Coherence-Based Reasoning and Rationality: A Neural Network Modelling Approach to Decision Making

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A key aspect of ecological rationality is fast and accurate learning in complex environments. Real-world decision makers are thereby often confronted with environments that are scarce in feedback, noisy, and dynamic. These factors are likely to complicate learning of cue-criterion relations. Other factors such as an optimal learning order could, in contrast, facilitate learning. Elaborating on previous work on the Parallel Constraint Satisfaction model of decision making (PCS), we will develop and investigate learning mechanisms for PCS and compare them to alternative models (i.e., reinforcement learning theory of strategy selection for fast and frugal heuristics, feedforward networks, regression and Bayes models). First, to assess the relative extent of ecological rationality, the models’ performance under these real-world conditions will be compared in a series of Monte-Carlo simulations. Second, the models’ ability to predict behavior will be tested in a series of subsequent empirical studies. We hypothesize that the suggested learning mechanism for PCS should allow learning even in dynamic environments with scarce and noisy feedback due to a) mechanisms of internal feedback, b) compensatory cue integration, and c) monitoring of all pieces of information.