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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We provide a theory-grounded, socio-geographic agent-based model to present a possible explanation for human movement in the Adriatic region within the Cetina phenomenon.

Focusing on ideas of social capital theory from Piere Bordieu (1986), we implement agent mobility in an abstract geography based on cultural capital (prestige) and social capital (social position). Agents hold myopic representations of social (Schaff, 2016) and geographical networks and decide in a heuristic way on moving (and where) or staying.

The model is implemented in a fork of the Laboratory for Simulation Development (LSD), appended with GIS capabilities (Pereira et. al. 2020).

AGENTS model is an agent-based computational framework designed to explore the socio-ecological and economic dynamics of agricultural production in the Byzantine Negev Highlands, with a focus on viticulture. It integrates historical, environmental, and social factors to simulate settlement sustainability, crop yields, and the impacts of varying climate conditions. The model is built in NetLogo and incorporates GIS-based topographical and hydrological data. Key features include the ability to assess climate impacts on crop profitability and settlement strategies, evaluate economic outputs of ancient vineyards, and simulate agent decision-making processes under diverse scenarios.

The AGENTS model is highly flexible, enabling users to simulate agricultural regimes with any two crops: one cash crop (a crop grown for profit, e.g., grapevines) and one staple crop (a crop grown for subsistence, e.g., wheat). While the default setup models viticulture and wheat cultivation in the Byzantine Negev Highlands, users can adapt the model to different environmental and socio-ecological contexts worldwide—both past and present.

Users can load external files to customize precipitation, evaporation, topography, and labor costs (measured as man-days per 0.1ha, converted to kg of wheat per model patch size area), and can also edit key parameters related to yield calculations. This includes modifying crop-specific yield formulas, soil and runoff indices, and any factors influencing crop performance. The model inherently simulates cash crops grown in floodplain regions and staple crops cultivated along riverbanks, providing a powerful tool to investigate societal resilience and responses to climate stressors across diverse environments.

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A flexible framework for Agent-Based Models (ABM), the ‘epiworldR’ package provides methods for prototyping disease outbreaks and transmission models using a ‘C++’ backend, making it very fast. It supports multiple epidemiological models, including the Susceptible-Infected-Susceptible (SIS), Susceptible-Infected-Removed (SIR), Susceptible-Exposed-Infected-Removed (SEIR), and others, involving arbitrary mitigation policies and multiple-disease models. Users can specify infectiousness/susceptibility rates as a function of agents’ features, providing great complexity for the model dynamics. Furthermore, ‘epiworldR’ is ideal for simulation studies featuring large populations.

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.

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.

ViSA 2.0.0 is an updated version of ViSA 1.0.0 aiming at integrating empirical data of a new use case that is much smaller than in the first version to include field scale analysis. Further, the code of the model is simplified to make the model easier and faster. Some features from the previous version have been removed.
It simulates decision behaviors of different stakeholders showing demands for ecosystem services (ESS) in agricultural landscape. It investigates conditions and scenarios that can increase the supply of ecosystem services while keeping the viability of the social system by suggesting different mixes of initial unit utilities and decision rules.

The ABM model is designed to model the adaptability of farmers in DTIM. This model includes two groups of farmers and local government admins agents. Farmers with different levels, with low WP of DTIM, are looking for economic benefits and reduced irrigation and production costs. Meanwhile, the government is looking for strategic goals to maintain water resources’ sustainability. The local government admins employ incentives (subsidies in this study) to encourage farmers to DTIM. In addition, it is used as a tool for supervision and training farmers’ performance. Farmers are currently harvesting water resources with irrigation systems and different levels of technology, and they intend to provide short-term benefits. Farmers adjust the existing approach based on their knowledge of the importance of DTIM and propensity to increase WP and cost-benefit evaluation. DTIM has an initial implementation fee. Every farmer can increase WP by using government subsidies. If none of the farmers create optimal use of water resources, access to water resources will be threatened in the long term. This is considered a hypothetical cost for farmers who do not participate in DTIM. With DTIM, considering that local government admins’ facilities cover an essential part of implementation costs, farmers may think of profiting from local government admins’ facilities by selling that equipment, especially if the farmers in the following conditions may consider selling their developed irrigation equipment. In this case, the technology of their irrigation system will return to the state before development.
- When the threshold of farmers’ propensity to DTIM is low (for example, in the conditions of scarcity of access to sufficient training about the new irrigation system or its role in reducing the cost and sustainability of water resources)
- When the share of government subsidy is high, and as a result, the profit from the sale of equipment is attractive, especially in conditions of inflation.
- Finally, farmers’ honesty threshold should be reduced based on the positive experience of profit-seeking and deception among neighbors.
Increasing the share of government subsidies can encourage farmers to earn profits. Therefore, the government can help increase farmers’ profits by considering the assessment teams at different levels with DTIM training . local government admins evaluations monitor the behavior of farmers. If farmers sell their improved irrigation system for profit, they may be deprived of some local government admins’ services and the possibility of receiving subsidies again. Assessments The local government admins can increase farmers’ honesty. Next, the ABM model evaluates local government admins policies to achieve a suitable framework for water resources management in the Miandoab region.

This model simulates the opinion dynamics of COVID-19 vaccination to examine especially how fears and cognitive bias contribute to the opinion polarisation and vaccination rate. In studying the opinion dynamics of COVID-19 vaccination, this model refers to the HUMAT framework (Antosz et al, 2019). Many psychological and social processes are included in the model, such as dynamical decision-making processes of information exchange and fear formation, satisfaction evaluation, preferred decision selection and dissonance reduction.