Previous research on organizations often focuses on either the individual, team, or organizational level. There is a lack of multidimensional research on emergent phenomena and interactions between the mechanisms at different levels. This paper takes a multifaceted perspective on individual learning and autonomous group formation and turnover. To analyze interactions between the two levels, we introduce an agent-based model that captures an organization with a population of heterogeneous agents who learn and are limited in their rationality. To solve a task, agents form a group that can be adapted from time to time. We explore organizations that promote learning and group turnover either simultaneously or sequentially and analyze the interactions between the activities and the effects on performance. We observe underproportional interactions when tasks are interdependent and show that pushing learning and group turnover too far might backfire and decrease performance significantly.

The purpose of the model is to simulate the cultural hitchhiking hypothesis to explore how neutral cultural traits linked with advantageous traits spread together over time

The purpose of this model is to explore the importance of geographic factors to the settlement choices of early Neolithic agriculturalists. In the model, each agriculturalist spreads to one of the best locations within a modeler specified radius. The best location is determined by choosing either one factor such as elevation or slope; or by ranking geographic factors in order of importance.

This model simulates different spread hypotheses proposed for the introduction of agriculture on the Iberian peninsula. We include three dispersal types: neighborhood, leapfrog, and ideal despotic distribution (IDD).

PSoup is an educational program in which evolution is demonstrated, on the desk-top, as you watch. Blind bugs evolve sophisticated heuristic search algorithms to be the best at finding food fast.

This MAS simulates the traffic of Barcelona Eixample. Uses a centralized AI system in order to control the traffic lights. Car agents are reactive and have no awareness of the intelligence of the system. They (try to) avoid collisions.

This is version 1 of the Parental Investment Model by Aktipis & Fernandez-Duque.