Cognitive Validity of a Causal Value Function with Loose Symmetry and Its Effectiveness for N-armed Bandit Problems

• Oyo Kuratomo

  Graduate School of Advanced Science and Technology, Tokyo Denki University

• Ichino Manabu

  School of Science and Engineering, Tokyo Denki University

• Takahashi Tatsuji

  School of Science and Engineering, Tokyo Denki University

Bibliographic Information

Other Title

• 緩い対称性を持つ因果的価値関数の認知的妥当性とN本腕バンディット問題におけるその有効性

Abstract

Cognitive psychology and behavioral economics have shown that humans have cognitive biases that deviate from normative systems such as classical logic and probability theory. Considering that humans have the ability to understand the world from sparse and/or imprecise data, it is natural to assume that the biases in human have some ecological merits in adaptation. We focus on two cognitive biases, symmetry and mutual exclusivity, that are considered peculiar to human. In this study, with the framework of empirical Bayes, we clarify the implication of a model of human causal cognition, the loosely symmetric (LS) model [Shinohara 07]) that implements the cognitive biases. We show that LS has great descriptive validity in inductive inference of causal relationship (causal induction) with a meta-analysis and an experiment in causal induction. The result of another experiment strongly suggests that humans use the inductively inferred causal relationship to decision-making. Then we show that LS effectively works in sequential decision-making under uncertainty (N-armed bandit problems). Operating LS as a simple value function under the greedy method in the framework of reinforcement learning, we analyze its behavior in terms of cognitive biases or heuristics under uncertainty. The three cognitive properties resulting from the loose symmetry, comparative valuation, satisficing, and prospect theory-like risk attitudes, are shown to be the key of the performance of LS. We parameterize the reference for satisficing and show that the quite intuitive parameter enables optimization.

Journal

• Transactions of the Japanese Society for Artificial Intelligence

  Transactions of the Japanese Society for Artificial Intelligence 30 (2), 403-416, 2015

  The Japanese Society for Artificial Intelligence

Keywords

• cognitive bias
• symmetry inference
• exploration-exploitation dilemma
• LS model
• cognitively inspired computing

Details

• CRID: 1390001205109257728
• NII Article ID: 130004927386
• DOI: 10.1527/tjsai.30.403
• ISSN: 13468030
• ISSN: 13460714
• Text Lang: ja
• Data Source:
  • JaLC
  • Crossref
  • CiNii Articles
  • KAKEN
• Abstract License Flag: Disallowed