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.
Displaying 10 of 123 results for "Joshua Alland" clear search
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.
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.
Three policy scenarios for urban expansion under the influences of the behaviours and decision modes of four agents and their interactions have been applied to predict the future development patterns of the Guangzhou metropolitan region.
Model explains both the final state and the dynamics of the development process of the wine sector in the Małopolska region in Poland. Model admits heterogeneous agents (regular farms,large and small vineyards).
The objective of the model is to evaluate the impact of seasonal forecasts on a farmer’s net agricultural income when their crop choices have different and variable costs and returns.
This is a simplified version of a Complex Model of Voter Turnout by Edmonds et al.(2014). It was developed to better understand the mechanisms at play on that complex model.
MayaSim is an agent-based, cellular automata and network model of the ancient Maya. Biophysical and anthropogenic processes interact to grow a complex social ecological system.
We represent commuters and their preferences for transportation cost, time and safety. Agents assess their options via their preferences, their environment, and the modes available. The model has policy levers to test impact on last-mile problem.
RaMDry allows to study the dynamic use of forage ressources by herbivores in semi-arid savanna with an emphasis on effects of change of climate and management. Seasonal dynamics affects the amount and the nutritional values of the available forage.
The purpose of this spatially-explicit agent-based model is to intervene in the debate about PES policy design, implementation and context. We use the case for a woodland-for-water payment for ecosystem services (PES) and model its implementation in a local area of Catalonia (NE Spain). The model is based on three sub-models. The structural contains four different designs of a PES policy. The social sub-model includes agent-based factors, by having four types of landowner categories managing or not the forests. This sub-model is based on behavioral studies and assumptions about reception and reaction to incentive policies from European-focused studies. The ecological sub-model is based on climate change data for the area. The output are the evolution of the ecological and social goals of the policy under different policy design scenarios. Our focus in Europe surges from the general context of land abandonment that many Mediterranean areas and Eastern countries are experiencing, and the growing interest from policy-makers and practitioners on the implementation of PES schemes to ameliorate this situation.
Displaying 10 of 123 results for "Joshua Alland" clear search