The social brain hypothesis posits that the evolution of big brains (neural complexity) in groups of social organisms is the evolutionary result of cognitive challenges associated with various complex interactions and the need to process and solve complex social tasks. This study aims to investigate the environmental and evolutionary conditions under which neural complexity evolves without sacrificing collective behavioral efficacy. Using an evolutionary collective robotics system this research evaluates the impact of imposing a fitness cost on evolving increased neural complexity in robot groups that must operate (accomplish cooperative tasks) in environments of varying complexity. Results indicate that for all environments tested, imposing a cost on neural complexity induces the evolution of smaller neural controllers that are comparably effective to more complex controllers.

This study reports work in progress on an Agent-Based Model (ABM) that critically explores specific theories that have gained prominence in the study of Egyptian state formation in the last two decades. The goal is to develop a model that simulates the evolution of complex social and economic networks by incorporating idiosyncrasies of human character and decision making, in order to create more plausible historical reconstructions. This study’s ABM focuses on wealth accumulation and loss in a simple agrarian society within an environment that simulates the Upper Egyptian landscape (ca. 4000 BC), the time period when clear evidence of economic and social inequality among Upper Egyptian households can be observed in the archaeological record. Understanding the establishment of permanent, entrenched inequality is crucial for exploring the development of social complexity and hierarchy.