Our mission is to help computational modelers at all levels engage in the establishment and adoption of community standards and good practices for developing and sharing computational models. Model authors can freely publish their model source code in the Computational Model Library alongside narrative documentation, open science metadata, and other emerging open science norms that facilitate software citation, reproducibility, interoperability, and reuse. Model authors can also request peer review of their computational models to receive a DOI.
All users of models published in the library must cite model authors when they use and benefit from their code.
We also maintain a curated database of over 7500 publications of agent-based and individual based models with additional detailed metadata on availability of code and bibliometric information on the landscape of ABM/IBM publications that we welcome you to explore.
Displaying 10 of 123 results strategies clear
MOOvPOPsurveillance was developed as a tool for wildlife agencies to guide collection and analysis of disease surveillance data that relies on non-probabilistic methods like harvest-based sampling.
This model analyzes two investors forming their expectations with heterogeneous strategies in order to optimize their portfolios by means of a Sharpe ratio maximization. Traders are distinguished according to their methodology used in forecasting. Two acknowledged algorithms of technical analysis have been implemented to compare portfolios performances and assess profitability of each technique.
This model was created to investigate the potential impacts of large-scale recreational and transport-related physical activity promotion strategies on six United Nations Sustainable Development Goals (SDGs) related outcomes—road traffic deaths (SDG 3), transportation mode share (SDG 9), convenient access to public transport, levels of fine particulate matter, and access to public open spaces (SDG 11), and levels of carbon dioxide emissions (SDG 13)—in three cities designed as abstract representations of common city types in high-, middle-, and low-income countries.
AMIRIS is the Agent-based Market model for the Investigation of Renewable and Integrated energy Systems.
It is an agent-based simulation of electricity markets and their actors.
AMIRIS enables researches to analyse and evaluate energy policy instruments and their impact on the actors involved in the simulation context.
Different prototypical agents on the electricity market interact with each other, each employing complex decision strategies.
AMIRIS allows to calculate the impact of policy instruments on economic performance of power plant operators and marketers.
This model is developed as a theoretical agent-based model to study the general phenomena of network-based targeting strategies on eco-innovation adoption and diffusion through inter-firm networks.
This model replicates the Axelrod prisoner’s dilemma tournaments. The model takes as input a file of strategies and pits them against each other to see who achieves the best payoff in the end. Change the payoff structure to see how it changes the tournament outcome!
This model simulates the Hawk-Dove game as first described by John Maynard Smith, and further elaborated by Richard Dawkins in “The Selfish Gene”. In the game, two strategies, Hawks and Doves, compete against each other, and themselves, for reproductive benefits. A third strategy can be introduced, Retaliators, which act like either Hawks or Doves, depending on the context.
The purpose of this model is to explore the dynamics of residency and eviction for households renting in the greater Phoenix (Arizona) metropolitan area. The model uses a representative population of renters modified from American Community Survey (ACS) data that includes demographic, housing and economic information. Each month, households pay their subsistence, rental and utility bills. If a household is unable to pay their monthly rent or utility bill they apply for financial assistance. This model provides a platform to understand the impact of various economic shock upon households. Also, the model includes conditions that occurred as a result of the Covid-19 pandemic which allows for the study of eviction mitigation strategies that were employed, such as the eviction moratorium and stimulus payments. The model allows us to make preliminary predictions concerning the number of households that may be evicted once the moratorium on evictions ends and the long-term effects on the number of evicted households in the greater Phoenix area going forward.
The Simulating Agroforestry Adoption in Rural Indonesia (SAFARI) model aims at exploring the adoption of illipe rubber agroforestry systems by farming households in the case study region in rural Indonesia. Thereby, the ABM simulates the interdependencies of agroforestry systems and local livelihoods, income, land use, biodiversity, and carbon fixation. The model contrasts development paths without agroforestry (business as usual (BAU) scenario), corresponding to a scenario where the government promotes rubber monoculture, with the introduction of illipe rubber agroforestry systems (IRA scenario) as an alternative. It aims to support policy-makers to assess the potential of IRA over larger temporal and spatial scales.
The SMASH model is an agent-based model of rural smallholder households. It models households’ evolving income and wealth, which they earn through crop sales. Wealth is carried in the form of livestock, which are grazed on an external rangeland (exogenous) and can be bought/sold as investment/coping mechanisms. The model includes a stylized representation of soil nutrient dynamics, modeling the inflows and outflows of organic and inorganic nitrogen from each household’s field.
The model has been applied to assess the resilience-enhancing effects of two different farm-level adaptation strategies: legume cover cropping and crop insurance. These two strategies interact with the model through different mechanims - legume cover cropping through ecological mechanisms and crop insurance through financial mechanisms. The model can be used to investigate the short- and long-term effects of these strategies, as well as how they may differently benefit different types of household.
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