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.

The purpose of the model is to collect information on human decision-making in the context of coalition formation games. The model uses a human-in-the-loop approach, and a single human is involved in each trial. All other agents are controlled by the ABMSCORE algorithm (Vernon-Bido and Collins 2020), which is an extension of the algorithm created by Collins and Frydenlund (2018). The glove game, a standard cooperative game, is used as the model scenario.

The intent of the game is to collection information on the human players behavior and how that compares to the computerized agents behavior. The final coalition structure of the game is compared to an ideal output (the core of the games).

MHCABM is an agent-based, multi-hazard risk interaction model with an integrated applied dynamic adaptive pathways planning component. It is designed to explore the impacts of climate change adaptation decisions on the form and function of a coastal human-environment system, using as a case study an idealised patch based representation of the Mount North-Omanu area of Tauranga city, New Zealand. The interacting hazards represented are erosion, inundation, groundwater intrusion driven by intermittent heavy rainfall / inundations (storm) impacts, and sea level rise.

Style_Net_01 is a spatial agent-based model designed to serve as a platform for exploring geographic patterns of tool transport and discard among seasonally mobile hunter-gatherer populations. The model has four main levels: artifact, person, group, and system. Persons make, use, and discard artifacts. Persons travel in groups within the geographic space of the model. The movements of groups represent a seasonal pattern of aggregation and dispersal, with all groups coalescing at an aggregation site during one point of the yearly cycle. The scale of group mobility is controlled by a parameter. The creation, use, and discard of artifacts is controlled by several parameters that specify how many tools each person carries in a personal inventory, how many times each tool can be used before it is discarded, and the frequency of tool usage. A lithic source (representing a geographically-specific, recognizable source of stone for tools) can be placed anywhere in the geographic space of the model.

A model that representa farmers potential to adopt bio-fuels in Georgia

The Agent-Based Model for Multiple Team Membership (ABMMTM) simulates design teams searching for viable design solutions, for a large design project that requires multiple design teams that are working simultaneously, under different organizational structures; specifically, the impact of multiple team membership (MTM). The key mechanism under study is how individual agent-level decision-making impacts macro-level project performance, specifically, wage cost. Each agent follows a stochastic learning approach, akin to simulated annealing or reinforcement learning, where they iteratively explore potential design solutions. The agent evaluates new solutions based on a random-walk exploration, accepting improvements while rejecting inferior designs. This iterative process simulates real-world problem-solving dynamics where designers refine solutions based on feedback.

As a proof-of-concept demonstration of assessing the macro-level effects of MTM in organizational design, we developed this agent-based simulation model which was used in a simulation experiment. The scenario is a system design project involving multiple interdependent teams of engineering designers. In this scenario, the required system design is split into three separate but interdependent systems, e.g., the design of a satellite could (trivially) be split into three components: power source, control system, and communication systems; each of three design team is in charge of a design of one of these components. A design team is responsible for ensuring its proposed component’s design meets the design requirement; they are not responsible for the design requirements of the other components. If the design of a given component does not affect the design requirements of the other components, we call this the uncoupled scenario; otherwise, it is a coupled scenario.

The model aims to investigate the role of Microfinance Institutes (MFIs) in strengthening the coping capacity of slum-dwellers (residents) in case of frequent disasters. The main purpose of the model is system understanding. It aids in understanding the following research question: Are the microcredits provided by MFI to start a small business helpful in increasing coping capacity of a slum dweller for recovering from frequent and intense disasters?

Zooarchaeological evidences indicate that rabbit hunting became prevalent during the Upper Palaeolithic in the Iberian Peninsula.

The purpose of the ABM is to test if warren hunting using nets as a collective strategy can explain the introduction of rabbits in the human diet in the Iberian Peninsula during this period. It is analyzed whether this hunting strategy has an impact on human diet breadth by affecting the relative abundance of other main taxa in the dietary spectrum.
Model validity is measured by comparing simulated diet breadth to the observed diet breadth in the zooarchaeological record.

The agent-based model is explicitly grounded on the Diet Breadth Model (DBM), from the Optimal Foraging Theory (OFT).
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A logging agent builds roads based on the location of high-value hotspots, and cuts trees based on road access. A forest monitor sanctions the logger on observed infractions, reshaping the pattern of road development.

Riparian forests are one of the most vulnerable ecosystems to the development of biological invasions, therefore limiting their spread is one of the main challenges for conservation. The main factors that explain plant invasions in these ecosystems are the capacity for both short- and long-distance seed dispersion, and the occurrence of suitable habitats that facilitate the establishment of the invasive species. Large floods constitute an abiotic filter for invasion.

This model simulates the spatio-temporal spread of the woody invader Gleditsia. triacanthos in the riparian forest of the National Park Esteros de Farrapos e Islas del Río Uruguay, a riparian system in the coast of the Uruguay river (South America). In this model, we represent different environmental conditions for the development of G. triacanthos, long- and short-distance spread of its fruits, and large floods as the main factor of mortality for fruit and early stages.

Field results show that the distribution pattern of this invasive species is limited by establishment, i.e. it spreads locally through the expansion of small areas, and remotely through new invasion foci. This model recreates this dispersion pattern. We use this model to derive management implications to control the spread of G. triacanthos