SeaROOTS ABM is a quite generic agent-based modeling system, for simulating and evaluating potential terrestrial and maritime mobility of artificial hominin groups, configured by available archaeological data and hypotheses. Necessary bathymetric, geomorphological and paleoenvironmental data are combined in order to reconstruct paleoshorelines for the study area and produce an archaeologically significant agent environment. Paleoclimatic and archaeological data are incorporated in the ABM in order to simulate maritime crossings and assess the emergent patterns of interaction between human agency and the sea.

SeaROOTS agent-based system includes completely autonomous, utility-based agents (Chliaoutakis & Chalkiadakis 2016), representing artificial hominin groups, with partial knowledge of their environment, for simulating their evolution and potential maritime mobility, utilizing alternative Least Cost Path analysis modeling techniques (Gustas & Supernant 2017, Gravel-Miguel & Wren 2021). Two groups of hominins, Neanderthals and Homo sapiens, are chosen in order to study the challenges and actions employed as a response to the fluctuating sea-levels, as well as probability scenarios with respect to sea-crossings via buoyant vessels (rafting) or the human body itself (swimming). SeaROOTS ABM aims to simulate various scenarios and investigate the degree climatic fluctuations influenced such activities and interactions in the Middle Paleolithic period.

The model focuses on simulating potential terrestrial and maritime routes, explore the interactions and relations between autonomous agents and their environment, as well as to test specific research questions; for example, when and under what conditions would Middle Paleolithic hominins be more likely to attempt a crossing and successfully reach the islands? By which agent type (Sapiens or Neanderthals) and how (e.g. swimming or by sea-vessels) could such short sea crossings be (mostly) attempted, and which (sea) routes were usually considered by the agents? When does a sea-crossing become a choice and when is it a result of forced migration, i.e. disaster- or conflict-induced displacement? Results show that the dynamic marine environment of the Inner Ionian, our case study in this work, played an important role in their decision-making process.

Transhumants move their herds based on strategies simultaneously considering several environmental and socio-economic factors. There is no agreement on the influence of each factor in these strategies. In addition, there is a discussion about the social aspect of transhumance and how to manage pastoral space. In this context, agent-based modeling can analyze herd movements according to the strategy based on factors favored by the transhumant. This article presents a reductionist agent-based model that simulates herd movements based on a single factor. Model simulations based on algorithms to formalize the behavioral dynamics of transhumants through their strategies. The model results establish that vegetation, water outlets and the socio-economic network of transhumants have a significant temporal impact on transhumance. Water outlets and the socio-economic network have a significant spatial impact. The significant impact of the socio-economic factor demonstrates the social dimension of Sahelian transhumance. Veterinarians and markets have an insignificant spatio-temporal impact. To manage pastoral space, water outlets should be at least 15 km
from each other. The construction of veterinary centers, markets and the securitization of transhumance should be carried out close to villages and rangelands.

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.

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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.

This model simulates the dynamics of agricultural land use change, specifically the transition between agricultural and non-agricultural land use in a spatial context. It explores the influence of various factors such as agricultural profitability, path dependency, and neighborhood effects on land use decisions.

The model operates on a grid of patches representing land parcels. Each patch can be in one of two states: exploited (green, representing agricultural land) or unexploited (brown, representing non-agricultural land). Agents (patches) transition between these states based on probabilistic rules. The main factors affecting these transitions are agricultural profitability, path dependency, and neighborhood effects.
-Agricultural Profitability: This factor is determined by the prob-agri function, which calculates the probability of a non-agricultural patch converting to agricultural based on income differences between agriculture and other sectors. -Path Dependency: Represented by the path-dependency parameter, it influences the likelihood of patches changing their state based on their current state. It’s a measure of inertia or resistance to change. -Neighborhood Effects: The neighborhood function calculates the number of exploited (agricultural) neighbors of a patch. This influences the decision of a patch to convert to agricultural land, representing the influence of surrounding land use on the decision-making process.

This ABM

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