Formalized Heuristic for Generation an Explanatory Typology

Abstract

The paper investigates the problems of formalizing the heuristics used to generate various types of typologies, in relation to the functional role of the types generated. Data analysis methods that study interpreted and causal models are basis for constructing explanatory typologies. This paper proposes an approach that implements plausible reasoning by logical means to inductively generate cause-and-effect relationships in limited (but potentially replenished) datasets. Some results of empirical typologization are presented that are useful for the formation of theoretical concepts and the development of applied recommendations in social policy.