Computational Model Library

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Tiebout sorting

Marco Janssen | Published Thursday, January 24, 2019

This Netlogo replication of Kollman, K., J.H. Miller and S.E. Page (1997) Political Institutions and Sorting in a Tiebout Model, American Economic Review 87(5): 977-992. The model consists of of citizens who can vote for partie and move to other jurisdictions if they expect their preferences are better served. Parties adjust their positions to increase their share in the elections.

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/.

Replication of an agent-based model using the Replication Standard

Derek Robinson Jiaxin Zhang | Published Sunday, January 20, 2019 | Last modified Saturday, July 18, 2020

This model is a replication model which is constructed based on the existing model used by the following article:
Brown, D.G. and Robinson, D.T., 2006. Effects of heterogeneity in residential preferences on an agent-based model of urban sprawl. Ecology and society, 11(1).
The original model is called SLUCE’s Original Model for Experimentation (SOME). In Brown and Robinson (2006)’s article, the SOME model was used to explore the impacts of heterogeneity in residential location selections on the research of urban sprawl. The original model was constructed using Objective-C language based on SWARM platform. This replication model is built by NetLogo language on NetLogo platform. We successfully replicate that model and demonstrated the reliability and replicability of it.

The model explores the relationship between ethnic density and health. It does this through exploring the potential pathway between racism, segregation, area deprivation and income.

Population dynamics

Marco Janssen | Published Monday, December 31, 2018

Simple population dynamics model used in Introduction to Agent-Based Modeling by Marco Janssen. For more information see https://intro2abm.com/

The model investigates conditions, scenarios and strategies for future planning of energy in Egypt, with an emphasis on alternative energy pathways and a sustainable electricity supply mix as part of an energy roadmap till the year 2100. It combines the multi-criteria decision analysis (MCDA) with agent-based modeling (ABM) and Geographic Information Systems (GIS) visualization to integrate the interactions of the decisions of multi-agents, the multi-criteria evaluation of sustainability, the time factor and the site factors to assess the transformation of energy landscapes.

Gentrilab

Adrian Lara | Published Monday, December 17, 2018

Development of a Multiagent System for the Analysis of Gentrification in Latin America, an Agent-Based Model

An agent based simple economy model that examines the effect of taxation and almsgiving (particularly Islamic almsgiving - zakat) for ameliorating wealth inequality.

BorealFireSIM Model

Liliana Perez Jonathan Gaudreau | Published Thursday, December 13, 2018

BorealFireSIM is a cellular automaton based model that serves to identify future fire patterns in the boreal forest of Quebec, Canada. The model simulates yearly fire seasons and adjusts decadal climate variables based on two future carbon pathways (RCP45 (low emissions) and RCP85 (business as usual)). The BorealFireSIM model simulates future fire patterns up to the year 2100.

Modeling Personal Carbon Trading with ABM

Roman Seidl | Published Friday, December 07, 2018 | Last modified Thursday, July 29, 2021

A simulated approach for Personal Carbon Trading, for figuring out what effects it might have if it will be implemented in the real world. We use an artificial population with some empirical data from international literature and basic assumptions about heterogeneous energy demand. The model is not to be used as simulating the actual behavior of real populations, but a toy model to test the effects of differences in various factors such as number of agents, energy price, price of allowances, etc. It is important to adapt the model for specific countries as carbon footprint and energy demand determines the relative success of PCT.

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