Probabilistic Formulation of the Take The Best Heuristic
This work provides a probabilistic framework for cognitive heuristics, which is incremental as it builds on existing bounded rationality concepts.
The paper tackled the problem of modeling the Take The Best heuristic within bounded rationality by formulating it as a probabilistic model, resulting in extensions like learning cue thresholds and handling biased feedback in simulations.
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.