This model combines decision making models of individual farmers with a model of the spatial spread between farms of blue tongue virus.

A multi-agent model of oligarchy in a spatial election simulation.

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

We built an agent-based model to foster the understanding of homeowners’ insulation activity.

The model proposes a translation of some Luhmann’s concepts (social sub-system, perturbation, dissipation, social communication and power) into a model using a stylized spatial-society as a metaphor of a Luhmann’s social subsystem. The model has been used to improve the social theory understanding and to evaluate the effect of different parameterization in the global stabilization and individual/social power distribution.

Previous work with the spatial iterated prisoner’s dilemma has shown that “walk away” cooperators are able to outcompete defectors as well as cooperators that do not respond to defection, but it remains to be seen just how robust the so-called walk away strategy is to ecologically important variables such as population density, error, and offspring dispersal. Our simulation experiments identify socio-ecological conditions in which natural selection favors strategies that emphasize forgiveness over flight in the spatial iterated prisoner’s dilemma. Our interesting results are best explained by considering how population density, error, and offspring dispersal affect the opportunity cost associated with walking away from an error-prone partner.

RHEA aims to provide a methodological platform to simulate the aggregated impact of households’ residential location choice and dynamic risk perceptions in response to flooding on urban land markets. It integrates adaptive behaviour into the spatial landscape using behavioural theories and empirical data sources. The platform can be used to assess: how changes in households’ preferences or risk perceptions capitalize in property values, how price dynamics in the housing market affect spatial demographics in hazard-prone urban areas, how structural non-marginal shifts in land markets emerge from the bottom up, and how economic land use systems react to climate change. RHEA allows direct modelling of interactions of many heterogeneous agents in a land market over a heterogeneous spatial landscape. As other ABMs of markets it helps to understand how aggregated patterns and economic indices result from many individual interactions of economic agents.
The model could be used by scientists to explore the impact of climate change and increased flood risk on urban resilience, and the effect of various behavioural assumptions on the choices that people make in response to flood risk. It can be used by policy-makers to explore the aggregated impact of climate adaptation policies aimed at minimizing flood damages and the social costs of flood risk.

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.

This model is to simulate and compare the admission effects of 3 school matching mechanisms, serial dictatorship, Boston mechanism, and Chinese Parallel, under different settings of information released.

The purpose of this hybrid ABM is to answer the question: where is the best place for a new swimming pool in a region of Krakow (in Poland)?

The model is well described in ODD protocol, that can be found in the end of my article published in JASSS journal (available online: http://jasss.soc.surrey.ac.uk/22/1/1.html ). Comparison of this kind of models with spatial interaction ones, is presented in the article. Before developing the model for different purposes, area of interest or services, I recommend reading ODD protocol and the article.

I published two films on YouTube that present the model: https://www.youtube.com/watch?v=iFWG2Xv20Ss , https://www.youtube.com/watch?v=tDTtcscyTdI&t=1s

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