A system of nonlinear differential equations, modelled in MATLAB Simulink, simulating the world of George Orwell’s 1984.

This model is an extension of the Artificial Long House Valley (ALHV) model developed by the authors (Swedlund et al. 2016; Warren and Sattenspiel 2020). The ALHV model simulates the population dynamics of individuals within the Long House Valley of Arizona from AD 800 to 1350. Individuals are aggregated into households that participate in annual agricultural and demographic cycles. The present version of the model incorporates features of the ALHV model including realistic age-specific fertility and mortality and, in addition, it adds the Black Mesa environment and population, as well as additional methods to allow migration between the two regions.

As is the case for previous versions of the ALHV model as well as the Artificial Anasazi (AA) model from which the ALHV model was derived (Axtell et al. 2002; Janssen 2009), this version makes use of detailed archaeological and paleoenvironmental data from the Long House Valley and the adjacent areas in Arizona. It also uses the same methods as the original AA model to estimate annual maize productivity of various agricultural zones within the Long House Valley. A new environment and associated methods have been developed for Black Mesa. Productivity estimates from both regions are used to determine suitable locations for households and farms during each year of the simulation.

We present an agent-based model that maps out and simulates the processes by which individuals within ecological restoration organizations communicate and collectively make restoration decisions.

An ABM to simulate the spatio-temporal distribution of cyclists across the road network of the city of Salzburg.

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 is a set of threshold public goods games models. Set consists of baseline model, endogenous shared punishment model, endogenous shared punishment model with activists and cooperation model. In each round, all agents are granted a budget of size set in GUI. Then they decide on how much they contribute to public goods and how much they keep. Public goods are provided only if the sum of contributions meets or exceeds the threshold defined in the GUI. After each round agents evaluate their strategy and payoff from this strategy.

This model is to explore how individuals’ cultural backgrounds may play a role in their Covid vaccination decision-making. Two cultural dimensions of collectivism/individualism and power distance are considered. Through the experimental scenarios, we find that Covid-vaccination opinions in collectivist societies can also be considerably polarised, if the power distance is less and authorities less centralised. This result complements the popular idea that cultural collectivism is usually associated with a high degree of social consensus. Hopefully, this study will help explain countries’ difference in the response of Covid vaccination programs.

This is a NetLogo replication of the hill-climbing version of the Lansing-Kremer model of Balinese irrigation.

This model implements a Spatial Prisoner’s Dilemma with the option to change whether agents interact deterministically or stochastically.

This Bicycle encounter model builds on the Salzburg Bicycle model (Wallentin & Loidl, 2015). It simulates cyclist flows and encounters, which are locations of potential accidents between cyclists.