Probabilistic Formulation of the Take The Best Heuristic

TL;DR

This paper presents a probabilistic model of the Take The Best heuristic within a bounded rationality framework, enabling learning, extension, and integration into larger models for decision making.

Contribution

It introduces a likelihood-based probabilistic formulation of TTB, allowing for learning, extensions with additional constraints, and embedding in larger probabilistic systems.

Findings

Probabilistic TTB can learn cue discrimination thresholds.

Model accounts for biased preferences in interactive tasks.

Extensible framework for bounded rational decision heuristics.

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.