This model illustrates the processes underlying the social construction of reality through an agent-based genetic algorithm. By simulating the interactions of agents within a structured environment, we have demonstrated how shared information and popularity contribute to the formation of emergent social structures with diverse cultures. The model illustrates how agents balance environmentally valid information with socially reliable information. It also highlights how social interaction leads to the formation of stable, yet diverse, social groups.

The purpose of the AdaptPumpa model is to analyze the robustness of the Pumpa irrigation system in Nepal to climate change.

This is an agent-based model designed to explore the evolution of cooperation under changes in resources availability for a given population

We propose an agent-based model where a fixed finite population of tagged agents play iteratively the Nash demand game in a regular lattice. The model extends the bargaining model by Axtell, Epstein and Young.

We used a computer simulation to measure how well different network structures (fully connected, small world, lattice, and random) find and exploit resource peaks in a variable environment.

We developed an agent-based model to explore underlying mechanisms of behavioral clustering that we observed in human online shopping experiments.

We model the relationship between natural resource user´s individual time preferences and their use of destructive extraction method in the context of small-scale fisheries.

MoPAgrIB model simulates the movement of cultivated patches in a savannah vegetation mosaic ; how they move and relocate through the landscape, depending on farming practices, population growth, social rules and vegetation growth.

Cultural group selection model used to evaluate the conditions for agents to evolve who have other-regarding preferences in making decisions in public good games.

A model to investigate the Evolution of Conditional Cooperation in a Spatial Public Goods Game. We consider two conditional cooperation strategies: one based on thresholds (Battu & Srinivasan, 2020) and another based on independent decisions for each number of cooperating neighbors. We examine the effects of productivity and conditional cooperation criteria on the trajectory of cooperation. Cooperation is evolving with no need for additional mechanisms apart from spatial structure when agents follow conditional strategies. We confirm the positive influence of productivity and cluster formation on the evolution of cooperation in spatial models. Results are robust for the two types of conditional cooperation strategies.