Results from the 2022 Existential Risk Persuasion Tournament
Today we’ve released "Forecasting Existential Risks: Evidence from a Long-Run Forecasting Tournament", which describes the results of the Existential-Risk Persuasion Tournament (XPT).
The XPT, which ran from June through October of 2022, brought together forecasters from two groups with distinctive claims to knowledge about humanity’s future — experts in various domains relevant to existential risk, and "superforecasters" with a track record of predictive accuracy over short time horizons. We asked tournament participants to predict the likelihood of global risks related to nuclear weapon use, biorisks, and AI, along with dozens of other related, shorter-run forecasts.
Some major takeaways from the XPT include:
The median domain expert predicted a 20% chance of catastrophe and a 6% chance of human extinction by 2100. The median superforecaster predicted a 9% chance of catastrophe and a 1% chance of extinction.
Superforecasters predicted considerably lower chances of both catastrophe and extinction than did experts, but the disagreement between experts and superforecasters was not uniform across topics. Experts and superforecasters were furthest apart (in percentage point terms) on AI risk, and most similar on the risk of nuclear war.
Predictions about risk were highly correlated across topics. For example, participants who gave higher risk estimates for AI also gave (on average) higher risk estimates for biorisks and nuclear weapon use.
Forecasters with higher “intersubjective accuracy”—i.e., those best at predicting the views of other participants—estimated lower probabilities of catastrophic and extinction risks from all sources.
Few minds were changed during the XPT, even among the most active participants, and despite monetary incentives for persuading others.
See the full working paper here.
FRI hopes that the XPT will not only inform our understanding of existential risks, but will also advance the science of forecasting by:
Collecting a large set of forecasts resolving on a long timescale, in a rigorous setting. This will allow us to measure correlations between short-run (2024), medium-run (2030) and longer-run (2050) accuracy in the coming decades.
Exploring the use of bonus payments for participants who both 1) produced persuasive rationales and 2) made accurate “intersubjective” forecasts (i.e., predictions of the predictions of other participants), which we are testing as early indicators of the reliability of long-range forecasts.
Encouraging experts and superforecasters to interact: to share knowledge, debate, and attempt to persuade each other. We plan to explore the value of these interactions in future work.
