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.
All users of models published in the library must cite model authors when they use and benefit from their code.
Please check out our model publishing tutorial and contact us if you have any questions or concerns about publishing your model(s) in the Computational Model Library.
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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FIsheries Simulation with Human COmplex DEcision-making (FISHCODE) is an agent-based model to depict and analyze current and future spatio-temporal dynamics of three German fishing fleets in the southern North Sea. Every agent (fishing vessel) makes daily decisions about if, what, and how long to fish. Weather, fuel and fish prices, as well as the actions of their colleagues influence agents’ decisions. To combine behavioral theories and enable agents to make dynamic decision, we implemented the Consumat approach, a framework in which agents’ decisions vary in complexity and social engagement depending on their satisfaction and uncertainty. Every agent has three satisfactions and two uncertainties representing different behavioral aspects, i.e. habitual behavior, profit maximization, competition, conformism, and planning insecurity. Availability of extensive information on fishing trips allowed us to parameterize many model parameters directly from data, while others were calibrated using pattern oriented modelling. Model validation showed that spatial and temporal aggregated ABM outputs were in realistic ranges when compared to observed data. Our ABM hence represents a tool to assess the impact of the ever growing challenges to North Sea fisheries and provides insight into fisher behavior beyond profit maximization.
The purpose of this model is to understand the role of trade networks and their interaction with different fish resources, for fish provision. The model is developed based on a multi-methods approach, combining agent-based modeling, network analysis and qualitative data based on a small-scale fisheries study case. The model can be used to investigate both how trade network structures are embedded in a social-ecological context and the trade processes that occur within them, to analyze how they lead to emergent outcomes related to the resilience of fish provision. The model processes are informed by qualitative data analysis, and the social network analysis of an empirical fish trade network. The network analysis can be used to investigate diverse network structures to perform model experiments, and their influence on model outcomes.
The main outcomes we study are 1) the overexploitation of fish resources and 2) the availability and variability of fish provision to satisfy different market demands, and 3) individual traders’ fish supply at the micro-level. The model has two types of trader agents, seller and dealer. The model reveals that the characteristics of the trade networks, linked to different trader types (that have different roles in those networks), can affect the resilience of fish provision.
Migration or other long-distance movement into other regions is a common strategy of fishers and fishworkers living and working on the coast to adapt to environmental change. This model attempts to understand the general dynamics of fisher mobility for over larger spatial scales. The model can be used for investigating the complex interplay that exists between mobility and fish stock heterogeneity across regions, and the associated outcomes of mobility at the system level.
The model design informed by the example of small-scale fisheries in the Gulf of California, Mexico but implements theoretical and stylized facts and can as such be used for different archetypical cases. Our methodological approach for designing the model aims to account for the complex causation, emergence and interdependencies in small-scale fisheries to explain the phenomenon of sequential overexploitation, i.e., overexploiting one resource after another. The model is intended to be used as a virtual laboratory to investigate when and how different levels of mobile fishers affect exploitation patterns of fisheries resources.
FIBE represents a simple fishery model. Fish that reproduce and fisher with different fishing styles that fish as their main source of income. The aim of the model is to reflect the different fishing behaviours as described and observed in the (Swedish) Baltic Sea fishery and explore the consequences of different approximations of human/fisher behaviour in under different environmental and managerial scenarios.
The overarching aim is to advance the incorporation and understanding of human behaviour (diversity) in fisheries research and management. In particular focusing on insights from social (fishery) science of fisher behaviour.
A fisher directed management system was describeded by Hart (2021). It was proposed that fishers should only be allowed to exploit a resource if they collaborated in a resource management system for which they would own and be collectively responsible for. As part of the system fishers would need to follow the rules of exploitation set by the group and provide a central unit with data with which to monitor the fishery. Any fisher not following the rules would at first be fined but eventually expelled from the fishery if he/she continued to act selfishly. This version of the model establishes the dynamics of a fleet of vessels and controls overfishing by imposing fines on fishers whose income is low and who are tempted to keep fishing beyond the set quota which is established each year depending on the abundance of the fish stock. This version will later be elaborated to have interactions between the fishers including pressure to comply with the norms set by the group and which could lead to a stable management system.
This model simulates the mechanisms of evolution, or how allele frequencies change in a population over time.
The FishCensus model simulates underwater visual census methods, where a diver estimates the abundance of fish. A separate model is used to shape species behaviours and save them to a file that can be shared and used by the counting model.
This model is an implementation of a predator-prey simulation using NetLogo programming language. It simulates the interaction between fish, lionfish, and zooplankton. Fish and lionfish are both represented as turtles, and they have their own energy level. In this simulation, lionfish eat fish, and fish eat zooplankton. Zooplankton are represented as green patches on the NetLogo world. Lionfish and fish can reproduce and gain energy by eating other turtles or zooplankton.
This model was created to help undergraduate students understand how simulation models might be helpful in addressing complex environmental problems. In this case, students were asked to use this model to make predictions about how the introduction of lionfish (considered an invasive species in some places) might alter the ecosystem.
(An empty output folder named “NETLOGOexperiment” in the same location with the LAKEOBS_MIX.nlogo file is required before the model can be run properly)
The model is motivated by regime shifts (i.e. abrupt and persistent transition) revealed in the previous paleoecological study of Taibai Lake. The aim of this model is to improve a general understanding of the mechanism of emergent nonlinear shifts in complex systems. Prelimnary calibration and validation is done against survey data in MLYB lakes. Dynamic population changes of function groups can be simulated and observed on the Netlogo interface.
Main functional groups in lake ecosystems were modelled as super-individuals in a space where they interact with each other. They are phytoplankton, zooplankton, submerged macrophyte, planktivorous fish, herbivorous fish and piscivorous fish. The relationships between these functional groups include predation (e.g. zooplankton-phytoplankton), competition (phytoplankton-macrophyte) and protection (macrophyte-zooplankton). Each individual has properties in size, mass, energy, and age as physiological variables and reproduce or die according to predefined criteria. A system dynamic model was integrated to simulate external drivers.
Set biological and environmental parameters using the green sliders first. If the data of simulation are to be logged, set “Logdata” as true and input the name of the file you want the spreadsheet(.csv) to be called. You will need create an empty folder called “NETLOGOexperiment” in the same level and location with the LAKEOBS_MIX.nlogo file. Press “setup” to initialise the system and “go” to start life cycles.
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.
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