Computational Model Library

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System Narrative
How do rebel groups control territory and engage with the local economy during civil war? Charles Tilly’s seminal War and State Making as Organized Crime (1985) posits that the process of waging war and providing governance resembles that of a protection racket, in which aspiring governing groups will extort local populations in order to gain power, and civilians or businesses will pay in order to ensure their own protection. As civil war research increasingly probes the mechanisms that fuel local disputes and the origination of violence, we develop an agent-based simulation model to explore the economic relationship of rebel groups with local populations, using extortion racket interactions to explain the dynamics of rebel fighting, their impact on the economy, and the importance of their economic base of support. This analysis provides insights for understanding the causes and byproducts of rebel competition in present-day conflicts, such as the cases of South Sudan, Afghanistan, and Somalia.

Model Description
The model defines two object types: RebelGroup and Enterprise. A RebelGroup is a group that competes for power in a system of anarchy, in which there is effectively no government control. An Enterprise is a local civilian-level actor that conducts business in this environment, whose objective is to make a profit. In this system, a RebelGroup may choose to extort money from Enterprises in order to support its fighting efforts. It can extract payments from an Enterprise, which fears for its safety if it does not pay. This adds some amount of money to the RebelGroup’s resources, and they can return to extort the same Enterprise again. The RebelGroup can also choose to loot the Enterprise instead. This results in gaining all of the Enterprise wealth, but prompts the individual Enterprise to flee, or leave the model. This reduces the available pool of Enterprises available to the RebelGroup for extortion. Following these interactions the RebelGroup can choose to AllocateWealth, or pay its rebel fighters. Depending on the value of its available resources, it can add more rebels or expel some of those which it already has, changing its size. It can also choose to expand over new territory, or effectively increase its number of potential extorting Enterprises. As a response to these dynamics, an Enterprise can choose to Report expansion to another RebelGroup, which results in fighting between the two groups. This system shows how, faced with economic choices, RebelGroups and Enterprises make decisions in war that impact conflict and violence outcomes.

Social norms and the dominance of Low-doers

Antonio Franco | Published Wednesday, July 13, 2016 | Last modified Sunday, December 02, 2018

The code for the paper “Social norms and the dominance of Low-doers”

Spatial rangeland model

Marco Janssen | Published Tuesday, January 22, 2019 | Last modified Friday, March 04, 2022

Spatial explicit model of a rangeland system, based on Australian conditions, where grass, woody shrubs and fire compete fore resources. Overgrazing can cause the system to flip from a healthy state to an unproductive shrub state. With the model one can explore the consequences of different movement rules of the livestock on the resilience of the system.

The model is discussed in Introduction to Agent-Based Modeling by Marco Janssen. For more information see https://intro2abm.com/.

Ornstein-Uhlenbeck Pandemic package

Peter Cotton | Published Friday, April 24, 2020 | Last modified Friday, May 08, 2020

Pandemic (pip install pandemic)

An agent model in which commuting, compliance, testing and contagion parameters drive infection in a population of thousands of millions. Agents follow Ornstein-Uhlenbeck processes in the plane and collisions drive transmission. Results are stored at SwarmPrediction.com for further analysis, and can be retrieved by anyone.

This is a very simple simulation that in a special case can be shown to be approximated by a compartmental model with time varying infection rate.

We use a threshold model to drive our simulated network analysis testing public support for candidates in invisible primaries. We assign voter thresholds for candidates and vary number of voters, attachment to candidates and decay. Results of the algorithm show effects of size of lead, attachment and size of decay.

A preliminary extension of the Hemelrijk 1996 model of reciprocal behavior to include feeding

Sean Barton | Published Monday, December 13, 2010 | Last modified Saturday, April 27, 2013

A more complete description of the model can be found in Appendix I as an ODD protocol. This model is an expansion of the Hemelrijk (1996) that was expanded to include a simple food seeking behavior.

FOUR SEASONS

Lars G Spang | Published Tuesday, March 28, 2017

Butterflies (turtles) goes through metamorphism and moves to corresponding patches each season of the year. The number of years and seasons are monitored.

A test-bed ecological model

Bruce Edmonds | Published Sunday, May 04, 2014 | Last modified Wednesday, May 15, 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.

Peer reviewed MIOvPOP

Aniruddha Belsare | Published Wednesday, September 18, 2019

An ABM simulating white-tailed deer population dynamics for selected Michigan counties. The model yields pre-harvest and post-harvest realistic population snapshots that can be used to initialize the surveillance model (MIOvPOPsurveillance) and the CWD transmission dynamics model (MIOvCWD) respectively.

MayaSim: An agent-based model of the ancient Maya social-ecological system

Scott Heckbert | Published Wednesday, July 11, 2012 | Last modified Tuesday, July 02, 2013

MayaSim is an agent-based, cellular automata and network model of the ancient Maya. Biophysical and anthropogenic processes interact to grow a complex social ecological system.

Displaying 10 of 867 results for "Am Vaes - Van De Hulsbeek" clear search

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