Implications of Human Irrationality for Reinforcement Learning
This work addresses how AI and machine learning methods can be enhanced by insights from human behavior, though it appears incremental as it builds on existing POMDP frameworks.
The paper tackles the problem of leveraging human decision-making, including apparent irrationalities, to improve reinforcement learning by proposing a novel POMDP model for contextual choice tasks, showing that a reinforcement learner can benefit from human decision patterns.
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.