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The framework of cognitively bounded rationality treats problem solving asfundamentally rational, but emphasises that it is constrained by cognitivearchitecture and the task environment. This paper investigates a simpledecision making heuristic, Take The Best (TTB), within that framework. Weformulate TTB as a likelihood-based probabilistic model, where the decisionstrategy arises by probabilistic inference based on the training data and themodel constraints. The strengths of the probabilistic formulation, in additionto providing a bounded rational account of the learning of the heuristic,include natural extensibility with additional cognitively plausible constraintsand prior information, and the possibility to embed the heuristic as a subpartof a larger probabilistic model. We extend the model to learn cuediscrimination thresholds for continuous-valued cues and experiment with usingthe model to account for biased preference feedback from a boundedly rationalagent in a simulated interactive machine learning task.

Probabilistic Formulation of the Take The Best Heuristic