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.
In the face of the COVID-19 pandemic, public health authorities around the world have experimented, in a short period of time, with various combinations of interventions at different scales. However, as the pandemic continues to progress, there is a growing need for tools and methodologies to quickly analyze the impact of these interventions and answer concrete questions regarding their effectiveness, range and temporality.
COMOKIT, the COVID-19 modeling kit, is such a tool. It is a computer model that allows intervention strategies to be explored in silico before their possible implementation phase. It can take into account important dimensions of policy actions, such as the heterogeneity of individual responses or the spatial aspect of containment strategies.
In COMOKIT, built using the agent-based modeling and simulation platform GAMA, the profiles, activities and interactions of people, person-to-person and environmental transmissions, individual clinical statuses, public health policies and interventions are explicitly represented and they all serve as a basis for describing the dynamics of the epidemic in a detailed and realistic representation of space.
Model of the Corona pandemic outbreak
The COVID-19 ABM aims to predict the qualitative behaviour of the CoViD-19 epidemic dynamics for the greater region of Salzburg City. Specifically, by means of scenario testing, it aims to help assessing how containment interventions can allow a stepwise relaxation of the lockdown without risking a new outbreak.
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.
Juan Castilla-Rho et al. (2015) developed a platform, named FLowLogo, which integrates a 2D, finite-difference solution of the governing equations of groundwater flow with agent-based simulation. We used this model for Rafsanjan Aquifer, which is located in an arid region in Iran. To use FLowLogo for a real case study, one needs to add GIS shapefiles of boundary conditions and modify the code written in NetLogo a little bit. The FlowLogo model used in our research is presented here.
In an associated paper which focuses on analyzing the structure of several egocentric networks of collective awareness platforms for sustainable innovation (CAPS), this model is developed. It answers the question whether the network structure is determinative for the sustainability of the created awareness. Based on a thorough literature review a model is developed to explain and operationalize the concept of sustainability of a social network in terms of importance, effectiveness and robustness. By developing this agent-based model, the expected outcomes after the dissolution of the CAPS are predicted and compared with the results of a network with the same participants but with different ties. Twitter data from different CAPS is collected and used to feed the simulation. The results show that the structure of the network is of key importance for its sustainability. With this knowledge and the ability to simulate the results after network changes have taken place, CAPS can assess the sustainability of their legacy and actively steer towards a longer lasting potential for social innovation. The retrieved knowledge urges organizations like the European Commission to adopt a more blended approach focusing not only on solving societal issues but on building a community to sustain the initiated development.
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.
We present an agent-based model for the sharing economy, in the short-time accommodations market, where peers participating as suppliers and demanders follow simple decision rules about sharing market participation, according to their heterogeneous characteristics. We consider the sharing economy mainly as a peer-to-peer market where the access is preferred to ownership, excluding professional agents using sharing platforms as Airbnb to promote their business.
The purpose of this agent-based model is to compare different variants of crowdworking in a general way, so that the obtained results are independent of specific details of the crowdworking platform. It features many adjustable parameters that can be used to calibrate the model to empirical data, but also when not calibrated it yields essential results about crowdworking in general.
Agents compete for contracts on a virtual crowdworking platform. Each agent is defined by various properties like qualification and income expectation. Agents that are unable to turn a profit have a chance to quit the crowdworking platform and new crowdworkers can replace them. Thus the model has features of an evolutionary process, filtering out the ill suited agents, and generating a realistic distribution of agents from an initially random one. To simulate a stable system, the amount of contracts issued per day can be set constant, as well as the number of crowdworkers. If one is interested in a dynamically changing platform, the simulation can also be initialized in a way that increases or decreases the number of crowdworkers or number of contracts over time. Thus, a large variety of scenarios can be investigated.
The command and control policy in natural resource management, including water resources, is a longstanding established policy that has been theoretically and practically argued from the point of view of social-ecological complex systems. With the intention of making a system ecologically resilient, these days, policymakers apply the top-down policies of controlling communities through regulations. To explore how these policies may work and to understand whether the ecological goal can be achieved via command and control policy, this research uses the capacity of Agent-Based Modeling (ABM) as an experimental platform in the Urmia Lake Basin (ULB) in Iran, which is a social-ecological complex system and has gone through a drought process.
Despite the uncertainty of the restorability capacity of the lake, there has been a consensus on the possibility to artificially restore the lake through the nationally managed Urmia Lake Restoratoin Program (ULRP). To reduce water consumption in the Basin, the ULRP widely targets the agricultural sector and proposes the project of changing crop patterns from high-water-demand (HWD) to low-water-demand (LWD), which includes a component to control water consumption by establishing water-police forces.
Using a wide range of multidisciplinary studies about Urmia Lake at the Basin and sub-basins as well as qualitative information at micro-level as the main conceptual sources for the ABM, the findings under different strategies indicate that targeting crop patterns change by legally limiting farmers’ access to water could force farmers to change their crop patterns for a short period of time as long as the number of police constantly increases. However, it is not a sustainable policy for either changing the crop patterns nor restoring the lake.
The model aims at reproducing the evolution of the land-use in an agricultural territory at the plot scale. It enables to simulate the affectation of land-use, the crop rotation and technical operations for each plot of the different farms of the territory. It allows as well for crop farms to simulate the daily state of plots (sowed, plowed, harvested, biomass indicator). The model is used as an input for the water pollution model allowing to determine the flow of nitrate, phosphorus and suspended matter in the territory according to the landscape configuration.