Computational Model Library

Agent-based model of risk behavior in adolescence

N Schuhmacher P Van Geert L Ballato | Published Mon Jun 24 16:33:51 2013 | Last modified Mon Apr 8 20:43:17 2019

The computer model simulates the development of a social network (i.e. formation of friendships and cliques), the (dyadic) interactions between pupils and the development of similarities and differences in their behavioral profiles.

NetLogo-R-Example for the Inititialisation of Agents with Correlated Random Numbers

Danilo Saft | Published Fri Feb 14 14:48:27 2014 | Last modified Mon Apr 8 20:42:57 2019

This is a short NetLogo example demonstrating how to initialize 500 agents with 4 correlated parameters each with random values by doing the necessary calculations in the program “R” and retrieving the results.

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 study investigates a possible nexus between inter-group competition and intra-group cooperation, which may be called “tribalism.” Building upon previous studies demonstrating a relationship between the environment and social relations, the present research incorporates a social-ecological model as a mediating factor connecting both individuals and communities to the environment. Cyclical and non-cyclical fluctuation in a simple, two-resource ecology drive agents to adopt either “go-it-alone” or group-based survival strategies via evolutionary selection. Novelly, this simulation employs a multilevel selection model allowing group-level dynamics to exert downward selective pressures on individuals’ propensity to cooperate within groups. Results suggest that cooperation and inter-group conflict are co-evolved in a triadic relationship with the environment. Resource scarcity increases inter-group competition, especially when resources are clustered as opposed to widely distributed. Moreover, the tactical advantage of cooperation in the securing of clustered resources enhanced selective pressure on cooperation, even if that implies increased individual mortality for the most altruistic warriors. Troubling, these results suggest that extreme weather, possibly as a result of climate change, could exacerbate conflict in sensitive, weather-dependent social-ecologies—especially places like the Horn of Africa where ecologically sensitive economic modalities overlap with high-levels of diversity and the wide-availability of small arms. As well, global development and foreign aid strategists should consider how plans may increase the value of particular locations where community resources are built or aid is distributed, potentially instigating tribal conflict. In sum, these factors, interacting with pre-existing social dynamics dynamics, may heighten inter-ethnic or tribal conflict in pluralistic but otherwise peaceful communities.

For special issue submission in JASSS.

Neolithic Spread Model Version 1.0

Sean Bergin Michael Barton Salvador Pardo Gordo Joan Bernabeu Auban | Published Thu Dec 11 19:12:19 2014 | Last modified Mon Dec 31 17:39:18 2018

This model simulates different spread hypotheses proposed for the introduction of agriculture on the Iberian peninsula. We include three dispersal types: neighborhood, leapfrog, and ideal despotic distribution (IDD).

Structure of Scientific Revolutions

Rogier De Langhe | Published Fri Sep 2 13:58:07 2016 | Last modified Tue Dec 4 20:36:38 2018

An agent-based model of Thomas Kuhn’s Structure of Scientific Revolutions

RAGE models a stylized common property grazing system. Agents follow a certain behavioral type. The model allows analyzing how household behavior with respect to a social norm on pasture resting affects long-term social-ecological system dynamics.

Peer reviewed The emergence of tag-mediated altruism in structured societies

Shade Shutters David Hales | Published Tue Jan 20 21:36:12 2015 | Last modified Mon Jun 1 20:13:51 2015

This abstract model explores the emergence of altruistic behavior in networked societies. The model allows users to experiment with a number of population-level parameters to better understand what conditions contribute to the emergence of altruism.

Social Closure and the Evolution of Cooperation via Indirect Reciprocity

Simone Righi Károly Takács | Published Sat Jun 9 14:14:48 2018 | Last modified Sat Jun 9 15:11:49 2018

Righi S., Takacs K., Social Closure and the Evolution of Cooperation via Indirect Reciprocity, Resubmitted after Revisions to Scientific Reports

NetLogo software for the Peer Review Game model. It represents a population of scientists endowed with a proportion of a fixed pool of resources. At each step scientists decide how to allocate their resources between submitting manuscripts and reviewing others’ submissions. Quality of submissions and reviews depend on the amount of allocated resources and biased perception of submissions’ quality. Scientists can behave according to different allocation strategies by simply reacting to the outcome of their previous submission process or comparing their outcome with published papers’ quality. Overall bias of selected submissions and quality of published papers are computed at each step.

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