CoMSES Net maintains cyberinfrastructure to foster FAIR data principles for access to and (re)use of computational models. Model authors can publish their model code in the Computational Model Library with documentation, metadata, and data dependencies and support these FAIR data principles as well as best practices for software citation. Model authors can also request that their model code be peer reviewed to receive a DOI. All users of models published in the library must cite model authors when they use and benefit from their code.
CoMSES Net also maintains a curated database of over 7500 publications of agent-based and individual based models with additional metadata on availability of code and bibliometric information on the landscape of ABM/IBM publications that we welcome you to explore.
The purpose of the model is to generate coalition structures of different glove games, using a specially designed algorithm. The coalition structures can be are later analyzed by comparing them to core partitions of the game used. Core partitions are coalition structures where no subset of players has an incentive to form a new coalition.
The algorithm used in this model is an advancement of the algorithm found in Collins & Frydenlund (2018). It was used used to generate the results in Vernon-Bido & Collins (2021).
A “Ger” is a yurt style house used by pastoralists in Mongolia. This model simulates seasonal movements, fission/fusion dynamics, social interaction between households and how these relate to climate impacts.
Schelling and Sakoda prominently proposed computational models suggesting that strong ethnic residential segregation can be the unintended outcome of a self-reinforcing dynamic driven by choices of individuals with rather tolerant ethnic preferences. There are only few attempts to apply this view to school choice, another important arena in which ethnic segregation occurs. In the current paper, we explore with an agent-based theoretical model similar to those proposed for residential segregation, how ethnic tolerance among parents can affect the level of school segregation. More specifically, we ask whether and under which conditions school segregation could be reduced if more parents hold tolerant ethnic preferences. We move beyond earlier models of school segregation in three ways. First, we model individual school choices using a random utility discrete choice approach. Second, we vary the pattern of ethnic segregation in the residential context of school choices systematically, comparing residential maps in which segregation is unrelated to parents’ level of tolerance to residential maps reflecting their ethnic preferences. Third, we introduce heterogeneity in tolerance levels among parents belonging to the same group. Our simulation experiments suggest that ethnic school segregation can be a very robust phenomenon, occurring even when about half of the population prefers mixed to segregated schools. However, we also identify a “sweet spot” in the parameter space in which a larger proportion of tolerant parents makes the biggest difference. This is the case when parents have moderate preferences for nearby schools and there is only little residential segregation. Further experiments are presented that unravel the underlying mechanisms.
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.
The purpose of the model is to collect information on human decision-making in the context of coalition formation games. The model uses a human-in-the-loop approach, and a single human is involved in each trial. All other agents are controlled by the ABMSCORE algorithm (Vernon-Bido and Collins 2020), which is an extension of the algorithm created by Collins and Frydenlund (2018). The glove game, a standard cooperative game, is used as the model scenario.
The intent of the game is to collection information on the human players behavior and how that compares to the computerized agents behavior. The final coalition structure of the game is compared to an ideal output (the core of the games).
This model makes it possible to explore how network clustering and resistance to changing existing status beliefs might affect the spontaneous emergence and diffusion of such beliefs as described by status construction theory.
B3GET simulates populations of virtual organisms evolving over generations, whose evolutionary outcomes reflect the selection pressures of their environment. The model simulates several factors considered important in biology, including life history trade-offs, investment in fighting ability and aggression, sperm competition, infanticide, and competition over access to food and mates. Downloaded materials include a starting genotype and population files. Edit the these files and see what changes occur in the behavior of virtual populations!
The Multilevel Group Selection I (MGS I) model simulates a population of contributing and non-contributing agents, competing on a social landscape for higher-value spots in an effort to withstand some selection pressure. It may be useful to both scientists and students in hypothesis testing, theory development, or more generally in understanding multilevel group selection.
The model is based on the influence function of the Leviathan model (Deffuant, Carletti, Huet 2013 and Huet and Deffuant 2017), considering that all the agents belong to the same ingroup. This agent-based model studies how sharing the same group identity reduce the potential negative effect of gossip.
We consider agents sharing a single group, having an opinion/esteem about each other, about themselves and about the group. During dyadic meetings, agents change their respective opinion about each other, about the group, 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. The expressed opinion of an agent about another one is a combination of the opinion about the other agent and the opinion about the group.
We show that the addition of the group in the Leviathan model reduce the discrepancy between reputations, even if the group is not very important for the agents. In addition, the homogenization of the opinions reduce the negative effect of gossip.