Probabilistic Formulation of the Take The Best Heuristic

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.Comment: Annual Meeting of the Cognitive Science Society, CogSci 2018 Proceeding

Probabilistic Formulation of the Take The Best Heuristic

The framework of cognitively bounded rationality treats prob-lem solving as fundamentally rational, but emphasises that itis constrained by cognitive architecture and the task environ-ment. This paper investigates a simple decision making heuris-tic, Take The Best (TTB), within that framework. We formu-late TTB as a likelihood-based probabilistic model, where thedecision strategy arises by probabilistic inference based on thetraining data and the model constraints. The strengths of theprobabilistic formulation, in addition to providing a boundedrational account of the learning of the heuristic, include naturalextensibility with additional cognitively plausible constraintsand prior information, and the possibility to embed the heuris-tic as a subpart of a larger probabilistic model. We extend themodel to learn cue discrimination thresholds for continuous-valued cues and experiment with using the model to accountfor biased preference feedback from a bounded rational agentin a simulated interactive machine learning task