Computational Model Library

Displaying 10 of 388 results for "Huw Vasey" clear search

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.

An empirical ABM for regional land use/cover change: a Dutch case study

Diego Valbuena | Published Saturday, March 12, 2011 | Last modified Thursday, November 11, 2021

This is an empirical model described in http://dx.doi.org/10.1016/j.landurbplan.2010.05.001. The objective of the model is to simulate how the decision-making of farmers/agents with different strategies can affect the landscape structure in a region in the Netherlands.

Peer reviewed Torsten Hägerstrand’s Spatial Innovation Diffusion Model

Sean Bergin | Published Friday, September 14, 2012 | Last modified Saturday, April 27, 2013

This model is a replication of Torsten Hägerstrand’s 1965 model–one of the earliest known calibrated and validated simulations with implicit “agent based” methodology.

A simple emulation-based computational model

Carlos M Fernández-Márquez Francisco J Vázquez | Published Tuesday, May 21, 2013 | Last modified Tuesday, February 05, 2019

Emulation is one of the simplest and most common mechanisms of social interaction. In this paper we introduce a descriptive computational model that attempts to capture the underlying dynamics of social processes led by emulation.

Exploring homeowners' insulation activity

Georg Holtz Emile Chappin Jonas Friege | Published Monday, June 01, 2015 | Last modified Monday, April 08, 2019

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

An Agent-Based Model of Flood Risk and Insurance

J Dubbelboer I Nikolic K Jenkins J Hall | Published Monday, July 27, 2015 | Last modified Monday, October 03, 2016

A model to show the effects of flood risk on a housing market; the role of flood protection for risk reduction; the working of the existing public-private flood insurance partnership in the UK, and the proposed scheme ‘Flood Re’.

A Model of Making

Bruce Edmonds | Published Friday, January 29, 2016 | Last modified Wednesday, December 07, 2016

This models provides the infrastructure to model the activity of making. Individuals use resources they find in their environment plus those they buy, to design, construct and deconstruct items. It represents plans and complex objects explicitly.

MCR Model

Davide Secchi Nuno R Barros De Oliveira | Published Friday, July 22, 2016 | Last modified Saturday, January 23, 2021

The aim of the model is to define when researcher’s assumptions of dependence or independence of cases in multiple case study research affect the results — hence, the understanding of these cases.

A simulation model for Dublin city

umesh7lowe | Published Friday, April 10, 2026

An agent-based model of urban travel behaviour in Dublin, Ireland, built in NetLogo and empirically grounded in 2016 travel survey data. Each agent represents a Dublin resident initialised with real socio-demographic attributes — including age, gender, household size and car ownership, income, driving licence status, and access to local amenities — alongside observed trip characteristics such as distance, travel time, and trip type (work, shopping, leisure).
At each time step, agents choose between four transport modes (car, public transport, cycling, and walking) across short, medium, and long trips. Mode choice is governed by a preference vector that weighs personal need satisfaction against social influence from neighbouring agents reflecting consumat framework. Satisfaction evolves dynamically based on cost (incorporating Irish motor tax bands and per-km operating rates), travel time, and trip-type suitability, with an uncertainty parameter capturing variability in perceived utility over time.
The model tracks aggregate modal shares and total CO2 emission at each tick, enabling exploration of how policy interventions — such as fuel taxation, public transport pricing, or active travel incentives — might shift the city’s travel demand profile over 100 simulated days.

The purpose of the OMOLAND-CA is to investigate the adaptive capacity of rural households in the South Omo zone of Ethiopia with respect to variation in climate, socioeconomic factors, and land-use at the local level.

Displaying 10 of 388 results for "Huw Vasey" clear search

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