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.

This model was designed to study resilience in organizations. Inspired by ethnographic work, it follows the simple goal to understand whether team structure affects the way in which tasks are performed. In so doing, it compares the ‘hybrid’ data-inspired structure with three more traditional structures (i.e. hierarchy, flexible/relaxed hierarchy, and anarchy/disorganization).

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.

Due to the role of education in promoting social status and facilitating upward social mobility, individuals and their families spare no effort to pursue better educational opportunities, especially in countries where education is highly competitive.

In China, the enrollment of senior high schools and universities mainly follows a ranking system based on students’ scores in national entrance exams (Zhongkao and Gaokao). Typically, students with higher scores have priority in choosing schools and endeavor to get into better senior high schools to increase their chances of entering a prestigious university.

However, students can only select “better” senior high schools based on their average Gaokao grades, which are strongly influenced by the initial performance (Zhongkao grades) of enrolled students. The true quality indicator of school education (schooling effect, defined as the grade improvement achieved through education at the senior high school) is unknowable. This raises the first question: will school rankings reflect the real educational quality of schools over decades of educational competition, or merely the initial quality of the students they enroll?

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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.

Food trade networks represent a complex system where food is periodically produced in different regions of the world. Food is continuously stocked and traded. Food security in a globalised world is vulnerable to shocks. We present DARTS, a new agent based model that models monthly dynamics of food production, trade, stocking, consumption and food security for different interconnected world regions and a city state. Agents in different regions differ in their harvest seasons, wealth (rich and poor), degree of urbanisation and connection to domestic and global markets. DARTS was specifically designed to model direct and indirect effects of shocks in the food system. We introduce a new typology of 6 distinct shock types and analyse their impact on food security, modelling local and global effects and short term and longer term effects. An second important scientific novelty of the model is that DARTS can also model indirect effects of shocks (cascading in space and in time, lag effects due to trade and food stock buffering). A third important scientific novelty of the model is its’ capability of modelling food security at different scales, in which the rural/urban divide and differences in (intra-annually varying) production and trade connections play a key role. At the time of writing DARTS is yet insufficiently parameterised for accurate prediction for real world regions and cities. Simulations for a hypothetical in silico world with 3 regions and a city state show that DARTS can reproduce rich and complex dynamics with analogues in the real world. The scientific interest is more on deepening insight in process dynamics and chains of events that lead to ultimate shock effects on food security.

This model simulates different trade dynamics in shellmound (sambaqui) builder communities in coastal Southern Brazil. It features two simulation scenarios, one in which every site is the same and another one testing different rates of cooperation. The purpose of the model is to analyze the networks created by the trade dynamics and explore the different ways in which sambaqui communities were articulated in the past.

How it Works?
There are a few rules operating in this model. In either mode of simulation, each tick the agents will produce an amount of resources based on the suitability of the patches inside their occupation-radius, after that the procedures depend on the trade dynamic selected. For BRN? the agents will then repay their owed resources, update their reputation value and then trade again if they need to. For GRN? the agents will just trade with a connected agent if they need to. After that the agents will then consume a random amount of resources that they own and based on that they will grow (split) into a new site or be removed from the simulation. The simulation runs for 1000 ticks. Each patch correspond to a 300x300m square of land in the southern coast of Santa Catarina State in Brazil. Each agent represents a shellmound (sambaqui) builder community. The data for the world were made from a SRTM raster image (1 arc-second) in ArcMap. The sites can be exported into a shapefile (.shp) vector to display in ArcMap. It uses a UTM Sirgas 2000 22S projection system.

Least Cost Path (LCP) analysis is a recurrent theme in spatial archaeology. Based on a cost of movement image, the user can interpret how difficult it is to travel around in a landscape. This kind of analysis frequently uses GIS tools to assess different landscapes. This model incorporates some aspects of the LCP analysis based on GIS with the capabilities of agent-based modeling, such as the possibility to simulate random behavior when moving. In this model the agent will travel around the coastal landscape of Southern Brazil, assessing its path based on the different cost of travel through the patches. The agents represent shellmound builders (sambaquieiros), who will travel mainly through the use of canoes around the lagoons.

How it works?
When the simulation starts the hiker agent moves around the world, a representation of the lagoon landscape of the Santa Catarina state in Southern Brazil. The agent movement is based on the travel cost of each patch. This travel cost is taken from a cost surface raster created in ArcMap to represent the different cost of movement around the landscape. Each tick the agent will have a chance to select the best possible patch to move in its Field of View (FOV) that will take it towards its target destination. If it doesn’t select the best possible patch, it will randomly choose one of the patches to move in its FOV. The simulation stops when the hiker agent reaches the target destination. The elevation raster file and the cost surface map are based on a 1 Arc-second (30m) resolution SRTM image, scaled down 5 times. Each patch represents a square of 150m, with an area of 0,0225km². The dataset uses a UTM Sirgas 2000 22S projection system. There are four different cost functions available to use. They change the cost surface used by the hikers to navigate around the world.

The model is based on Swann and Buhrmester’s Identity Fusion behavioural theory, which seeks to explain why an individual puts the group’s priorities above their personal expectations. In order to observe the theory and validate group behaviour, a case study was carried out focusing on scenarios of group violence in football stadiums in Brazil. For the modelling, each agent has a distribution of levels of identification with the group to which they belong, with their level of fusion varying between 1 and 5. According to behavioural theory, an individual’s degree of fusion with the group directly interferes with their behaviour of replicating actions and absorbing group beliefs.

Transhumants move their herds based on strategies simultaneously considering several environmental and socio-economic factors. There is no agreement on the influence of each factor in these strategies. In addition, there is a discussion about the social aspect of transhumance and how to manage pastoral space. In this context, agent-based modeling can analyze herd movements according to the strategy based on factors favored by the transhumant. This article presents a reductionist agent-based model that simulates herd movements based on a single factor. Model simulations based on algorithms to formalize the behavioral dynamics of transhumants through their strategies. The model results establish that vegetation, water outlets and the socio-economic network of transhumants have a significant temporal impact on transhumance. Water outlets and the socio-economic network have a significant spatial impact. The significant impact of the socio-economic factor demonstrates the social dimension of Sahelian transhumance. Veterinarians and markets have an insignificant spatio-temporal impact. To manage pastoral space, water outlets should be at least 15 km
from each other. The construction of veterinary centers, markets and the securitization of transhumance should be carried out close to villages and rangelands.