Computational Models of Belief Systems & Cultural Systems

Work on belief systems is similar to the research on cultural systems – both use agent-based models to explore how complex systems evolve given a simple set of actor rules and interactions – there are important conceptual differences between the two lines of work.

Research on cultural systems takes a maco-level approach, seeking to explain if, when, and how, distinctive communities of similar traits emerge, while research on belief systems uses comparable methods to understand if, when, and how distinctive individuals come to agree on a given point.

The difference between these approaches is subtle but notable. The cultural systems approach begins with the observation that distinctive cultures do exist, despite local tendencies for convergence, while research on belief systems begins from the observation that groups of people are capable of working together, despite heterogeneous opinions and interests.

In his foundational work on cultural systems, Axelrod begins, “despite tendencies towards convergence, differences between individuals and groups continue to exist in beliefs, attitudes, and behavior” (Axelrod, 1997).

Compare this to how DeGroot begins his exploration of belief systems: “consider a group of individuals who must act together as a team or committee, and suppose that each individual in the group has his own subjective probability distribution for the unknown value of some parameter. A model is presented which describes how the group might reach agreement on a common subjective probability distribution parameter by pooling their individual opinions” (DeGroot, 1974).

In other words, while cultural models seek to explain the presence of homophily and other system-level traits, belief systems more properly seek to capture deliberative exchange. The important methodological difference here is that cultural systems model agent change as function of similarity, while belief systems model agent change as a process of reasoning.

 

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