NEURAL-NETWORK MODELS OF RELIGIOUS BELIEF

This paper applies neural network technology, a standard approach in c omputer science that has been unaccountably ignored by sociologists, t o the problem of developing rigorous sociological theories. A simulati on program employing a ''varimax'' model of human learning and decisio n-making models central elements of the Stark-Bainbridge theory of rel igion. Individuals in a micro-society of 24 simulated people learn whi ch categories of potential exchange partners to seek for each of four material rewards which in fact can be provided by other actors in the society. However, when they seek eternal life, they me unable to find suitable human exchange partners who can provide it to them, so they p ostulate the existence of supernatural exchange partners as substitute s. The explanation of how the particular neural net works, including r eference to module arithmetic, introduces some aspects of this new tec hnology to sociology, and this paper invites readers to explore the wi de range of other neural net techniques that may be of value for socia l scientists.