Exploring Minds. Modes of Modelling and Simulation in Artificial Intelligence

The aim of this paper is to grasp the relevant distinctions between various ys in which models and simulations in Artificial Intelligence (AI) relate to cognitive phenomena. In order to get a systematic picture, a taxonomy is developed that is based on the coordinates of formal versus material analogies and theory-guided versus pre-theoretic models in science. These distinctions have parallels in the computational versus mimetic aspects and in analytic versus exploratory types of computer simulation. This taxonomy cuts across the traditional dichotomies between symbolic and embodied AI, general intelligence and cognitive simulation and human and non-human-like AI. According to the taxonomy proposed here, one can distinguish between four distinct general approaches that figured prominently in early and classical AI, and that have partly developed into distinct research programmes: first, phenomenal simulations (e.g., Turing’s “imitation game”); second, simulations that explore general-level formal isomorphisms in pursuit of a general theory of intelligence (e.g., logic-based AI); third, simulations as exploratory material models that serve to develop theoretical accounts of cognitive processes (e.g., Marr’s stages of visual processing and classical connectionism); and fourth, simulations as strictly formal models of a theory of computation that postulates cognitive processes to be isomorphic with computational processes (strong symbolic AI). In continuation of pragmaticist views of the modes of modelling and simulating world affairs (Humphreys, Winsberg), this taxonomy of approaches to modelling in AI helps to elucidate how available computational concepts and simulational resources contribute to the modes of representation and theory development in AI research – and what made that research programme uniquely dependent on these resources