A Workflow for Building Computationally Rational Models of Human Behavior

Suyog Chandramouli*, Danqing Shi, Aini Putkonen, Sebastiaan De Peuter, Shanshan Zhang, Jussi P. P. Jokinen, Andrew Howes, Antti Oulasvirta

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

11 Downloads (Pure)

Abstract

Computational rationality explains human behavior as arising due to the maximization of expected utility under the constraints imposed by the environment and limited cognitive resources. This simple assumption, when instantiated via partially observable Markov decision processes (POMDPs), gives rise to a powerful approach for modeling human adaptive behavior, within which a variety of internal models of cognition can be embedded. In particular, such an instantiation enables the use of methods from reinforcement learning (RL) to approximate the optimal policy solution to the sequential decision-making problems posed to the cognitive system in any given setting; this stands in contrast to requiring ad hoc hand-crafted rules for capturing adaptive behavior in more traditional cognitive architectures. However, despite their successes and promise for modeling human adaptive behavior across everyday tasks, computationally rational models that use RL are not easy to build. Being a hybrid of theoretical cognitive models and machine learning (ML) necessitates that model building take into account appropriate practices from both cognitive science and ML. The design of psychological assumptions and machine learning decisions concerning reward specification, policy optimization, parameter inference, and model selection are all tangled processes rife with pitfalls that can hinder the development of valid and effective models. Drawing from a decade of work on this approach, a workflow is outlined for tackling this challenge and is accompanied by a detailed discussion of the pros and cons at key decision points.

Original languageEnglish
Pages (from-to)399-419
Number of pages21
JournalComputational Brain and Behavior
Volume7
Issue number3
Early online date15 Aug 2024
DOIs
Publication statusPublished - Sept 2024
MoE publication typeA1 Journal article-refereed

Keywords

  • Computational rationality
  • Modeling workflow
  • POMDPs
  • Resource rationality

Fingerprint

Dive into the research topics of 'A Workflow for Building Computationally Rational Models of Human Behavior'. Together they form a unique fingerprint.
  • Subjective Functions: Subjective Functions

    Oulasvirta, A. (Principal investigator), Putkonen, A.-M. (Project Member), Li, F. (Project Member) & Luo, X. (Project Member)

    01/09/202331/08/2027

    Project: Academy of Finland: Other research funding

  • Human Automata: Simulator-based Methods for Collaborative AI

    Oulasvirta, A. (Principal investigator), Shiripour, M. (Project Member), Putkonen, A.-M. (Project Member), Rastogi, A. (Project Member), Hegemann, L. (Project Member), Iyer, A. (Project Member), Santala, S. (Project Member), Dayama, N. (Project Member), Laine, M. (Project Member), Halasinamara Chandramouli, S. (Project Member), Li, C. (Project Member), Zhu, Y. (Project Member), Liao, Y.-C. (Project Member), Kylmälä, J. (Project Member), Nioche, A. (Project Member) & Kompatscher, J. (Project Member)

    01/01/202031/12/2023

    Project: Academy of Finland: Other research funding

  • -: Finnish Center for Artificial Intelligence

    Kaski, S. (Principal investigator)

    01/01/201931/12/2022

    Project: Academy of Finland: Other research funding

Cite this