The model constructs a complex network of traffic based on the main urban area of Zhengzhou, China, and simulates the urban rainfall process using the ABM model to analyse the real-time risk of flooding hazards in the nodes of the complex network.

An Agent-Based Model to simulate agent reactions to threatening information based on the anxiety-to-approach framework of Jonas et al. (2014).
The model showcases the framework of BIS/BAS (inhibitory and approach motivated behavior) for the case of climate information, including parameters for anxiety, environmental awareness, climate scepticism and pro-environmental behavior intention.

Agents receive external information according to threat-level and information frequency. The population dynamic is based on the learning from that information as well as social contagion mechanisms through a scale-free network topology.

The model uses Netlogo 6.2 and the network extension.
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This model is an implementation of a predator-prey simulation using NetLogo programming language. It simulates the interaction between fish, lionfish, and zooplankton. Fish and lionfish are both represented as turtles, and they have their own energy level. In this simulation, lionfish eat fish, and fish eat zooplankton. Zooplankton are represented as green patches on the NetLogo world. Lionfish and fish can reproduce and gain energy by eating other turtles or zooplankton.

This model was created to help undergraduate students understand how simulation models might be helpful in addressing complex environmental problems. In this case, students were asked to use this model to make predictions about how the introduction of lionfish (considered an invasive species in some places) might alter the ecosystem.

NeoCOOP is an iteration-based ABM that uses Reinforcement Learning and Artificial Evolution as adaptive-mechanisms to simulate the emergence of resource trading beliefs among Neolithic-inspired households.

The model explores food distribution patterns that emerge in a small-scale non-agricultural group when individuals follow a set of spatially explicit sharing interaction rules derived from a theory on the evolution of the egalitarian social instinct.

An agent-based model of individual consumers making choices between five possible diets: omnivore, flexitarian, pescatarian, vegetarian, or vegan. Each consumer makes decisions based on personal constraints and values, and their perceptions of how well each diet matches with those values. Consumers can also be influenced by each other’s perceptions via interaction across three social networks: household members, friends, and acquaintances.

This model system aims to simulate the whole process of task allocation, task execution and evaluation in the team system through a feasible method. On the basis of Complex Adaptive Systems (CAS) theory and Agent-based Modelling (ABM) technologies and tools, this simulation system attempts to abstract real-world teams into MAS models. The author designs various task allocation strategies according to different perspectives, and the interaction among members is concerned during the task-performing process. Additionally, knowledge can be acquired by such an interaction process if members encounter tasks they cannot handle directly. An artificial computational team is constructed through ABM in this simulation system, to replace real teams and carry out computational experiments. In all, this model system has great potential for studying team dynamics, and model explorers are encouraged to expand on this to develop richer models for research.

The HERB model simulates the retrofit behavior of homeowners in a neighborhood. The model initially parameterizes a neighborhood and households with technical factors such as energy standard, the availability of subsidies, and neighbors’ retrofit activity. Then, these factors are translated into psychological variables such as perceived comfort gain, worry about affording the retrofit, and perceiving the current energy standard of the home as wasteful. These psychological variables moderate the transition between four different stages of deciding to retrofit, as suggested by a behavioral model specific to household energy retrofitting identified based on a large population survey in Norway. The transition between all stages eventually leads to retrofitting, which affects both the household’s technical factors and friends and neighbors, bringing the model “full circle”. The model assumes that the energy standard of the buildings deteriorates over time, forcing households to retrofit regularly to maintain a certain energy standard.

Because experiment datafiles are about 15GB, they are available at https://doi.org/10.18710/XOSAMD

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 spatio-temporal Agent Based Modeling (ABM) framework is developed to probabilistically predict farmers’ decisions in the context of climate-induced water scarcity under varying utility optimization functions. The proposed framework forecasts farmers’ behavior assuming varying utility functions. The framework allows decision makers to forecast the behavior of farmers through a user-friendly platform with clear output visualization. The functionality of the proposed ABM is illustrated in an agriculturally dominated plain along the Eastern Mediterranean coastline.

Study area GIS data available upon request to [email protected]