WeDiG Sim- Weighted Directed Graph Simulator - is an open source application that serves to simulate complex systems. WeDiG Sim reflects the behaviors of those complex systems that put stress on scale-free, weightedness, and directedness. It has been implemented based on “WeDiG model” that is newly presented in this domain. The WeDiG model can be seen as a generalized version of “Barabási-Albert (BA) model”. WeDiG not only deals with weighed directed systems, but also it can handle the […]

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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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 model accompanies a paper looking at the role and limits of values and norms for modeling realistic social agents. Based on literature we synthesize a theory on norms and a theory that combines both values and norms. In contrast to previous work, these theories are checked against data on human behavior obtained from a psychological experiment on dividing money: the ultimatum game. We found that agents that act according to a theory that combines both values and norms, produce behavior quite similar to that of humans. Furthermore, we found that this theory is more realistic than theories solely concerned with norms or theories solely concerned with values. However, to explain the amount of money people accept in this ultimatum game we will eventually need an even more realistic theory. We propose that a theory that explains when people exactly choose to use norms instead of values could provide this realism.

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.

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.

An ABM to simulate the behaviour of households within a village and observe the emerging properties of the system in terms of food security. The model quantifies food availability, access, utilisation and stability.

The current rate of production and consumption of meat poses a problem both to peoples’ health and to the environment. This work aims to develop a simulation of peoples’ meat consumption behaviour in Britain using agent-based modelling. The agents represent individual consumers. The key variables that characterise agents include sex, age, monthly income, perception of the living cost, and concerns about the impact of meat on the environment, health, and animal welfare. A process of peer influence is modelled with respect to the agents’ concerns. Influence spreads across two eating networks (i.e. co-workers and household members) depending on the time of day, day of the week, and agents’ employment status. Data from a representative sample of British consumers is used to empirically ground the model. Different experiments are run simulating interventions of application of social marketing campaigns and a rise in price of meat. The main outcome is the average weekly consumption of meat per consumer. A secondary outcome is the likelihood of eating meat.

While the world’s total urban population continues to grow, this growth is not equal. Some cities are declining, resulting in urban shrinkage which is now a global phenomenon. Many problems emerge due to urban shrinkage including population loss, economic depression, vacant properties and the contraction of housing markets. To explore this issue, this paper presents an agent-based model stylized on spatially explicit data of Detroit Tri-county area, an area witnessing urban shrinkage. Specifically, the model examines how micro-level housing trades impact urban shrinkage by capturing interactions between sellers and buyers within different sub-housing markets. The stylized model results highlight not only how we can simulate housing transactions but the aggregate market conditions relating to urban shrinkage (i.e., the contraction of housing markets). To this end, the paper demonstrates the potential of simulation to explore urban shrinkage and potentially offers a means to test polices to alleviate this issue.

Resilience of humans in the Upper Paleolithic could provide insights in how to defend against today’s environmental threats. Approximately 13,000 years ago, the Laacher See volcano located in present-day western Germany erupted cataclysmically. Archaeological evidence suggests that this is eruption – potentially against the background of a prolonged cold spell – led to considerable culture change, especially at some distance from the eruption (Riede, 2017). Spatially differentiated and ecologically mediated effects on contemporary social networks as well as social transmission effects mediated by demographic changes in the eruption’s wake have been proposed as factors that together may have led to, in particular, the loss of complex technologies such as the bow-and-arrow (Riede, 2014; Riede, 2009).

This model looks at the impact of the interaction between climate change trajectory and an extreme event, such as the Laacher See eruption, on the generational development of hunter-gatherer bands. Historic data is used to model the distribution and population dynamics of hunter-gatherer bands during these circumstances.