Probabilistic Formulation of the Take The Best Heuristic

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Abstract

The framework of cognitively bounded rationality treats problem solving as fundamentally rational, but emphasises that it is constrained by cognitive architecture and the task environment. This paper investigates a simple decision making heuristic, Take The Best (TTB), within that framework. We formulate TTB as a likelihood-based probabilistic model, where the decision strategy arises by probabilistic inference based on the training data and the model constraints. The strengths of the probabilistic formulation, in addition to providing a bounded rational account of the learning of the heuristic, include natural extensibility with additional cognitively plausible constraints and prior information, and the possibility to embed the heuristic as a subpart of a larger probabilistic model. We extend the model to learn cue discrimination thresholds for continuous-valued cues and experiment with using the model to account for biased preference feedback from a boundedly rational agent in a simulated interactive machine learning task.
Original languageEnglish
Title of host publicationCogSci 2018 Proceedings
PublisherCOGNITIVE SCIENCE SOCIETY
Pages2214-2219
ISBN (Electronic)978-0-9911967-8-4
Publication statusPublished - 2018
MoE publication typeA4 Article in a conference publication
EventAnnual Meeting of the Cognitive Science Society - Madison, United States
Duration: 25 Jul 201828 Jul 2018
Conference number: 40

Conference

ConferenceAnnual Meeting of the Cognitive Science Society
Abbreviated titleCogSci
CountryUnited States
CityMadison
Period25/07/201828/07/2018

Keywords

  • Bayesian models
  • bounded rationality
  • heuristics
  • Take The Best

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