Replication of the well known Artificial Anasazi model that simulates the population dynamics between 800 and 1350 in the Long House Valley in Arizona.

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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The Emergent Firm (EF) model is based on the premise that firms arise out of individuals choosing to work together to advantage themselves of the benefits of returns-to-scale and coordination. The Emergent Firm (EF) model is a new implementation and extension of Rob Axtell’s Endogenous Dynamics of Multi-Agent Firms model. Like the Axtell model, the EF model describes how economies, composed of firms, form and evolve out of the utility maximizing activity on the part of individual agents. The EF model includes a cash-in-advance constraint on agents changing employment, as well as a universal credit-creating lender to explore how costs and access to capital affect the emergent economy and its macroeconomic characteristics such as firm size distributions, wealth, debt, wages and productivity.

This NetLogo model illustrates the cultural evolution of pro-environmental behaviour patterns. It illustrates how collective behaviour patterns evolve from interactions between agents and agents (in a social network) as well as agents and the affordances (action opportunities provided by the environment) within a niche. More specifically, the cultural evolution of behaviour patterns is understood in this model as a product of:

1. The landscape of affordances provided by the material environment,
2. Individual learning and habituation,
3. Social learning and network structure,
4. Personal states (such as habits and attitudes), and
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Is the mass shooter a maniac or a relatively normal person in a state of great stress? According to the FBI report (Silver, J., Simons, A., & Craun, S. (2018). A Study of the Pre-Attack Behaviors of Active Shooters in the United States Between 2000 – 2013. Federal Bureau of Investigation, U.S. Department of Justice,Washington, D.C. 20535.), only 25% of the active shooters were known to have been diagnosed by a mental health professional with a mental illness of any kind prior to the offense.
The main objects of the model are the humans and the guns. The main factors influencing behavior are the population size, the number of people with mental disabilities (“psycho” in the model terminology) per 100,000 population, the total number of weapons (“guns”) in the population, the availability of guns for humans, the intensity of stressors affecting humans and the threshold level of stress, upon reaching which a person commits an act of mass shooting.
The key difference (in the model) between a normal person and a psycho is that a psycho accumulates stressors and, upon reaching a threshold level, commits an act of mass shooting. A normal person is exposed to stressors, but reaching the threshold level for killing occurs only when the simultaneous effect of stressors on him exceeds this level.
The population dynamics are determined by the following factors: average (normally distributed) life expectancy (“life_span” attribute of humans) and population growth with the percentage of newborns set by the value of the TickReprRatio% slider of the current population volume from 16 to 45 years old.Thus, one step of model time corresponds to a year.

MIOvCWD is a spatially-explicit, agent-based model designed to simulate the spread of chronic wasting disease (CWD) in Michigan’s white-tailed deer populations. CWD is an emerging prion disease of North American cervids (white-tailed deer Odocoileus virginianus, mule deer Odocoileus hemionus, and elk Cervus elaphus) that is being actively managed by wildlife agencies in most states and provinces in North America, including Michigan. MIOvCWD incorporates features like deer population structure, social organization and behavior that are particularly useful to simulate CWD dynamics in regional deer populations.

This is a model of plant communities in urban and suburban residential neighborhoods. These plant communities are of interest because they provide many benefits to human residents and also provide habitat for wildlife such as birds and pollinators. The model was designed to explore the social factors that create spatial patterns in biodiversity in yards and gardens. In particular, the model was originally developed to determine whether mimicry behaviors–-or neighbors copying each other’s yard design–-could produce observed spatial patterns in vegetation. Plant nurseries and socio-economic constraints were also added to the model as other potential sources of spatial patterns in plant communities.

The idea for the model was inspired by empirical patterns of spatial autocorrelation that have been observed in yard vegetation in Chicago, Illinois (USA), and other cities, where yards that are closer together are more similar than yards that are farther apart. The idea is further supported by literature that shows that people want their yards to fit into their neighborhood. Currently, the yard attribute of interest is the number of plant species, or species richness. Residents compare the richness of their yards to the richness of their neighbors’ yards. If a resident’s yard is too different from their neighbors, the resident will be unhappy and change their yard to make it more similar.

The model outputs information about the diversity and identity of plant species in each yard. This can be analyzed to look for spatial autocorrelation patterns in yard diversity and to explore relationships between mimicry behaviors, yard diversity, and larger scale diversity.

This project is based on a Jupyter Notebook that describes the stepwise implementation of the EWA model in bi-matrix ( 2×2 ) strategic-form games for the simulation of economic learning processes. The output is a dataset with the simulated values of Attractions, Experience, selected strategies, and payoffs gained for the desired number of rounds and periods. The notebook also includes exploratory data analysis over the simulated output based on equilibrium, strategy frequencies, and payoffs.

The Non-Deterministic model of affordable housing Negotiations (NoD-Neg) is designed for generating hypotheses about the possible outcomes of negotiating affordable housing obligations in new developments in England. By outcomes we mean, the probabilities of failing the negotiation and/or the different possibilities of agreement.
The model focuses on two negotiations which are key in the provision of affordable housing. The first is between a developer (DEV) who is submitting a planning application for approval and the relevant Local Planning Authority (LPA) who is responsible for reviewing the application and enforcing the affordable housing obligations. The second negotiation is between the developer and a Registered Social Landlord (RSL) who buys the affordable units from the developer and rents them out. They can negotiate the price of selling the affordable units to the RSL.
The model runs the two negotiations on the same development project several times to enable agents representing stakeholders to apply different negotiation tactics (different agendas and concession-making tactics), hence, explore the different possibilities of outcomes.
The model produces three types of outputs: (i) histograms showing the distribution of the negotiation outcomes in all the simulation runs and the probability of each outcome; (ii) a data file with the exact values shown in the histograms; and (iii) a conversation log detailing the exchange of messages between agents in each simulation run.

This model has been developed together with the publication ‘Modelling Value Change - An Exploratory Approach’

Value change and moral change have increasingly become topics of interest in the philosophical literature. Several theoretical accounts have been proposed. Such accounts are usually based on certain theoretical and conceptual assumptions and their strengths and weaknesses are often hard to determine and compare, also because they are based on limited empirical evidence.

We propose that a step forward can be made with the help of agent-based modelling (ABM). ABM can be used to investigate whether a simulation model based on a specific account of value change can reproduce relevant phenomena. To illustrate this approach, we built a model based on the pragmatist account of value change proposed in van de Poel and Kudina (2022). We show that this model can reproduce four relevant phenomena, namely 1) the inevitability and stability of values, 2) how different societies may react differently to external shocks, 3) moral revolutions, and 4) lock-in.