Rationality vs. Learning in the Evolution of Solidarity Networks: A Theoretical Comparison

In this paper we analyze the evolution of solidarity relations between dissimilar actors by means of a cellular automaton framework. We assume that actors face two types of decisions in the course of an iterated game. First, actors&2018; solidarity decisions constitute mutual support relations between neighbors. Second, by migrating in a two dimensional world, actors select between potential solidarity partners. Moreover, actors are dissimilar with respect to their neediness class, i.e., their need for help. Hegselmann (1996) demonstrated by computer simulation that under these assumptions the behavior of (boundedly) rational egoists may lead to the emergence of a solidarity network that is characterized by class segregation. In the present paper, we explore whether the macro phenomenon of segregation depends on the micro assumption of rationality. We replace Hegselmann&2018;s rational egoist by an adaptive egoist, who takes solidarity and migration decisions on basis of the &2018;law of effect&2019;. A stochastic learning model (e.g., Flache and Macy, 1996) is used to simulate adaptive decision making. Our model of learning behavior, we show, entails the emergence of class segregated solidarity networks. At the same time, however, the evolving networks are considerably more fragile and less extended than those arising amongst rational egoists. While critics of the rational choice approach often argue that rational egoist models tend to underestimate the level of social solidarity, we showed that in this particular analysis relaxing the assumption of rationality may entail the prediction of less rather than more solidarity.