This agent-based model examines the impact of seasonal aggregation, dispersion, and learning opportunities on the richness and evenness of artifact styles under random social learning (unbiased transmission).

The simulation model LAMDA investigates the influences of varying cognitive abilities of the decision maker on the truth-inducing effect of the Groves mechanism. Bounded rationality concepts are represented by information states and learning models.

The purpose of this model is to explore the impact of combining archaeological palimpsests with different methods of cultural transmission on the visibility of prehistoric social networks. Up until recently, Paleolithic archaeologists have relied on stylistic similarities of artifacts to reconstruct social networks. However, this method - which is successfully applied to more recent ceramic assemblages - may not be applicable to Paleolithic assemblages, as several of those consist of palimpsests of occupations. Therefore, this model was created to study how palimpsests of occupation affect our social network reconstructions.

The model simplifies inter-groups interactions between populations who share cultural traits as they produce artifacts. It creates a proxy archaeological record of artifacts with stylistic traits that can then be used to reconstruct interactions. One can thus use this model to compare the networks reconstructed through stylistic similarities with direct contact.

We consider scientific communities where each scientist employs one of two characteristic methods: an “adequate” method (A) and a “superior” method (S). The quality of methodology is relevant to the epistemic products of these scientists, and generate credit for their users. Higher-credit methods tend to be imitated, allowing to explore whether communities will adopt one method or the other. We use the model to examine the effects of (1) bias for existing methods, (2) competence to assess relative value of competing methods, and (3) two forms of interdisciplinarity: (a) the tendency for members of a scientific community to receive meaningful credit assignment from those outside their community, and (b) the tendency to consider new methods used outside their community. The model can be used to show how interdisciplinarity can overcome the effects of bias and incompetence for the spread of superior methods.

Agents co-operate or defect towards other agents in a prisoner’s dilemma, with strategy choice depending on whether agents share tags or are kin in different social structures.

This is a preliminary attempt in creating an Agent-Based Model of capital flows. This is based on the theory of capital flows based on interest-rate differentials. Foreign capital flows to a country with higher interest rates relative to another. The model shows how capital volatilty and wealth concentration are affected by the speed of capital flow, number of investors, magnitude of changes in interest rate due to capital flows and the interest differential threshold that investors set in deciding whether to move capital or not. Investors in the model are either “regional” investors (only investing in neighboring countries) and “global” investors (those who invest anywhere in the world).

In the future, the author hopes to extend this model to incorporate capital flow based on changes in macroeconomic fundamentals, exchange rate volatility, behavioral finance (for instance, herding behavior) and the presence of capital controls.

Discriminators who have limited tolerance for helping dissimilar others are necessary for the evolution of costly cooperation in a one-shot Prisoner’s Dilemma. Existing research reports that trust in

How can species evolve a cooperative network to keep the environment suitable for life?

This is a model of innovation implementation inside an organization. It characterizes an innovation as a set of distributed and technically interdependent tasks performed by a number of different and socially interconnected frontline workers.

Continuing on from the Adder model, this adaptation explores how rationality, learning and uncertainty influence the exploration of complex landscapes representing technological evolution.