The model is an agent-based artificial stock market where investors connect in a dynamic network. The network is dynamic in the sense that the investors, at specified intervals, decide whether to keep their current adviser (those investors they receive trading advise from). The investors also gain information from a private source and share public information about the risky asset. Investors have different tendencies to follow the different information sources, consider differing amounts of history, and have different thresholds for investing.

This NetLogo model simulates trait-based biotic responses to climate change in an environmentally heterogeneous continent in an evolving clade, the species of which are each represented by local populations that disperse and interbreed; they also are subject to selection, genetic drift, and local extirpation. We simulated mammalian herbivores, whose success depends on tooth crown height, vegetation type, precipitation and grit. This model investigates the role of dispersal, selection, extirpation, and other factors contribute to resilience under three climate change scenarios.

This model aims to replicate the evolution of opinions and behaviours on a communal plan over time. It also aims to foster community dialogue on simulation outcomes, promoting inclusivity and engagement. Individuals (referred to as agents), grouped based on Sinus Milieus (Groh-Samberg et al., 2023), face a binary choice: support or oppose the plan. Motivated by experiential, social, and value needs (Antosz et al., 2019), their decision is influenced by how well the plan aligns with these fundamental needs.

MASTOC is a replication of the Tragedy of the Commons by G. Hardin, programmed in NetLogo 4.0.4, based on behavioral game theory and Nash solution.

This model looks at the effects of a “control” on agent populations. Much like farmers spraying pesticides/herbicides to manage pest populations, the user sets a control management regiment to be use

Default Initial skill, read ODD for more info. The purpose of the model presented by Salau is to study the ’player profit vs. club benefit’ dilemma present in professional soccer organizations.

Simulates the construction of scientific journal publications, including authors, references, contents and peer review. Also simulates collective learning on a fitness landscape. Described in: Watts, Christopher & Nigel Gilbert (forthcoming) “Does cumulative advantage affect collective learning in science? An agent-based simulation”, Scientometrics.

This paper builds on a basic ABM for a revolution and adds a combination of behaviors to its agents such as military benefits, citizen’s grievances, geographic vision, empathy, personality type and media impact.

This model aims to understand the interaction between particulate matter and leaves of trees. The particles collide with the leaf and can either be absorbed with a certain probability, otherwise they bounce off it. The absorptions are detected in a counter.
The movement of the particles depends mainly on the strength and direction of the wind and the air temperature. They also show a certain random movement, but the proportion is negligible.
In a collision with the leaf, the particles are absorbed with a certain probability (absorption-probability), otherwise repelled.

Both models simulate n-person prisoner dilemma in groups (left figure) where agents decide to C/D – using a stochastic threshold algorithm with reinforcement learning components. We model fixed (single group ABM) and dynamic groups (bad-barrels ABM). The purpose of the bad-barrels model is to assess the impact of information during meritocratic matching. In the bad-barrels model, we incorporated a multidimensional structure in which agents are also embedded in a social network (2-person PD). We modeled a random and homophilous network via a random spatial graph algorithm (right figure).