Implications of Human Irrationality for Reinforcement Learning

TL;DR

This paper explores how human decision-making, often seen as irrational, can inform the design of reinforcement learning models by leveraging context-dependent choices and POMDP frameworks.

Contribution

It introduces a novel POMDP model for contextual choice tasks that utilizes human-like decision patterns to enhance reinforcement learning.

Findings

Reinforcement learners can exploit human decision strategies.

Human irrationalities can inspire AI architecture improvements.

A new POMDP model captures context-dependent decision-making.

Abstract

Recent work in the behavioural sciences has begun to overturn the long-held belief that human decision making is irrational, suboptimal and subject to biases. This turn to the rational suggests that human decision making may be a better source of ideas for constraining how machine learning problems are defined than would otherwise be the case. One promising idea concerns human decision making that is dependent on apparently irrelevant aspects of the choice context. Previous work has shown that by taking into account choice context and making relational observations, people can maximize expected value. Other work has shown that Partially observable Markov decision processes (POMDPs) are a useful way to formulate human-like decision problems. Here, we propose a novel POMDP model for contextual choice tasks and show that, despite the apparent irrationalities, a reinforcement learner can take advantage of the way that humans make decisions. We suggest that human irrationalities may offer a productive source of inspiration for improving the design of AI architectures and machine learning methods.