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.

The model of Chinese and Western civilization patterns can help understand how civilizations formed, how they evolved by themselves, and the difference between the unity of China and the disunity of the Western. The previous research had examined historical phenomena about civilization patterns with subjective, static, local, and inductive methods. Therefore, we propose a general model of history dynamics for civilizations pattern, which contains both China and the West, to improve our understanding of civilization formation and the factors influencing the pattern of civilization. And at the same time, the model is used to find the boundary conditions of two different patterns.

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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We provide a theory-grounded, socio-geographic agent-based model to present a possible explanation for human movement in the Adriatic region within the Cetina phenomenon.

Focusing on ideas of social capital theory from Piere Bordieu (1986), we implement agent mobility in an abstract geography based on cultural capital (prestige) and social capital (social position). Agents hold myopic representations of social (Schaff, 2016) and geographical networks and decide in a heuristic way on moving (and where) or staying.

The model is implemented in a fork of the Laboratory for Simulation Development (LSD), appended with GIS capabilities (Pereira et. al. 2020).

The main function of this simulation model is to simulate the onset of individual panic in the context of a public health event, and in particular to simulate how an individual’s panic develops and dies out in the context of a dual information contact network of online social media information and offline in-person perception information. In this model, eight different scenarios are set up by adjusting key parameters according to the difference in the amount and nature of information circulating in the dual information network, in order to observe how the agent’s panic behavior will change under different information exposure situations.

A model to investigate the Evolution of Conditional Cooperation in a Spatial Public Goods Game. We consider two conditional cooperation strategies: one based on thresholds (Battu & Srinivasan, 2020) and another based on independent decisions for each number of cooperating neighbors. We examine the effects of productivity and conditional cooperation criteria on the trajectory of cooperation. Cooperation is evolving with no need for additional mechanisms apart from spatial structure when agents follow conditional strategies. We confirm the positive influence of productivity and cluster formation on the evolution of cooperation in spatial models. Results are robust for the two types of conditional cooperation strategies.

This agent-based model simulates the implementation of a Transfer of Development Rights (TDR) mechanism in a stylized urban environment inspired by Dublin. It explores how developer agents interact with land parcels under spatial zoning, conservation protections, and incentive-based policy rules. The model captures emergent outcomes such as compact growth, green and heritage zone preservation, and public cost-efficiency. Built in NetLogo, the model enables experimentation with variable FSI bonuses, developer behavior, and spatial alignment of sending/receiving zones. It is intended as a policy sandbox to test market-aligned planning tools under behavioral and spatial uncertainty.

The agent based model matches origins and destinations using employment search methods at the individual level.

This model represents informal information transmission networks among medieval Genoese investors used to inform each other about cheating merchants they employed as part of long-distance trade operations.

This model extends the original Artifical Anasazi (AA) model to include individual agents, who vary in age and sex, and are aggregated into households. This allows more realistic simulations of population dynamics within the Long House Valley of Arizona from AD 800 to 1350 than are possible in the original model. The parts of this model that are directly derived from the AA model are based on Janssen’s 1999 Netlogo implementation of the model; the code for all extensions and adaptations in the model described here (the Artificial Long House Valley (ALHV) model) have been written by the authors. The AA model included only ideal and homogeneous “individuals” who do not participate in the population processes (e.g., birth and death)–these processes were assumed to act on entire households only. The ALHV model incorporates actual individual agents and all demographic processes affect these individuals. Individuals are aggregated into households that participate in annual agricultural and demographic cycles. Thus, the ALHV model is a combination of individual processes (birth and death) and household-level processes (e.g., finding suitable agriculture plots).

As is the case for the AA model, the ALHV model makes use of detailed archaeological and paleoenvironmental data from the Long House Valley and the adjacent areas in Arizona. It also uses the same methods as the original model (from Janssen’s Netlogo implementation) to estimate annual maize productivity of various agricultural zones within the valley. These estimates are used to determine suitable locations for households and farms during each year of the simulation.