Probabilistic Formulation of the Take The Best Heuristic

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Researchers

Research units

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.

Details

Original languageEnglish
Title of host publicationCogSci 2018 Proceedings
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

    Research areas

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

ID: 26824320