The model is based on the influence function of the Leviathan model (Deffuant, Carletti, Huet 2013 and Huet and Deffuant 2017). We aim at better explaining some patterns generated by this model, using a derived mathematical approximation of the evolution of the opinions averaged.

We consider agents having an opinion/esteem about each other and about themselves. During dyadic meetings, agents change their respective opinion about each other, and possibly about other agents they gossip about, with a noisy perception of the opinions of their interlocutor. Highly valued agents are more influential in such encounters.

We show that the inequality of reputations among agents have a negative effect on the opinions about the agents of low status.The mathematical analysis of the opinion dynamic shows that the lower the status of the agent, the more detrimental the interactions are for the opinions about this agent, especially when gossip is activated, while the interactions always tend to increase the opinions about agents of high status.

The Holmestrand model is an epidemiological agent-based model. Its aim is to test hypotheses related to how the social and physical environment of a residential school for children with disabilities might influence the spread of an infectious disease epidemic among students and staff. Annual reports for the Holmestrand School for the Deaf (Norway) are the primary sources of inspiration for the modeled school, with additional insights drawn from other archival records for schools for children with disabilities in early 20th century Norway and data sources for the 1918 influenza pandemic. The model environment consists of a simplified boarding school that includes residential spaces for students and staff, classrooms, a dining room, common room, and an outdoor area. Students and staff engage in activities reflecting hourly schedules suggested by school reports. By default, a random staff member is selected as the first case and is infected with disease. Subsequent transmission is determined by agent movement and interactions between susceptible and infectious pairs.

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.

We study cultural dissemination in the context of an Axelrod-like agent-based model describing the spread of cultural traits across a society, with an added element of social influence. This modification produces absorbing states exhibiting greater variation in number and size of distinct cultural regions compared to the original Axelrod model, and we identify the mechanism responsible for this amplification in heterogeneity. We develop several new metrics to quantitatively characterize the heterogeneity and geometric qualities of these absorbing states. Additionally, we examine the dynamical approach to absorbing states in both our Social Influence Model as well as the Axelrod Model, which not only yields interesting insights into the differences in behavior of the two models over time, but also provides a more comprehensive view into the behavior of Axelrod’s original model. The quantitative metrics introduced in this paper have broad potential applicability across a large variety of agent-based cultural dissemination models.

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

HOW IT WORKS

This model consists of three agents, and each agent type operates per business theories as below.
a. New technologies(Tech): It evolves per sustaining or disruptive technology trajectory with the constraint of project management triangle (Scope, Time, Quality, and Cost).
b. Entrepreneurs(Entre): It builds up the solution by combining Tech components per its own strategy (Exploration, Exploitation, or Ambidex).
c. Consumer(Consumer): It selects the solution per its own preference due to Diffusion of innovation theory (Innovators, Early Adopters, Early Majority, Late Majority, Laggards)
…

This is a simulation of an insurance market where the premium moves according to the balance between supply and demand. In this model, insurers set their supply with the aim of maximising their expected utility gain while operating under imperfect information about both customer demand and underlying risk distributions.

There are seven types of insurer strategies. One type follows a rational strategy within the bounds of imperfect information. The other six types also seek to maximise their utility gain, but base their market expectations on a chartist strategy. Under this strategy, market premium is extrapolated from trends based on past insurance prices. This is subdivided according to whether the insurer is trend following or a contrarian (counter-trend), and further depending on whether the trend is estimated from short-term, medium-term, or long-term data.

Customers are modelled as a whole and allocated between insurers according to available supply. Customer demand is calculated according to a logit choice model based on the expected utility gain of purchasing insurance for an average customer versus the expected utility gain of non-purchase.

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

Hybrid attacks coordinate the exploitation of vulnerabilities across domains to undermine trust in authorities and cause social unrest. Whilst such attacks have primarily been seen in active conflict zones, there is growing concern about the potential harm that can be caused by hybrid attacks more generally and a desire to discover how better to identify and react to them. In addressing such threats, it is important to be able to identify and understand an adversary’s behaviour. Game theory is the approach predominantly used in security and defence literature for this purpose. However, the underlying rationality assumption, the equilibrium concept of game theory, as well as the need to make simplifying assumptions can limit its use in the study of emerging threats. To study hybrid threats, we present a novel agent-based model in which, for the first time, agents use reinforcement learning to inform their decisions. This model allows us to investigate the behavioural strategies of threat agents with hybrid attack capabilities as well as their broader impact on the behaviours and opinions of other agents.

We represent commuters and their preferences for transportation cost, time and safety. Agents assess their options via their preferences, their environment, and the modes available. The model has policy levers to test impact on last-mile problem.