Heuristics for clueless agents: how to get away with ignoring what matters most in ordinary decision-making
David Thorstad and Andreas Mogensen (Global Priorities Institute, Oxford University)
GPI Working Paper No. 2-2020
Even our most mundane decisions have the potential to significantly impact the long-term future, but we are often clueless about what this impact may be. In this paper, we aim to characterize and solve two problems raised by recent discussions of cluelessness, which we term the Problems of Decision Paralysis and the Problem of Decision-Making Demandingness. After reviewing and rejecting existing solutions to both problems, we argue that the way forward is to be found in the distinction between procedural and substantive rationality. Clueless agents have access to a variety of heuristic decision-making procedures which are often rational responses to the decision problems that they face. By simplifying or even ignoring information about potential long-term impacts, heuristics produce effective decisions without demanding too much of ordinary decision-makers. We outline two classes of problem features bearing on the rationality of decision-making procedures for clueless agents, and show how these features can be used to shed light on our motivating problems.
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The surrogate index method allows policymakers to estimate long-run treatment effects before long-run outcomes are observable. We meta-analyse this approach over nine long-run RCTs in development economics, comparing surrogate estimates to estimates from actual long-run RCT outcomes. We introduce the M-lasso algorithm for constructing the surrogate approach’s first-stage predictive model and compare its performance with other surrogate estimation methods. …
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Some worry that advanced artificial agents may resist being shut down. The Incomplete Preferences Proposal (IPP) is an idea for ensuring that does not happen. A key part of the IPP 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…