Two steps to risk sensitivity

TL;DR

This paper explores risk sensitivity in decision making using distributional reinforcement learning, focusing on CVaR, and introduces methods to ensure time consistency for better modeling of human and animal behavior.

Contribution

It reanalyzes human decision-making with CVaR, identifies issues with risk aversion, and proposes alternative CVaR forms that are time consistent.

Findings

Revealed hidden risk aversion in human choices using CVaR analysis.

Showed that certain CVaR forms lack time consistency, affecting modeling accuracy.

Simulations demonstrated differences in planning implications between CVaR variants.

Abstract

Distributional reinforcement learning (RL) -- in which agents learn about all the possible long-term consequences of their actions, and not just the expected value -- is of great recent interest. One of the most important affordances of a distributional view is facilitating a modern, measured, approach to risk when outcomes are not completely certain. By contrast, psychological and neuroscientific investigations into decision making under risk have utilized a variety of more venerable theoretical models such as prospect theory that lack axiomatically desirable properties such as coherence. Here, we consider a particularly relevant risk measure for modeling human and animal planning, called conditional value-at-risk (CVaR), which quantifies worst-case outcomes (e.g., vehicle accidents or predation). We first adopt a conventional distributional approach to CVaR in a sequential setting and reanalyze the choices of human decision-makers in the well-known two-step task, revealing substantial risk aversion that had been lurking under stickiness and perseveration. We then consider a further critical property of risk sensitivity, namely time consistency, showing alternatives to this form of CVaR that enjoy this desirable characteristic. We use simulations to examine settings in which the various forms differ in ways that have implications for human and animal planning and behavior.