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.
This model represnts an unique human-aquifer interactions model for the Li-extraction in Salar de Atacama, Chile. It describes the local actors’ experience of mining-induced changes in the socio-ecological system, especially on groundwater changes and social stressors. Social interactions are designed specifically according to a long-term local fieldwork by Babidge et al. (2019, 2020). The groundwater system builds on the FlowLogo model by Castilla-Rho et al. (2015), which was then parameterized and calibrated with local hydrogeological inputs in Salar de Atacama, Chile. The social system of the ABM is defined and customozied based on empirical studies to reflect three major stressors: drought stress, population stress, and mining stress. The model reports evolution of groundwater changes and associated social stress dynamics within the modeled time frame.
A minimal genetic algorithm was preliminarily developed to search for the solution of an elementary arithmetic problem. It has been modified to explore the effect of a mutator gene and the consequent entrance into a hypermutation state. The phenomenon is particularly important in some types of tumorigenesis and in a more general way, in cells and tissues submitted to chronic sublethal environmental or genomic stress.
Since a long time, some scholars suppose that organisms speed up their own evolution by varying mutation rate, but evolutionary biologists are not convinced that evolution can select a mechanism promoting more (often harmful) mutations looking forward an environmental challenge. The aim of the model is to shed light on these controversial points of views.
Genetic algorithms try to solve a computational problem following some principles of organic evolution. This model has educational purposes; it can give us an answer to the simple arithmetic problem on how to find the highest natural number composed by a given number of digits. We approach the task using a genetic algorithm, where the candidate solutions to the problem are represented by agents, that in logo programming environment are usually known as “turtles”.
This model builds on inquisitiveness as a key individual disposition to expand the bounds of their rationality. It represents a system where teams are formed around problems and inquisitive agents integrate competencies to find ‘emergent’ solutions.
The Garbage Can Model of Organizational Choice (GCM) is a fundamental model of organizational decision-making originally propossed by J.D. Cohen, J.G. March and J.P. Olsen in 1972. In their model, decisions are made out of random meetings of decision-makers, opportunities, solutions and problems within an organization.
With this model, these very same agents are supposed to meet in society at large where they make decisions according to GCM rules. Furthermore, under certain additional conditions decision-makers, opportunities, solutions and problems form stable organizations. In this artificial ecology organizations are born, grow and eventually vanish with time.
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.
We reconstruct Cohen, March and Olsen’s Garbage Can model of organizational choice as an agent-based model. We add another means for avoiding making decisions: buck-passing difficult problems to colleagues.
While the world’s total urban population continues to grow, this growth is not equal. Some cities are declining, resulting in urban shrinkage which is now a global phenomenon. Many problems emerge due to urban shrinkage including population loss, economic depression, vacant properties and the contraction of housing markets. To explore this issue, this paper presents an agent-based model stylized on spatially explicit data of Detroit Tri-county area, an area witnessing urban shrinkage. Specifically, the model examines how micro-level housing trades impact urban shrinkage by capturing interactions between sellers and buyers within different sub-housing markets. The stylized model results highlight not only how we can simulate housing transactions but the aggregate market conditions relating to urban shrinkage (i.e., the contraction of housing markets). To this end, the paper demonstrates the potential of simulation to explore urban shrinkage and potentially offers a means to test polices to alleviate this issue.
The model simulates the decisions of residents and a water authority to respond to socio-hydrological hazards. Residents from neighborhoods are located in a landscape with topographic complexity and two problems: water scarcity in the peripheral neighborhoods at high altitude and high risk of flooding in the lowlands, at the core of the city. The role of the water authority is to decide where investments in infrastructure should be allocated to reduce the risk to water scarcity and flooding events in the city, and these decisions are made via a multi-objective site selection procedure. This procedure accounts for the interdependencies and feedback between the urban landscape and a policy scenario that defines the importance, or priorities, that the authority places on four criteria.
Neighborhoods respond to the water authority decisions by protesting against the lack of investment and the level of exposure to water scarcity and flooding. Protests thus simulate a form of feedback between local-level outcomes (flooding and water scarcity) and higher-level decision-making. Neighborhoods at high altitude are more likely to be exposed to water scarcity and lack infrastructure, whereas neighborhoods in the lowlands tend to suffer from recurrent flooding. The frequency of flooding is also a function of spatially uniform rainfall events. Likewise, neighborhoods at the periphery of the urban landscape lack infrastructure and suffer from chronic risk of water scarcity.
The model simulates the coupling between the decision-making processes of institutional actors, socio-political processes and infrastructure-related hazards. In the documentation, we describe details of the implementation in NetLogo, the description of the procedures, scheduling, and the initial conditions of the landscape and the neighborhoods.
This work was supported by the National Science Foundation under Grant No. 1414052, CNH: The Dynamics of Multi-Scalar Adaptation in Megacities (PI Hallie Eakin).
This model represents an agent-based social simulation for citizenship competences. In this model people interact by solving different conflicts and a conflict is solved or not considering two possible escenarios: when individual citizenship competences are considered and when not. In both cases the TKI conflict resolution styles are considered. Each conflict has associated a competence and the information about the conflicts and their competences is retrieved from an ontology which was developed in Protégé. To do so, a NetLogo extension was developed using the Java programming language and the JENA API (to make queries over the ontology).