A Picit Jeu is an agent-based model (ABM) developed as a supporting tool for a role-playing game of the same name. The game is intended for stakeholders involved in land management and fire prevention at a municipality level. It involves four different roles: farmers, forest technicians, municipal administrators and forest private owners. The model aims to show the long-term effects of their different choices about forest and pasture management on fire hazard, letting them test different management strategies in an economically constraining context. It also allows the players to explore different climatic and economic scenarios. A Picit Jeu ABM reproduces the ecological, social and economic characteristics and dynamics of an Alpine valley in north-west Italy. The model should reproduce a primary general pattern: the less players undertake landscape management actions, by thinning and cutting forests or grazing pastures, the higher the probability that a fire will burn a large area of land.

A letter sending model with historically informed initial positions to reconstruct communication and archiving processes in the Republic of Letters, the 15th to 17th century form of scholarship.

The model is aimed at historians, willing to formalize historical assumptions about the letter sending process itself and allows in principle to set heterogeneous social roles, e.g. to evaluate the role of gender or social status in the formation of letter exchange networks. The model furthermore includes a pruning process to simulate the loss of letters to critically asses the role of biases e.g. in relation to gender, geographical regions, or power structures, in the creation of empirical letter archives.

Each agent has an initial random topic vector, expressed as a RGB value. The initial positions of the agents are based on a weighted random draw based on data from [2]. In each step, agents generate two neighbourhoods for sending letters and potential targets to move towards. The probability to send letters is a self-reinforcing process. After each sending the internal topic of the receiver is updated as a movement in abstract space by a random amount towards the letters topic.

…

Our aim is to demonstrate how conversational AI systems, exemplified by ChatGPT, can support the conceptualisation of Agent-Based Social Simulation (ABSS) models, leading to a full ABSS model design document. Through advanced prompt engineering and adherence to the Engineering ABSS framework (Siebers and Klügl 2017), we have constructed a comprehensive script that is easy to use and that supports the design of ABSS models with or even by AI. The performance of the script is demonstrated through an illustrative case study related to the use of adaptive architecture in museums. The repository contains (1) the comprehensive script in a format that allows copying and pasting prompts for use with ChatGPT, (2) the results of the illustrative case study in the form of two conceptual ABSS models, the ground truth and the autogenerated version.

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.

Agent-based models of organizational search have long investigated how exploitative and exploratory behaviors shape and affect performance on complex landscapes. To explore this further, we build a series of models where agents have different levels of expertise and cognitive capabilities, so they must rely on each other’s knowledge to navigate the landscape. Model A investigates performance results for efficient and inefficient networks. Building on Model B, it adds individual-level cognitive diversity and interaction based on knowledge similarity. Model C then explores the performance implications of coordination spaces. Results show that totally connected networks outperform both hierarchical and clustered network structures when there are clear signals to detect neighbor performance. However, this pattern is reversed when agents must rely on experiential search and follow a path-dependent exploration pattern.

The model’s aim is to represent the price dynamics under very simple market conditions, given the values adopted by the user for the model parameters. We suppose the market of a financial asset contains agents on the hypothesis they have zero-intelligence. In each period, a certain amount of agents are randomly selected to participate to the market. Each of these agents decides, in a equiprobable way, between proposing to make a transaction (talk = 1) or not (talk = 0). Again in an equiprobable way, each participating agent decides to speak on the supply (ask) or the demand side (bid) of the market, and proposes a volume of assets, where this number is drawn randomly from a uniform distribution. The granularity depends on various factors, including market conventions, the type of assets or goods being traded, and regulatory requirements. In some markets, high granularity is essential to capture small price movements accurately, while in others, coarser granularity is sufficient due to the nature of the assets or goods being traded

ViSA 2.0.0 is an updated version of ViSA 1.0.0 aiming at integrating empirical data of a new use case that is much smaller than in the first version to include field scale analysis. Further, the code of the model is simplified to make the model easier and faster. Some features from the previous version have been removed.
It simulates decision behaviors of different stakeholders showing demands for ecosystem services (ESS) in agricultural landscape. It investigates conditions and scenarios that can increase the supply of ecosystem services while keeping the viability of the social system by suggesting different mixes of initial unit utilities and decision rules.

The model is intended to simulate visitor spatial and temporal dynamics, encompassing their numbers, activities, and distribution along a coastline influenced by beach landscape design. Our primary focus is understanding how the spatial distribution of services and recreational facilities (e.g., beach width, entrance location, recreational facilities, parking availability) impacts visitation density. Our focus is not on tracking the precise visitation density but rather on estimating the areas most affected by visitor activity. This comprehension allows for assessing the diverse influences of beach layouts on spatial visitor density and, consequently, on the landscape’s biophysical characteristics (e.g., vegetation, fauna, and sediment features).

The model explores the impact of public disclosure on tax compliance among diverse agents, including individual taxpayers and a tax authority. It incorporates heterogeneous preferences and income endowments among taxpayers, captured through a utility function that considers psychic costs subtracted from expected pecuniary utility. These costs include moral, reciprocity, and stigma costs associated with norm violations, leading to variations in taxpayers’ risk attitudes and related parameters. The tax authority’s attributes, such as the frequency of random audits, penalty rate, and the choice between partial or full disclosure, remain fixed throughout the simulation. Income endowments and preference parameters are randomly assigned to taxpayers at the outset.

Taxpayers maximize their expected utility by reporting income, taking into account tax, penalty, and audit rates. They make annual decisions based on their own and their peers’ behaviors from the previous year. Taxpayers indirectly interact at the societal level through public disclosure conducted by the tax authority, exchanging tax information among peers. Each period in the simulation collects data on total reported income, average compliance rates per income group, distribution of compliance rates, counts of compliers, full evaders, partial evaders, and the numbers of taxpayers experiencing guilt and anger. The model evaluates whether public disclosure positively or negatively impacts compliance rates and quantifies this impact based on aggregated individual reporting behaviors.

Abstract: The notion of physical space has long been central in geographical theories. However, the widespread adoption of information and communication technologies (ICTs) has freed human dynamics from purely physical to also relational and cyber spaces. While researchers increasingly recognize such shifts, rarely have studies examined how the information propagates in these hybrid spaces (i.e., physical, relational, and cyber). By exploring the vaccine opinion dynamics through agent-based modeling, this study is the first that combines all hybrid spaces and explores their distinct impacts on human dynamics from an individual’s perspective. Our model captures the temporal dynamics of vaccination progress with small errors (MAE=2.45). Our results suggest that all hybrid spaces are indispensable in vaccination decision making. However, in our model, most of the agents tend to give more emphasis to the information that is spread in the physical instead of other hybrid spaces. Our study not only sheds light on human dynamics research but also offers a new lens to identifying vaccinated individuals which has long been challenging in disease-spread models. Furthermore, our study also provides responses for practitioners to develop vaccination outreach policies and plan for future outbreaks.