Empirical content as a criterion for evaluating models

Abstract

Hypotheses derived from models can be tested in an empirical study: If the model reliably fails to predict behavior, it can be dismissed or modified. Models can also be evaluated before data are collected: More useful models have a high level of empirical content (Popper, 1934), i.e., they make precise predictions (degree of precision) for many events (level of universality). I apply these criteria to reflect on some critical aspects of Kirsch’s (2019) unifying computational model of decision making.

Publication
Cognitive Processing, 20, 273-275
Date
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