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.

Large outbreaks of Shigella sonnei among children in Haredi Jewish (ultra-Orthodox) communities in Brooklyn, New York have occurred every 3–5 years since at least the mid-1980s. These outbreaks are partially attributable to large numbers of young children in these communities, with transmission highest in child care and school settings, and secondary transmission within households. As these outbreaks have been prolonged and difficult to control, we developed an agent-based model of shigellosis transmission among children in these communities to support New York City Department of Health and Mental Hygiene staff. Simulated children were assigned an initial susceptible, infectious, or recovered (immune) status and interacted and moved between their home, child care program or school, and a community site. We calibrated the model according to observed case counts as reported to the Health Department. Our goal was to better understand the efficacy of existing interventions and whether limited outreach resources could be focused more effectively.

Identifying how organisms respond to environmental stressors remains of central importance as human impacts continue to shift the environmental conditions for countless species. Some mammals are able to mitigate these environmental stressors at the cellular level, but the mechanisms by which cells are able to do this and how these strategies vary among species is not well understood. At the cellular level, it is difficult to identify the temporal dynamics of the system through empirical data because fine-grained time course samples are both incomplete and limited by available resources. To help identify the mechanisms by which animal cells mitigate extreme environmental conditions, we propose an agent-based model to capture the dynamics of the system. In the model, agents are regulatory elements and genes, and are able to impact the behaviors of each other. Rather than imposing rules for these interactions among agents, we will begin with randomized sets of rules and calibrate the model based on empirical data of cellular responses to stress. We will apply a common-garden framework to cultured cells from 16 mammalian species, which will yield genomic data and measures of cell morphology and physiology when exposed to different levels of temperature, glucose, and oxygen. These species include humans, dolphins, bats, and camels, among others, which vary in how they respond to environmental stressors, offering a comparative approach for identifying mechanistic rules whereby cells achieve robustness to environmental stressors. For calibration of the model, we will iteratively select for rules that best lead to the emergent outcomes observed in the cellular assays. Our model is generalized for any species, any cell type, and any environmental stressor, offering many applications of the model beyond our study. This study will increase our understanding of how organisms mitigate environmental stressors at the cellular level such that we can better address how organisms are impacted by and respond to extreme environmental conditions.

Viable North Sea (ViNoS) is an Agent-based Model of the German North Sea Small-scale Fisheries in a Social-Ecological Systems framework focussing on the adaptive behaviour of fishers facing regulatory, economic, and resource changes. Small-scale fisheries are an important part both of the cultural perception of the German North Sea coast and of its fishing industry. These fisheries are typically family-run operations that use smaller boats and traditional fishing methods to catch a variety of bottom-dwelling species, including plaice, sole, and brown shrimp. Fisheries in the North Sea face area competition with other uses of the sea – long practiced ones like shipping, gas exploration and sand extractions, and currently increasing ones like marine protection and offshore wind farming. German authorities have just released a new maritime spatial plan implementing the need for 30% of protection areas demanded by the United Nations High Seas Treaty and aiming at up to 70 GW of offshore wind power generation by 2045. Fisheries in the North Sea also have to adjust to the northward migration of their established resources following the climate heating of the water. And they have to re-evaluate their economic balance by figuring in the foreseeable rise in oil price and the need for re-investing into their aged fleet.

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.

According to the philosopher of science K. Popper “All life is problem solving”. Genetic algorithms aim to leverage Darwinian selection, a fundamental mechanism of biological evolution, so as to tackle various engineering challenges.
Flibs’NFarol is an Agent Based Model that embodies a genetic algorithm applied to the inherently ill-defined “El Farol Bar” problem. Within this context, a group of agents operates under bounded rationality conditions, giving rise to processes of self-organization involving, in the first place, efficiency in the exploitation of available resources. Over time, the attention of scholars has shifted to equity in resource distribution, as well. Nowadays, the problem is recognized as paradigmatic within studies of complex evolutionary systems.
Flibs’NFarol provides a platform to explore and evaluate factors influencing self-organized efficiency and fairness. The model represents agents as finite automata, known as “flibs,” and offers flexibility in modifying the number of internal flibs states, which directly affects their behaviour patterns and, ultimately, the diversity within populations and the complexity of the system.

This is a generic sub-model of animal territory formation. It is meant to be a reusable building block, but not in the plug-and-play sense, as amendments are likely to be needed depending on the species and region. The sub-model comprises a grid of cells, reprenting the landscape. Each cell has a “quality” value, which quantifies the amount of resources provided for a territory owner, for example a tiger. “Quality” could be prey density, shelter, or just space. Animals are located randomly in the landscape and add grid cells to their intial cell until the sum of the quality of all their cells meets their needs. If a potential new cell to be added is owned by another animal, competition takes place. The quality values are static, and the model does not include demography, i.e. mortality, mating, reproduction. Also, movement within a territory is not represented.

What policy measures are effective in redistributing essential resources during crisis situations such as climate change impacts? We model a collective action institution with different rules for designing and organizing it, and make our analysis specific to various societal contexts.

Our model captures a generic societal context of unequal vulnerability and climate change impact in a stylized form. We represent a community of people who harvest and consume an essential resource to maintain their well-being. However, their ability to harvest the resource is not equal; people are characterized by a ‘resource access’ attribute whose values are uniformly distributed from 0 to 1 in the population. A person’s resource access value determines the amount of resource units they are able to harvest, and therefore the welfare levels they are able to attain. People travel to the centralized resource region and derive well-being or welfare, represented as an energy gain, by harvesting and consuming resource units.

The community is subject to a climate change impact event that occurs with a certain periodicity and over a certain duration. The capacity of resource units to regenerate diminishes during the impact events. Unequal capacities to access the essential resource results in unequal vulnerability among people with regards to their ability to maintain a sufficient welfare level, especially during impact events.

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This is a replication of the SequiaBasalto model, originally built in Cormas by Dieguez Cameroni et al. (2012, 2014, Bommel et al. 2014 and Morales et al. 2015). The model aimed to test various adaptations of livestock producers to the drought phenomenon provoked by climate change. For that purpose, it simulates the behavior of one livestock farm in the Basaltic Region of Uruguay. The model incorporates the price of livestock, fodder and paddocks, as well as the growth of grass as a function of climate and seasons (environmental submodel), the life cycle of animals feeding on the pasture (livestock submodel), and the different strategies used by farmers to manage their livestock (management submodel). The purpose of the model is to analyze to what degree the common management practices used by farmers (i.e., proactive and reactive) to cope with seasonal and interannual climate variations allow to maintain a sustainable livestock production without depleting the natural resources (i.e., pasture). Here, we replicate the environmental and livestock submodel using NetLogo.

One year is 368 days. Seasons change every 92 days. Each day begins with the growth of grass as a function of climate and season. This is followed by updating the live weight of cows according to the grass height of their patch, and grass consumption, which is determined based on the updated live weight. After consumption, cows grow and reproduce, and a new grass height is calculated. Cows then move to the patch with less cows and with the highest grass height. This updated grass height value will be the initial grass height for the next day.

A replication in Netlogo 5.2 of the classic model, Sugarscape (Epstein & Axtell, 1996).