An unintended consequence of low cost maritime travel may be hyperconnectedness, creating social situations where information can be readily passed before it is verified- an issue not limited to modern digitally connected societies. In traditional Coast Salish societies, the peoples of what is now Western Washington and Southwestern British Columbia, oral traditions were vertified through a process called witnessing. Witnesses would be trained to recount and verify oral history and traditional teachings at high fidelity. Here, a simple model based on dual inheritance approaches to genes and culture, is used to compare this specific form of verifying socially important information compared to modern mass communication. The model suggests that witnessing is a high fidelity form of transmitting knowledge with a low error rate, more in line with modern apprenticeships than mass communication. Social mechanisms such as witnessing provide solutions to issues faced in contemporary discourse where the validity of information and even fact checking mechanisms may be biased or counterfactual. This effort also demonstrates the utillity of using modeling approaches to highlight how specific, historically contingent institutions such as witnesses can be drawn upon to model potential solutions to contemporary issues solved in the past in traditional Coast Salish practice.

A system that receives from an agent-based social simulation the agent’s emotional data, their emotional-related data such as motivations and beliefs, as well as their location, and visualizes of all this information in a two dimensional map of the geographic region the agents inhabit as well as on graphs along the time dimension.

In this Repast model the ‘Consumat’ cognitive framework is applied to an ABM of the Dutch car market. Different policy scenarios can be selected or created to examine their effect on the diffusion of EVs.

The impacts of income inequality can be seen everywhere, regardless of the country or the level of economic development. According to the literature review, income inequality has negative impacts in economic, social, and political variables. Notwithstanding of how well or not countries have done in reducing income inequality, none have been able to reduce it to a Gini Coefficient level of 0.2 or less.
This is the promise that a novel approach called Counterbalance Economics (CBE) provides without the need of increased taxes.
Based on the simulation, introducing the CBE into the Australian, UK, US, Swiss or German economies would result in an overall GDP increase of under 1% however, the level of inequality would be reduced from an average of 0.33 down to an average of 0.08. A detailed explanation of how to use the model, software, and data dependencies along with all other requirements have been included as part of the info tab in the model.

Existing studies on prejudice, which is important in multi-group dynamics in societies, focus on the social-psychological knowledge behind the processes involving prejudice and its propagation. We instead create a multi-agent framework that simulates the propagation of prejudice and measures its tangible impact on the prosperity of individuals as well as of larger social structures, including groups and factions within. Groups in society help us define prejudice, and factions represent smaller tight-knit circles of individuals with similar opinions. We model social interactions using the Continuous Prisoner’s Dilemma (CPD) and a type of agent called a prejudiced agent, whose cooperation is affected by a prejudice attribute, updated over time based both on the agent’s own experiences and those of others in its faction. This model generates various results that both provide new insights into intergroup prejudice and its effects, as well as highlight and reinforce certain existing notions of prejudice.

The model is suitable to investigate the effects of different characteristics of apprenticeship programmes both in historical and contemporary societies. The model is built considering five societies, using an agent-based simulation model, we identified six main characteristics which impact the success of an apprenticeship programme in a society, which we measured by considering three parameters, namely the number of skilled agents produced by the apprenticeships, programme completion, and the contribution of programmes in the Gross Domestic Income (GDI) of the society. We investigate different definitions for success of an apprenticeship and some hypothetical societies to test some common beliefs about apprenticeships performance. The model also shows the number of unemployed agents given their work-based skills, wages, and the number of small and large companies who participate in training agents. The model enables exploring the impact of parameters, such as initial wages and the number of training years, along with the stated policies on the system.

Using chains of replicas of Atwood’s Machine, this model explores implications of the Maximum Power Principle. It is one of a series of models exploring the dynamics of sustainable economics – PSoup, ModEco, EiLab, OamLab, MppLab, TpLab, EiLab.

Using webs of replicas of Atwood’s Machine, we explore implications of the Maximum Power Principle. This is one of a series of models exploring the dynamics of sustainable economics – PSoup, ModEco, EiLab, OamLab, MppLab, TpLab, CmLab.

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.

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.