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 is an extension of the Artificial Long House Valley (ALHV) model developed by the authors (Swedlund et al. 2016; Warren and Sattenspiel 2020). The ALHV model simulates the population dynamics of individuals within the Long House Valley of Arizona from AD 800 to 1350. Individuals are aggregated into households that participate in annual agricultural and demographic cycles. The present version of the model incorporates features of the ALHV model including realistic age-specific fertility and mortality and, in addition, it adds the Black Mesa environment and population, as well as additional methods to allow migration between the two regions.
As is the case for previous versions of the ALHV model as well as the Artificial Anasazi (AA) model from which the ALHV model was derived (Axtell et al. 2002; Janssen 2009), this version makes use of detailed archaeological and paleoenvironmental data from the Long House Valley and the adjacent areas in Arizona. It also uses the same methods as the original AA model to estimate annual maize productivity of various agricultural zones within the Long House Valley. A new environment and associated methods have been developed for Black Mesa. Productivity estimates from both regions are used to determine suitable locations for households and farms during each year of the simulation.
This model extends the original Artifical Anasazi (AA) model to include individual agents, who vary in age and sex, and are aggregated into households. This allows more realistic simulations of population dynamics within the Long House Valley of Arizona from AD 800 to 1350 than are possible in the original model. The parts of this model that are directly derived from the AA model are based on Janssen’s 1999 Netlogo implementation of the model; the code for all extensions and adaptations in the model described here (the Artificial Long House Valley (ALHV) model) have been written by the authors. The AA model included only ideal and homogeneous “individuals” who do not participate in the population processes (e.g., birth and death)–these processes were assumed to act on entire households only. The ALHV model incorporates actual individual agents and all demographic processes affect these individuals. Individuals are aggregated into households that participate in annual agricultural and demographic cycles. Thus, the ALHV model is a combination of individual processes (birth and death) and household-level processes (e.g., finding suitable agriculture plots).
As is the case for the AA model, the ALHV model makes use of detailed archaeological and paleoenvironmental data from the Long House Valley and the adjacent areas in Arizona. It also uses the same methods as the original model (from Janssen’s Netlogo implementation) to estimate annual maize productivity of various agricultural zones within the valley. These estimates are used to determine suitable locations for households and farms during each year of the simulation.
The purpose of the model is to simulate the cultural hitchhiking hypothesis to explore how neutral cultural traits linked with advantageous traits spread together over time
This model explores different aspects of the formation of urban neighbourhoods where residents believe in values distant from those dominant in society. Or, at least, this is what the Danish government beliefs when they discuss their politics about parallel societies. This simulation is set to understand (a) whether these alternative values areas form and what determines their formation, (b) if they are linked to low or no income residents, and (c) what happens if they disappear from the map. All these three points are part of the Danish government policy. This agent-based model is set to understand the boundaries and effects of this policy.
The model represents migration of the green sea turtle, Chelonia mydas, between foraging and breeding sites in the Southwest Indian Ocean. The purpose of the model is to investigate the impact of local environmental conditions, including the quality of foraging sites and ocean currents, on emerging migratory corridors and reproductive output and to thereby identify conservation priority sites.
Corresponding article to found here: https://onlinelibrary.wiley.com/doi/epdf/10.1002/ece3.5552
Investigate spatial adaptive behaviors of narco-trafficking networks in response to various counterdrug interdiction strategies within the cocaine transit zone of Central America and associated maritime areas. Through the novel application of the ‘complex adaptive systems’ paradigm, we implement a potentially transformative coupled agent-based and interdiction optimization modeling approach to compellingly demonstrate: (a) how current efforts to disrupt narco-trafficking networks are in fact making them more widespread, resilient, and economically powerful; (b) the potential for alternative interdiction approaches to weaken and contain traffickers.
We develop an IBM that predicts how interactions between elephants, poachers, and law enforcement affect poaching levels within a virtual protected area. The model is theoretical at this stage and is not meant to provide a realistic depiction of poaching, but instead to demonstrate how IBMs can expand upon the existing modelling work done in this field, and to provide a framework for future research. The model could be further developed into a useful management support tool to predict the outcomes of various poaching mitigation strategies at real-world locations. The model was implemented in NetLogo version 6.1.0.
We first compared a scenario in which poachers have prescribed, non-adaptive decision-making and move randomly across the landscape, to one in which poachers adaptively respond to their memories of elephant locations and where other poachers have been caught by law enforcement. We then compare a situation in which ranger effort is distributed unevenly across the protected area to one in which rangers patrol by adaptively following elephant matriarchal herds.
The CHIME ABM explores information distribution networks and agents’ protective decision making in the context of hurricane landfall.
Brazil has initiated two territorial public policies for a rural sustainable development, the National Program for Sustainable Development of the Rural Territories (PRONAT) and Citizenship Territory Program (PTC). These public policies aims, as a condition for its effectiveness, the equilibrium of the power relations between actors which participate in the Collegiate for Territorial Development (CODETER) of each Rural Territory. Our research studies the hypotheses that, in the Rural Territories submitted to the PRONAT and PTC public policies, the power and reciprocity relations between actors engaged in the CODETER effectively have evolved in favor of the civil society representatives to the detriment of the public powers, notably the mayors.
The SocLab approach has been applied in two case studies and four models representing the Southern Rural Territory of Sergipe (TRSS) and the São Francisco Rural Territory (TRBSF) were designed for two referential periods, 2008-2012 and 2013-2017. These models were developed to evaluate the empowerment of the civil society in these rural territories due to thes two public policies, PRONAT and PTC.
RHEA aims to provide a methodological platform to simulate the aggregated impact of households’ residential location choice and dynamic risk perceptions in response to flooding on urban land markets. It integrates adaptive behaviour into the spatial landscape using behavioural theories and empirical data sources. The platform can be used to assess: how changes in households’ preferences or risk perceptions capitalize in property values, how price dynamics in the housing market affect spatial demographics in hazard-prone urban areas, how structural non-marginal shifts in land markets emerge from the bottom up, and how economic land use systems react to climate change. RHEA allows direct modelling of interactions of many heterogeneous agents in a land market over a heterogeneous spatial landscape. As other ABMs of markets it helps to understand how aggregated patterns and economic indices result from many individual interactions of economic agents.
The model could be used by scientists to explore the impact of climate change and increased flood risk on urban resilience, and the effect of various behavioural assumptions on the choices that people make in response to flood risk. It can be used by policy-makers to explore the aggregated impact of climate adaptation policies aimed at minimizing flood damages and the social costs of flood risk.