experts and superforecasters are shown. <br>*N is the number of responses for relative risk. 110 experts provided forecasts on the probability of the events occurring.<\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Several other cruxes (including the USA withdrawing from NATO or ROKUS, increasing entanglement of nuclear and non-nuclear forces, vertical proliferation, and states other than North Korea conducting weapons tests) were also associated with smaller increases in the risk of nuclear catastrophe. For more detail, see <a href=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=201\" id=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=85\" target=\"_blank\" rel=\"noreferrer noopener\">Appendix 11<\/a>.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"factors-that-generally-did-not-influence-forecasts\">4.3.3\nFactors that generally did not influence forecasts<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">Perhaps the most striking finding is that many of the participants\nwouldn\u2019t change their forecast of nuclear risk if many of the cruxes\nwere to occur. As discussed earlier, these cruxes included an accidental\nnon-test detonation. They also included whether nuclear-armed states do\nor do not have no-first-use policies. Figure 18 shows how participants\u2019\nforecasts of nuclear catastrophe changed conditional on the USA having a\nno-first-use policy by 2030. Most participants thought that this\nwouldn\u2019t affect risk at all. Some thought it would reduce risk, and a\nsmaller number thought it would increase risk.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/forecastingresearch.org\/wp-content\/uploads\/2026\/03\/working-paper_2024-10-29_nuclear-risk_fig-18.png\" alt=\"Figure 18: Plot shows how individual respondents\u2019 forecasts of nuclear catastrophe would change conditional on the USA having a no-first-use policy before 2030. The blue and orange dots show the baseline forecasts, and the tips of the arrows show the forecast conditional on the event. Yellow dots indicate forecasts that would not change conditional on the event. The y-axis uses a logarithmic scale to informatively show variation in forecasts in the 0\u201310% range.\"\/><figcaption class=\"wp-element-caption\"><strong>Figure 18:<\/strong> Plot shows how individual respondents\u2019 forecasts of nuclear catastrophe would change conditional on the USA having a no-first-use policy before 2030. The blue and orange dots show the baseline forecasts, and the tips of the arrows show the forecast conditional on the event. Yellow dots indicate forecasts that would not change conditional on the event. The y-axis uses a logarithmic scale to informatively show variation in forecasts in the 0\u201310% range.<\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Other potential cruxes that the median participant thought would not\naffect their forecast of catastrophe were:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Summits between adversarial countries<\/li>\n\n\n\n<li>A terrorist attack in India that is blamed on Pakistan<\/li>\n\n\n\n<li>A nuclear weapons test by North Korea<\/li>\n\n\n\n<li>Ballistic missile submarines becoming more detectable<\/li>\n\n\n\n<li>The US rejoining JCPOA or a similar agreement<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">As with the USA having a no-first-use policy, many participants did think that these cruxes would influence the risk of nuclear catastrophe, although there wasn\u2019t consensus in which direction (see Figure 19). However, in most of these cases, a plurality of respondents thought that the event occurring would have no impact on the probability of nuclear catastrophe. Details on how participants responded to all of the cruxes are in <a href=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=201\" id=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=85\" target=\"_blank\" rel=\"noreferrer noopener\">Appendix 11<\/a>.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/forecastingresearch.org\/wp-content\/uploads\/2026\/03\/working-paper_2024-10-29_nuclear-risk_fig-19.png\" alt=\"Figure 19: Plot shows the proportion of respondents who would increase, decrease, or not change their forecast of nuclear catastrophe, if they knew that different events would occur by 2030. For each of the events in this plot, the median participant\u2019s relative risk was one, indicating the event would not change their forecast of nuclear catastrophe.\"\/><figcaption class=\"wp-element-caption\"><strong>Figure 19:<\/strong> Plot shows the proportion of respondents who would increase, decrease, or not change their forecast of nuclear catastrophe, if they knew that different events would occur by 2030. For each of the events in this plot, the median participant\u2019s relative risk was one, indicating the event would not change their forecast of nuclear catastrophe.<\/figcaption><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"views-on-policies\">5. Views on policies<\/h2>\n\n\n\n<div class=\"wp-block-group\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h4 class=\"wp-block-heading\" id=\"box-key-points-2\">Key points<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Of the policies we asked about, the two most popular amongst\nexperts and superforecasters were:\n<ul class=\"wp-block-list\">\n<li>A secure multilateral crisis communications center<\/li>\n\n\n\n<li>All nuclear-armed states conducting failsafe reviews<\/li>\n<\/ul>\n<\/li>\n\n\n\n<li>These two policies were thought to be the most effective in\nreducing the risk of a nuclear catastrophe, had the best average ranks,\nand had at least a 20% chance of being implemented, conditional on\nfunding aimed at their implementation.<\/li>\n\n\n\n<li>If six policies were to be implemented, the median expert thought\nthat the risk of nuclear catastrophe could be halved, and the median\nsuperforecaster thought it could be reduced by 40%.<\/li>\n<\/ul>\n<\/div><\/div>\n\n\n\n<p class=\"wp-block-paragraph\">We investigated views on 23 different policies that have been suggested as mechanisms to reduce the risk of nuclear catastrophe. We included six general policies (i.e., not specific to any adversarial domain) and 17 domain-specific policies. The full description of the policies is in <a href=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=142\" id=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=85\" target=\"_blank\" rel=\"noreferrer noopener\">Appendix 4<\/a>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">As different domain-specific policies were answered by different\nparticipants, and the sample size for each of these domain-specific\npolicies was relatively small, we advise caution in interpreting the\ndomain-specific policy results and particularly in making comparisons\nacross the adversarial domains. For this reason, we present the results\nof the general policies and the domain-specific policies separately.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"general-policies\">5.1 General policies<\/h3>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"policy-impact-on-risk-of-nuclear-catastrophe\">5.1.1 Policy\nimpact on risk of nuclear catastrophe<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">Most expert participants thought each of these six policies would\nreduce the risk of a nuclear catastrophe by between 9% and 25% relative\nto their unconditional forecast. In general, compared to experts,\nsuperforecasters thought policies would make less of a difference to\nnuclear risk. The median superforecaster thought most policies would\nreduce risk by between 3% and 15%. Figures 20 and 21 show how the median\nexpert and median superforecaster\u2019s estimate of the probability of\nnuclear catastrophe changes when considering each of these six policies.\nWe also asked participants how their forecast of the probability of\nnuclear catastrophe would change if <em>all<\/em> of these six policies\nwere implemented. Although this scenario is very unlikely to occur, it\ngives an indication of how nuclear risk could change if drastic actions\nwere taken. The median participant thought these six policies\nimplemented together could halve the probability of nuclear catastrophe\nby 2045. Table 7 shows the median participants\u2019 views on the\neffectiveness of the six general policies in terms of relative and\nabsolute changes in risk.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/forecastingresearch.org\/wp-content\/uploads\/2026\/03\/working-paper_2024-10-29_nuclear-risk_fig-20.png\" alt=\"Figure 20: Median relative change in probability of nuclear catastrophe conditional on policy implementation, for experts (blue) and superforecasters (orange). The difference between the height of the bar associated with the policy and the bar labeled \u201cunconditional\u201d represents relative reduction in risk of nuclear catastrophe associated with the policy by the median expert (left) and superforecaster (right). E.g., the median expert would reduce their forecast of nuclear catastrophe by 25% if a crisis communications network were to be established.\"\/><figcaption class=\"wp-element-caption\"><strong>Figure 20:<\/strong> Median relative change in probability of nuclear catastrophe conditional on policy implementation, for experts (blue) and superforecasters (orange). The difference between the height of the bar associated with the policy and the bar labeled \u201cunconditional\u201d represents relative reduction in risk of nuclear catastrophe associated with the policy by the median expert (left) and superforecaster (right). E.g., the median expert would reduce their forecast of nuclear catastrophe by 25% if a crisis communications network were to be established.<\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/forecastingresearch.org\/wp-content\/uploads\/2026\/03\/working-paper_2024-10-29_nuclear-risk_fig-02.png\" alt=\"Figure 21: Violin plots showing distribution of relative risk associated with each policy. The relative risk is the relative reduction in probability of nuclear catastrophe conditional on policy implementation. The group median relative risk is shown in text. The thicker bar within each violin shows the interquartile range (25th to 75th percentile forecasts), and the thin line shows the range of forecasts minus outliers.\"\/><figcaption class=\"wp-element-caption\"><strong>Figure 21:<\/strong> Violin plots showing distribution of relative risk associated with each policy. The relative risk is the relative reduction in probability of nuclear catastrophe conditional on policy implementation. The group median relative risk is shown in text. The thicker bar within each violin shows the interquartile range (25<sup>th<\/sup> to 75<sup>th<\/sup> percentile forecasts), and the thin line shows the range of forecasts minus outliers.<\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-table\"><div class=\"table-wrapper\"><table class=\"has-fixed-layout\"><tbody><tr><td><\/td><td colspan=\"3\"><strong>Expert<\/strong> <p>Median forecast (IQR)<\/p><\/td><td colspan=\"3\"><strong>Superforecaster<\/strong> <p>Median forecast (IQR)<\/p><\/td><\/tr><tr><td><strong>Policy<\/strong><\/td><td><strong>N*<\/strong><\/td><td><strong>Relative change in\nrisk<\/strong><\/td><td><strong>Absolute change in\nrisk<\/strong><\/td><td><strong>N<\/strong><\/td><td><strong>Relative change in\nrisk<\/strong><\/td><td><strong>Absolute change in\nrisk<\/strong><\/td><\/tr><tr><td>AI risk assessment<\/td><td>94<\/td><td>0.8x<br>(0.6\u20130.96x)<\/td><td>0.5p<span style=\"font-family: inherit;font-size: inherit;font-weight: inherit;color: initial\">.p.<\/span><br>(0\u20133p.p.)<\/td><td>37<\/td><td>0.93x<br>(0.83\u20130.98x)<\/td><td>0.04p.p.<br>(0.004\u20130.2p.p.)<\/td><\/tr><tr><td>Crisis communications network<\/td><td>96<\/td><td>0.75x<br>(0.49\u20130.9x)<\/td><td>0.85p.p. <br>(0.02\u20134.4p.p.)<\/td><td>37<\/td><td>0.85x<br>(0.71\u20130.95x)<\/td><td>0.1p.p. <br>(0.02\u20130.65p.p.)<\/td><\/tr><tr><td>CTBT is ratified<\/td><td>95<\/td><td>0.9x<br>(0.67\u20131x)<\/td><td>0.05p.p.<br>(0\u20133p.p.)<\/td><td>37<\/td><td>0.95x<br>(0.8\u20131x)<\/td><td>0.01p.p.<br>(0\u20130.25p.p.)<\/td><\/tr><tr><td>Failsafe reviews<\/td><td>96<\/td><td>0.8x<br>(0.61\u20130.94x)<\/td><td>0.5p.p.<br>(0\u20132p.p.)<\/td><td>37<\/td><td>0.<span style=\"font-family: inherit;font-size: inherit;font-weight: inherit;color: initial\">9<\/span>x<br>(0.75\u20130.98x)<\/td><td>0.1p.p. <br>(0.01\u20130.4p.p.)<\/td><\/tr><tr><td>FMCT is signed<\/td><td>95<\/td><td>0<span style=\"font-family: inherit;font-size: inherit;font-weight: inherit;color: initial\">.91x<\/span><br><span style=\"font-family: inherit;font-size: inherit;font-weight: inherit;color: initial\">(0.78\u20131x)<\/span><\/td><td>0.01p.p.<br>(0\u20132p.p.)<\/td><td>37<\/td><td>0.97x<br>(0.89\u20131x)<\/td><td>0.01p.p.<br>(0\u20130.1p.p.)<\/td><\/tr><tr><td>USA removes \u2018sole authority\u2019<\/td><td>96<\/td><td>0.9x<br>(0.75\u20131x)<\/td><td>0.1p.p.<br>(0\u20131p.p.)<\/td><td>37<\/td><td>0.97x<br>(0.83\u20131x)<\/td><td>0.02p.p.<br>(0\u20130.13p.p.)<\/td><\/tr><tr><td>All six policies together<\/td><td>96<\/td><td>0.5x<br>(0.2\u20130.69x)<\/td><td>2p.p.<br>(0.18\u20135p.p.)<\/td><td>37<\/td><td>0.58x<br>(0.25\u20130.76x)<\/td><td>0.3p.p.<br>(0.09\u20131.5p.p.)<\/td><\/tr><\/tbody><\/table><\/div><figcaption class=\"wp-element-caption\"><strong>Table 7:<\/strong> Views on effects of policies on probability of nuclear catastrophe. The median and interquartile range (25<sup>th<\/sup> percentile and 75<sup>th<\/sup> percentile estimates) are shown for the relative risk (or relative change in risk) and the absolute risk reduction (in percentage points (p.p.)). <br>*The values in this column show the number of experts whose data was used in the relative risk summary statistics. The number for the absolute change in risk is higher by two (as two experts gave an unconditional forecast of catastrophe of zero).<\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Some respondents thought that some of the policies would increase risk. Of the general policies we asked about, the USA removing its \u201csole authority\u201d policy that allows the President to launch a nuclear weapon without approval from others was most often thought to increase risk. 8% of experts and 11% of superforecasters thought this policy would increase risk. Rationales from these respondents argued that the policy would compromise the USA\u2019s ability to act quickly in times of crisis, and would reduce the credibility of US nuclear deterrence (see <a href=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=205\" id=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=85\" target=\"_blank\" rel=\"noreferrer noopener\">Appendix 12<\/a> for more detail). Several expert forecasters believed that all six policies being implemented would increase risk, as this would be a destabilizing change. Table 8 and Figure 22 show the proportion of respondents who thought that each policy would decrease, increase, or not affect risk.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><div class=\"table-wrapper\"><table class=\"has-fixed-layout\"><tbody><tr><td><\/td><td colspan=\"4\"><strong>Expert<\/strong><\/td><td colspan=\"4\"><strong>Superforecaster<\/strong><\/td><\/tr><tr><td><strong>Policy<\/strong><\/td><td><strong>N<\/strong><\/td><td><strong>Decrease risk<\/strong><\/td><td><strong>Not affect risk<\/strong><\/td><td><strong>Increase risk<\/strong><\/td><td><strong>N<\/strong><\/td><td><strong>Decrease risk<\/strong><\/td><td><strong>Not affect risk<\/strong><\/td><td><strong>Increase risk<\/strong><\/td><\/tr><tr><td>AI risk assessment<\/td><td>94<\/td><td>78%<\/td><td>22%<\/td><td>0%<\/td><td>37<\/td><td>86%<\/td><td>14%<\/td><td>0%<\/td><\/tr><tr><td>Crisis communications network<\/td><td>96<\/td><td>89%<\/td><td>11%<\/td><td>0%<\/td><td>37<\/td><td>95%<\/td><td>5%<\/td><td>0%<\/td><\/tr><tr><td>CTBT is ratified<\/td><td>95<\/td><td>61%<\/td><td>39%<\/td><td>0%<\/td><td>37<\/td><td>68%<\/td><td>32%<\/td><td>0%<\/td><\/tr><tr><td>Failsafe reviews<\/td><td>96<\/td><td>78%<\/td><td>22%<\/td><td>0%<\/td><td>37<\/td><td>95%<\/td><td>5%<\/td><td>0%<\/td><\/tr><tr><td>FMCT is signed<\/td><td>95<\/td><td>58%<\/td><td>42%<\/td><td>0%<\/td><td>37<\/td><td>65%<\/td><td>35%<\/td><td>0%<\/td><\/tr><tr><td>USA removes \u2018sole authority\u2019<\/td><td>96<\/td><td>64%<\/td><td>28%<\/td><td>8%<\/td><td>37<\/td><td>62%<\/td><td>27%<\/td><td>11%<\/td><\/tr><tr><td>All six general policies<\/td><td>96<\/td><td>93%<\/td><td>4%<\/td><td>3%<\/td><td>37<\/td><td>100%<\/td><td>0%<\/td><td>0%<\/td><\/tr><\/tbody><\/table><\/div><figcaption class=\"wp-element-caption\"><strong>Table 8:<\/strong> Proportion of respondents who thought the policy would decrease risk (relative risk &lt; 1), not affect risk (relative risk = 1), and increase risk (relative risk &gt; 1).<\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/forecastingresearch.org\/wp-content\/uploads\/2026\/03\/working-paper_2024-10-29_nuclear-risk_fig-22.png\" alt=\"Figure 22: Plot shows the proportion of respondents who would increase, decrease, or not change their forecast of nuclear catastrophe, conditional on policies being implemented.\"\/><figcaption class=\"wp-element-caption\"><strong>Figure 22:<\/strong> Plot shows the proportion of respondents who would increase, decrease, or not change their forecast of nuclear catastrophe, conditional on policies being implemented.<\/figcaption><\/figure>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"probability-of-policy-implementation-and-effects-of-funding\">5.1.2\nProbability of policy implementation and effects of funding<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">We asked participants to forecast the probability that each of these\npolicies would be implemented. When participants were forecasting the\neffects of the policies, we asked them to make their forecast as if work\nto implement the policy would begin immediately. The description of each\npolicy included a date by which implementation would be complete. When\nwe asked participants to forecast the probability that the policy would\nbe implemented, we pushed this date back by three years. So, we asked\nparticipants to forecast the probability that the policy would be\nimplemented by three years later than the original date we described\nwhen asking participants to forecast the effects of policies. We asked\nfor an unconditional forecast and a forecast conditional on a nonprofit\nteam being given $500 million with the goal of getting the policy\nimplemented. Table 9 and Figure 23 summarize these results.<\/p>\n\n\n\n<figure class=\"wp-block-table\"><div class=\"table-wrapper\"><table class=\"has-fixed-layout\"><tbody><tr><td rowspan=\"3\"><strong>Policy<\/strong><\/td><td colspan=\"4\"><strong>Expert<\/strong><br>Median (IQR)<\/td><td colspan=\"4\"><strong>Superforecaster<\/strong><br>Median (IQR)<\/td><\/tr><tr><td rowspan=\"2\"><strong>N<\/strong><\/td><td colspan=\"2\"><strong>Probability implemented<\/strong><\/td><td rowspan=\"2\"><strong>Funding multiplier<\/strong><\/td><td rowspan=\"2\"><strong>N<\/strong><\/td><td colspan=\"2\"><strong>Probability implemented<\/strong><\/td><td rowspan=\"2\"><strong>Funding multiplier<\/strong><\/td><\/tr><tr><td><strong>Baseline<\/strong><\/td><td><strong>With funding<\/strong><\/td><td><strong>Baseline<\/strong><\/td><td><strong>With funding<\/strong><\/td><\/tr><tr><td>AI risk assessment<\/td><td>102<\/td><td>20%<br>(10\u201340%)<\/td><td>30%<br>(10\u201353.8%)<\/td><td>1.5x<br>(1.1\u20131.9x)<\/td><td>39<\/td><td>9%<br>(3\u201321.5%)<\/td><td>12%<br>(4\u201331.5%)<\/td><td>1.3x<br>(1.1\u20131.5x)<\/td><\/tr><tr><td>Crisis comms. network<\/td><td>104<\/td><td>15%<br>(5\u201330%)<\/td><td>25%<br>(10\u201350%)<\/td><td>1.4x<br>(1.1\u20131.8x)<\/td><td>39<\/td><td>10%<br>(4\u201325%)<\/td><td>18%<br>(6\u201336.5%)<\/td><td>1.3x<br>(1.1\u20132x)<\/td><\/tr><tr><td>CTBT is ratified<\/td><td>97<\/td><td>6.5%<br>(1\u201315%)<\/td><td>10%<br>(2.6\u201328.8%)<\/td><td>1.3x<br>(1\u20132x)<\/td><td>36<\/td><td>5%<br>(1\u20139.5%)<\/td><td>7%<br>(2\u201310%)<\/td><td>1.1x<br>(1\u20131.4x)<\/td><\/tr><tr><td>Failsafe reviews<\/td><td>103<\/td><td>15%<br>(10\u201330%)<\/td><td>30%<br>(15\u201350%)<\/td><td>1.5x<br>(1.2\u20132x)<\/td><td>38<\/td><td>7%<br>(3.5\u201316%)<\/td><td>10%<br>(5.1\u201323%)<\/td><td>1.4x<br>(1.1\u20132x)<\/td><\/tr><tr><td>FMCT is signed<\/td><td>97<\/td><td>5%<br>(1\u201310%)<\/td><td>6.5%<br>(2\u201315.8%)<\/td><td>1.2x<br>(1\u20131.8x)<\/td><td>38<\/td><td>5%<br>(1\u201313.5%)<\/td><td>9%<br>(2\u201315%)<\/td><td>1.1x<br>(1\u20131.5x)<\/td><\/tr><tr><td>USA removes sole authority<\/td><td>98<\/td><td>10%<br>(3\u201320%)<\/td><td>20%<br>(6\u201335%)<\/td><td>1.5x<br>(1.2\u20132x)<\/td><td>35<\/td><td>4%<br>(0.5\u201310%)<\/td><td>5%<br>(1\u201316.5%)<\/td><td>1.3x<br>(1\u20131.9x)<\/td><\/tr><\/tbody><\/table><\/div><figcaption class=\"wp-element-caption\"><strong>Table 9:<\/strong> Views on the probability of policies being implemented (with implementation on a time scale that suggests a decision to implement is made within the next three years). The table shows the unconditional forecast of policy implementation and the forecast conditional on $500 million of funding going to a nonprofit tasked with getting the policy implemented. The median and interquartile range (25th percentile and 75th percentile estimates) are shown for each value.<\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/forecastingresearch.org\/wp-content\/uploads\/2026\/03\/working-paper_2024-10-29_nuclear-risk_fig-23.png\" alt=\"Figure 23: Violin plots showing distribution of forecasts of the probability of policies being implemented, unconditionally (top) and conditional on $500 million in funding being provided to a non-profit group with the goal of getting the policy implemented (bottom). The thick line next to each violin shows the interquartile range (25th to 75th percentile forecasts), the thin line shows the range of forecasts minus outliers, and the text shows the median forecast.\"\/><figcaption class=\"wp-element-caption\"><strong>Figure 23:<\/strong> Violin plots showing distribution of forecasts of the probability of policies being implemented, unconditionally (top) and conditional on $500 million in funding being provided to a non-profit group with the goal of getting the policy implemented (bottom). The thick line next to each violin shows the interquartile range (25<sup>th<\/sup> to 75<sup>th<\/sup> percentile forecasts), the thin line shows the range of forecasts minus outliers, and the text shows the median forecast.<\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Beliefs about the probability that these policies would be\nimplemented vary widely, both among participants and among policies.\nHowever, the median expert thought that, for the three policies believed\nto be most effective in reducing risk (crisis communications, failsafe\nreviews, and AI risk), there was at least a 15% chance of the policy\nbeing implemented. The median superforecaster thought there was at least\na 7% probability that these policies are implemented. Both groups\nthought that funding could make a meaningful difference to the\nprobability a policy is implemented. The median expert thought the\nprobability could increase by 20-50% depending on the policy, and the\nmedian superforecaster thought it could increase by 11-41%.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"policy-ranking\">5.1.3 Policy ranking<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">We asked participants to rank the policies they were shown in two\nways: first, by how much they would like the policy to be implemented,\nand second, by how much they would like $500 million to go to a\nhypothetical nonprofit aiming to have the policy implemented. When\nranking for funding, participants were asked to consider the effects of\nthe policy, probability the policy would be implemented, and the\ndifference funding would make.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Table 10 shows the average rank and the proportion of participants\nwho ranked each policy within the top three, for implementation and for\nfunding. These values include the domain-specific policies in the ranks\n(rank is out of nine).<\/p>\n\n\n\n<figure class=\"wp-block-table\"><div class=\"table-wrapper\"><table class=\"has-fixed-layout\"><tbody><tr><td><\/td><td colspan=\"4\"><strong>Experts<\/strong><br>(N = 109)<\/td><td colspan=\"4\"><strong>Superforecasters<\/strong><br>(N = 39)<\/td><\/tr><tr><td><\/td><td colspan=\"2\"><strong>Average rank<\/strong><\/td><td colspan=\"2\"><strong>Rank in top 3<\/strong><\/td><td colspan=\"2\"><strong>Average rank<\/strong><\/td><td colspan=\"2\"><strong>Rank in top 3<\/strong><\/td><\/tr><tr><td><strong>Policy<\/strong><\/td><td>To implement<\/td><td>For funding<\/td><td>To implement<\/td><td>For funding<\/td><td>To implement<\/td><td>For funding<\/td><td>To implement<\/td><td>For funding<\/td><\/tr><tr><td>AI risk assessment<\/td><td>4.6<\/td><td>3.6<\/td><td>39%<\/td><td>52%<\/td><td>4.7<\/td><td>3.9<\/td><td>33%<\/td><td>46%<\/td><\/tr><tr><td>Crisis comms. network<\/td><td>3.3<\/td><td>2.9<\/td><td>58%<\/td><td>70%<\/td><td>3.0<\/td><td>2.4<\/td><td>62%<\/td><td>77%<\/td><\/tr><tr><td>CTBT is ratified<\/td><td>4.3<\/td><td>4.7<\/td><td>41%<\/td><td>28%<\/td><td>4.7<\/td><td>4.8<\/td><td>33%<\/td><td>26%<\/td><\/tr><tr><td>Failsafe reviews<\/td><td>3.8<\/td><td>3.5<\/td><td>50%<\/td><td>61%<\/td><td>3.7<\/td><td>3.3<\/td><td>51%<\/td><td>62%<\/td><\/tr><tr><td>FMCT is signed<\/td><td>5.2<\/td><td>5.7<\/td><td>28%<\/td><td>17%<\/td><td>4.9<\/td><td>4.9<\/td><td>31%<\/td><td>18%<\/td><\/tr><tr><td>USA removes sole authority<\/td><td>6.7<\/td><td>6.3<\/td><td>13%<\/td><td>11%<\/td><td>6.0<\/td><td>6.4<\/td><td>28%<\/td><td>18%<\/td><\/tr><\/tbody><\/table><\/div><figcaption class=\"wp-element-caption\"><strong>Table 10:<\/strong> Results of ranking exercises. Participants were asked to rank the policies in two ways: by how much they would like the policy to be implemented and by how much they would like $500 million in funding to go to a nonprofit that had the goal of getting the policy implemented. For both types of ranking we show the average rank for experts and superforecasters and the proportion of each group who ranked the policy within the top 3.<\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Figures 24 and 25 show experts\u2019 and superforecasters\u2019 ranking of the\nsix general policies by how much participants would like to see funding\ngo towards having the policy implemented. Ranks closer to one indicate a\nstronger preference for that policy relative to others. Participants\nalso ranked the three domain-specific policies they answered questions\non. The ranks shown in Figures 24 and 25 exclude those domain-specific\npolicies (and so ranks are shown out of six).<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/forecastingresearch.org\/wp-content\/uploads\/2026\/03\/working-paper_2024-10-29_nuclear-risk_fig-24.png\" alt=\"Figure 24: Experts\u2019 ranking of the six general policies when considering where they would like $500 million funding to go to a nonprofit group who has the goal of getting the policy implemented. These are listed in order of average rank (most favored to least favored). The values inside the squares show the proportion of expert respondents who gave the policy that rank out of the six general policies.\"\/><figcaption class=\"wp-element-caption\"><strong>Figure 24:<\/strong> Experts\u2019 ranking of the six general policies when considering where they would like $500 million funding to go to a nonprofit group who has the goal of getting the policy implemented. These are listed in order of average rank (most favored to least favored). The values inside the squares show the proportion of expert respondents who gave the policy that rank out of the six general policies.<\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/forecastingresearch.org\/wp-content\/uploads\/2026\/03\/working-paper_2024-10-29_nuclear-risk_fig-25.png\" alt=\"Figure 25: Superforecasters\u2019 ranking of the six general policies when considering where they would like $500 million funding to go to a nonprofit group who has the goal of getting the policy implemented. These are listed in order of average rank (most favored to least favored). The values inside the squares show the proportion of expert respondents who gave the policy that rank out of the general policies.\"\/><figcaption class=\"wp-element-caption\"><strong>Figure 25:<\/strong> Superforecasters\u2019 ranking of the six general policies when considering where they would like $500 million funding to go to a nonprofit group who has the goal of getting the policy implemented. These are listed in order of average rank (most favored to least favored). The values inside the squares show the proportion of expert respondents who gave the policy that rank out of the general policies.<\/figcaption><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"the-two-most-popular-policies\">5.2 The two most popular\npolicies<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">Of the six general policies, two were clearly favored by both groups\nof participants: establishing a crisis communications network, and all\nnuclear-armed countries conducting failsafe reviews. These policies were\ngenerally seen as more effective in reducing risk and more likely to be\nimplemented than others. These policies were ranked within the top three\nby more than half of all participants. When asked about which policies\nthey\u2019d prefer to see funding go towards, these two policies were ranked\nwithin the top three by at least 60% of participants in both groups.<\/p>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"crisis-communications-network\">5.2.1 Crisis communications\nnetwork<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">This policy would see a secure multilateral crisis communications network established with all nuclear-armed states participating. The full details of the policy (as it was described to participants) is available in <a href=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=142\" id=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=85\" target=\"_blank\" rel=\"noreferrer noopener\">Appendix 4<\/a>. In summary, the policy would see that a secure multilateral crisis communication network (such as the proposed CATALINK network)<sup data-fn=\"33715d4d-a106-44fa-9bcc-e75ea03073c0\" class=\"fn\"><a href=\"#33715d4d-a106-44fa-9bcc-e75ea03073c0\" id=\"33715d4d-a106-44fa-9bcc-e75ea03073c0-link\">37<\/a><\/sup> is established. The network would be encrypted and robust to threats, and would allow for direct leader-to-leader communication, with the ability to conduct bilateral or multilateral communications. All nuclear-armed states would be actively participating in the network. This policy would build on existing \u201chotlines\u201d between adversaries by providing a more secure connection that was more actively maintained and allowed for multilateral communications.<\/p>\n\n\n\n<h5 class=\"wp-block-heading\" id=\"effect-of-policy\"><strong>Effect of policy<\/strong><\/h5>\n\n\n\n<p class=\"wp-block-paragraph\">This policy was associated with the largest relative risk reduction\nfor both the median expert and the median superforecaster. Figure 26\nshows the distribution of relative risk reduction attributed to this\npolicy. It shows that roughly 80% of superforecasters and 60% of experts\nthought that this policy would reduce risk by up to 50%. Figure 27 shows\nhow each individual participants\u2019 forecast would change conditional on\nthe policy being implemented.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/forecastingresearch.org\/wp-content\/uploads\/2026\/03\/working-paper_2024-10-29_nuclear-risk_fig-26.png\" alt=\"Figure 26: Plot shows the proportion of respondents whose relative risk (relative change in risk of nuclear catastrophe) for the crisis communications network policy falls into each of the ranges.\"\/><figcaption class=\"wp-element-caption\"><strong>Figure 26:<\/strong> Plot shows the proportion of respondents whose relative risk (relative change in risk of nuclear catastrophe) for the crisis communications network policy falls into each of the ranges.<\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/forecastingresearch.org\/wp-content\/uploads\/2026\/03\/working-paper_2024-10-29_nuclear-risk_fig-27.png\" alt=\"Figure 27: Plot shows how individual respondents\u2019 forecasts of nuclear catastrophe would change conditional on a crisis communications network being established. The blue and orange dots show the baseline forecasts, and the tips of the arrows show the forecast conditional on the event. Yellow dots indicate forecasts that would not change conditional on the event. The y-axis uses a logarithmic scale to informatively show variation in forecasts in the 0\u201310% range.\"\/><figcaption class=\"wp-element-caption\"><strong>Figure 27:<\/strong> Plot shows how individual respondents\u2019 forecasts of nuclear catastrophe would change conditional on a crisis communications network being established. The blue and orange dots show the baseline forecasts, and the tips of the arrows show the forecast conditional on the event. Yellow dots indicate forecasts that would not change conditional on the event. The y-axis uses a logarithmic scale to informatively show variation in forecasts in the 0\u201310% range.<\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">When explaining the rationales for their forecasts, participants who thought this policy would likely reduce risk substantially cited the importance of secure and prompt communication to prevent misunderstandings and the benefits of building trust and transparency between states. Many participants pointed to the Cuban Missile Crisis as an example of the importance of effective communication during crises. Participants who were less optimistic about the policy\u2019s effects suggested that some states may not use the network (as some states have not used bilateral hotlines), or may misuse it to spread misinformation or sow confusion. Some also noted that this policy doesn\u2019t affect the underlying drivers of nuclear risk. For more details of arguments made in rationales, see <a href=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=205\" id=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=85\" target=\"_blank\" rel=\"noreferrer noopener\">Appendix 12<\/a>.<\/p>\n\n\n\n<h5 class=\"wp-block-heading\" id=\"probability-of-policy-implementation-1\"><strong>Probability of\npolicy implementation<\/strong><\/h5>\n\n\n\n<p class=\"wp-block-paragraph\">The median superforecaster thought that, of the six general policies,\na crisis communications network was the most likely to be implemented\n(with or without funding). They forecast a 10% probability of the policy\nbeing implemented, with that figure rising to 18% with dedicated\nfunding. Experts were more optimistic about the chances of the policy\nbeing implemented, with a median unconditional forecast of 15%, which\nrose to 25% with funding. Figure 28 shows how forecasts of the\nprobability of the policy being implemented change with funding.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Participants who gave higher forecasts for the probability of this\npolicy being implemented suggested that this policy is a sensible and\nrelatively easy step to take, especially as it builds on existing\nbilateral hotlines. Those who gave lower forecasts suggested that\nexisting tensions would make cooperation difficult, especially as some\nstates favor policies of strategic ambiguity. Some suggested that having\n<em>all<\/em> nuclear-armed states participate was a high bar.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/forecastingresearch.org\/wp-content\/uploads\/2026\/03\/working-paper_2024-10-29_nuclear-risk_fig-28.png\" alt=\"Figure 28: Plots show forecasts of the probability of a crisis communications network being established, unconditionally and conditional on $500 million funding going to a hypothetical nonprofit with the goal of getting the policy implemented. The median forecast is provided in text. The boxes show the 25th\u201375th percentile forecasts, and the lines the range of forecasts minus outliers. We jitter the data points horizontally to allow for better visualization of the distribution of forecasts. Horizontal variation within each group serves no other empirical purpose.\"\/><figcaption class=\"wp-element-caption\"><strong>Figure 28:<\/strong> Plots show forecasts of the probability of a crisis communications network being established, unconditionally and conditional on $500 million funding going to a hypothetical nonprofit with the goal of getting the policy implemented. The median forecast is provided in text. The boxes show the 25<sup>th<\/sup>\u201375<sup>th<\/sup> percentile forecasts, and the lines the range of forecasts minus outliers. We jitter the data points horizontally to allow for better visualization of the distribution of forecasts. Horizontal variation within each group serves no other empirical purpose.<\/figcaption><\/figure>\n\n\n\n<h4 class=\"wp-block-heading\" id=\"failsafe-reviews\">5.2.2 Failsafe reviews<\/h4>\n\n\n\n<p class=\"wp-block-paragraph\">This policy would see all national governments of nuclear-armed states establish a review mechanism to identify risks of inadvertent or accidental nuclear use and develop plans for mitigation of these risks. This would include, but is not limited to, false alarms, technical malfunctions, and human error. Each government would conduct an analysis to identify potential pathways to unintentional launches of nuclear weapons or false perceptions of being under attack, and would then consider areas of intervention to reduce the likelihood of these outcomes. The main focus should be on measures the government can take unilaterally to reduce risk from their systems. But if the reviews identify opportunities for risk mitigation that require multilateral action, these should be proposed to other nations\u2019 governments. This policy was loosely based on the \u201cindependent review of the safety, security, and reliability of U.S. nuclear weapons, NC3, and integrated tactical warning\/attack assessment systems\u201d announced in the US 2022 Nuclear Posture Review.<sup data-fn=\"b5117a2d-6ec6-45ba-b711-b4870db57b0f\" class=\"fn\"><a href=\"#b5117a2d-6ec6-45ba-b711-b4870db57b0f\" id=\"b5117a2d-6ec6-45ba-b711-b4870db57b0f-link\">38<\/a><\/sup> The description of this policy provided to respondents is available in <a href=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=142\" id=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=85\" target=\"_blank\" rel=\"noreferrer noopener\">Appendix 4<\/a>.<\/p>\n\n\n\n<h5 class=\"wp-block-heading\" id=\"effect-of-policy-1\"><strong>Effect of policy<\/strong><\/h5>\n\n\n\n<p class=\"wp-block-paragraph\">This policy was associated with the second largest relative risk\nreduction for both the median expert and the median superforecaster.\nFigure 29 shows how the distribution of forecasts on the probability of\nnuclear catastrophe shifts conditional on this policy being implemented.\nFigure 30 shows how each individual participants\u2019 forecast would change\nconditional on the policy being implemented.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/forecastingresearch.org\/wp-content\/uploads\/2026\/03\/working-paper_2024-10-29_nuclear-risk_fig-29.png\" alt=\"Figure 29: Plot shows the proportion of respondents whose relative risk (relative change in risk of nuclear catastrophe) for the Failsafe Reviews policy falls into each of the ranges.\"\/><figcaption class=\"wp-element-caption\"><strong>Figure 29:<\/strong> Plot shows the proportion of respondents whose relative risk (relative change in risk of nuclear catastrophe) for the Failsafe Reviews policy falls into each of the ranges.<\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Rationales for forecasts indicated that many participants thought that failsafe reviews could reduce the risk of accidental or inadvertent nuclear detonations and, by identifying ways to improve decision-making processes, reduce the risk of deliberate escalation during a crisis. Those who thought this policy would do little to reduce nuclear risk suggested that this policy primarily addresses accidental risks, which contribute little to nuclear risk, and do not affect risks of deliberate use. They also noted that nuclear-armed states regularly conduct their own reviews and that the quality of the reviews could vary between states. For more details of arguments made in rationales, see <a href=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=205\" id=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=85\" target=\"_blank\" rel=\"noreferrer noopener\">Appendix 12<\/a>.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/forecastingresearch.org\/wp-content\/uploads\/2026\/03\/working-paper_2024-10-29_nuclear-risk_fig-30.png\" alt=\"Figure 30: Plot shows how individual respondents\u2019 forecasts of nuclear catastrophe would change conditional on the Failsafe Reviews policy being implemented before 2030. The blue and orange dots show the baseline forecasts, and the tips of the arrows show the forecast conditional on the event. Yellow dots indicate forecasts that would not change conditional on the event. The y-axis uses a logarithmic scale to informatively show variation in forecasts in the 0\u201310% range.\"\/><figcaption class=\"wp-element-caption\"><strong>Figure 30:<\/strong> Plot shows how individual respondents\u2019 forecasts of nuclear catastrophe would change conditional on the Failsafe Reviews policy being implemented before 2030. The blue and orange dots show the baseline forecasts, and the tips of the arrows show the forecast conditional on the event. Yellow dots indicate forecasts that would not change conditional on the event. The y-axis uses a logarithmic scale to informatively show variation in forecasts in the 0\u201310% range.<\/figcaption><\/figure>\n\n\n\n<h5 class=\"wp-block-heading\" id=\"probability-of-policy-implementation-2\"><strong>Probability of\npolicy implementation<\/strong><\/h5>\n\n\n\n<p class=\"wp-block-paragraph\">The median expert forecasted a 15% probability of this policy being\nimplemented, with this increasing to 30% with funding. Superforecasters\nforecasted a 7% probability unconditionally and a 10% probability with\nfunding. Figure 31 shows how the distribution of forecasts of the\nprobability of the policy being implemented changes with funding.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Those who were more optimistic about this policy being implemented\nsuggested that this is a relatively uncontroversial policy that is\nlow-risk and provides a way for states to demonstrate responsibility.\nMany also suggested that wariness about cyber threats and the impact of\nAI might motivate states to conduct such a review. Those who were less\noptimistic suggested that countries like North Korea and Israel are\nunlikely to participate and that there is little incentive for states to\nagree to conduct a review.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/forecastingresearch.org\/wp-content\/uploads\/2026\/03\/working-paper_2024-10-29_nuclear-risk_fig-31.png\" alt=\"Figure 31: Plots show forecasts of the probability of the Failsafe Reviews policy being implemented, unconditionally and conditional on $500 million funding going to a hypothetical nonprofit with the goal of getting the policy implemented. The median forecast is provided in text. The boxes show the 25th\u201375th percentile forecasts, and the lines the range of forecasts minus outliers. We jitter the data points horizontally to allow for better visualization of the distribution of forecasts. Horizontal variation within each group serves no other empirical purpose.\"\/><figcaption class=\"wp-element-caption\"><strong>Figure 31:<\/strong> Plots show forecasts of the probability of the Failsafe Reviews policy being implemented, unconditionally and conditional on $500 million funding going to a hypothetical nonprofit with the goal of getting the policy implemented. The median forecast is provided in text. The boxes show the 25<sup>th<\/sup>\u201375<sup>th<\/sup> percentile forecasts, and the lines the range of forecasts minus outliers. We jitter the data points horizontally to allow for better visualization of the distribution of forecasts. Horizontal variation within each group serves no other empirical purpose.<\/figcaption><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"domain-specific-policies\">5.3 Domain-specific policies<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">As different domain-specific policy questions were answered by\ndifferent participants, and the sample size for each of these\ndomain-specific policies was relatively small, we advise caution in\ninterpreting these results, and particularly in making comparisons\nacross the adversarial domains.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">While noting the need for caution with these results, there are a few\nfindings worth commenting on. Participants generally ranked\ndomain-specific policies less favorably than general policies. The\naverage rank for general policies was 4.6, and the average rank for\ndomain-specific policies was 5.8 (when ranking for funding the values\nwere 4.4 and 6.2). However, there were some exceptions to this.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Among the 25 experts who were shown the policy of Russia and the USA\nsigning an arms control agreement similar to New START, the average rank\nwas 3.5, slightly lower than the average rank for the failsafe review\npolicy (3.8). The median expert thought this policy would reduce the\nprobability of catastrophe by 20%, and that it had a 20% probability of\nbeing implemented, with this increasing to 25% with funding.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Among the 10 experts who answered on the China and the USA domain,\nthe policy that would see the establishment of regular high-level\ndialogue between the USA and China was notably popular. The median\nexpert in this group thought the policy would reduce the risk of nuclear\ncatastrophe by 25% and had a 45% probability of being implemented\n(rising to 55% with additional funding). These experts gave it an\naverage rank of 3.4 (or 4 when ranking for funding).<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">More detail on the rationales provided for forecasts relating to these two policies is available in <a href=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=233\" id=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=85\" target=\"_blank\" rel=\"noreferrer noopener\">Appendix 13<\/a>. Domain-specific policy results are provided in more detail in <a href=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=240\" id=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=240\" target=\"_blank\" rel=\"noreferrer noopener\">Appendix 14<\/a> and quantitative forecasts from all policies are provided in <a href=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=250\" id=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=85\" target=\"_blank\" rel=\"noreferrer noopener\">Appendix 15<\/a>. We also asked participants which policies they would have liked to see included in the survey. The responses are in <a href=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=260\" id=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=85\" target=\"_blank\" rel=\"noreferrer noopener\">Appendix 16<\/a>.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"factors-influencing-forecasts\">6. Factors influencing\nforecasts<\/h2>\n\n\n\n<div class=\"wp-block-group\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h4 class=\"wp-block-heading\" id=\"box-key-points-3\">Key points<\/h4>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Compared to experts, superforecasters generally gave lower\nforecasts for the probability of nuclear catastrophe and thought that\npolicies would make less difference to the risk and were less likely to\nbe implemented.<\/li>\n\n\n\n<li>There was no significant difference in forecasts of nuclear\ncatastrophe for different age groups or, for experts, years of\nexperience in the nuclear weapons field. However, experts who had more\nexperience were more likely to be skeptical about the impacts of\npolicies.<\/li>\n\n\n\n<li>Participants who were better at predicting the median expert\nforecast of catastrophe generally gave lower forecasts of catastrophe.\nHowever, participants who were better at predicting the median expert\nforecast of 2030 crux questions generally gave higher forecasts of\ncatastrophe.<\/li>\n<\/ul>\n<\/div><\/div>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"demographics-and-expertise\">6.1 Demographics and expertise<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">As noted earlier, superforecaster participants put lower\nprobabilities on nuclear catastrophe by 2045 than did subject matter\nexperts. Their median forecast was 1%, compared to the median expert\nforecast of 5%.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Compared to superforecasters, experts also thought that policies\nwould make a greater difference to the probability of nuclear\ncatastrophe. To get a general indication of a participant\u2019s beliefs\nabout how much of a difference policies could make, we took the average\nof the relative risks assigned to the general policies (i.e., not\nincluding the domain-specific policies), except for the policy that\nwould see the USA relinquish the US President\u2019s sole authority to launch\nnuclear weapons (Sole Authority). Given that many participants thought\nthis policy would increase risk, we excluded it from this calculation.\nWhen taking the average of the relative risk reduction for these five\ngeneral policies, the median expert result is 0.75 (indicating a 25%\nreduction in risk), while the median superforecaster result is 0.82\n(indicating an 18% reduction in risk).<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">There was no significant difference in forecasts of nuclear\ncatastrophe for different age groups (including when superforecasters\nare excluded from the sample) or, for experts, years of experience in\nthe nuclear weapons field (see figures 32 and 33). However, experts who\nhad more experience were more likely to be skeptical about the impacts\nof policies, with higher average relative risk for the general policies\n(excluding Sole Authority) (see Figure 33).<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/forecastingresearch.org\/wp-content\/uploads\/2026\/03\/working-paper_2024-10-29_nuclear-risk_fig-32.png\" alt=\"Figure 32: Forecasts on the probability of catastrophe disaggregated by age group (experts only). The median forecast is provided in text. The 18\u201325 age group for experts is not displayed in the chart due to insufficient sample size (n=1), which does not meet the minimum threshold (n=3) for inclusion in the boxplot visualization. The boxes show the 25th\u201375th percentile forecasts, and the lines the range of forecasts minus outliers. We jitter the data points horizontally to allow for better visualization of the distribution of forecasts. Horizontal variation within each group serves no other empirical purpose. The y-axis uses a logarithmic scale to informatively show variation in forecasts in the 0\u201310% range.\"\/><figcaption class=\"wp-element-caption\"><strong>Figure 32:<\/strong> Forecasts on the probability of catastrophe disaggregated by age group (experts only). The median forecast is provided in text. The 18\u201325 age group for experts is not displayed in the chart due to insufficient sample size (n=1), which does not meet the minimum threshold (n=3) for inclusion in the boxplot visualization. The boxes show the 25<sup>th<\/sup>\u201375<sup>th<\/sup> percentile forecasts, and the lines the range of forecasts minus outliers. We jitter the data points horizontally to allow for better visualization of the distribution of forecasts. Horizontal variation within each group serves no other empirical purpose. The y-axis uses a logarithmic scale to informatively show variation in forecasts in the 0\u201310% range.<\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/forecastingresearch.org\/wp-content\/uploads\/2026\/03\/working-paper_2024-10-29_nuclear-risk_fig-33.png\" alt=\"Figure 33: Correlation between years of relevant experience and forecast of probability of catastrophe (left) and average relative risk for five general policies (minus Sole Authority) (right). This only shows data from experts.\"\/><figcaption class=\"wp-element-caption\"><strong>Figure 33:<\/strong> Correlation between years of relevant experience and forecast of probability of catastrophe (left) and average relative risk for five general policies (minus Sole Authority) (right). This only shows data from experts.<\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">There was a trend toward participants who reported affiliation with government giving lower forecasts of the probability of nuclear catastrophe, and those working in advocacy organizations giving higher responses. However, given the small sample sizes, these differences were not statistically significant (see Figure 34, and <a href=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=264\" id=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=85\" target=\"_blank\" rel=\"noreferrer noopener\">Appendix 17<\/a> for further detail). There was no difference in average relative risk for the general policies, and no clear trend across organizations (see <a href=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=264\" id=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=85\" target=\"_blank\" rel=\"noreferrer noopener\">Appendix 17<\/a>).<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/forecastingresearch.org\/wp-content\/uploads\/2026\/03\/working-paper_2024-10-29_nuclear-risk_fig-34.png\" alt=\"Figure 34: Forecasts on the probability of catastrophe disaggregated by type of affiliated organization (experts only). The median forecast is provided in text. The boxes show the 25th\u201375th percentile forecasts, and the lines the range of forecasts minus outliers. We jitter the data points horizontally to allow for better visualization of the distribution of forecasts. Horizontal variation within each group serves no other empirical purpose. The y-axis uses a logarithmic scale to informatively show variation in forecasts in the 0\u201310% range.\"\/><figcaption class=\"wp-element-caption\"><strong>Figure 34:<\/strong> Forecasts on the probability of catastrophe disaggregated by type of affiliated organization (experts only). The median forecast is provided in text. The boxes show the 25<sup>th<\/sup>\u201375<sup>th<\/sup> percentile forecasts, and the lines the range of forecasts minus outliers. We jitter the data points horizontally to allow for better visualization of the distribution of forecasts. Horizontal variation within each group serves no other empirical purpose. The y-axis uses a logarithmic scale to informatively show variation in forecasts in the 0\u201310% range.<\/figcaption><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"beliefs-about-contentious-issues-1\">6.2 Beliefs about\ncontentious issues<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">We analyzed how participants\u2019 stated beliefs about contentious issues\nin nuclear weapons policy interacted with their forecast of the\nprobability of nuclear catastrophe by 2045. Experts who thought that\ndeterrence is inherently fragile and that nuclear escalation is very\nlikely had higher median forecasts of nuclear catastrophe, but the\ndifference was not statistically significant. Superforecasters showed\nthe opposite pattern, but this was also not statistically significant.\nFigures 35 and 36 show the distribution of forecasts disaggregated by\nviews on nuclear deterrence and the likelihood of nuclear\nescalation.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/forecastingresearch.org\/wp-content\/uploads\/2026\/03\/working-paper_2024-10-29_nuclear-risk_fig-35.png\" alt=\"Figure 35: Forecasts on the probability of catastrophe disaggregated by beliefs about the fragility \/ robustness of nuclear deterrence. The median forecast is provided in text. The boxes show the 25th\u201375th percentile forecasts, and the lines the range of forecasts minus outliers. We jitter the data points horizontally to allow for better visualization of the distribution of forecasts. Horizontal variation within each group serves no other empirical purpose. The y-axis uses a logarithmic scale to informatively show variation in forecasts in the 0\u201310% range.\"\/><figcaption class=\"wp-element-caption\"><strong>Figure 35:<\/strong> Forecasts on the probability of catastrophe disaggregated by beliefs about the fragility \/ robustness of nuclear deterrence. The median forecast is provided in text. The boxes show the 25<sup>th<\/sup>\u201375<sup>th<\/sup> percentile forecasts, and the lines the range of forecasts minus outliers. We jitter the data points horizontally to allow for better visualization of the distribution of forecasts. Horizontal variation within each group serves no other empirical purpose. The y-axis uses a logarithmic scale to informatively show variation in forecasts in the 0\u201310% range.<\/figcaption><\/figure>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/forecastingresearch.org\/wp-content\/uploads\/2026\/03\/working-paper_2024-10-29_nuclear-risk_fig-36.png\" alt=\"Figure 36: Forecasts on the probability of catastrophe disaggregated by beliefs about the likelihood of nuclear escalation after an initial nuclear strike. The median forecast is provided in text. The boxes show the 25th\u201375th percentile forecasts, and the lines the range of forecasts minus outliers. We jitter the data points horizontally to allow for better visualization of the distribution of forecasts. Horizontal variation within each group serves no other empirical purpose. The y-axis uses a logarithmic scale to informatively show variation in forecasts in the 0\u201310% range.\"\/><figcaption class=\"wp-element-caption\"><strong>Figure 36:<\/strong> Forecasts on the probability of catastrophe disaggregated by beliefs about the likelihood of nuclear escalation after an initial nuclear strike. The median forecast is provided in text. The boxes show the 25<sup>th<\/sup>\u201375<sup>th<\/sup> percentile forecasts, and the lines the range of forecasts minus outliers. We jitter the data points horizontally to allow for better visualization of the distribution of forecasts. Horizontal variation within each group serves no other empirical purpose. The y-axis uses a logarithmic scale to informatively show variation in forecasts in the 0\u201310% range.<\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Table 11 compares the mean (average) rank given to the two most popular policies by participants who had selected the opposing statements for the contentious policy issues. Superforecasters who agreed with the statement that deterrence is inherently fragile ranked the crisis communications network significantly more favorably than did superforecasters who agreed with the statement that deterrence is robust. There was no significant difference (or any clear trend) in the average relative risk of the five general policies (minus Sole Authority) across the different views on the contentious policy issues (see <a href=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=264\" id=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=85\" target=\"_blank\" rel=\"noreferrer noopener\">Appendix 17<\/a> for details).<\/p>\n\n\n\n<figure class=\"wp-block-table\"><div class=\"table-wrapper\"><table class=\"has-fixed-layout\"><tbody><tr><td rowspan=\"3\"><strong>Statement selected as closest match to views<\/strong><sup data-fn=\"491235a4-0c43-40ac-ba49-52a5e9d67ca4\" class=\"fn\"><a href=\"#491235a4-0c43-40ac-ba49-52a5e9d67ca4\" id=\"491235a4-0c43-40ac-ba49-52a5e9d67ca4-link\">39<\/a><\/sup><\/td><td colspan=\"4\"><strong>Crisis communications Network<\/strong><\/td><td colspan=\"4\"><strong>Failsafe reviews<\/strong><\/td><\/tr><tr><td colspan=\"2\"><strong>Expert<\/strong><\/td><td colspan=\"2\"><strong>Superforecaster<\/strong><\/td><td colspan=\"2\"><strong>Expert<\/strong><\/td><td colspan=\"2\"><strong>Superforecaster<\/strong><\/td><\/tr><tr><td><strong>Mean rank<\/strong><\/td><td><strong>p-value<\/strong><\/td><td><strong>Mean rank<\/strong><\/td><td><strong>p-value<\/strong><\/td><td><strong>Mean rank<\/strong><\/td><td><strong>p-value<\/strong><\/td><td><strong>Mean rank<\/strong><\/td><td><strong>p-value<\/strong><\/td><\/tr><tr><td colspan=\"9\"><strong>Deterrence<\/strong><\/td><\/tr><tr><td>Deterrence is inherently fragile<\/td><td>3.42<\/td><td rowspan=\"2\">0.75<\/td><td>1.67<\/td><td rowspan=\"2\">0.005<\/td><td>4.33<\/td><td rowspan=\"2\">0.05<\/td><td>3.33<\/td><td rowspan=\"2\">0.31<\/td><\/tr><tr><td>Deterrence is robust<\/td><td>3.26<\/td><td>3.47<\/td><td>3.41<\/td><td>4.12<\/td><\/tr><tr><td colspan=\"9\"><strong>Escalation<\/strong><\/td><\/tr><tr><td>Nuclear escalation is very likely<\/td><td>3.53<\/td><td rowspan=\"2\">0.15<\/td><td>2.71<\/td><td rowspan=\"2\">0.61<\/td><td>4.07<\/td><td rowspan=\"2\">0.05<\/td><td>3.77<\/td><td rowspan=\"2\">0.85<\/td><\/tr><tr><td>Nuclear escalation can be prevented<\/td><td>2.83<\/td><td>3.09<\/td><td>3.11<\/td><td>3.91<\/td><\/tr><\/tbody><\/table><\/div><figcaption class=\"wp-element-caption\"><strong>Table 11:<\/strong> Mean rank (out of 9) for the two most popular policies, disaggregated by beliefs on contentious issues. <br>*The p-values compare the responses for the participants who selected the two responses as being closest to their views (it does not compare experts and superforecasters). The p-value is derived from Welch\u2019s t-test, evaluating for difference in the distribution of rank between respondents choosing each statement, within expert and superforecaster groups.<\/figcaption><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"reciprocal-scores\">6.3 Reciprocal scores<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\">As mentioned, participants were asked to predict what the median\nexpert in the study would forecast for the probability of nuclear\ncatastrophe by 2045, and to predict the median expert\u2019s forecast on five\nof the crux questions that will resolve in 2030. We used these results\nto generate \u201creciprocal scores\u201d for each participant.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">We found that, compared to experts, superforecasters had better\nreciprocal scores when predicting the expert median forecast of nuclear\ncatastrophe, but that experts had better reciprocal scores when\npredicting the expert median forecast of the crux questions. So, the\nsuperforecasters were generally better at predicting what experts would\nsay about the probability of a nuclear catastrophe by 2045. But experts\nwere better at predicting experts&#8217; views on whether different events\nrelated to nuclear risk will or won\u2019t occur by 2030. To calculate each\nparticipant&#8217;s accuracy on this \u201creciprocal scoring\u201d exercise, we rank\neach participant\u2019s accuracy on each question and then average their\naccuracy rank across each question. Figure 37 shows the ranking of\nreciprocal scores for forecasts of catastrophe and the ranking of\nreciprocal scores for forecasts of the crux questions. Ranks closer to\none indicate greater accuracy on these questions. Because there were 151\nparticipants who filled out Survey 1, the worst possible rank a\nparticipant could receive is 151: this would mean that on all questions,\nthey were less accurate than all other participants at predicting the\ngroup\u2019s beliefs.<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/forecastingresearch.org\/wp-content\/uploads\/2026\/03\/working-paper_2024-10-29_nuclear-risk_fig-37.png\" alt=\"Figure 37: Reciprocal scores for predicting the expert median forecast of the probability of nuclear catastrophe by 2045 (left) and for predicting the expert median forecasts for the resolution of crux questions (right). Lower scores indicate greater accuracy. The median forecast is provided in text. The boxes show the 25th\u201375th percentile forecasts, and the lines the range of forecasts minus outliers. We jitter the data points horizontally to allow for better visualization of the distribution of forecasts. Horizontal variation within each group serves no other empirical purpose.\"\/><figcaption class=\"wp-element-caption\"><strong>Figure 37:<\/strong> Reciprocal scores for predicting the expert median forecast of the probability of nuclear catastrophe by 2045 (left) and for predicting the expert median forecasts for the resolution of crux questions (right). Lower scores indicate greater accuracy. The median forecast is provided in text. The boxes show the 25<sup>th<\/sup>\u201375<sup>th<\/sup> percentile forecasts, and the lines the range of forecasts minus outliers. We jitter the data points horizontally to allow for better visualization of the distribution of forecasts. Horizontal variation within each group serves no other empirical purpose.<\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">Figure 38 shows how forecasts of nuclear catastrophe vary according\nto reciprocal scores. Expert participants who were within the bottom\nthird, in terms of ability to predict the expert median forecast of\nnuclear catastrophe, gave much higher forecasts of nuclear catastrophe\nthan did others (a median of 25%, compared to 2% for the top third of\nperformers and 0.5% for the middle third. However, experts who were\nbetter at predicting expert forecasts of crux resolution generally gave\nhigher forecasts for the probability of nuclear catastrophe by 2045. In\nsuperforecasters, there was a slight trend in the opposite direction:\nthe superforecasters who were better at predicting experts\u2019 crux\nforecasts generally gave slightly lower forecasts for the probability of\ncatastrophe (see Figure 38).<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/forecastingresearch.org\/wp-content\/uploads\/2026\/03\/working-paper_2024-10-29_nuclear-risk_fig-38.png\" alt=\"Figure 38: Probability of catastrophe predictions from the top, middle, and bottom third of expert and superforecaster reciprocal scoring performers (based on both probability of catastrophe reciprocal scoring accuracy and crux question reciprocal scoring accuracy. Each groups\u2019 top, middle and bottom thirds are determined within-group (i.e., the top third of experts is composed of the best performers from the Expert camp, even if some of the same individuals wouldn\u2019t rank in the top third of overall performers). The median forecast is provided in text. The boxes show the 25th\u201375th percentile forecasts, and the lines the range of forecasts minus outliers. We jitter the data points horizontally to allow for better visualization of the distribution of forecasts. Horizontal variation within each group serves no other empirical purpose. The y-axis uses a logarithmic scale to informatively show variation in forecasts in the 0\u201310% range.\"\/><figcaption class=\"wp-element-caption\"><strong>Figure 38:<\/strong> Probability of catastrophe predictions from the top, middle, and bottom third of expert and superforecaster reciprocal scoring performers (based on both probability of catastrophe reciprocal scoring accuracy and crux question reciprocal scoring accuracy. Each groups\u2019 top, middle and bottom thirds are determined within-group (i.e., the top third of experts is composed of the best performers from the Expert camp, even if some of the same individuals wouldn\u2019t rank in the top third of overall performers). The median forecast is provided in text. The boxes show the 25<sup>th<\/sup>\u201375<sup>th<\/sup> percentile forecasts, and the lines the range of forecasts minus outliers. We jitter the data points horizontally to allow for better visualization of the distribution of forecasts. Horizontal variation within each group serves no other empirical purpose. The y-axis uses a logarithmic scale to informatively show variation in forecasts in the 0\u201310% range.<\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">We also found that expert participants who performed better at predicting experts\u2019 forecasts of crux resolution tended to forecast a higher probability that policies would be implemented. Conversely, experts who performed better at predicting forecasts of nuclear catastrophe tended to forecast a lower probability that policies would be implemented (Figure 39). There was no significant correlation between superforecasters\u2019 reciprocal scores and their views on the likelihood of policy implementation. There was also no significant correlation between reciprocal scoring ranks (of either group) and views on the effectiveness of policies (see <a href=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=264\" id=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=85\" target=\"_blank\" rel=\"noreferrer noopener\">Appendix 17<\/a> for more detail).<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img decoding=\"async\" src=\"https:\/\/forecastingresearch.org\/wp-content\/uploads\/2026\/03\/working-paper_2024-10-29_nuclear-risk_fig-39.png\" alt=\"Figure 39: Correlation between average forecasts of policy implementation (five general policies, excluding \u201cSole Authority\u201d) and reciprocal scoring ranks. Reciprocal scoring rank for predicting the median expert\u2019s forecast of nuclear catastrophe is shown on the left and reciprocal scoring rank for predicting the median expert\u2019s forecasts of crux resolution is shown on the right. This only shows data from experts.\"\/><figcaption class=\"wp-element-caption\"><strong>Figure 39:<\/strong> Correlation between average forecasts of policy implementation (five general policies, excluding \u201cSole Authority\u201d) and reciprocal scoring ranks. Reciprocal scoring rank for predicting the median expert\u2019s forecast of nuclear catastrophe is shown on the left and reciprocal scoring rank for predicting the median expert\u2019s forecasts of crux resolution is shown on the right. This only shows data from experts.<\/figcaption><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">While we do have some evidence that accuracy in predicting the aggregate forecast of a group is predictive of actual accuracy on other geopolitical forecasting questions,<sup data-fn=\"f491b3ff-4d59-46f4-9b62-9183c828f28e\" class=\"fn\"><a href=\"#f491b3ff-4d59-46f4-9b62-9183c828f28e\" id=\"f491b3ff-4d59-46f4-9b62-9183c828f28e-link\">40<\/a><\/sup> this evidence base is limited and may not extend to longer-run questions like the ones in this study. As the questions from this study resolve, the results will inform our understanding of this method of assessing forecasting accuracy.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"limitations\">7. Limitations<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">This study has several important limitations. Despite active efforts\nto recruit a diverse group of expert participants, the final sample was\ndisproportionately from the USA, and to a lesser extent western Europe.\nAlthough we had some success with efforts to recruit participants from\nSouth Asia, there were very few participants from eastern Europe and\neastern Asia. Given the importance of Russia, China, and North and South\nKorea to the global nuclear weapons situation, it is disappointing that\nwe have few participants with direct experience within these countries\nrepresented in the survey. If we were to conduct a similar survey in the\nfuture, we would consider partnering with organizations with connections\nin these countries, and we would consider translating the survey. The\nsurvey was only available in English, which was likely an important\nfactor limiting participation from some regions.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Our sample also largely represented experts from academia and think\ntanks. Although some participants had experience in government, they\nwere in the minority. Future surveys could be strengthened by increased\nefforts to engage experts within government and the military to\nunderstand the perspectives of these groups.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">More generally, the number of participants limited some of the\nconclusions we could draw from this data. Although this was the largest\nforecasting study of nuclear weapons experts, we would need a bigger\nsample to determine whether some of the trends we identified are\nstatistically significant (rather than due to chance). This was most\nnotably an issue for the questions on the effects of policies. As we\nallowed participants to choose a domain to answer questions on, there\nwere some questions about some domains that few participants\nanswered.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This study investigated a global perspective on nuclear weapons. This\nbroad perspective is valuable in providing a holistic assessment of\nnuclear weapons risk. However, it does mean that the depth to which we\ncould explore aspects of nuclear weapons policy was limited. For\nexample, a study of similar size to this one could be conducted\nspecifically on any one of the adversarial domains we investigated.\nLimiting the scope of future studies could allow specific topics to be\nexplored in greater depth. We believe there is value in both broader\nstudies such as this one and more focused studies.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">While our study included a large number of questions, covering over\n20 different topics in the crux questions and over 20 different\npolicies, there are many more questions we could have included. While we\ntried to achieve a balanced viewpoint in the questions, it\u2019s possible\nthat the ultimate list of policies leans towards policies aimed at\nreducing nuclear capacities, rather than strengthening nuclear arsenals,\nwhich some experts believe would reduce the odds of a nuclear\ncatastrophe through increased deterrence. We did include policies\ninvolving Russia and the USA increasing the role within their arsenals\nof low-yield nuclear weapons, but generally, policies leaned \u201cdove\u201d-ish\nin their approach to nuclear weapons policy.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The focus on a single type of nuclear weapons event was another\nconstraint. We did not explore the probability of smaller, more probable\nincidents or, on the other end of the scale, catastrophes of an even\ngreater magnitude than our main outcome. Given the research on nuclear\nwinters, it is possible that a nuclear war could kill many more than 10\nmillion people. When considering the potential benefits of policies\naiming at reducing the risk of nuclear weapons, low-probability but\nhigh-consequence events will likely account for much of the expected\nvalue of interventions. Therefore, we caution against using these\nresults to estimate the cost-effectiveness of the policies we explored\nin the survey.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"next-steps\">8. Next steps<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Every January the Bulletin of the Atomic Scientists updates the \u201cDoomsday Clock\u201d to indicate how close the world is to a nuclear catastrophe.<sup data-fn=\"8ca66c13-c487-4fa2-a006-d4ac0507bee4\" class=\"fn\"><a href=\"#8ca66c13-c487-4fa2-a006-d4ac0507bee4\" id=\"8ca66c13-c487-4fa2-a006-d4ac0507bee4-link\">41<\/a><\/sup> Some experts might pose the question, \u201cSo you&#8217;re recreating the \u2018Doomsday Clock\u2019?\u201d And our answer is\u2026 not quite.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Our attempt to quantify risk does not solely aim to sound the alarm\non nuclear weapons risk. Decision-makers often face a range of threats,\neach with varying degrees of probability and impact. A quantified risk\nframework helps clarify which threats are more immediate or severe. It\nalso introduces a systematic approach that minimizes biases, promotes\nobjectivity, and mitigates the influence of noise in the decision-making\nprocess. Without a structured approach to quantifying risks,\ndecision-makers may disproportionately emphasize some risks while\nunderestimating or overlooking others. To this end, we hope to provide\nan illuminating tool to help governments and other decision-makers with\ncompeting priorities allocate attention and resources.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The Doomsday Clock update is based on a survey of around 20 experts.\nThis study, in a sense, provides a more molecular reading of the status\nof nuclear risk by incorporating more experts and enabling them to\nsystematically assess and express risks using probabilities. However,\nnuclear weapons risk is a large and complicated topic. Our study offers\na broad overview of the risk landscape, but much more could be done to\ninvestigate specific aspects of this landscape in greater depth. For\nexample, future work could explore how a multilateral crisis\ncommunications network should operate. How should the center attempt to\novercome problems that have plagued bilateral communications between\nadversaries, such as a lack of engagement and a lack of trust between\nleaders? How should leaders respond when such crises arise? Future\nstudies could focus on these and other more detailed questions. Rather\nthan providing all the answers, our study serves as a basis to build\nupon with future work.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">More engagement with policymakers could also be highly beneficial. It\nwould be ideal to include policymakers in the process of designing\nstudies\u2014particularly the forecasting questions and policies to be\nevaluated\u2014as well as have them participate in surveys. Improved, future\nversions of these studies could help track views on nuclear weapons with\nenough granularity to inform policy choices.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">There is scope for a wider range of activities that bring a\nsystematic approach to assessing nuclear risks. For example, researchers\ncould use foresight exercises and scenario planning sessions to further\ninterrogate significant findings from the study. There is also potential\nto combine insights from open-source information to understand and\nmonitor early warning indicators for nuclear escalation.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"conclusion\">9. Conclusion<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">This year, 2024, has witnessed the intensification of two major\ngeopolitical conflicts: Russia-Ukraine on the one hand, and Israel-Gaza\n(and now Lebanon) on the other. Policymakers and diplomats are seeking\nto broker ceasefire, de-escalation, and d\u00e9tente, but the biggest\nchallenge they face is the degradation of communication channels. As we\nwrite this in October 2024, the recent rounds of escalation in the\nMiddle East have demonstrated the difficulty of exercising restraint.\nThis highlights the importance of one of the most popular policies in\nthis study, a crisis communications network. The other most popular\npolicy, failsafe reviews, suggests a proactive approach to identifying\nareas where misunderstandings or miscommunication might arise. While\nnuclear weapons are a complex problem, the participants in this survey\nwere optimistic that steps can be taken to reduce the risk they\npose.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">It\u2019s difficult to compare highly complex and abstract threats like\nnuclear war to other existential risks without a common metric. Using\nprobabilities allows for a more nuanced understanding of how nuclear\nrisks stack up against other potentially catastrophic events.\nQuantifying the risk of a nuclear catastrophe alongside other\nexistential risks provides a clearer framework for understanding and\ncommunicating which threats require immediate action, sustained\nattention, or strategic monitoring. By translating abstract concerns\ninto measurable probabilities, it becomes easier to engage in informed,\nrational prioritization amid a chaotic and noisy political\nenvironment.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Notes<\/h2>\n\n\n<ol class=\"wp-block-footnotes\"><li id=\"0e558f0c-0266-4b03-8306-a59806414564\">We define an outlier as any observation that falls below (Q1 \u2013 1.5\u00d7IQR) or above (Q3 + 1.5\u00d7IQR), where Q1 and Q3 are the first and third quartiles and IQR is the interquartile range. <a href=\"#0e558f0c-0266-4b03-8306-a59806414564-link\" aria-label=\"Jump to footnote reference 1\">\u21a9\ufe0e<\/a><\/li><li id=\"b5cb55a3-a6f5-4e1b-b9fd-41ae9c0feaaa\">This policy would see all nuclear-armed states participating in a secure (to damage to physical infrastructure and cyberattacks) communications network that allowed bilateral and multilateral communication between country leaders. See <a href=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=142\" id=\"https:\/\/forecastingresearch.org\/wp-content\/uploads\/2024\/10\/nuclear-risk.pdf#page=85\" target=\"_blank\" rel=\"noreferrer noopener\">Appendix 4<\/a> for more detail. <a href=\"#b5cb55a3-a6f5-4e1b-b9fd-41ae9c0feaaa-link\" aria-label=\"Jump to footnote reference 2\">\u21a9\ufe0e<\/a><\/li><li id=\"c6f13b70-38ab-4909-b351-5ff855b35099\">This policy would see all nuclear-armed states establish a review mechanism based on national defense guidelines to identify and develop plans to mitigate risks of inadvertent or accidental nuclear use. See <a href=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=142\" id=\"https:\/\/forecastingresearch.org\/wp-content\/uploads\/2024\/10\/nuclear-risk.pdf#page=85\" target=\"_blank\" rel=\"noreferrer noopener\">Appendix 4<\/a> for more detail. <a href=\"#c6f13b70-38ab-4909-b351-5ff855b35099-link\" aria-label=\"Jump to footnote reference 3\">\u21a9\ufe0e<\/a><\/li><li id=\"758e9bb7-1fcd-4147-97ed-463e43451c07\">\u201cSuperforecasters\u201d outperformed experts and intelligence analysts in forecasting tournaments held by the Good Judgment Project, or had equivalent forecasting skill according to follow-up work by Good Judgment Inc. See Tetlock, Philip E., Barbara A. Mellers, Nick Rohrbaugh, and Eva Chen. &#8220;Forecasting Tournaments: Tools for Increasing Transparency and Improving the Quality of Debate.&#8221; <em>Current Directions in Psychological Science<\/em> 23, no. 4 (2014): 290\u2013295. <a href=\"https:\/\/doi.org\/10.1177\/0963721414534257\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/doi.org\/10.1177\/0963721414534257<\/a>. <a href=\"#758e9bb7-1fcd-4147-97ed-463e43451c07-link\" aria-label=\"Jump to footnote reference 4\">\u21a9\ufe0e<\/a><\/li><li id=\"45752514-46e5-48b1-9fd3-6f7088fedcf4\">These were non-experts or laypeople who had some interest in current affairs but lacked specialized<br>training. <a href=\"#45752514-46e5-48b1-9fd3-6f7088fedcf4-link\" aria-label=\"Jump to footnote reference 5\">\u21a9\ufe0e<\/a><\/li><li id=\"c8e4b196-3698-4204-911b-001d3bf6c52f\">Tetlock, Philip E., Christopher Karvetski, Ville A. Satop\u00e4\u00e4, and Kevin Chen. &#8220;Long-Range Subjective-Probability Forecasts of Slow-Motion Variables in World Politics: Exploring Limits on Expert Judgment.&#8221; <em>Futures &amp; Foresight Science<\/em> 6 (2024): e157. <a href=\"https:\/\/doi.org\/10.1002\/ffo2.157\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/doi.org\/10.1002\/ffo2.157<\/a>. <a href=\"#c8e4b196-3698-4204-911b-001d3bf6c52f-link\" aria-label=\"Jump to footnote reference 6\">\u21a9\ufe0e<\/a><\/li><li id=\"a7e8e02f-375c-42e9-a2aa-f4ab2d49adb9\">Lugar, Richard G. &#8220;The Lugar Survey on Proliferation Threats and Responses.&#8221; Washington, D.C.: United States Senate Foreign Relations Committee, 2005. <a href=\"https:\/\/irp.fas.org\/threat\/lugar_survey.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/irp.fas.org\/threat\/lugar_survey.pdf<\/a>.<br> <a href=\"#a7e8e02f-375c-42e9-a2aa-f4ab2d49adb9-link\" aria-label=\"Jump to footnote reference 7\">\u21a9\ufe0e<\/a><\/li><li id=\"60dd9d0a-e48a-44c5-8657-d5fd82954159\">Project for the Study of the 21st Century. &#8220;Great Power Conflict Report.&#8221; November 12, 2015. <a href=\"https:\/\/projects21.org\/2015\/11\/12\/ps21-survey-experts-see-increased-risk-of-nuclear-war\/\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/projects21.org\/2015\/11\/12\/ps21-survey-experts-see-increased-risk-of-nuclear-war\/<\/a>. <a href=\"#60dd9d0a-e48a-44c5-8657-d5fd82954159-link\" aria-label=\"Jump to footnote reference 8\">\u21a9\ufe0e<\/a><\/li><li id=\"aab97946-4875-4de3-aa06-7b31c520e260\">Karger, Ezra, Josh Rosenberg, Zach Jacobs, et al. \u201cForecasting Existential Risks: Evidence from a Long-Run Forecasting Tournament.\u201d FRI Working Paper #1. Forecasting Research Institute, 2023.\u00a0<a href=\"https:\/\/forecastingresearch.org\/research\/existential-risk-persuasion-tournament\" id=\"876\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/forecastingresearch.org\/research\/existential-risk-persuasion-tournament<\/a>. <a href=\"#aab97946-4875-4de3-aa06-7b31c520e260-link\" aria-label=\"Jump to footnote reference 9\">\u21a9\ufe0e<\/a><\/li><li id=\"604b594e-b78f-4a24-bc60-74eee3ab6d6c\">Karger et al., <a href=\"https:\/\/forecastingresearch.org\/research\/existential-risk-persuasion-tournament\" id=\"876\"><a href=\"https:\/\/forecastingresearch.org\/research\/existential-risk-persuasion-tournament\" id=\"876\" target=\"_blank\" rel=\"noreferrer noopener\">XPT<\/a><\/a>, 44. <a href=\"#604b594e-b78f-4a24-bc60-74eee3ab6d6c-link\" aria-label=\"Jump to footnote reference 10\">\u21a9\ufe0e<\/a><\/li><li id=\"8c4963a1-4c22-4c9b-8b77-9b10a3ee2a39\">The Russian invasion of Ukraine began on February 24th, 2022. <a href=\"#8c4963a1-4c22-4c9b-8b77-9b10a3ee2a39-link\" aria-label=\"Jump to footnote reference 11\">\u21a9\ufe0e<\/a><\/li><li id=\"dcc54637-bd3f-4634-b836-75867518b3eb\">YouGov. &#8220;How Likely Do You Think We Are to Get into a Nuclear War within the Next Ten Years?&#8221; Survey, February 28, 2022. <a href=\"https:\/\/today.yougov.com\/topics\/politics\/survey-results\/daily\/2022\/02\/28\/c6993\/3\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/today.yougov.com\/topics\/politics\/survey-results\/daily\/2022\/02\/28\/c6993\/3<\/a>. Statista. &#8220;How Likely Do You Think We Are to Get into a Nuclear War within the Next Ten Years?&#8221; Survey, February 1\u20137, 2024. <a href=\"https:\/\/www.statista.com\/statistics\/1308926\/us-opinion-likelihood-nuclear-war\/\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/www.statista.com\/statistics\/1308926\/us-opinion-likelihood-nuclear-war\/<\/a>. \u201cHow likely do you think we are to get into a nuclear war within the next ten years?,\u201d survey,<br>February 1 to 7, 2024, https:\/\/www.statista.com\/statistics\/1308926\/us-opinion-likelihood-nuclear-war\/. <a href=\"#dcc54637-bd3f-4634-b836-75867518b3eb-link\" aria-label=\"Jump to footnote reference 12\">\u21a9\ufe0e<\/a><\/li><li id=\"e20baa58-8279-4da2-8319-351c40d6b416\">FOMnibus. &#8220;\u042f\u0434\u0435\u0440\u043d\u043e\u0435 \u043e\u0440\u0443\u0436\u0438\u0435.&#8221; Survey conducted October 27\u201329, 2023. Fond Obshchestvennoe Mnenie (Public Opinion Foundation), November 10, 2023. <a href=\"https:\/\/fom.ru\/Bezopasnost-i-pravo\/14942\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/fom.ru\/Bezopasnost-i-pravo\/14942<\/a>. <a href=\"#e20baa58-8279-4da2-8319-351c40d6b416-link\" aria-label=\"Jump to footnote reference 13\">\u21a9\ufe0e<\/a><\/li><li id=\"e02fc319-5841-41cf-8d24-40b326b3945e\">For crowd forecasts. Retrieval date for ongoing forecasts. <a href=\"#e02fc319-5841-41cf-8d24-40b326b3945e-link\" aria-label=\"Jump to footnote reference 14\">\u21a9\ufe0e<\/a><\/li><li id=\"d5bd4ed7-715e-48cb-8400-985fd7281cd8\">\u201cNot just a warning shot &#8211; a targeting of military or civilian targets.\u201d <a href=\"#d5bd4ed7-715e-48cb-8400-985fd7281cd8-link\" aria-label=\"Jump to footnote reference 15\">\u21a9\ufe0e<\/a><\/li><li id=\"3db2aa6e-57c4-44e0-b1c4-ad3c922a12a9\">Wyden, Peter H. <em>Bay of Pigs: The Untold Story.<\/em> Simon and Schuster, 1979.Peter H. Wyden, Bay of Pigs: The Untold Story (New York: Simon and Schuster, 1979). <a href=\"#3db2aa6e-57c4-44e0-b1c4-ad3c922a12a9-link\" aria-label=\"Jump to footnote reference 16\">\u21a9\ufe0e<\/a><\/li><li id=\"7b32080a-5826-40e8-93e8-2d985b5c1d02\">Mauboussin, Andrew, and Michael J. Mauboussin. &#8220;If You Say Something Is &#8216;Likely,&#8217; How Likely Do People Think It Is?&#8221; <em>Harvard Business Review<\/em>, July 3, 2018. <a href=\"https:\/\/hbr.org\/2018\/07\/if-you-say-something-is-likely-how-likely-do-people-think-it-is\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/hbr.org\/2018\/07\/if-you-say-something-is-likely-how-likely-do-people-think-it-is<\/a>. <a href=\"#7b32080a-5826-40e8-93e8-2d985b5c1d02-link\" aria-label=\"Jump to footnote reference 17\">\u21a9\ufe0e<\/a><\/li><li id=\"6b75f296-e069-42f0-8c2d-d2ac026eb77c\">Cited in Gleditsch, Kristian Skrede. &#8220;One without the Other? Prediction and Policy in International Studies.&#8221; <em>International Studies Quarterly<\/em> 66, no. 3 (2022): sqac036. <a href=\"https:\/\/doi.org\/10.1093\/isq\/sqac036\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/doi.org\/10.1093\/isq\/sqac036<\/a>. <a href=\"#6b75f296-e069-42f0-8c2d-d2ac026eb77c-link\" aria-label=\"Jump to footnote reference 18\">\u21a9\ufe0e<\/a><\/li><li id=\"ad362f26-fa6a-44b4-bffc-6adbc13a12dd\">Friedman, Jeffrey A., Joshua D. Baker, Barbara A. Mellers, Philip E. Tetlock, and Richard Zeckhauser. &#8220;The Value of Precision in Probability Assessment: Evidence from a Large-Scale Geopolitical Forecasting Tournament.&#8221; International Studies Quarterly 62, no. 2 (2018): 410\u2013422. <a href=\"https:\/\/doi.org\/10.1093\/isq\/sqx078\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/doi.org\/10.1093\/isq\/sqx078<\/a>. <a href=\"#ad362f26-fa6a-44b4-bffc-6adbc13a12dd-link\" aria-label=\"Jump to footnote reference 19\">\u21a9\ufe0e<\/a><\/li><li id=\"ca439d97-db4b-4c13-b741-4c7f4f02de74\">Tetlock, Philip E. <em>Expert Political Judgment: How Good Is It? How Can We Know?<\/em> Princeton University Press, 2005. <a href=\"#ca439d97-db4b-4c13-b741-4c7f4f02de74-link\" aria-label=\"Jump to footnote reference 20\">\u21a9\ufe0e<\/a><\/li><li id=\"c3431a5f-d200-4c78-901c-300e6dbad1e7\">Mellers, Barbara A., Philip E. Tetlock, Joshua D. Baker, Jeffrey A. Friedman, and Richard Zeckhauser. &#8220;Chapter 12. Improving the Accuracy of Geopolitical Risk Assessments.&#8221; In <em>The Future of Risk Management<\/em>, edited by Howard Kunreuther, Robert J. Meyer, and Erwann O. Michel-Kerjan. University of Pennsylvania Press, 2019. <a href=\"https:\/\/doi.org\/10.9783\/9780812296228-013\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/doi.org\/10.9783\/9780812296228-013<\/a>. <a href=\"#c3431a5f-d200-4c78-901c-300e6dbad1e7-link\" aria-label=\"Jump to footnote reference 21\">\u21a9\ufe0e<\/a><\/li><li id=\"ade75780-9b56-4138-8584-78ad76b4a001\">Muehlhauser, Luke. &#8220;How Feasible Is Long-Range Forecasting?&#8221; Open Philanthropy, October 10, 2019. <a href=\"https:\/\/www.openphilanthropy.org\/research\/how-feasible-is-long-range-forecasting\/\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/www.openphilanthropy.org\/research\/how-feasible-is-long-range-forecasting\/<\/a>. <a href=\"#ade75780-9b56-4138-8584-78ad76b4a001-link\" aria-label=\"Jump to footnote reference 22\">\u21a9\ufe0e<\/a><\/li><li id=\"e8ec8ee0-8a6a-4db8-89f6-c57bdd9dd861\">Karger et al., <a href=\"https:\/\/forecastingresearch.org\/research\/existential-risk-persuasion-tournament\" id=\"876\" target=\"_blank\" rel=\"noreferrer noopener\"><a href=\"https:\/\/forecastingresearch.org\/research\/existential-risk-persuasion-tournament\" id=\"876\" target=\"_blank\" rel=\"noreferrer noopener\">XPT<\/a><\/a>. <a href=\"#e8ec8ee0-8a6a-4db8-89f6-c57bdd9dd861-link\" aria-label=\"Jump to footnote reference 23\">\u21a9\ufe0e<\/a><\/li><li id=\"20c227aa-b640-45e0-9e72-dc9f5bf15327\">Ruhl, Christian. &#8220;Philanthropy to the Right of Boom.&#8221; Founders Pledge, February 13, 2023. <a href=\"https:\/\/www.founderspledge.com\/research\/philanthropy-to-the-right-of-boom\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/www.founderspledge.com\/research\/philanthropy-to-the-right-of-boom<\/a>. <a href=\"#20c227aa-b640-45e0-9e72-dc9f5bf15327-link\" aria-label=\"Jump to footnote reference 24\">\u21a9\ufe0e<\/a><\/li><li id=\"ffeb8537-c72d-4014-95d8-be11f18d0e7c\">These organizations included but were not limited to Nuclear Threat Initiative, Arms Control Association, Founders Pledge, Open Philanthropy, Carnegie Endowment, Chatham House, and the Union of Concerned Scientists. <a href=\"#ffeb8537-c72d-4014-95d8-be11f18d0e7c-link\" aria-label=\"Jump to footnote reference 25\">\u21a9\ufe0e<\/a><\/li><li id=\"019937a7-6537-49a3-b2d2-45c55bece462\">Each participant was allocated five of the general (i.e. not domain-specific) crux questions. <a href=\"#019937a7-6537-49a3-b2d2-45c55bece462-link\" aria-label=\"Jump to footnote reference 26\">\u21a9\ufe0e<\/a><\/li><li id=\"089e2614-bb58-4ada-8324-090737ec8534\">Karger, Ezra, Joshua Monrad, Barbara Mellers, and Philip Tetlock. &#8220;Reciprocal Scoring: A Method for Forecasting Unanswerable Questions.&#8221; SSRN Working Paper, 2021. <a href=\"https:\/\/dx.doi.org\/10.2139\/ssrn.3954498\" id=\"https:\/\/dx.doi.org\/10.2139\/ssrn.3954498\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/dx.doi.org\/10.2139\/ssrn.3954498<\/a>. <a href=\"#089e2614-bb58-4ada-8324-090737ec8534-link\" aria-label=\"Jump to footnote reference 27\">\u21a9\ufe0e<\/a><\/li><li id=\"c0960c9f-6ab5-47ed-b9df-3bc33fe0e4b9\">Organizations that kindly helped with distribution of the survey were: Emerging Voices Network at<br>BASIC, European Leadership Network, Asia Pacific Leadership Network, Younger Generation Leaders<br>Network, Project on Nuclear Issues at CSIS, and the Pacific Forum. <a href=\"#c0960c9f-6ab5-47ed-b9df-3bc33fe0e4b9-link\" aria-label=\"Jump to footnote reference 28\">\u21a9\ufe0e<\/a><\/li><li id=\"f5fb56b4-48f0-464f-bb93-5305547cd093\">A full list of the organizations and reports we reviewed is available in <a href=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=82\" id=\"https:\/\/forecastingresearch.org\/wp-content\/uploads\/2024\/10\/nuclear-risk.pdf#page=82\" target=\"_blank\" rel=\"noreferrer noopener\">Appendix 2<\/a>. <a href=\"#f5fb56b4-48f0-464f-bb93-5305547cd093-link\" aria-label=\"Jump to footnote reference 29\">\u21a9\ufe0e<\/a><\/li><li id=\"11446d22-b021-4ec9-a89c-f66fd1840fff\">Tetlock, Philip E., Barbara A. Mellers, Nick Rohrbaugh, and Eva Chen. &#8220;Forecasting Tournaments: Tools for Increasing Transparency and Improving the Quality of Debate.&#8221; <em>Current Directions in Psychological Science<\/em> 23, no. 4 (2014): 290\u2013295. <a href=\"https:\/\/doi.org\/10.1177\/0963721414534257\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/doi.org\/10.1177\/0963721414534257<\/a>. <a href=\"#11446d22-b021-4ec9-a89c-f66fd1840fff-link\" aria-label=\"Jump to footnote reference 30\">\u21a9\ufe0e<\/a><\/li><li id=\"26b45041-471d-4824-9a22-0399db4b0191\">We believe that this very low proportion of female superforecaster participants is largely a function of recruitment from the study that originally identified superforecasters among a broad population of participants (Mellers, Barbara, Lyle Ungar, Jonathan Baron, et al. &#8220;Psychological Strategies for Winning a Geopolitical Forecasting Tournament.&#8221; <em>Psychological Science<\/em> 25, no. 5 (2014): 1106\u20131115. <a href=\"https:\/\/doi.org\/10.1177\/0956797614524255\">https:\/\/doi.org\/10.1177\/0956797614524255<\/a>.) Of the participants in that study, 83% were male. <a href=\"#26b45041-471d-4824-9a22-0399db4b0191-link\" aria-label=\"Jump to footnote reference 31\">\u21a9\ufe0e<\/a><\/li><li id=\"3484acb4-565d-4e85-8ee4-a427963b3770\">In some places, we report the average or mean. These are instances where participants are asked to<br>distribute votes, ranks, or probabilities (i.e., saying how likely several mutually exclusive but collectively exhaustive events are, such that the total probabilities sum to 100%). <a href=\"#3484acb4-565d-4e85-8ee4-a427963b3770-link\" aria-label=\"Jump to footnote reference 32\">\u21a9\ufe0e<\/a><\/li><li id=\"151cbe3e-71d5-4c1b-b612-39a074dbb49e\">Karger et al., <a href=\"https:\/\/forecastingresearch.org\/research\/xpt\" id=\"876\"><a href=\"https:\/\/forecastingresearch.org\/research\/existential-risk-persuasion-tournament\" id=\"876\" target=\"_blank\" rel=\"noreferrer noopener\">XPT<\/a><\/a>, 20-22. <a href=\"#151cbe3e-71d5-4c1b-b612-39a074dbb49e-link\" aria-label=\"Jump to footnote reference 33\">\u21a9\ufe0e<\/a><\/li><li id=\"e6bb5c35-0dca-43d8-81f5-036ee994b8e2\">Here we report averages, rather than medians, as the aggregate group measure. This is done so that<br>the totals across the domains sum to 100%. <a href=\"#e6bb5c35-0dca-43d8-81f5-036ee994b8e2-link\" aria-label=\"Jump to footnote reference 34\">\u21a9\ufe0e<\/a><\/li><li id=\"ca6ff985-c41c-4231-a5f1-bd56d5d38d60\">Participants were also asked how their probability of nuclear catastrophe by 2045 would change if they knew that this event would occur. Therefore, these changes in forecasts shouldn\u2019t be taken as<br>representing the causal effect of the event. For example, it&#8217;s possible that a participant who would reduce their probability of catastrophe if an arms control agreement occurred might not think that the agreement itself would cause any change in risk. Instead, they might think that an arms control agreement would indicate that the relationship between the countries has improved, and they might reduce their predicted risk of catastrophe for that reason. <a href=\"#ca6ff985-c41c-4231-a5f1-bd56d5d38d60-link\" aria-label=\"Jump to footnote reference 35\">\u21a9\ufe0e<\/a><\/li><li id=\"be7abff7-058e-417d-8fa4-f381cb8708b9\">Barrett, Anthony M., Seth D. Baum, and Kelly Hostetler. &#8220;Analyzing and Reducing the Risks of Inadvertent Nuclear War Between the United States and Russia.&#8221; <em>Science &amp; Global Security<\/em> 21, no. 2 (2013): 106\u2013133. <a href=\"https:\/\/doi.org\/10.1080\/08929882.2013.798984\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/doi.org\/10.1080\/08929882.2013.798984<\/a>. <a href=\"#be7abff7-058e-417d-8fa4-f381cb8708b9-link\" aria-label=\"Jump to footnote reference 36\">\u21a9\ufe0e<\/a><\/li><li id=\"33715d4d-a106-44fa-9bcc-e75ea03073c0\">Institute for Security and Technology. &#8220;CATALINK.&#8221; Accessed October 17, 2024. <a href=\"https:\/\/securityandtechnology.org\/catalink\/\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/securityandtechnology.org\/catalink\/<\/a>. <a href=\"#33715d4d-a106-44fa-9bcc-e75ea03073c0-link\" aria-label=\"Jump to footnote reference 37\">\u21a9\ufe0e<\/a><\/li><li id=\"b5117a2d-6ec6-45ba-b711-b4870db57b0f\">U.S. Department of Defense. &#8220;2022 National Defense Strategy of the United States of America.&#8221; U.S. Department of Defense, October 27, 2022. <a href=\"https:\/\/media.defense.gov\/2022\/Oct\/27\/2003103845\/-1\/-1\/1\/2022-NATIONAL-DEFENSE-STRATEGY-NPR-MDR.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/media.defense.gov\/2022\/Oct\/27\/2003103845\/-1\/-1\/1\/2022-NATIONAL-DEFENSE-STRATEGY-NPR-MDR.pdf<\/a>. <a href=\"#b5117a2d-6ec6-45ba-b711-b4870db57b0f-link\" aria-label=\"Jump to footnote reference 38\">\u21a9\ufe0e<\/a><\/li><li id=\"491235a4-0c43-40ac-ba49-52a5e9d67ca4\">Summaries of the full statements are presented here. We suggest reading the full statements, which<br>can be found in <a href=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=142\" id=\"https:\/\/forecastingresearch.org\/wp-content\/uploads\/2024\/10\/nuclear-risk.pdf#page=85\" target=\"_blank\" rel=\"noreferrer noopener\">Appendix 4<\/a>. <a href=\"#491235a4-0c43-40ac-ba49-52a5e9d67ca4-link\" aria-label=\"Jump to footnote reference 39\">\u21a9\ufe0e<\/a><\/li><li id=\"f491b3ff-4d59-46f4-9b62-9183c828f28e\">Atanasov, Pavel D., Ezra Karger, and Philip Tetlock. &#8220;Full Accuracy Scoring Accelerates the Discovery of Skilled Forecasters.&#8221; SSRN Working Paper, February 13, 2023. <a href=\"https:\/\/dx.doi.org\/10.2139\/ssrn.4357367\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/dx.doi.org\/10.2139\/ssrn.4357367<\/a>. <a href=\"#f491b3ff-4d59-46f4-9b62-9183c828f28e-link\" aria-label=\"Jump to footnote reference 40\">\u21a9\ufe0e<\/a><\/li><li id=\"8ca66c13-c487-4fa2-a006-d4ac0507bee4\">Bulletin of the Atomic Scientists. &#8220;Doomsday Clock.&#8221; Accessed October 17, 2024. <a href=\"https:\/\/thebulletin.org\/doomsday-clock\/current-time\/\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/thebulletin.org\/doomsday-clock\/current-time\/<\/a>. <a href=\"#8ca66c13-c487-4fa2-a006-d4ac0507bee4-link\" aria-label=\"Jump to footnote reference 41\">\u21a9\ufe0e<\/a><\/li><\/ol>\n\n\n<div class=\"wp-block-buttons is-layout-flex wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a class=\"btn orange\" href=\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">The Appendix is available in the full PDF report <svg width=\"7\" height=\"9\" viewBox=\"0 0 7 9\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n  <path d=\"M0.000156283 8.60806L4.22416 4.33606V4.24006L0.000156283 6.10352e-05H1.80816L6.06416 4.28806L1.80816 8.60806H0.000156283Z\" fill=\"#102B23\"\/>\n<\/svg>\n<svg width=\"8\" height=\"10\" viewBox=\"0 0 8 10\" fill=\"none\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\">\n  <path d=\"M0.601719 8.85794L4.82572 4.58594V4.48994L0.601719 0.249939H2.40972L6.66572 4.53794L2.40972 8.85794H0.601719Z\" fill=\"#102B23\"\/>\n<\/svg><\/a><\/div>\n<\/div>\n\n\n\n<p class=\"wp-block-paragraph\"><\/p>\n","protected":false},"excerpt":{"rendered":"This study systematically assessed expert beliefs about the probability of a nuclear weapons catastrophe by 2045. Domain experts and superforecasters predicted the likelihood of nuclear conflict, explained the mechanisms underlying their predictions, and forecast the impact of specific tractable policies on the chance of nuclear catastrophe.","protected":false},"featured_media":858,"template":"","meta":{"footnotes":"[{\"id\":\"0e558f0c-0266-4b03-8306-a59806414564\",\"content\":\"We define an outlier as any observation that falls below (Q1 \u2013 1.5\u00d7IQR) or above (Q3 + 1.5\u00d7IQR), where Q1 and Q3 are the first and third quartiles and IQR is the interquartile range.\"},{\"content\":\"This policy would see all nuclear-armed states participating in a secure (to damage to physical infrastructure and cyberattacks) communications network that allowed bilateral and multilateral communication between country leaders. See <a href=\\\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=142\\\" id=\\\"https:\/\/forecastingresearch.org\/wp-content\/uploads\/2024\/10\/nuclear-risk.pdf#page=85\\\" target=\\\"_blank\\\" rel=\\\"noreferrer noopener\\\">Appendix 4<\/a> for more detail.\",\"id\":\"b5cb55a3-a6f5-4e1b-b9fd-41ae9c0feaaa\"},{\"content\":\"This policy would see all nuclear-armed states establish a review mechanism based on national defense guidelines to identify and develop plans to mitigate risks of inadvertent or accidental nuclear use. See <a href=\\\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=142\\\" id=\\\"https:\/\/forecastingresearch.org\/wp-content\/uploads\/2024\/10\/nuclear-risk.pdf#page=85\\\" target=\\\"_blank\\\" rel=\\\"noreferrer noopener\\\">Appendix 4<\/a> for more detail.\",\"id\":\"c6f13b70-38ab-4909-b351-5ff855b35099\"},{\"id\":\"758e9bb7-1fcd-4147-97ed-463e43451c07\",\"content\":\"\u201cSuperforecasters\u201d outperformed experts and intelligence analysts in forecasting tournaments held by the Good Judgment Project, or had equivalent forecasting skill according to follow-up work by Good Judgment Inc. See Tetlock, Philip E., Barbara A. Mellers, Nick Rohrbaugh, and Eva Chen. \\\"Forecasting Tournaments: Tools for Increasing Transparency and Improving the Quality of Debate.\\\" <em>Current Directions in Psychological Science<\/em> 23, no. 4 (2014): 290\u2013295. <a href=\\\"https:\/\/doi.org\/10.1177\/0963721414534257\\\" target=\\\"_blank\\\" rel=\\\"noreferrer noopener\\\">https:\/\/doi.org\/10.1177\/0963721414534257<\/a>.\"},{\"id\":\"45752514-46e5-48b1-9fd3-6f7088fedcf4\",\"content\":\"These were non-experts or laypeople who had some interest in current affairs but lacked specialized<br>training.\"},{\"id\":\"c8e4b196-3698-4204-911b-001d3bf6c52f\",\"content\":\"Tetlock, Philip E., Christopher Karvetski, Ville A. Satop\u00e4\u00e4, and Kevin Chen. \\\"Long-Range Subjective-Probability Forecasts of Slow-Motion Variables in World Politics: Exploring Limits on Expert Judgment.\\\" <em>Futures &amp; Foresight Science<\/em> 6 (2024): e157. <a href=\\\"https:\/\/doi.org\/10.1002\/ffo2.157\\\" target=\\\"_blank\\\" rel=\\\"noreferrer noopener\\\">https:\/\/doi.org\/10.1002\/ffo2.157<\/a>.\"},{\"id\":\"a7e8e02f-375c-42e9-a2aa-f4ab2d49adb9\",\"content\":\"Lugar, Richard G. \\\"The Lugar Survey on Proliferation Threats and Responses.\\\" Washington, D.C.: United States Senate Foreign Relations Committee, 2005. <a href=\\\"https:\/\/irp.fas.org\/threat\/lugar_survey.pdf\\\" target=\\\"_blank\\\" rel=\\\"noreferrer noopener\\\">https:\/\/irp.fas.org\/threat\/lugar_survey.pdf<\/a>.<br>\"},{\"id\":\"60dd9d0a-e48a-44c5-8657-d5fd82954159\",\"content\":\"Project for the Study of the 21st Century. \\\"Great Power Conflict Report.\\\" November 12, 2015. <a href=\\\"https:\/\/projects21.org\/2015\/11\/12\/ps21-survey-experts-see-increased-risk-of-nuclear-war\/\\\" target=\\\"_blank\\\" rel=\\\"noreferrer noopener\\\">https:\/\/projects21.org\/2015\/11\/12\/ps21-survey-experts-see-increased-risk-of-nuclear-war\/<\/a>.\"},{\"content\":\"Karger, Ezra, Josh Rosenberg, Zach Jacobs, et al. \u201cForecasting Existential Risks: Evidence from a Long-Run Forecasting Tournament.\u201d FRI Working Paper #1. Forecasting Research Institute, 2023.\u00a0<a href=\\\"https:\/\/forecastingresearch.org\/research\/existential-risk-persuasion-tournament\\\" type=\\\"research\\\" id=\\\"876\\\" target=\\\"_blank\\\" rel=\\\"noreferrer noopener\\\">https:\/\/forecastingresearch.org\/research\/existential-risk-persuasion-tournament<\/a>.\",\"id\":\"aab97946-4875-4de3-aa06-7b31c520e260\"},{\"content\":\"Karger et al., <a href=\\\"https:\/\/forecastingresearch.org\/research\/existential-risk-persuasion-tournament\\\" type=\\\"research\\\" id=\\\"876\\\"><a href=\\\"https:\/\/forecastingresearch.org\/research\/existential-risk-persuasion-tournament\\\" type=\\\"research\\\" id=\\\"876\\\" target=\\\"_blank\\\" rel=\\\"noreferrer noopener\\\">XPT<\/a><\/a>, 44.\",\"id\":\"604b594e-b78f-4a24-bc60-74eee3ab6d6c\"},{\"id\":\"8c4963a1-4c22-4c9b-8b77-9b10a3ee2a39\",\"content\":\"The Russian invasion of Ukraine began on February 24th, 2022.\"},{\"id\":\"dcc54637-bd3f-4634-b836-75867518b3eb\",\"content\":\"YouGov. \\\"How Likely Do You Think We Are to Get into a Nuclear War within the Next Ten Years?\\\" Survey, February 28, 2022. <a href=\\\"https:\/\/today.yougov.com\/topics\/politics\/survey-results\/daily\/2022\/02\/28\/c6993\/3\\\" target=\\\"_blank\\\" rel=\\\"noreferrer noopener\\\">https:\/\/today.yougov.com\/topics\/politics\/survey-results\/daily\/2022\/02\/28\/c6993\/3<\/a>. Statista. \\\"How Likely Do You Think We Are to Get into a Nuclear War within the Next Ten Years?\\\" Survey, February 1\u20137, 2024. <a href=\\\"https:\/\/www.statista.com\/statistics\/1308926\/us-opinion-likelihood-nuclear-war\/\\\" target=\\\"_blank\\\" rel=\\\"noreferrer noopener\\\">https:\/\/www.statista.com\/statistics\/1308926\/us-opinion-likelihood-nuclear-war\/<\/a>. \u201cHow likely do you think we are to get into a nuclear war within the next ten years?,\u201d survey,<br>February 1 to 7, 2024, https:\/\/www.statista.com\/statistics\/1308926\/us-opinion-likelihood-nuclear-war\/.\"},{\"id\":\"e20baa58-8279-4da2-8319-351c40d6b416\",\"content\":\"FOMnibus. \\\"\u042f\u0434\u0435\u0440\u043d\u043e\u0435 \u043e\u0440\u0443\u0436\u0438\u0435.\\\" Survey conducted October 27\u201329, 2023. Fond Obshchestvennoe Mnenie (Public Opinion Foundation), November 10, 2023. <a href=\\\"https:\/\/fom.ru\/Bezopasnost-i-pravo\/14942\\\" target=\\\"_blank\\\" rel=\\\"noreferrer noopener\\\">https:\/\/fom.ru\/Bezopasnost-i-pravo\/14942<\/a>.\"},{\"id\":\"e02fc319-5841-41cf-8d24-40b326b3945e\",\"content\":\"For crowd forecasts. Retrieval date for ongoing forecasts.\"},{\"id\":\"d5bd4ed7-715e-48cb-8400-985fd7281cd8\",\"content\":\"\u201cNot just a warning shot - a targeting of military or civilian targets.\u201d\"},{\"id\":\"3db2aa6e-57c4-44e0-b1c4-ad3c922a12a9\",\"content\":\"Wyden, Peter H. <em>Bay of Pigs: The Untold Story.<\/em> Simon and Schuster, 1979.Peter H. Wyden, Bay of Pigs: The Untold Story (New York: Simon and Schuster, 1979).\"},{\"id\":\"7b32080a-5826-40e8-93e8-2d985b5c1d02\",\"content\":\"Mauboussin, Andrew, and Michael J. Mauboussin. \\\"If You Say Something Is 'Likely,' How Likely Do People Think It Is?\\\" <em>Harvard Business Review<\/em>, July 3, 2018. <a href=\\\"https:\/\/hbr.org\/2018\/07\/if-you-say-something-is-likely-how-likely-do-people-think-it-is\\\" target=\\\"_blank\\\" rel=\\\"noreferrer noopener\\\">https:\/\/hbr.org\/2018\/07\/if-you-say-something-is-likely-how-likely-do-people-think-it-is<\/a>.\"},{\"id\":\"6b75f296-e069-42f0-8c2d-d2ac026eb77c\",\"content\":\"Cited in Gleditsch, Kristian Skrede. \\\"One without the Other? Prediction and Policy in International Studies.\\\" <em>International Studies Quarterly<\/em> 66, no. 3 (2022): sqac036. <a href=\\\"https:\/\/doi.org\/10.1093\/isq\/sqac036\\\" target=\\\"_blank\\\" rel=\\\"noreferrer noopener\\\">https:\/\/doi.org\/10.1093\/isq\/sqac036<\/a>.\"},{\"id\":\"ad362f26-fa6a-44b4-bffc-6adbc13a12dd\",\"content\":\"Friedman, Jeffrey A., Joshua D. Baker, Barbara A. Mellers, Philip E. Tetlock, and Richard Zeckhauser. \\\"The Value of Precision in Probability Assessment: Evidence from a Large-Scale Geopolitical Forecasting Tournament.\\\" International Studies Quarterly 62, no. 2 (2018): 410\u2013422. <a href=\\\"https:\/\/doi.org\/10.1093\/isq\/sqx078\\\" target=\\\"_blank\\\" rel=\\\"noreferrer noopener\\\">https:\/\/doi.org\/10.1093\/isq\/sqx078<\/a>.\"},{\"id\":\"ca439d97-db4b-4c13-b741-4c7f4f02de74\",\"content\":\"Tetlock, Philip E. <em>Expert Political Judgment: How Good Is It? How Can We Know?<\/em> Princeton University Press, 2005.\"},{\"id\":\"c3431a5f-d200-4c78-901c-300e6dbad1e7\",\"content\":\"Mellers, Barbara A., Philip E. Tetlock, Joshua D. Baker, Jeffrey A. Friedman, and Richard Zeckhauser. \\\"Chapter 12. Improving the Accuracy of Geopolitical Risk Assessments.\\\" In <em>The Future of Risk Management<\/em>, edited by Howard Kunreuther, Robert J. Meyer, and Erwann O. Michel-Kerjan. University of Pennsylvania Press, 2019. <a href=\\\"https:\/\/doi.org\/10.9783\/9780812296228-013\\\" target=\\\"_blank\\\" rel=\\\"noreferrer noopener\\\">https:\/\/doi.org\/10.9783\/9780812296228-013<\/a>.\"},{\"id\":\"ade75780-9b56-4138-8584-78ad76b4a001\",\"content\":\"Muehlhauser, Luke. \\\"How Feasible Is Long-Range Forecasting?\\\" Open Philanthropy, October 10, 2019. <a href=\\\"https:\/\/www.openphilanthropy.org\/research\/how-feasible-is-long-range-forecasting\/\\\" target=\\\"_blank\\\" rel=\\\"noreferrer noopener\\\">https:\/\/www.openphilanthropy.org\/research\/how-feasible-is-long-range-forecasting\/<\/a>.\"},{\"content\":\"Karger et al., <a href=\\\"https:\/\/forecastingresearch.org\/research\/existential-risk-persuasion-tournament\\\" type=\\\"research\\\" id=\\\"876\\\" target=\\\"_blank\\\" rel=\\\"noreferrer noopener\\\"><a href=\\\"https:\/\/forecastingresearch.org\/research\/existential-risk-persuasion-tournament\\\" type=\\\"research\\\" id=\\\"876\\\" target=\\\"_blank\\\" rel=\\\"noreferrer noopener\\\">XPT<\/a><\/a>.\",\"id\":\"e8ec8ee0-8a6a-4db8-89f6-c57bdd9dd861\"},{\"id\":\"20c227aa-b640-45e0-9e72-dc9f5bf15327\",\"content\":\"Ruhl, Christian. \\\"Philanthropy to the Right of Boom.\\\" Founders Pledge, February 13, 2023. <a href=\\\"https:\/\/www.founderspledge.com\/research\/philanthropy-to-the-right-of-boom\\\" target=\\\"_blank\\\" rel=\\\"noreferrer noopener\\\">https:\/\/www.founderspledge.com\/research\/philanthropy-to-the-right-of-boom<\/a>.\"},{\"id\":\"ffeb8537-c72d-4014-95d8-be11f18d0e7c\",\"content\":\"These organizations included but were not limited to Nuclear Threat Initiative, Arms Control Association, Founders Pledge, Open Philanthropy, Carnegie Endowment, Chatham House, and the Union of Concerned Scientists.\"},{\"id\":\"019937a7-6537-49a3-b2d2-45c55bece462\",\"content\":\"Each participant was allocated five of the general (i.e. not domain-specific) crux questions.\"},{\"id\":\"089e2614-bb58-4ada-8324-090737ec8534\",\"content\":\"Karger, Ezra, Joshua Monrad, Barbara Mellers, and Philip Tetlock. \\\"Reciprocal Scoring: A Method for Forecasting Unanswerable Questions.\\\" SSRN Working Paper, 2021. <a href=\\\"https:\/\/dx.doi.org\/10.2139\/ssrn.3954498\\\" id=\\\"https:\/\/dx.doi.org\/10.2139\/ssrn.3954498\\\" target=\\\"_blank\\\" rel=\\\"noreferrer noopener\\\">https:\/\/dx.doi.org\/10.2139\/ssrn.3954498<\/a>.\"},{\"id\":\"c0960c9f-6ab5-47ed-b9df-3bc33fe0e4b9\",\"content\":\"Organizations that kindly helped with distribution of the survey were: Emerging Voices Network at<br>BASIC, European Leadership Network, Asia Pacific Leadership Network, Younger Generation Leaders<br>Network, Project on Nuclear Issues at CSIS, and the Pacific Forum.\"},{\"content\":\"A full list of the organizations and reports we reviewed is available in <a href=\\\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=82\\\" id=\\\"https:\/\/forecastingresearch.org\/wp-content\/uploads\/2024\/10\/nuclear-risk.pdf#page=82\\\" target=\\\"_blank\\\" rel=\\\"noreferrer noopener\\\">Appendix 2<\/a>.\",\"id\":\"f5fb56b4-48f0-464f-bb93-5305547cd093\"},{\"id\":\"11446d22-b021-4ec9-a89c-f66fd1840fff\",\"content\":\"Tetlock, Philip E., Barbara A. Mellers, Nick Rohrbaugh, and Eva Chen. \\\"Forecasting Tournaments: Tools for Increasing Transparency and Improving the Quality of Debate.\\\" <em>Current Directions in Psychological Science<\/em> 23, no. 4 (2014): 290\u2013295. <a href=\\\"https:\/\/doi.org\/10.1177\/0963721414534257\\\" target=\\\"_blank\\\" rel=\\\"noreferrer noopener\\\">https:\/\/doi.org\/10.1177\/0963721414534257<\/a>.\"},{\"id\":\"26b45041-471d-4824-9a22-0399db4b0191\",\"content\":\"We believe that this very low proportion of female superforecaster participants is largely a function of recruitment from the study that originally identified superforecasters among a broad population of participants (Mellers, Barbara, Lyle Ungar, Jonathan Baron, et al. \\\"Psychological Strategies for Winning a Geopolitical Forecasting Tournament.\\\" <em>Psychological Science<\/em> 25, no. 5 (2014): 1106\u20131115. <a href=\\\"https:\/\/doi.org\/10.1177\/0956797614524255\\\">https:\/\/doi.org\/10.1177\/0956797614524255<\/a>.) Of the participants in that study, 83% were male.\"},{\"id\":\"3484acb4-565d-4e85-8ee4-a427963b3770\",\"content\":\"In some places, we report the average or mean. These are instances where participants are asked to<br>distribute votes, ranks, or probabilities (i.e., saying how likely several mutually exclusive but collectively exhaustive events are, such that the total probabilities sum to 100%).\"},{\"content\":\"Karger et al., <a href=\\\"https:\/\/forecastingresearch.org\/research\/xpt\\\" id=\\\"876\\\"><a href=\\\"https:\/\/forecastingresearch.org\/research\/existential-risk-persuasion-tournament\\\" type=\\\"research\\\" id=\\\"876\\\" target=\\\"_blank\\\" rel=\\\"noreferrer noopener\\\">XPT<\/a><\/a>, 20-22.\",\"id\":\"151cbe3e-71d5-4c1b-b612-39a074dbb49e\"},{\"id\":\"e6bb5c35-0dca-43d8-81f5-036ee994b8e2\",\"content\":\"Here we report averages, rather than medians, as the aggregate group measure. This is done so that<br>the totals across the domains sum to 100%.\"},{\"id\":\"ca6ff985-c41c-4231-a5f1-bd56d5d38d60\",\"content\":\"Participants were also asked how their probability of nuclear catastrophe by 2045 would change if they knew that this event would occur. Therefore, these changes in forecasts shouldn\u2019t be taken as<br>representing the causal effect of the event. For example, it's possible that a participant who would reduce their probability of catastrophe if an arms control agreement occurred might not think that the agreement itself would cause any change in risk. Instead, they might think that an arms control agreement would indicate that the relationship between the countries has improved, and they might reduce their predicted risk of catastrophe for that reason.\"},{\"id\":\"be7abff7-058e-417d-8fa4-f381cb8708b9\",\"content\":\"Barrett, Anthony M., Seth D. Baum, and Kelly Hostetler. \\\"Analyzing and Reducing the Risks of Inadvertent Nuclear War Between the United States and Russia.\\\" <em>Science &amp; Global Security<\/em> 21, no. 2 (2013): 106\u2013133. <a href=\\\"https:\/\/doi.org\/10.1080\/08929882.2013.798984\\\" target=\\\"_blank\\\" rel=\\\"noreferrer noopener\\\">https:\/\/doi.org\/10.1080\/08929882.2013.798984<\/a>.\"},{\"id\":\"33715d4d-a106-44fa-9bcc-e75ea03073c0\",\"content\":\"Institute for Security and Technology. \\\"CATALINK.\\\" Accessed October 17, 2024. <a href=\\\"https:\/\/securityandtechnology.org\/catalink\/\\\" target=\\\"_blank\\\" rel=\\\"noreferrer noopener\\\">https:\/\/securityandtechnology.org\/catalink\/<\/a>.\"},{\"id\":\"b5117a2d-6ec6-45ba-b711-b4870db57b0f\",\"content\":\"U.S. Department of Defense. \\\"2022 National Defense Strategy of the United States of America.\\\" U.S. Department of Defense, October 27, 2022. <a href=\\\"https:\/\/media.defense.gov\/2022\/Oct\/27\/2003103845\/-1\/-1\/1\/2022-NATIONAL-DEFENSE-STRATEGY-NPR-MDR.pdf\\\" target=\\\"_blank\\\" rel=\\\"noreferrer noopener\\\">https:\/\/media.defense.gov\/2022\/Oct\/27\/2003103845\/-1\/-1\/1\/2022-NATIONAL-DEFENSE-STRATEGY-NPR-MDR.pdf<\/a>.\"},{\"content\":\"Summaries of the full statements are presented here. We suggest reading the full statements, which<br>can be found in <a href=\\\"https:\/\/forecastingresearch.org\/pdf\/nuclear-risk.pdf#page=142\\\" id=\\\"https:\/\/forecastingresearch.org\/wp-content\/uploads\/2024\/10\/nuclear-risk.pdf#page=85\\\" target=\\\"_blank\\\" rel=\\\"noreferrer noopener\\\">Appendix 4<\/a>.\",\"id\":\"491235a4-0c43-40ac-ba49-52a5e9d67ca4\"},{\"id\":\"f491b3ff-4d59-46f4-9b62-9183c828f28e\",\"content\":\"Atanasov, Pavel D., Ezra Karger, and Philip Tetlock. \\\"Full Accuracy Scoring Accelerates the Discovery of Skilled Forecasters.\\\" SSRN Working Paper, February 13, 2023. <a href=\\\"https:\/\/dx.doi.org\/10.2139\/ssrn.4357367\\\" target=\\\"_blank\\\" rel=\\\"noreferrer noopener\\\">https:\/\/dx.doi.org\/10.2139\/ssrn.4357367<\/a>.\"},{\"id\":\"8ca66c13-c487-4fa2-a006-d4ac0507bee4\",\"content\":\"Bulletin of the Atomic Scientists. \\\"Doomsday Clock.\\\" Accessed October 17, 2024. <a href=\\\"https:\/\/thebulletin.org\/doomsday-clock\/current-time\/\\\" target=\\\"_blank\\\" rel=\\\"noreferrer noopener\\\">https:\/\/thebulletin.org\/doomsday-clock\/current-time\/<\/a>.\"}]"},"research_type":[4],"class_list":["post-1042","research","type-research","status-publish","has-post-thumbnail","hentry","research_type-working-paper"],"acf":[],"yoast_head":"<title>Can Humanity Achieve a Century of Nuclear Peace? &#8211; Forecasting Research Institute<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/forecastingresearch.org\/research\/nuclear-risk\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Can Humanity Achieve a Century of Nuclear Peace? &#8211; Forecasting Research Institute\" \/>\n<meta property=\"og:description\" content=\"This study systematically assessed expert beliefs about the probability of a nuclear weapons catastrophe by 2045. 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