ASPM2: Progress Toward the Analysis of Symbolic Parameter Models

Abstract

Abstract : Model-fitting, the problem of finding parameter settings that cause a model to fit given data as closely as possible, is a hard but important problem in cognitive science in general, and in cognitive diagnosis in particular. Efficient solutions have been found for certain types of model-fitting problems (e.g., linear and integer programming) that involve specific types of parameters (usually continuous) and models (usually linear). But these techniques usually do not apply to computational cognitive models whose internal workings must be treated as a black box for the purposes of fitting. We present ASPM (Analysis of Symbolic Parameter Models), a suite of computational tools for fitting and analyzing such symbolic parameter models, show how it can be used to fit and analyze computational models with well over 10 billion parameter settings, and describe a few changes in the initial design that will make it even more powerful as well as easier to use.