This is a model of the occurrence of disorganization and its impact on individual goal setting and problem-solving. This model therefore, explores the effects of disorganization on goal achievement.

I extend Lazer’s model by adding agent’s two kinds of imitation strategies: selective imitation and structurally equivalent imitation. I examined the effect of interaction of network with agent behavi

C++ and Netlogo models presented in G. Bravo (2011), “Agents’ beliefs and the evolution of institutions for common-pool resource management”. Rationality and Society 23(1).

Ants in the genus Temnothorax use tandem runs (rather than pheromone trails) to recruit to food sources. This model explores the collective consequences of this linear recruitment (as opposed to highly nonlinear pheromone trails).

This model is a small extension (rectangular layout) of Joshua Epstein’s (2001) model on development of thoughtless conformity in an artificial society of agents.

This model simulates the emergence of a dual market structure from firm-level interaction. Firms are profit-seeking, and demand is represented by a unimodal distribution of consumers along a set of taste positions.

We build a computational model to investigate, in an evolutionary setting, a series of questions pertaining to happiness.

This model simulates the heterogeneity of preferences in a PG game and how the interaction between them affects the dynamics of voluntary contributions. Model is based on the results of a human-based experiment.

Individually parameterized mussels (Mytilus californianus) recruit, grow, move and die in a 3D environment while facing predation (in the form of seastar agents), heat and desiccation with increased tide height, and storms. Parameterized with data collected by Wootton, Paine, Kandur, Donahue, Robles and others. See my 2019 CoMSES video presentation to learn more.

This model is programmed in Python 3.6. We model how different consensus protocols and trade network topologies affect the performance of a blockchain system. The model consists of multiple trader and miner agents (Trader.py and Tx.py), and one system agent (System.py). We investigated three consensus protocols, namely proof-of-work (PoW), proof-of-stake (PoS), and delegated proof-of-stake (DPoS). We also examined three common trade network topologies: random, small-world, and scale-free. To reproduce our results, you may need to create some databases using, e.g., MySQL; or read and write some CSV files as model configurations.