This version adds a Maslowian entropy to each agent decision based on Kendrick et. al. Rudimentary implementation assumes agents with lower scores are more likely to make decisions autonomously rather than sociotropically.

This is the code for a simulation model of the radicalisation process based on the IVEE theoretical framework.

In this agent-based model, agents decide to adopt a new product according to a utility function that depends on two kinds of social influences. First, there is a local influence exerted on an agent by her closest neighbors that have already adopted, and also by herself if she feels the product suits her personal needs. Second, there is a global influence which leads agents to adopt when they become aware of emerging trends happening in the system. For this, we endow agents with a reflexive capacity that allows them to recognize a trend, even if they can not perceive a significant change in their neighborhood.

Results reveal the appearance of slowdown periods along the adoption rate curve, in contrast with the classic stylized bell-shaped behavior. Results also show that network structure plays an important role in the effect of reflexivity: while some structures (e.g., scale-free networks) may amplify it, others (e.g., small-world structure) weaken such an effect.

The model explores food distribution patterns that emerge in a small-scale non-agricultural group when sharing individuals engage in intentional consumption leveling with a given probability.

This model examines an important but underappreciated mechanism affecting urban segregation and integration: urban venues. The venue- an area where urbanites interact- is an essential aspect of city life that tends to influence how satisfactory any location is. We study the venue/segregation relationship by installing venues into Schelling’s classic agent-based segregation model.

ICARUS is a multi-agent compliance inspection model (ICARUS - Inspecting Compliance to mAny RUleS). The model is applicable to environments where an inspection agency, via centrally coordinated inspections, examines compliance in organizations which must comply with multiple provisions (rules). The model (ICARUS) contains 3 types of agents: entities, inspection agency and inspectors / inspections. ICARUS describes a repeated, simultaneous, non-cooperative game of pure competition. Agents have imperfect, incomplete, asymmetric information. Entities in each move (tick) choose a pure strategy (comply/violate) for each rule, depending on their own subjective assessment of the probability of the inspection. The Inspection Agency carries out the given inspection strategy.

A more detailed description of the model is available in the .nlogo file.
Full description of the model (in line with the ODD+D protocol) and the analysis of the model (including verification, validation and sensitivity analysis) can be found in the attached documentation.

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

At the heart of a study of Social-Ecological Systems, this model is built by coupling together two independently developed models of social and ecological phenomena. The social component of the model is an abstract model of interactions of a governing agent and several user agents, where the governing agent aims to promote a particular behavior among the user agents. The ecological model is a spatial model of spread of the Mountain Pine Beetle in the forests of British Columbia, Canada. The coupled model allowed us to simulate various hypothetical management scenarios in a context of forest insect infestations. The social and ecological components of this model are developed in two different environments. In order to establish the connection between those components, this model is equipped with a ‘FlipFlop’ - a structure of storage directories and communication protocols which allows each of the models to process its inputs, send an output message to the other, and/or wait for an input message from the other, when necessary. To see the publications associated with the social and ecological components of this coupled model please see the References section.

We present an Agent-Based Stock Flow Consistent Multi-Country model of a Currency Union to analyze the impact of changes in the fiscal regimes that is permanent changes in the deficit-to-GDP targets that governments commit to comply.

This is a simulation model of communication between two groups of managers in the course of project implementation. The “world” of the model is a space of interaction between project participants, each of which belongs either to a group of work performers or to a group of customers. Information about the progress of the project is publicly available and represents the deviation Earned value (EV) from the planned project value (cost baseline).
The key elements of the model are 1) persons belonging to a group of customers or performers, 2) agents that are communication acts. The life cycle of persons is equal to the time of the simulation experiment, the life cycle of the communication act is 3 periods of model time (for the convenience of visualizing behavior during the experiment). The communication act occurs at a specific point in the model space, the coordinates of which are realized as random variables. During the experiment, persons randomly move in the model space. The communication act involves persons belonging to a group of customers and a group of performers, remote from the place of the communication act at a distance not exceeding the value of the communication radius (MaxCommRadius), while at least one representative from each of the groups must participate in the communication act. If none are found, the communication act is not carried out. The number of potential communication acts per unit of model time is a parameter of the model (CommPerTick).

The managerial sense of the feedback is the stimulating effect of the positive value of the accumulated communication complexity (positive background of the project implementation) on the productivity of the performers. Provided there is favorable communication (“trust”, “mutual understanding”) between the customer and the contractor, it is more likely that project operations will be performed with less lag behind the plan or ahead of it.
The behavior of agents in the world of the model (change of coordinates, visualization of agents’ belonging to a specific communicative act at a given time, etc.) is not informative. Content data are obtained in the form of time series of accumulated communicative complexity, the deviation of the earned value from the planned value, average indicators characterizing communication - the total number of communicative acts and the average number of their participants, etc. These data are displayed on graphs during the simulation experiment.
The control elements of the model allow seven independent values to be varied, which, even with a minimum number of varied values (three: minimum, maximum, optimum), gives 3^7 = 2187 different variants of initial conditions. In this case, the statistical processing of the results requires repeated calculation of the model indicators for each grid node. Thus, the set of varied parameters and the range of their variation is determined by the logic of a particular study and represents a significant narrowing of the full set of initial conditions for which the model allows simulation experiments.
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