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

A fisher directed management system was describeded by Hart (2021). It was proposed that fishers should only be allowed to exploit a resource if they collaborated in a resource management system for which they would own and be collectively responsible for. As part of the system fishers would need to follow the rules of exploitation set by the group and provide a central unit with data with which to monitor the fishery. Any fisher not following the rules would at first be fined but eventually expelled from the fishery if he/she continued to act selfishly. This version of the model establishes the dynamics of a fleet of vessels and controls overfishing by imposing fines on fishers whose income is low and who are tempted to keep fishing beyond the set quota which is established each year depending on the abundance of the fish stock. This version will later be elaborated to have interactions between the fishers including pressure to comply with the norms set by the group and which could lead to a stable management system.

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.

This is a model of organizational behavior in the hierarchy in which personnel decisions are made.
The idea of the model is that the hierarchy, busy with operations, is described by such characteristics as structure (number and interrelation of positions) and material, filling these positions (persons with their individual performance). A particular hierarchy is under certain external pressure (performance level requirement) and is characterized by the internal state of the material (the distribution of the perceptions of others over the ensemble of persons).
The World of the model is a four-level hierarchical structure, consisting of shuff positions of the top manager (zero level of the hierarchy), first-level managers who are subordinate to the top manager, second-level managers (subordinate to the first-level managers) and positions of employees (the third level of the hierarchy). ) subordinated to the second-level managers. Such a hierarchy is a tree, i.e. each position, with the exception of the position of top manager, has a single boss.
Agents in the model are persons occupying the specified positions, the number of persons is set by the slider (HumansQty). Personas have some operational performance (harisma, an unfortunate attribute name left over from the first edition of the model)) and a sense of other personas’ own perceptions. Performance values are distributed over the ensemble of persons according to the normal law with some mean value and variance.
The value of perception by agents of each other is positive or negative (implemented in the model as numerical values equal to +1 and -1). The distribution of perceptions over an ensemble of persons is implemented as a random variable specified by the probability of negative perception, the value of which is set by the control elements of the model interface. The numerical value of the probability equal to 0 corresponds to the case in which all persons positively perceive each other (the numerical value of the random variable is equal to 1, which corresponds to the positive perception of the other person by the individual).
The hierarchy is occupied with operational activity, the degree of intensity of which is set by the external parameter Difficulty. The level of productivity of each manager OAIndex is equal to the level of productivity of the department he leads and is the ratio of the sum of productivity of employees subordinate to the head to the level of complexity of the work Difficulty. An increase in the numerical value of Difficulty leads to a decrease in the OAIndex for all subdivisions of the hierarchy. The managerial meaning of the OAIndex indicator is the percentage of completion of the load specified for the hierarchy as a whole, i.e. the ratio of the actual performance of the structural subdivisions of the hierarchy to the required performance, the level of which is specified by the value of the Difficulty parameter.
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This model simulates a foraging system based on Middle Stone Age plant and shellfish foraging in South Africa.

The Communication-Based Model of Perceived Descriptive Norm Dynamics in Digital Networks (COMM-PDND) is an agent-based model specifically created to examine the dynamics of perceived descriptive norms in the context of digital network structures. The model, developed as part of a master’s thesis titled “The Dynamics of Perceived Descriptive Norms in Digital Network Publics: An Agent-Based Simulation,” emphasizes the critical role of communication processes in norm formation. It focuses on the role of communicative interactions in shaping perceived descriptive norms.

The COMM-PDND is tuned to explore the effects of normative deviance in digital social networks. It provides functionalities for manipulating agents according to their network position, and has a versatile set of customizable parameters, making it adaptable to a wide range of research contexts.

IDEAL: Agent-Based Model of Residential Land Use Change where the choice of new residential development in based on the Ideal-point decision rule.

This agent-based model examines the impact of seasonal aggregation, dispersion, and learning opportunities on the richness and evenness of artifact styles under random social learning (unbiased transmission).

The model is a combination of a spatially explicit, stochastic, agent-based model for wild boars (Sus scrofa L.) and an epidemiological model for the Classical Swine Fever (CSF) virus infecting the wild boars.

The original model (Kramer-Schadt et al. 2009) was used to assess intrinsic (system immanent host-pathogen interaction and host life-history) and extrinsic (spatial extent and density) factors contributing to the long-term persistence of the disease and has further been used to assess the effects of intrinsic dynamics (Lange et al. 2012a) and indirect transmission (Lange et al. 2016) on the disease course. In an applied context, the model was used to test the efficiency of spatiotemporal vaccination regimes (Lange et al. 2012b) as well as the risk of disease spread in the country of Denmark (Alban et al. 2005).

References: See ODD model description.

A minimal genetic algorithm was previously developed in order to solve an elementary arithmetic problem. It has been modified to explore the effect of a mutator gene and the consequent entrance into a hypermutation state. The phenomenon seems relevant in some types of tumorigenesis and in a more general way, in cells and tissues submitted to chronic sublethal environmental or genomic stress.
For a long time, some scholars suppose that organisms speed up their own evolution by varying mutation rate, but evolutionary biologists are not convinced that evolution can select a mechanism promoting more (often harmful) mutations looking forward to an environmental challenge.
The model aims to shed light on these controversial points of view and it provides also the features required to check the role of sex and genetic recombination in the mutator genes diffusion.