We build a stylized model of a network of business angel investors and start-up entrepreneurs. Decisions are based on trust as a decision making tool under true uncertainty.

The program simulate the functioning of an italian health and social public information office (SPUN) on the basis of the real data collected in the first five years of functioning.

Studies of colonization processes in past human societies often use a standard population model in which population is represented as a single quantity. Real populations in these processes, however, are structured with internal classes or stages, and classes are sometimes created based on social differentiation. In this present work, information about the colonization of old Providence Island was used to create an agent-based model of the colonization process in a heterogeneous environment for a population with social differentiation. Agents were socially divided into two classes and modeled with dissimilar spatial clustering preferences. The model and simulations assessed the importance of gregarious behavior for colonization processes conducted in heterogeneous environments by socially-differentiated populations. Results suggest that in these conditions, the colonization process starts with an agent cluster in the largest and most suitable area. The spatial distribution of agents maintained a tendency toward randomness as simulation time increased, even when gregariousness values increased. The most conspicuous effects in agent clustering were produced by the initial conditions and behavioral adaptations that increased the agent capacity to access more resources and the likelihood of gregariousness. The approach presented here could be used to analyze past human colonization events or support long-term conceptual design of future human colonization processes with small social formations into unfamiliar and uninhabited environments.

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.

Designed to capture the evolutionary forces of global society.

The DITCH model has been developed to investigate partner selection processes, focusing on individual preferences, opportunities for contact, and group size to uncover how these may lead to differential rates of inter-­ethnic marriage.

We model the epistemic dynamics preceding political uprising. Before deciding whether to start protests, agents need to estimate the amount of discontent with the regime. This model simulates the dynamics of group knowledge about general discontent.

This is a model of a game of Telephone (also known as Chinese Whishpers in the UK), with agents representing people that can be asked, to play. The first player selects a word from their internal vocabulary and “whispers” it to the next player, who may mishear it depending on the current noise level, who whispers that word to the next player, and so on.

When the game ends, the word chosen by the first player is compared to the word heard by the last player. If they match exactly, all players earn large prize. If the words do not match exactly, a small prize is awarded to all players for each part of the words that do match. Players change color to reflect their current prize-count. A histogram shows the distribution of colors over all the players.

The user can decide on factors like
* how many players there are,
…

This model is an extended version of the matching problem including the mate search problem, which is the generalization of a traditional optimization problem. The matching problem is extended to a form of asymmetric two-sided matching problem.

We construct an agent-based model to investigate and understand the roles of green attachment, engagement in local ecological investment (i.e., greening), and social feedback.