I model a forest and a community of loggers. Agents follow different kinds of rules in order to log. I compare the impact of endogenous and of exogenous institutions on the state of the forest and on the profit of the users, representing different scenarios of participatory conservation projects.

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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Studies of colonization processes in past human societies often use a standard population model in which population is represented as a single quantity. Real populations in these processes, however, are structured with internal classes or stages, and classes are sometimes created based on social differentiation. In this present work, information about the colonization of old Providence Island was used to create an agent-based model of the colonization process in a heterogeneous environment for a population with social differentiation. Agents were socially divided into two classes and modeled with dissimilar spatial clustering preferences. The model and simulations assessed the importance of gregarious behavior for colonization processes conducted in heterogeneous environments by socially-differentiated populations. Results suggest that in these conditions, the colonization process starts with an agent cluster in the largest and most suitable area. The spatial distribution of agents maintained a tendency toward randomness as simulation time increased, even when gregariousness values increased. The most conspicuous effects in agent clustering were produced by the initial conditions and behavioral adaptations that increased the agent capacity to access more resources and the likelihood of gregariousness. The approach presented here could be used to analyze past human colonization events or support long-term conceptual design of future human colonization processes with small social formations into unfamiliar and uninhabited environments.

Country-by-Country Reporting and Automatic Exchange of Information have recently been implemented in European Union (EU) countries. These international tax reforms increase tax compliance in the short term. In the long run, however, taxpayers will continue looking abroad to avoid taxation and, countries, looking for additional revenues, will provide opportunities. As a result, tax competition intensifies and the initial increase in compliance could reverse. To avoid international tax reforms being counteracted by tax competition, this paper suggests bilateral responsive regulation to maximize compliance. This implies that countries would use different tax policy instruments toward other countries, including tax and secrecy havens.

To assess the effectiveness of fully or partially enforce tax policies, this agent based model has been ran many times under different enforcement rules, which influence the perceived enforced- and voluntary compliance, as the slippery-slope model prescribes. Based on the dynamics of this perception and the extent to which agents influence each other, the annual amounts of tax evasion, tax avoidance and taxes paid are calculated over longer periods of time.

The agent-based simulation finds that a differentiated policy response could increase tax compliance by 6.54 percent, which translates into an annual increase of €105 billion in EU tax revenues on income, profits, and capital gains. Corporate income tax revenues in France, Spain, and the UK alone would already account for €35 billion.

RAGE models a stylized common property grazing system. Agents follow a certain behavioral type. The model allows analyzing how household behavior with respect to a social norm on pasture resting affects long-term social-ecological system dynamics.

The simulation is a variant of the “ToRealSim OD variants - base v2.7” base model, which is based on the standard DW opinion dynamics model (but with the differences that rather than one agent per tick randomly influencing another, all agents randomly influence one other per tick - this seems to make no difference to the outcomes other than to scale simulation time). Influence can be made one-way by turning off the two-way? switch

Various additional variations and sources of noise are possible to test robustness of outcomes to these (compared to DW model).
In this version agent opinions change following the empirical data collected in some experiments (Takács et al 2016).

Such an algorithm leaves no role for the uncertainties in other OD models. [Indeed the data from (Takács et al 2016) indicates that there can be influence even when opinion differences are large - which violates a core assumption of these]. However to allow better comparison with other such models there is a with-un? switch which allows uncertainties to come into play. If this is on, then influence (according to above algorithm) is only calculated if the opinion difference is less than the uncertainty. If an agent is influenced uncertainties are modified in the same way as standard DW models.

Our aim is to demonstrate how conversational AI systems, exemplified by ChatGPT, can support the conceptualisation of Agent-Based Social Simulation (ABSS) models, leading to a full ABSS model design document. Through advanced prompt engineering and adherence to the Engineering ABSS framework (Siebers and Klügl 2017), we have constructed a comprehensive script that is easy to use and that supports the design of ABSS models with or even by AI. The performance of the script is demonstrated through an illustrative case study related to the use of adaptive architecture in museums. The repository contains (1) the comprehensive script in a format that allows copying and pasting prompts for use with ChatGPT, (2) the results of the illustrative case study in the form of two conceptual ABSS models, the ground truth and the autogenerated version.

The TechNet_04 is an abstract model that embeds a simple cultural tranmission process in an environment where interaction is structured by spatially-situated networks.

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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We develop an agent-based model to explore the effect of perceived intergroup conflict escalation on the number of extremists. The proposed model builds on the 2D bounded confidence model proposed by Huet et al (2008).