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.
PoliSEA represents a continuous policy process cycle, integrated with the dynamics of a fishery social-ecological system. The policy process in the model is represented by interactions between policymakers and interest groups and subsequent voting during which policymaker decide to increase or decrease the fishing quota for the next season. Policymakers’ positions can be influenced by lobbying of interest groups or interest group coalitions. The quota adopted through the policy process determines the amount of fish that can be harvested from the fish population during the season.
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 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).
MigrAgent simulates migration flows of a population from a home country to a host country and mutual adaptation of a migrant and local population post-migration. Agents accept interactions in intercultural networks depending on their degree of conservatism. Conservatism is a group-level parameter normally distributed within each ethnic group. Individual conservatism changes as function of reciprocity of interaction in intergroup experiences of acceptance or rejection.
The aim of MigrAgent is to unfold different outcomes of integration, assimilation, separation and marginalization in terms of networks as effect of different degrees of conservatism in each group and speed of migration flows.
Modeling an economy with stable macro signals, that works as a benchmark for studying the effects of the agent activities, e.g. extortion, at the service of the elaboration of public policies..
We present a network agent-based model of ethnocentrism and intergroup cooperation in which agents from two groups (majority and minority) change their communality (feeling of group solidarity), cooperation strategy and social ties, depending on a barrier of “likeness” (affinity). Our purpose was to study the model’s capability for describing how the mechanisms of preexisting markers (or “tags”) that can work as cues for inducing in-group bias, imitation, and reaction to non-cooperating agents, lead to ethnocentrism or intergroup cooperation and influence the formation of the network of mixed ties between agents of different groups. We explored the model’s behavior via four experiments in which we studied the combined effects of “likeness,” relative size of the minority group, degree of connectivity of the social network, game difficulty (strength) and relative frequencies of strategy revision and structural adaptation. The parameters that have a stronger influence on the emerging dominant strategies and the formation of mixed ties in the social network are the group-tag barrier, the frequency with which agents react to adverse partners, and the game difficulty. The relative size of the minority group also plays a role in increasing the percentage of mixed ties in the social network. This is consistent with the intergroup ties being dependent on the “arena” of contact (with progressively stronger barriers from e.g. workmates to close relatives), and with measures that hinder intergroup contact also hindering mutual cooperation.
This is a modification of a model published previous by Barton and Riel-Salvatore (2012). In this model, we simulate six regional populations within Last Glacial Maximum western Europe. Agents interact through reproduction and genetic markers attached to each of six regions mix through subsequent generations as a way to track population dynamics, mobility, and gene flow. In addition, the landscape is heterogeneous and affects agent mobility and, under certain scenarios, their odds of survival.
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.
Agents are linked in a social-network and make decisions on which of 2 types of behavior to adopt. We explore consequences of different information feedback and providing targeted feedback to individuals.
The purpose of the OMOLAND-CA is to investigate the adaptive capacity of rural households in the South Omo zone of Ethiopia with respect to variation in climate, socioeconomic factors, and land-use at the local level.