This paper presents an agent-based model to study the dynamics of city-state systems in a constrained environment with limited space and resources. The model comprises three types of agents: city-states, villages, and battalions, where city-states, the primary decision-makers, can build villages for food production and recruit battalions for defense and aggression. In this setting, simulation results, generated through a multi-parameter grid sampling, suggest that risk-seeking strategies are more effective in high-cost scenarios, provided that the production rate is sufficiently high. Also, the model highlights the role of output productivity in defining which strategic preferences are successful in a long-term scenario, with higher outputs supporting more aggressive expansion and military actions, while resource limitations compel more conservative strategies focused on survival and resource conservation. Finally, the results suggest the existence of a non-linear effect of diminishing returns in strategic investments on successful strategies, emphasizing the need for careful resource allocation in a competitive environment.

This is the same model as used in the article ‘Modelling Society’s Evolutionary Forces’ except the Fertility graph has been corrected. The Fertility graph was not used in the published article.

Our model shows how disinformation spreads on a random network of individuals. The network is weighted and directed. We are looking at how different factors affect how fast, or how many people get “infected” with the misinformation. One of the main factors that we were curious about was perceived trustworthiness. This is because we want to see if people of power, or a high degree of perceived trustworthiness, were able to push misinformation to more people and convert more people to believe the information.

This model is a more comprehensive version of the original model; descriptions and expanations are added

Simple population dynamics model used in Introduction to Agent-Based Modeling by Marco Janssen. For more information see https://intro2abm.com/

This model is an application of Brantingham’s neutral model to a real landscape with real locations of potential sources. The sources are represented as their sizes during current conditions, and from marine geophysics surveys, and the agent starts at a random location in Mossel Bay Region (MBR) surrounding the Archaeological Pinnacle Point (PP) locality, Western Cape, South Africa. The agent moves at random on the landscape, picks up and discards raw materials based only upon space in toolkit and probability of discard. If the agent happens to encounter the PP locality while moving at random the agent may discard raw materials at it based on the discard probability.

Agents can influence each other if they are close enough in knowledge. The probability to convince with good knowledge and number of agents have an impact on the dissemination of knowledge.

This is a model intended to demonstrate the function of scramble crossings and a more efficient flow of pedestrian traffic with the presence of diagonal crosswalks.

This is a very simple foraging model used to illustrate the features of Netlogo’s Profiler extension.

3 simple models to illustrate diffusion of innovations.
The models are discussed in Introduction to Agent-Based Modeling by Marco Janssen. For more information see https://intro2abm.com/