Our mission is to help computational modelers at all levels engage in the establishment and adoption of community standards and good practices for developing and sharing computational models. Model authors can freely publish their model source code in the Computational Model Library alongside narrative documentation, open science metadata, and other emerging open science norms that facilitate software citation, reproducibility, interoperability, and reuse. Model authors can also request peer review of their computational models to receive a DOI.
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We also maintain a curated database of over 7500 publications of agent-based and individual based models with additional detailed metadata on availability of code and bibliometric information on the landscape of ABM/IBM publications that we welcome you to explore.
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Model of influence of access to social information spread via social network on decisions in a two-person game.
Sahelian transhumance is a type of socio-economic and environmental pastoral mobility. It involves the movement of herds from their terroir of origin (i.e., their original pastures) to one or more host terroirs, followed by a return to the terroir of origin. According to certain pastoralists, the mobility of herds is planned to prevent environmental degradation, given the continuous dependence of these herds on their environment. However, these herds emit Greenhouse Gases (GHGs) in the spaces they traverse. Given that GHGs contribute to global warming, our long-term objective is to quantify the GHGs emitted by Sahelian herds. The determination of these herds’ GHG emissions requires: (1) the artificial replication of the transhumance, and (2) precise knowledge of the space used during their transhumance.
This article presents the design of an artificial replication of the transhumance through an agent-based model named MSTRANS. MSTRANS determines the space used by transhumant herds, based on the decision-making process of Sahelian transhumants.
MSTRANS integrates a constrained multi-objective optimization problem and algorithms into an agent-based model. The constrained multi-objective optimization problem encapsulates the rationality and adaptability of pastoral strategies. Interactions between a transhumant and its socio-economic network are modeled using algorithms, diffusion processes, and within the multi-objective optimization problem. The dynamics of pastoral resources are formalized at various spatio-temporal scales using equations that are integrated into the algorithms.
The results of MSTRANS are validated using GPS data collected from transhumant herds in Senegal. MSTRANS results highlight the relevance of integrated models and constrained multi-objective optimization for modeling and monitoring the movements of transhumant herds in the Sahel. Now specialists in calculating greenhouse gas emissions have a reproducible and reusable tool for determining the space occupied by transhumant herds in a Sahelian country. In addition, decision-makers, pastoralists, veterinarians and traders have a reproducible and reusable tool to help them make environmental and socio-economic decisions.
The model is based on Swann and Buhrmester’s Identity Fusion behavioural theory, which seeks to explain why an individual puts the group’s priorities above their personal expectations. In order to observe the theory and validate group behaviour, a case study was carried out focusing on scenarios of group violence in football stadiums in Brazil. For the modelling, each agent has a distribution of levels of identification with the group to which they belong, with their level of fusion varying between 1 and 5. According to behavioural theory, an individual’s degree of fusion with the group directly interferes with their behaviour of replicating actions and absorbing group beliefs.
The Urban Traffic Simulator is an agent-based model developed in the Unity platform. The model allows the user to simulate several autonomous vehicles (AVs) and tune granular parameters such as vehicle downforce, adherence to speed limits, top speed in mph and mass. The model allows researchers to tune these parameters, run the simulator for a given period and export data from the model for analysis (an example is provided in Jupyter Notebook).
The data the model is currently able to output are the following:
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The model provides instruments for the simulation of interbank network evolution. There are tools for dynamic network analysis, allowing to evaluate graph topological invariants, thermodynamic network features and combinational node-based features.
This thesis presents an abstract spatial simulation model of the Maya Central Lowlands coupled human and natural system from 1000 BCE to the present day. It’s name is the Climatically Heightened but Anothropogenically Achieved Historical Kerplunk model (CHAAHK). The simulation features features virtual human groups, population centers, transit routes, local resources, and imported resources. Despite its embryonic state, the model demonstrates how certain anthropogenic characteristics of a landscape can interact with externally induced trauma and result in a prolonged period of relative sociopolitical uncomplexity. Analysis of batch simulation output suggests decreasing empirical uncertainties about ancient wetland modification warrants more investment. This first submission of CHAAHK’s code represents the simulation’s implementation that was featured in the author’s master’s thesis.
This is a simulator for the unified opinion dynamics framework, as developed by Adam Coates, Anthony Kleerekoper, and Liangxiu Han.
MoPAgrIB model simulates the movement of cultivated patches in a savannah vegetation mosaic ; how they move and relocate through the landscape, depending on farming practices, population growth, social rules and vegetation growth.
This simulation model is associated with the journal paper “A First Approach on Modelling Staff Proactiveness in Retail Simulation Models” to appear in the Journal of Artificial Societies and Social Simulation 14 (2) 2. The authors are Peer-Olaf Siebers ([email protected]) and Uwe Aickelin ([email protected]).
This is a stationarity test, it tests whether a given moment is constant during the time series (null hypothesis). The Wald Wolfowitz nonparametric fitness test is applied to time series.
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