Towards shutdownable agents via stochastic choice
Elliott Thornley (Global Priorities Institute, University of Oxford), Alexander Roman (New College of Florida), Christos Ziakas (Imperial College, London), Leyton Ho (Brown University) and Louis Thomson (University of Oxford)
GPI Working Paper No. 16-2024
The POST-Agents Proposal (PAP) is an idea for ensuring that advanced artificial agents never resist shutdown. A key part of the PAP is using a novel ‘Discounted Reward for Same-Length Trajectories (DReST)’ reward function to train agents to (1) pursue goals effectively conditional on each trajectory-length (be ‘USEFUL’), and (2) choose stochastically between different trajectory-lengths (be ‘NEUTRAL’ about trajectory-lengths). In this paper, we propose evaluation metrics for USEFULNESS and NEUTRALITY. We use a DReST reward function to train simple agents to navigate gridworlds, and we find that these agents learn to be USEFUL and NEUTRAL. Our results thus provide some initial evidence that DReST reward functions could train advanced agents to be USEFUL and NEUTRAL. Our theoretical work suggests that these agents would be useful and shutdownable.
Other working papers
Future Suffering and the Non-Identity Problem – Theron Pummer (University of St Andrews)
I present and explore a new version of the Person-Affecting View, according to which reasons to do an act depend wholly on what would be said for or against this act from the points of view of particular individuals. According to my view, (i) there is a morally requiring reason not to bring about lives insofar as they contain suffering (negative welfare), (ii) there is no morally requiring reason to bring about lives insofar as they contain happiness (positive welfare), but (iii) there is a permitting reason to bring about lives insofar as they…
Tough enough? Robust satisficing as a decision norm for long-term policy analysis – Andreas Mogensen and David Thorstad (Global Priorities Institute, Oxford University)
This paper aims to open a dialogue between philosophers working in decision theory and operations researchers and engineers whose research addresses the topic of decision making under deep uncertainty. Specifically, we assess the recommendation to follow a norm of robust satisficing when making decisions under deep uncertainty in the context of decision analyses that rely on the tools of Robust Decision Making developed by Robert Lempert and colleagues at RAND …
A bargaining-theoretic approach to moral uncertainty – Owen Cotton-Barratt (Future of Humanity Institute, Oxford University), Hilary Greaves (Global Priorities Institute, Oxford University)
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”…