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.
Please check out our model publishing tutorial and contact us if you have any questions or concerns about publishing your model(s) in the Computational Model Library.
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.
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How do households alter their spending patterns when they experience changes in income? This model answers this question using a random assignment scheme where spending patterns are copied from a household in the new income bracket.
The ABM model is designed to model the adaptability of farmers in DTIM. This model includes two groups of farmers and local government admins agents. Farmers with different levels, with low WP of DTIM, are looking for economic benefits and reduced irrigation and production costs. Meanwhile, the government is looking for strategic goals to maintain water resources’ sustainability. The local government admins employ incentives (subsidies in this study) to encourage farmers to DTIM. In addition, it is used as a tool for supervision and training farmers’ performance. Farmers are currently harvesting water resources with irrigation systems and different levels of technology, and they intend to provide short-term benefits. Farmers adjust the existing approach based on their knowledge of the importance of DTIM and propensity to increase WP and cost-benefit evaluation. DTIM has an initial implementation fee. Every farmer can increase WP by using government subsidies. If none of the farmers create optimal use of water resources, access to water resources will be threatened in the long term. This is considered a hypothetical cost for farmers who do not participate in DTIM. With DTIM, considering that local government admins’ facilities cover an essential part of implementation costs, farmers may think of profiting from local government admins’ facilities by selling that equipment, especially if the farmers in the following conditions may consider selling their developed irrigation equipment. In this case, the technology of their irrigation system will return to the state before development.
- When the threshold of farmers’ propensity to DTIM is low (for example, in the conditions of scarcity of access to sufficient training about the new irrigation system or its role in reducing the cost and sustainability of water resources)
- When the share of government subsidy is high, and as a result, the profit from the sale of equipment is attractive, especially in conditions of inflation.
- Finally, farmers’ honesty threshold should be reduced based on the positive experience of profit-seeking and deception among neighbors.
Increasing the share of government subsidies can encourage farmers to earn profits. Therefore, the government can help increase farmers’ profits by considering the assessment teams at different levels with DTIM training . local government admins evaluations monitor the behavior of farmers. If farmers sell their improved irrigation system for profit, they may be deprived of some local government admins’ services and the possibility of receiving subsidies again. Assessments The local government admins can increase farmers’ honesty. Next, the ABM model evaluates local government admins policies to achieve a suitable framework for water resources management in the Miandoab region.
This model was developed to study the combination of electric vehicles (EVs) and intermitten renewable energy sources. The model presents an EV fleet in a fictional area, divided into a residential area, an office area and commercial area. The area has renewable energy sources: wind and PV solar panels. The agents can be encouraged to charge their electric vehicles at times of renewable energy surplus by introducing different policy interventions. Other interesting variables in the model are the installed renewable energy sources, EV fleet composition and available charging infrastructure. Where possible, use emperical data as input for our model. We expand upon previous models by incorporating environmental self-identity and range anxiety as agent variables.
Righi S., Takacs K., Social Closure and the Evolution of Cooperation via Indirect Reciprocity, Resubmitted after Revisions to Scientific Reports
The Olympic Peninsula ABM works as a virtual laboratory to simulate the existing forestland management practices as followed by different forestland owner groups in the Olympic Peninsula, Washington, and explore how they could shape the future provisions of multifunctional ecosystem services such as Carbon storage and revenue generation under the business-as-usual scenario as well as by their adaptation to interventions. Forestlands are socio-ecological systems that interact with economic, socio-cultural, and policy systems. Two intervention scenarios were introduced in this model to simulate the adaptation of landowner behavior and test the efficacy of policy instruments in promoting sustainable forest practices and fostering Carbon storage and revenue generation. (1) A market-linked carbon offset scheme that pays the forestland owners a financial incentive in the form of a yearly carbon rent. (2) An institutional intervention policy that allows small forest owners (SFLO) to cooperate for increased market access and benefits under carbon rent scenario. The model incorporates the heterogeneous contexts within which the forestland owners operate and make their forest management decisions by parameterizing relevant agent attributes and contextualizing their unique decision-making processes.
This model is a replication of that described by Peterson (2002) and illustrates the ‘spread’ feedback loop type described in Millington (2013).
Scholars have written extensively about hierarchical international order, on the one hand, and war on the other, but surprisingly little work systematically explores the connection between the two. This disconnect is all the more striking given that empirical studies have found a strong relationship between the two. We provide a generative computational network model that explains hierarchy and war as two elements of a larger recursive process: The threat of war drives the formation of hierarchy, which in turn shapes states’ incentives for war. Grounded in canonical theories of hierarchy and war, the model explains an array of known regularities about hierarchical order and conflict. Surprisingly, we also find that many traditional results of the IR literature—including institutional persistence, balancing behavior, and systemic self-regulation—emerge from the interplay between hierarchy and war.
Signaling chains are a special case of Lewis’ signaling games on networks. In a signaling chain, a sender tries to send a single unit of information to a receiver through a chain of players that do not share a common signaling system.
A simple model of random encounters of materials that produces distributions as found in the archaeological record.
In CmLab we explore the implications of the phenomenon of Conservation of Money in a modern economy. This is one of a series of models exploring the dynamics of sustainable economics – PSoup, ModEco, EiLab, OamLab, MppLab, TpLab, CmLab.
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