This project is an interactive agent-based model simulating consumption of a shared, renewable resource using a game-theoretic framework with environmental feedback. The primary function of this model was to test how resource-use among AI and human agents degrades the environment, and to explore the socio-environmental feedback loops that lead to complex emergent system dynamics. We implemented a classic game theoretic matrix which decides agents´ strategies, and added a feedback loop which switches between strategies in pristine vs degraded environments. This leads to cooperation in bad environments, and defection in good ones.

Despite this use, it can be applicable for a variety of other scenarios including simulating climate disasters, environmental sensitivity to resource consumption, or influence of environmental degradation to agent behaviour.
The ABM was inspired by the Weitz et. al. (2016, https://pubmed.ncbi.nlm.nih.gov/27830651/) use of environmental feedback in their paper, as well as the Demographic Prisoner’s Dilemma on a Grid model (https://mesa.readthedocs.io/stable/examples/advanced/pd_grid.html#demographic-prisoner-s-dilemma-on-a-grid). The main innovation is the added environmental feedback with local resource replenishment.

Beyond its theoretical insights into coevolutionary dynamics, it serves as a versatile tool with several practical applications. For urban planners and policymakers, the model can function as a ”digital sandbox” for testing the impacts of locating high-consumption industrial agents, such as data centers, in proximity to residential communities. It allows for the exploration of different urban densities, and the evaluation of policy interventions—such as taxes on defection or subsidies for cooperation—by directly modifying the agents’ resource consumptions to observe effects on resource health. Furthermore, the model provides a framework for assessing the resilience of such socio-environmental systems to external shocks.
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LogoClim is a NetLogo model designed to be integrated into other simulations through the LevelSpace extension (Hjorth et al., 2020), providing high resolution climate data from sources validated and used by the Intergovernmental Panel on Climate Change (IPCC).

The model simplifies and standardizes the integration of climate data into NetLogo, allowing researchers to focus their efforts on the model itself with the assurance of using reliable and widely recognized data. Although its main use is as a component of larger simulations, LogoClim also has its own graphical interface for monitoring and checking the datasets.

The climate data comes from the WorldClim 2.1 project (Fick & Hijmans, 2017), for which LogoClim works as an interface to NetLogo. The model supports all three WorldClim data series: (1) Historical Climate Data (1970 to 2000), with 12 monthly points for minimum, mean, and maximum temperature, precipitation, solar radiation, wind speed, vapor pressure, elevation, and bioclimatic variables; (2) Historical Monthly Weather Data (1951 to 2024), based on downscaling of CRU-TS-4.09, developed by the Climatic Research Unit at the University of East Anglia (Harris et al., 2020), with minimum and maximum temperature and total precipitation; and (3) Future Climate Data, based on downscaling climate projections derived from global climate models of the Coupled Model Intercomparison Project Phase 6 (CMIP6) (Eyring et al., 2016) for four future periods (2021 to 2040, 2041 to 2060, 2061 to 2080, and 2081 to 2100) and four scenarios based on the Shared Socioeconomic Pathways (SSPs 126, 245, 370, and 585), covering minimum and maximum temperature, total precipitation, and bioclimatic variables. All series are available at multiple spatial resolutions, from 10 minutes (about 340 km² at the equator) to 30 seconds (about 1 km² at the equator).

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This agent-based model (ABM), developed in NetLogo and available on the COMSES repository, simulates a stylized, competitive electricity market to explore the effects of carbon pricing policies under conditions of technological innovation. Unlike traditional models that treat innovation as exogenous, this ABM incorporates endogenous innovation dynamics, allowing clean technology costs to evolve based on cumulative deployment (Wright’s Law) or time (Moore’s Law). Electricity generation companies act as agents, making investment decisions across coal, gas, wind, and solar PV technologies based on expected returns and market conditions. The model evaluates three policy scenarios—No Policy, Emissions Trading System (ETS), and Carbon Tax—within a merit-order market framework. It is partially empirically grounded, using real-world data for technology costs and emissions caps. By capturing emergent system behavior, this model offers a flexible and transparent tool for analyzing the transition to low-carbon electricity systems.

ABM model studying impact of social cohesion on wellbeing of a society. Ibn Khaldun’s cyclical theory of history is being used as the theoretical lens along with some other theories. Social cohesion is measured as TSC = (TVE + 2 * (TPI * TPL - TNI * TNL))/((TPI+TNI))
Where
TSC total-social-cohesion ; Variable for social cohesion
TPI total-positive-interactions ; Count of positive interactions
TNI total-negative-interactions ; Count of negative interactions
TPL total-positive-learning ; Count of positive learning outcomes
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This model examines language dynamics within a social network using simulation techniques to represent the interplay of language adoption, social influence, economic incentives, and language policies. The agent-based model (ABM) focuses on interactions between agents endowed with specific linguistic attributes, who engage in communication based on predefined rules. A key feature of our model is the incorporation of network analysis, structuring agent relationships as a dynamic network and leveraging network metrics to capture the evolving inter-agent connections over time. This integrative approach provides nuanced insights into emergent behaviors and system dynamics, offering an analytical framework that extends beyond traditional modeling approaches. By combining agent-based modeling with network analysis, the model sheds light on the underlying mechanisms governing complex language systems and can be effectively paired with sociolinguistic observational data.

This NetLogo model simulates how coral reefs around the islands of Palau would develop under different emission scenarios and with selected adaptation strategies. Reef health is indicated by coral cover (%) and is affected by four major climate change impacts: increasing sea surface temperature, sea level rise, ocean acidification, and more intense typhoons. The model differentiates between inner and outer reefs, with the former naturally adapted to warmer, more acidic waters. The simulation includes bleaching events and possible recovery. In addition, the user can choose between different coral transplantation strategies as well as regulate natural thermal adaptation rates.

Overview

The Weather model is a procedural generation model designed to create realistic daily weather data for socioecological simulations. It generates synthetic weather time series for solar radiation, temperature, and precipitation using algorithms based on sinusoidal and double logistic functions. The model incorporates stochastic variation to mimic unpredictable weather patterns and aims to provide realistic yet flexible weather inputs for exploring diverse climate scenarios.

The Weather model can be used independently or integrated into larger models, providing realistic weather patterns without extensive coding or data collection. It can be customized to meet specific requirements, enabling users to gain a better understanding of the underlying mechanisms and have greater confidence in their applications.

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This model simulates household water consumption patterns in an urban environment. Its current setup compares monthly water consumption data, and the results of a daily heuristic water demand model with the simulation results produced by household demographics that is fine tuned via some base demand model. It’s designed to estimate and analyze water demand based on various factors including household demographics, daily routines of residents (working, weekending, vacation patterns), weather conditions (temperature and precipitation), appliance usage patterns, seasonal variations, and special periods such as weekends and holidays. The model aims to help understand how different factors influence residential water consumption and can be used for water demand forecasting and management.

Grasslands have a large share of the world’s land cover and their sustainable management is important for the protection and provisioning of grassland ecosystem services. The question of how to manage grassland sustainably is becoming increasingly important, especially in view of climate change, which on the one hand extends the vegetation period (and thus potentially allows use intensification) and on the other hand causes yield losses due to droughts. Fertilization plays an important role in grassland management and decisions are usually made at farm level. Data on fertilizer application rates are crucial for an accurate assessment of the effects of grassland management on ecosystem services. However, these are generally not available on farm/field scale. To close this gap, we present an agent-based model for Fertilization In Grasslands (FertIG). Based on animal, land-use, and cutting data, the model estimates grassland yields and calculates field-specific amounts of applied organic and mineral nitrogen on grassland (and partly cropland). Furthermore, the model considers different legal requirements (including fertilization ordinances) and nutrient trade among farms. FertIG was applied to a grassland-dominated region in Bavaria, Germany comparing the effects of changes in the fertilization ordinance as well as nutrient trade. The results show that the consideration of nutrient trade improves organic fertilizer distribution and leads to slightly lower Nmin applications. On a regional scale, recent legal changes (fertilization ordinance) had limited impacts. Limiting the maximum applicable amount of Norg to 170 kg N/ha fertilized area instead of farm area as of 2020 hardly changed fertilizer application rates. No longer considering application losses in the calculation of fertilizer requirements had the strongest effects, leading to lower supplementary Nmin applications. The model can be applied to other regions in Germany and, with respective adjustments, in Europe. Generally, it allows comparing the effects of policy changes on fertilization management at regional, farm and field scale.

The code and data in this repository are associated with the article titled:

Leal, Diego F. 2025. “Locating Cultural Holes Brokers in Diffusion Dynamics across Bright Symbolic Boundaries.” Sociological Methods & Research OnlineFirst https://doi.org/10.1177/00491241251322517

The NetLogo code (version 6.4.0) is designed to be a standalone piece of code although it uses the ‘nw’ and ‘matrix’ extensions that come integrated with NetLogo 6.4.0. The code was ran on a Windows 10 x 64 machine.