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.
SiFlo is an ABM dedicated to simulate flood events in urban areas. It considers the water flowing and the reaction of the inhabitants. The inhabitants would be able to perform different actions regarding the flood: protection (protect their house, their equipment and furniture…), evacuation (considering traffic model), get and give information (considering imperfect knowledge), etc. A special care was taken to model the inhabitant behavior: the inhabitants should be able to build complex reasoning, to have emotions, to follow or not instructions, to have incomplete knowledge about the flood, to interfere with other inhabitants, to find their way on the road network. The model integrates the closure of roads and the danger a flooded road can represent. Furthermore, it considers the state of the infrastructures and notably protection infrastructures as dyke. Then, it allows to simulate a dyke breaking.
The model intends to be generic and flexible whereas provide a fine geographic description of the case study. In this perspective, the model is able to directly import GIS data to reproduce any territory. The following sections expose the main elements of the model.
A simulated approach for Personal Carbon Trading, for figuring out what effects it might have if it will be implemented in the real world. We use an artificial population with some empirical data from international literature and basic assumptions about heterogeneous energy demand. The model is not to be used as simulating the actual behavior of real populations, but a toy model to test the effects of differences in various factors such as number of agents, energy price, price of allowances, etc. It is important to adapt the model for specific countries as carbon footprint and energy demand determines the relative success of PCT.
This NetLogo ABM builds on Elena Vallino’s model of Loggers using community-based natural resource management for a forest ecosystem. In it we introduce an alternative mechanism for Logger cheating and enforcement of CBNRM rules.
The Simulating Agroforestry Adoption in Rural Indonesia (SAFARI) model aims at exploring the adoption of illipe rubber agroforestry systems by farming households in the case study region in rural Indonesia. Thereby, the ABM simulates the interdependencies of agroforestry systems and local livelihoods, income, land use, biodiversity, and carbon fixation. The model contrasts development paths without agroforestry (business as usual (BAU) scenario), corresponding to a scenario where the government promotes rubber monoculture, with the introduction of illipe rubber agroforestry systems (IRA scenario) as an alternative. It aims to support policy-makers to assess the potential of IRA over larger temporal and spatial scales.
The purpose of the model is to explore the influence of the design of circular business models (CBMs) on CBM viability. The model represents an Industrial Symbiosis Network (ISN) in which a processor uses the organic waste from suppliers to produce biogas and nutrient rich digestate for local reuse. CBM viability is expressed as value captured (e.g., cash flow/tonne waste/agent) and the survival of the network over time (shown in the interface).
In the model, the value captured is calculated relative to the initial state, using incineration costs as a benchmark. Moderating variables are interactions with the waste incinerator and actor behaviour factors. Actors may leave the network when the waste supply for local production is too low, or when personal economic benefits are too low. When the processor decides to leave, the network fails. Theory of planned behaviour can be used to include agent behaviour in the simulations.
Risk assessments are designed to measure cumulative risk and promotive factors for delinquency and recidivism, and are used by criminal and juvenile justice systems to inform sanctions and interventions. Yet, these risk assessments tend to focus on individual risk and often fail to capture each individual’s environmental risk. This agent-based model (ABM) explores the interaction of individual and environmental risk on the youth. The ABM is based on an interactional theory of delinquency and moves beyond more traditional statistical approaches used to study delinquency that tend to rely on point-in-time measures, and to focus on exploring the dynamics and processes that evolve from interactions between agents (i.e., youths) and their environments. Our ABM simulates a youth’s day, where they spend time in schools, their neighborhoods, and families. The youth has proclivities for engaging in prosocial or antisocial behaviors, and their environments have likelihoods of presenting prosocial or antisocial opportunities.
Aqua.MORE (Agent-based MOdelling of REsources in Socio-Hydrological Systems) is an agent based modelling (ABM) approach to simulate the resource flow and social interaction in a coupled natural and social system of water supply and demand. The model is able to simulate the two-way feedback as socio-economic agents influence the natural resource flow and the availability of this resource influences the agents in their behaviour.
This model is based on the Narragansett Bay, RI recreational fishery. The two types of agents are piscivorous fish and fishers (shore and boat fishers are separate “breeds”). Each time step represents one week. Open season is weeks 1-26, assuming fishing occurs during half the year. At each weekly time step, fish agents grow, reproduce, and die. Fisher agents decide whether or not to fish based on their current satisfaction level, and those that do go fishing attempt to catch a fish. If they are successful, they decide whether to keep or release the fish. In our publication, this model was linked to an Ecopath with Ecosim food web model where the commercial harvest of forage fish affected the biomass of piscivorous fish - which then became the starting number of piscivorous fish for this ABM. The number of fish caught in a season of this ABM was converted to a fishing pressure and input back into the food web model.
The purpose of the model is to explore the influence of actor behaviour, combined with environment and business model design, on the survival rates of Industrial Symbiosis Networks (ISN), and the cash flows of the agents. We define an ISN to be robust, when it is able to run for 10 years, without falling apart due to leaving agents.
The model simulates the implementation of local waste exchange collaborations for compost production, through the ISN implementation stages of awareness, planning, negotiation, implementation, and evaluation.
One central firm plays the role of waste processor in a local composting initiative. This firm negotiates with other firms to become a supplier of their organic residual streams. The waste suppliers in the model can decide to join the initiative, or to have the waste brought to the external waste incinerator. The focal point of the model are the company-level interactions during the implementation or ending of synergies.
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: