The HUMan impact on LANDscapes (HUMLAND) model has been developed to track and quantify the intensity of different impacts on landscapes at the continental level. This agent-based model focuses on determining the most influential factors in the transformation of interglacial vegetation with a specific emphasis on burning organized by hunter-gatherers. HUMLAND integrates various spatial datasets as input and target for the agent-based model results. Additionally, the simulation incorporates recently obtained continental-scale estimations of fire return intervals and the speed of vegetation regrowth. The obtained results include maps of possible scenarios of modified landscapes in the past and quantification of the impact of each agent, including climate, humans, megafauna, and natural fires.

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.

Municipal waste management (MWM) is essential for urban development. Efficient waste management is essential for providing a healthy and clean environment, for reducing GHGs and for increasing the amount of material recycled. Waste separation at source is perceived as an effective MWM strategy that relays on the behaviour of citizens to separate their waste in different fractions. The strategy is straightforward, and many cities have adopted the strategy or are working to implement it. However, the success of such strategy depends on adequate understanding of the drivers of the behaviour of proper waste sorting. The Theory of Planned Behaviour (TPB) has been extensively applied to explain the behaviour of waste sorting and contributes to determining the importance of different psychological constructs. Although, evidence shows its validity in different contexts, without exploring how urban policies and the built environment affect the TPB, its application to urban challenges remains unlocked. To date, limited research has focused in exposing how different urban situations such as: distance to waste bins, conditions of recycling facilities or information campaigns affect the planned behaviour of waste separation. To fill this gap, an agent-based model (ABM) of residents capable of planning the behaviour of waste separation is developed. The study is a proof of concept that shows how the TPB can be combined with simulations to provide useful insights to evaluate different urban planning situations. In this paper we depart from a survey to capture TPB constructs, then Structural Equation Modelling (SEM) is used to validate the TPB hypothesis and extract the drivers of the behaviour of waste sorting. Finally, the development of the ABM is detailed and the drivers of the TPB are used to determine how the residents behave. A low-density and a high-density urban scenario are used to extract policy insights. In conclusion, the integration between the TPB into ABMs can help to bridge the knowledge gap between can provide a useful insight to analysing and evaluating waste management scenarios in urban areas. By better understanding individual waste sorting behaviour, we can develop more effective policies and interventions to promote sustainable waste management practices.

The MML is a hybrid modeling environment that couples an agent-based model of small-holder agropastoral households and a cellular landscape evolution model that simulates changes in erosion/deposition, soils, and vegetation.

this agent-based model explores the dynamics of volunteer participation in urban community gardens, by combining behavioral theory and institutional theory

AMBAWA simulates the flows of biomass between crop and livestock systems at the field, farm, and village scales in order to showcase innovating management practices of soil fertility in West Africa.

The Communicating Hazard Information in the Modern Environment (CHIME) agent-based model (ABM) is a Netlogo program that facilitates the analysis of information flow and protective decisions across space and time during hazardous weather events. CHIME ABM provides a platform for testing hypotheses about collective human responses to weather forecasts and information flow, using empirical data from historical hurricanes. The model uses real world geographical and hurricane data to set the boundaries of the simulation, and it uses historical hurricane forecast information from the National Hurricane Center to initiate forecast information flow to citizen agents in the model.

AncientS-ABM is an agent-based model for simulating and evaluating the potential social organization of an artificial past society, configured by available archaeological data. Unlike most existing agent-based models used in archaeology, our ABM framework includes completely autonomous, utility-based agents. It also incorporates different social organization paradigms, different decision-making processes, and also different cultivation technologies used in ancient societies. Equipped with such paradigms, the model allows us to explore the transition from a simple to a more complex society by focusing on the historical social dynamics; and to assess the influence of social organization on agents’ population growth, agent community numbers, sizes and distribution.

AncientS-ABM also blends ideas from evolutionary game theory with multi-agent systems’ self-organization. We model the evolution of social behaviours in a population of strategically interacting agents in repeated games where they exchange resources (utility) with others. The results of the games contribute to both the continuous re-organization of the social structure, and the progressive adoption of the most successful agent strategies. Agent population is not fixed, but fluctuates over time, while agents in stage games also receive non-static payoffs, in contrast to most games studied in the literature. To tackle this, we defined a novel formulation of the evolutionary dynamics via assessing agents’ rather than strategies’ fitness.

As a case study, we employ AncientS-ABM to evaluate the impact of the implemented social organization paradigms on an artificial Bronze Age “Minoan” society, located at different geographical parts of the island of Crete, Greece. Model parameter choices are based on archaeological evidence and studies, but are not biased towards any specific assumption. Results over a number of different simulation scenarios demonstrate better sustainability for settlements consisting of and adopting a socio-economic organization model based on self-organization, where a “heterarchical” social structure emerges. Results also demonstrate that successful agent societies adopt an evolutionary approach where cooperation is an emergent strategic behaviour. In simulation scenarios where the natural disaster module was enabled, we observe noticeable changes in the settlements’ distribution, relating to significantly higher migration rates immediately after the modeled Theran eruption. In addition, the initially cooperative behaviour is transformed to a non-cooperative one, thus providing support for archaeological theories suggesting that the volcanic eruption led to a clear breakdown of the Minoan socio-economic system.

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MayaSim is an agent-based, cellular automata and network model of the ancient Maya. Biophysical and anthropogenic processes interact to grow a complex social ecological system.

Model of a very serious conflict about the relevance of a dam to impede its construction, between the client, the prime contractor, State, legalist opponents and activist opponents.