We present a model for the diffusion of management fads and other technologies which lack clear objective evidence about their merits. The choices made by non-Bayesian adopters reflect both their own evaluations and the social in°uence of their peers. We show, both analytically and computationally, that the dynamics lead to outcomes that appear to be deterministic in spite of being governed by a stochastic process. In other words, when the objective evidence about a technology is weak, the evolution of this process quickly settles down to a fraction of adopters that is not predetermined. When the objective evidence is strong, the proportion of adopters is determined by the quality of the evidence and the adopters'competence.
In this paper we propose a model that is consistent with all of Camerer's observations and so is an alternative to canonical herding models. Thus our agents exhibit normatively desirable and empirically plausible monotonicity properties: in particular, the more the social cues favor innovation A over B, the more likely it is that an agent will select A, ceteris paribus. Yet the reasoning that underlies such choices is adaptively rational rather than fully rational. Moreover, unlike many adaptive models of fads, the present model generates analytical solutions, not just computational ones. Many---perhaps most---adaptive models of fads are what has come to be called agent-based models'' and it is virtually a defining feature of such models that they be computational. (For a survey of agent-based models, including several applied to fads, see @cite_12 .)
@cite_12
2167951823
■ Abstract Sociologists often model social processes as interactions among variables. We review an alternative approach that models social life as interactions among adaptive agents who influence one another in response to the influence they receive. These agent-based models (ABMs) show how simple and predictable local interactions can generate familiar but enigmatic global patterns, such as the diffusion of information, emergence of norms, coordination of conventions, or participation in collective action. Emergent social patterns can also appear unexpectedly and then just as dramatically transform or disappear, as happens in revolutions, market crashes, fads, and feeding frenzies. ABMs provide theoretical leverage where the global patterns of interest are more than the aggregation of individual attributes, but at the same time, the emergent pattern cannot be understood without a bottom up dynamical model of the microfoundations at the relational level. We begin with a brief historical sketch of the shift from “factors” to “actors” in computational sociology that shows how agent-based modeling differs fundamentally from earlier sociological uses of computer simulation. We then review recent contributions focused on the emergence of social structure and social order out of local interaction. Although sociology has lagged behind other social sciences in appreciating this new methodology, a distinctive sociological contribution is evident in the papers we review. First, theoretical interest focuses on dynamic social networks that shape and are shaped by agent interaction. Second, ABMs are used to perform virtual experiments that test macrosociological theories by manipulating structural factors like network topology, social stratification, or spatial mobility. We conclude our review with a series of recommendations for realizing the rich sociological potential of this approach.