Computational Model Library

A test-bed ecological model

Bruce Edmonds | Published Sun May 4 13:22:47 2014 | Last modified Wed May 15 14:18:58 2019

This is a multi-patch meta-population ecological model. It intended as a test-bed in which to test the impact of humans with different kinds of social structure.

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

OLIGO

Mason Wright | Published Thu Oct 24 21:55:58 2013 | Last modified Mon Apr 8 20:40:48 2019

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

Stochastic vs. Deterministic Spatial PD

Andrew Bausch | Published Fri Nov 1 20:35:06 2013 | Last modified Mon Apr 8 20:39:01 2019

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

Stylized agricultural land-use model for resilience exploration

Patrick Bitterman | Published Tue Jun 14 15:18:33 2016 | Last modified Mon Apr 8 20:38:38 2019

This model is a highly stylized land use model in the Clear Creek Watershed in Eastern Iowa, designed to illustrate the construction of stability landscapes within resilience theory.

Exploring homeowners' insulation activity

Jonas Friege Emile Chappin Georg Holtz | Published Mon Jun 1 08:34:32 2015 | Last modified Mon Apr 8 20:38:19 2019

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.

Bicycle encounter model

Gudrun Wallentin | Published Sat Oct 29 20:45:47 2016 | Last modified Fri Mar 29 19:38:22 2019

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.

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