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 examines how financial and social top-down interventions interplay with the internal self-organizing dynamics of a fishing community. The aim is to transform from hierarchical fishbuyer-fisher relationship into fishing cooperatives.
This model is a replication model which is constructed based on the existing model used by the following article:
Brown, D.G. and Robinson, D.T., 2006. Effects of heterogeneity in residential preferences on an agent-based model of urban sprawl. Ecology and society, 11(1).
The original model is called SLUCE’s Original Model for Experimentation (SOME). In Brown and Robinson (2006)’s article, the SOME model was used to explore the impacts of heterogeneity in residential location selections on the research of urban sprawl. The original model was constructed using Objective-C language based on SWARM platform. This replication model is built by NetLogo language on NetLogo platform. We successfully replicate that model and demonstrated the reliability and replicability of it.
This model simulates the form and function of an idealised estuary with associated barrier-spit complex on the north east coast of New Zealand’s North Island (from Bream Bay to central Bay of Plenty) during the years 2010 - 2050 CE. It combines variables from social, ecological and geomorphic systems to simulate potential directions of change in shallow coastal systems in response to external forcing from land use, climate, pollution, population density, demographics, values and beliefs. The estuary is over 1000Ha, making it a large estuary according to Hume et al. (2007) - there are 12 large estuaries in the Auckland region alone (Suyadi et al., 2019). The model was developed as part of Andrew Allison’s PhD Thesis in Geography from the School of Environment and Institute of Marine Science, University of Auckland, New Zealand. The model setup allows for alteration of geomorphic, ecological and social variables to suit the specific conditions found in various estuaries along the north east coast of New Zealand’s North Island.
This model is not a predictive or forecasting model. It is designed to investigate potential directions of change in complex shallow coastal systems. This model must not be used for any purpose other than as a heuristic to facilitate researcher and stakeholder learning and for developing system understanding (as per Allison et al., 2018).
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.
TunaFisher ABM simulates the decisions of fishing companies and fishing vessels of the Philippine tuna purse seinery operating in the Celebes and Sulu Seas.
High fishing effort remains in many of the world’s fisheries, including the Philippine tuna purse seinery, despite a variety of policies that have been implemented to reduce it. These policies have predominantly focused on models of cause and effect which ignore the possibility that the intended outcomes are altered by social behavior of autonomous agents at lower scales.
This model is a spatially explicit Agent-based Model (ABM) for the Philippine tuna purse seine fishery, specifically designed to include social behavior and to study its effects on fishing effort, fish stock and industry profit. The model includes economic and social factors of decision making by companies and fishing vessels that have been informed by interviews.
The SMASH model is an agent-based model of rural smallholder households. It models households’ evolving income and wealth, which they earn through crop sales. Wealth is carried in the form of livestock, which are grazed on an external rangeland (exogenous) and can be bought/sold as investment/coping mechanisms. The model includes a stylized representation of soil nutrient dynamics, modeling the inflows and outflows of organic and inorganic nitrogen from each household’s field.
The model has been applied to assess the resilience-enhancing effects of two different farm-level adaptation strategies: legume cover cropping and crop insurance. These two strategies interact with the model through different mechanims - legume cover cropping through ecological mechanisms and crop insurance through financial mechanisms. The model can be used to investigate the short- and long-term effects of these strategies, as well as how they may differently benefit different types of household.
The Bronze Age Collapse model (BACO model) is written using free NetLogo software v.6.0.3. The purpose of using the BACO model is to develop a tool to identify and analyse the main factors that made the Late Bronze Age and Early Iron Age socio-ecological system resilient or vulnerable in the face of the environmental aridity recorded in the Aegean. The model explores the relationship between dependent and independent variables. Independent variables are: a) inter-annual rainfall variability for the Late Bronze Age and Early Iron Age in the eastern Mediterranean, b) intensity of raiding, c) percentage of marine, agricultural and other calorie sources included in the diet, d) soil erosion processes, e) farming assets, and d) storage capacity. Dependent variables are: a) human pressure for land, b) settlement patterns, c) number of commercial exchanges, d) demographic behaviour, and e) number of migrations.
B3GET simulates populations of virtual organisms evolving over generations, whose evolutionary outcomes reflect the selection pressures of their environment. The model simulates several factors considered important in biology, including life history trade-offs, investment in fighting ability and aggression, sperm competition, infanticide, and competition over access to food and mates. Downloaded materials include a starting genotype and population files. Edit the these files and see what changes occur in the behavior of virtual populations!
This model simulates the mechanisms of evolution, or how allele frequencies change in a population over time.
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.