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

Imperfect Recall and AI Delegation – Eric Olav Chen (Global Priorities Institute, University of Oxford), Alexis Ghersengorin (Global Priorities Institute, University of Oxford) and Sami Petersen (Department of Economics, University of Oxford)

A principal wants to deploy an artificial intelligence (AI) system to perform some task. But the AI may be misaligned and aim to pursue a conflicting objective. The principal cannot restrict its options or deliver punishments. Instead, the principal is endowed with the ability to impose imperfect recall on the agent. The principal can then simulate the task and obscure whether it is real or part of a test. This allows the principal to screen misaligned AIs during testing and discipline their behaviour in deployment. By increasing the…

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This paper explores a new approach to the problem of decision under relevant moral uncertainty. We treat the case of an agent making decisions in the face of moral uncertainty on the model of bargaining theory, as if the decision-making process were one of bargaining among different internal parts of the agent, with different parts committed to different moral theories. The resulting approach contrasts interestingly with the extant “maximise expected choiceworthiness”…

Shutdownable Agents through POST-Agency – Elliott Thornley (Global Priorities Institute, University of Oxford)

Many fear that future artificial agents will resist shutdown. I present an idea – the POST-Agents Proposal – for ensuring that doesn’t happen. I propose that we train agents to satisfy Preferences Only Between Same-Length Trajectories (POST). I then prove that POST – together with other conditions – implies Neutrality+: the agent maximizes expected utility, ignoring the probability distribution over trajectory-lengths. I argue that Neutrality+ keeps agents shutdownable and allows them to be useful.