It Only Takes One: The Psychology of Unilateral Decisions
Joshua Lewis (New York University), Carter Allen (UC Berkeley), Christoph Winter (ITAM, Harvard University and Institute for Law & AI) and Lucius Caviola (Global Priorities Institute, Oxford University)
GPI Working Paper No. 14-2024
Sometimes, one decision can guarantee that a risky event will happen. For instance, it only took one team of researchers to synthesize and publish the horsepox genome, thus imposing its publication even though other researchers might have refrained for biosecurity reasons. We examine cases where everybody who can impose a given event has the same goal but different information about whether the event furthers that goal. Across 8 experiments (including scenario studies with elected policymakers, doctors, artificial-intelligence researchers, and lawyers and judges and economic games with laypeople, N = 1,518, and 3 supplemental studies, N = 847) people behave suboptimally, balancing two factors. First, people often impose events with expected utility only slightly better than the alternative based on the information available to them, even when others might know more. This approach is insufficiently cautious, leading people to impose too frequently, a situation termed the unilateralist’s curse. Second, counteracting the first factor, people avoid sole responsibility for unexpectedly bad outcomes, sometimes declining to impose seemingly desirable events. The former heuristic typically dominates and people unilaterally impose too often, succumbing to the unilateralist’s curse. But when only few people can impose, who know the stakes are high, responsibility aversion reduces over-imposing.
Other working papers
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Recent years have seen increasing concern that artificial intelligence may soon pose an existential risk to humanity. One leading ground for concern is that artificial agents may be power-seeking, aiming to acquire power and in the process disempowering humanity. A range of power-seeking theorems seek to give formal articulation to the idea that artificial agents are likely to be power-seeking. I argue that leading theorems face five challenges, then draw lessons from this result.
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