Computational Model Library

Our mission is to help computational modelers develop, document, and share their computational models in accordance with community standards and good open science and software engineering practices. Model authors can publish their model source code in the Computational Model Library with narrative documentation as well as metadata that supports open science and emerging norms that facilitate software citation, computational reproducibility / frictionless reuse, and interoperability. Model authors can also request private peer review of their computational models. Models that pass peer review receive a DOI once published.

All users of models published in the library must cite model authors when they use and benefit from their code.

Please check out our model publishing tutorial and feel free to contact us if you have any questions or concerns about publishing your model(s) in the Computational Model Library.

Displaying 10 of 1209 results for "Ian M Hamilton" clear search

Social distancing is a strategy to mitigate the spread of contagious disease, but it bears negative impacts on people’s social well-being, resulting in non-compliance. This paper uses an integrated behavioral simulation model, called HUMAT, to identify a sweet spot
that balances strictness of and obedience to social distancing rules.

A novel agent-based model was developed that aims to explore social interaction while it is constrained by visitor limitations (due to Dutch COVID measures). Specifically, the model aims to capture the interaction between the need for social contact and the support for the visitors measure. The model was developed using the HUMAT integrated framework, which offered a psychological and sociological foundation for the behavior of the agents.

Model implemented in Lammers, W., Pattyn, V., Ferrari, S. et al. Evidence for policy-makers: A matter of timing and certainty?. Policy Sci 57, 171–191 (2024). https://doi.org/10.1007/s11077-024-09526-9

Peer reviewed Online Protest and Repression in Authoritarian Settings (OPRAS)

Nanda Wijermans Annie Waldherr Aytalina Kulichkina | Published Tuesday, January 27, 2026 | Last modified Tuesday, April 07, 2026

This agent-based model, developed for the study “Online Protest and Repression in Authoritarian Settings,” examines how online protest and repression evolve in authoritarian contexts and how these dynamics affect ordinary users’ attitudes and behavior on social media. The model integrates key theoretical and empirical insights into social media use and core political factors that shape digital contention in authoritarian settings. The following questions are addressed: (1) how online protest–repression dynamics unfold across different levels of authoritarianism and varying compositions of committed accounts, and (2) how ordinary users’ internal propensity to protest and their perceived probability of successful repression change during online protest-repression contestation. The model is evaluated against two empirically grounded macro patterns observed in the real world. The first is enduring protest: online protest becomes dominant as vocal protesters grow to outnumber vocal repressors, shrinking the pool of silent users and stabilizing a pro-protest majority. The second is suppressed protest: online dissent is contained as vocal repression and silence expand in response to protest, yielding a sustained majority of repressive and silent accounts. Together, these dynamics demonstrate how dissenting voices are empowered and suppressed online in authoritarian settings.

An Agent-Based Model of Corruption: Micro Approach

Valery Dzutsati | Published Friday, January 30, 2015 | Last modified Sunday, September 27, 2015

Endogenous social transition from a high-corruption state to a low-corruption state, replication of Hammond 2009

Building upon the distance-based Hotelling’s differentiation idea, we describe the behavioral experience of several prototypes of consumers, who walk a hypothetical cognitive path in an attempt to maximize their satisfaction.

This is an agent-based model of the implementation of the self-enforcing agreement in cooperative teams.

Village level varietal dynamics of Sorghum in Mali

Geraldine Abrami | Published Thursday, December 03, 2009 | Last modified Saturday, April 27, 2013

Final project version - still needs a bit of work for being completly operational

Nudging agents in social networks for collective action

Marco Janssen | Published Sunday, August 14, 2011 | Last modified Sunday, March 17, 2019

Agents are linked in a social-network and make decisions on which of 2 types of behavior to adopt. We explore consequences of different information feedback and providing targeted feedback to individuals.

This model is used to simulate the influence of spatially and temporally variable sedimentary processes on the distribution of dated archaeological features in a surface context.

Takács, K. and Squazzoni, F. 2015. High Standards Enhance Inequality in Idealized Labor Markets. Journal of Artificial Societies and Social Simulation, 18(4), 2, http://jasss.soc.surrey.ac.uk/18/4/2.html
We built a simple model of an idealized labor market, in which there is no objective difference in average quality between groups and hiring decisions are not biased in favor of any particular group. Our results show that inequality in employment emerges necessarily also in such idealized situations due to the limited supply of high quality individuals and asymmetric information. Inequalities are exacerbated when employers have high standards and keep only the best workers in house. We found that ambitious workers get higher quality jobs even if ambition does not correlate or even negatively correlates with internal quality. Our findings help to corroborate empirical findings on higher employment discrepancies in high rather than low status jobs.

Displaying 10 of 1209 results for "Ian M Hamilton" clear search

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