Two new chapters just published in a Springer volume titled Advances in Social Simulation.

The first is “InCREDulity in Artificial Societies” with Ivan Puga-Gonzalez, Wesley J. Wildman, Kevin McCaffree, and Ryan Cragun.

Here is the abstract: “This paper describes an artificial society in which the simulated agents behave and interact based on a computational architecture informed by insights from one of the leading social psychological theories in the scientific study of secularization and religion: “credibility-enhancing displays” (or CREDs) theory. After introducing the key elements of the theory and outlining the computational architecture of our CRED model, we present some of our initial experimental simulation results. These efforts are intended to advance the quest within social simulation for more authentic artificial societies and more plausible human-like agents with complex interactive and interpretative capacities.”

You can download the chapter here.

The second is “Computational Demography of Religion” with Wesley J. Wildman and Saikou Diallo.

Here is the abstract: “This paper proposes a new approach to the demography of religion and non-religion that builds on and expands agent-based modeling and social simulation techniques developed in prior work by the research teams led by the authors. Traditional demographic approaches to religion and non-religion understandably focus attention on self-reports of religious identity or affiliation, where longitudinal data is most readily available, and they employ a cohort-component methodology to make projections. We argue that demographic projections of religion and non-religion could be enhanced by using multi-agent artificial intelligence models of societies. After artificial societies with suitably cognitively complex agents are validated using existing demographic data, projections of religion and non-religion could be made by measuring religiosity within the artificial society not only as affiliation but also in three other dimensions: belief, service attendance, and private religious practices. Artificial-society religious demographic projection could also take account of non-linear feedback loops and interaction of variables, produce narrower error estimates, and integrate a rich array of disciplinary insights relevant to religious and non-religious identity and change – all of which are weaknesses in traditional religious demographic projections.”

You can download the chapter here.