On the empirical indistinguishability of knowledge structures

• Published in: British journal of mathematical & statistical psychology Vol. 74; no. 3; pp. 465 - 486
• Main Authors: Stefanutti, Luca, Spoto, Andrea
• Format: Journal Article
• Language: English
• Published: England British Psychological Society 01.11.2021
• Subjects: Combinatorial analysis; empirical indistinguishability; forward and backward gradedness; identifiability; Knowledge; knowledge space theory; Mathematical models; Parameter identification; Probability distribution; knowledge space theory; forward and backward gradedness; identifiability; empirical indistinguishability
• ISSN: 0007-1102; 2044-8317; 2044-8317
• DOI: 10.1111/bmsp.12235

In recent years a number of articles have focused on the identifiability of the basic local independence model. The identifiability issue usually concerns two model parameter sets predicting an identical probability distribution on the response patterns. Both parameter sets are applied to the same knowledge structure. However, nothing is known about cases where different knowledge structures predict the same probability distribution. This situation is referred to as ʻempirical indistinguishabilityʼ between two structures and is the main subject of the present paper. Empirical indistinguishability is a stronger form of unidentifiability, which involves not only the parameters, but also the structural and combinatorial properties of the model. In particular, as far as knowledge structures are concerned, a consequence of empirical indistinguishability is that the existence of certain knowledge states cannot be empirically established. Most importantly, it is shown that model identifiability cannot guarantee that a certain knowledge structure is empirically distinguishable from others. The theoretical findings are exemplified in a number of different empirical scenarios.