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.
Other working papers
Egyptology and Fanaticism – Hayden Wilkinson (Global Priorities Institute, University of Oxford)
Various decision theories share a troubling implication. They imply that, for any finite amount of value, it would be better to wager it all for a vanishingly small probability of some greater value. Counterintuitive as it might be, this fanaticism has seemingly compelling independent arguments in its favour. In this paper, I consider perhaps the most prima facie compelling such argument: an Egyptology argument (an analogue of the Egyptology argument from population ethics). …
Will AI Avoid Exploitation? – Adam Bales (Global Priorities Institute, University of Oxford)
A simple argument suggests that we can fruitfully model advanced AI systems using expected utility theory. According to this argument, an agent will need to act as if maximising expected utility if they’re to avoid exploitation. Insofar as we should expect advanced AI to avoid exploitation, it follows that we should expected advanced AI to act as if maximising expected utility. I spell out this argument more carefully and demonstrate that it fails, but show that the manner of its failure is instructive…
Estimating long-term treatment effects without long-term outcome data – David Rhys Bernard (Paris School of Economics)
Estimating long-term impacts of actions is important in many areas but the key difficulty is that long-term outcomes are only observed with a long delay. One alternative approach is to measure the effect on an intermediate outcome or a statistical surrogate and then use this to estimate the long-term effect. …