CoMSES Net maintains cyberinfrastructure to foster FAIR data principles for access to and (re)use of computational models. Model authors can publish their model code in the Computational Model Library with documentation, metadata, and data dependencies and support these FAIR data principles as well as best practices for software citation. Model authors can also request that their model code be peer reviewed to receive a DOI. All users of models published in the library must cite model authors when they use and benefit from their code.
CoMSES Net also maintains a curated database of over 7500 publications of agent-based and individual based models with additional metadata on availability of code and bibliometric information on the landscape of ABM/IBM publications that we welcome you to explore.
The name of the model, CoComForest, stands for COllaborative COMmunity FOREST management. The purposes of this model are to expose local resource harvesters to the competition with external resource harvesters, called outsiders, and to provide them the opportunity to collectively discuss on resource management. The model, which is made of a set of interconnected entities, including (i) community forest habitat, (ii) resource harvesters, (iii) market, and (iv) firebreak. More details about the CoComForest model are described based on the Overview, Design concept, and Details (ODD) protocol uploaded with the model.
The Retail Competition Agent-based Model (RC-ABM) is designed to simulate the retail competition system in the Region of Waterloo, Ontario, Canada, which which explicitly represents store competition behaviour. Through the RC-ABM, we aim to answer 4 research questions: 1) What is the level of correspondence between market share and revenue acquisition for an agent-based approach compared to a traditional location-allocation-based approach? 2) To what degree can the observed store spatial pattern be reproduced by competition? 3) To what degree are their path dependent patterns of retail success? 4) What is the relationship between retail survival and the endogenous geographic characteristics of stores and consumer expenditures?
B3GET simulates populations of virtual organisms evolving over generations, whose evolutionary outcomes reflect the selection pressures of their environment. The model simulates several factors considered important in biology, including life history trade-offs, investment in fighting ability and aggression, sperm competition, infanticide, and competition over access to food and mates. Downloaded materials include a starting genotype and population files. Edit the these files and see what changes occur in the behavior of virtual populations!
Under the Kyoto Protocol, governments agreed on and accepted CO2 reduction targets in order to counter climate change. In Europe one of the main policy instruments to meet the agreed reduction targets is CO2 emission-trading (CET), which was implemented as of January 2005. In this system, companies active in specific sectors must be in the possession of CO2 emission rights to an amount equal to their CO2 emission. In Europe, electricity generation accounts for one-third of CO2 emissions. Since the power generation sector, has been liberalized, reregulated and privatized in the last decade, around Europe autonomous companies determine the sectors’ CO2 emission. Short-term they adjust their operation, long-term they decide on (dis)investment in power generation facilities and technology selection. An agent-based model is presented to elucidate the effect of CET on the decisions of power companies in an oligopolistic market. Simulations over an extensive scenario-space show that there CET does have an impact. A long-term portfolio shift towards less-CO2 intensive power generation is observed. However, the effect of CET is relatively small and materializes late. The absolute emissions from power generation rise under most scenarios. This corresponds to the dominant character of current capacity expansion planned in the Netherlands (50%) and in Germany (68%), where companies have announced many new coal based power plants. Coal is the most CO2 intensive option available and it seems surprising that even after the introduction of CET these capacity expansion plans indicate a preference for coal. Apparently in power generation the economic effect of CO2 emission-trading is not sufficient to outweigh the economic incentives to choose for coal.
The TERROIR agent-based model was built for the multi-level analysis of biomass and nutrient flows within agro-sylvo-pastoral villages in West Africa. It explicitly takes into account both human organization and spatial extension of such flows.
This model inspects the performance of firms as the product attribute space changes, which evolves as a consequence of firms’ actions. Firms may create new product variants by dragging demand from other existing variants. Firms decide whether to open new product variants, to invade existing ones, or to keep their variant portfolio. At each variant there is a Cournot competition each round. Competition is nested since many firms compete at many variants simultaneously, affecting firm composition at each location (variant).
After the Cournot outcomes, at each round firms decide whether to (i) keep their existing product variant niche, (ii) invade an existing variant, (iii) create a new variant, or (iv) abandon a variant. Firms’ profits across their niche take into consideration the niche-width cost and the cost of opening a new variant.
This a model developed as a part of the paper Mejía, G. & García-Díaz, C. (2018). Market-level effects of firm-level adaptation and intermediation in networked markets of fresh foods: a case study in Colombia. Agricultural Systems 160: 132-142.
It simulates the competition dynamics of the potato market in Bogotá, Colombia. The model explores the economic impact of intermediary actors on the potato supply chain.
This model aims to simulate Competition and Displacement of Online Interpersonal Communication Platforms process from a bottom-up angle. Individual interpersonal communication platform adoption and abandonment serve as the micro-foundation of the simulation model. The evolution mode of platform user online communication network determines how present platform users adjust their communication relationships as well as how new users join that network. This evolution mode together with innovations proposed by individual interpersonal communication platforms would also have impacts on the platform competition and displacement process and result by influencing individual platform adoption and abandonment behaviors. Three scenes were designed to simulate some common competition situations occurred in the past and current time, that two homogeneous interpersonal communication platforms competed with each other when this kind of platforms first came into the public eye, that a late entrant platform with a major innovation competed with the leading incumbent platform during the following days, as well as that both the leading incumbent and the late entrant continued to propose many small innovations to compete in recent days, respectively.
Initial parameters are as follows: n(Nmax in the paper), denotes the final node number of the online communication network node. mi (m in the paper), denotes the initial degree of those initial network nodes and new added nodes. pc(Pc in the paper), denotes the proportion of links to be removed and added in each epoch. pst(Pv in the paper), denotes the proportion of nodes with a viscosity to some platforms. comeintime(Ti in the paper), denotes the epoch when Platform 2 joins the market. pit(Pi in the paper), denotes the proportion of nodes adopting Platform 2 immediately at epoch comeintime(Ti). ct(Ct in the paper), denotes the Innovation Effective Period length. In Scene 2, There is only one major platform proposed by Platform 2, and ct describes that length. However, in Scene 3, Platform 2 and 1 will propose innovations alternately. And so, we set ct=10000 in simulation program, and every jtt epochs, we alter the innovation proposer from one platform to the other. Hence in this scene, jtt actually denotes the Innovation Effective Period length instead of ct.
How do rebel groups control territory and engage with the local economy during civil war? Charles Tilly’s seminal War and State Making as Organized Crime (1985) posits that the process of waging war and providing governance resembles that of a protection racket, in which aspiring governing groups will extort local populations in order to gain power, and civilians or businesses will pay in order to ensure their own protection. As civil war research increasingly probes the mechanisms that fuel local disputes and the origination of violence, we develop an agent-based simulation model to explore the economic relationship of rebel groups with local populations, using extortion racket interactions to explain the dynamics of rebel fighting, their impact on the economy, and the importance of their economic base of support. This analysis provides insights for understanding the causes and byproducts of rebel competition in present-day conflicts, such as the cases of South Sudan, Afghanistan, and Somalia.
The model defines two object types: RebelGroup and Enterprise. A RebelGroup is a group that competes for power in a system of anarchy, in which there is effectively no government control. An Enterprise is a local civilian-level actor that conducts business in this environment, whose objective is to make a profit. In this system, a RebelGroup may choose to extort money from Enterprises in order to support its fighting efforts. It can extract payments from an Enterprise, which fears for its safety if it does not pay. This adds some amount of money to the RebelGroup’s resources, and they can return to extort the same Enterprise again. The RebelGroup can also choose to loot the Enterprise instead. This results in gaining all of the Enterprise wealth, but prompts the individual Enterprise to flee, or leave the model. This reduces the available pool of Enterprises available to the RebelGroup for extortion. Following these interactions the RebelGroup can choose to AllocateWealth, or pay its rebel fighters. Depending on the value of its available resources, it can add more rebels or expel some of those which it already has, changing its size. It can also choose to expand over new territory, or effectively increase its number of potential extorting Enterprises. As a response to these dynamics, an Enterprise can choose to Report expansion to another RebelGroup, which results in fighting between the two groups. This system shows how, faced with economic choices, RebelGroups and Enterprises make decisions in war that impact conflict and violence outcomes.