Computational Model Library

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.

This version of the accumulated copying error (ACE) model is designed to address the following research question: how does finite population size (N) affect the coefficient of variation (CV) of a continuous cultural trait under the assumptions that the only source of copying error is visual perception error and that the continuous trait can take any positive value (i.e., it has no upper bound)? The model allows one to address this question while assuming the continuous trait is transmitted via vertical transmission, unbiased transmission, prestige biased transmission, mean conformist transmission, or median conformist transmission. By varying the parameter, p, one can also investigate the effect of population size under a mix of vertical and non-vertical transmission, whereby on average (1-p)N individuals learn via vertical transmission and pN individuals learn via either unbiased transmission, prestige biased transmission, mean conformist transmission, or median conformist transmission.

Food Safety Inspection Model - Stores Signal

Sara Mcphee-Knowles | Published Wed Mar 5 20:47:06 2014 | Last modified Mon Aug 26 23:13:09 2019

The Inspection Model represents a basic food safety system where inspectors, consumers and stores interact. The purpose of the model is to provide insight into an optimal level of inspectors in a food system by comparing three search strategies.

Food Safety Inspection Model - Stores Signal with Errors

Sara Mcphee-Knowles | Published Wed Mar 5 20:45:00 2014 | Last modified Mon Apr 8 20:40:48 2019

The Inspection Model represents a basic food safety system where inspectors, consumers and stores interact. The purpose of the model is to provide insight into an optimal level of inspectors in a food system by comparing three search strategies.

Food Safety Inspection Model - Random Strategy

Sara Mcphee-Knowles | Published Wed Mar 5 20:32:44 2014 | Last modified Mon Apr 8 20:40:48 2019

The Inspection Model represents a basic food safety system where inspectors, consumers and stores interact. The purpose of the model is to provide insight into an optimal level of inspectors in a food system by comparing three search strategies.

Previous work with the spatial iterated prisoner’s dilemma has shown that “walk away” cooperators are able to outcompete defectors as well as cooperators that do not respond to defection, but it remains to be seen just how robust the so-called walk away strategy is to ecologically important variables such as population density, error, and offspring dispersal. Our simulation experiments identify socio-ecological conditions in which natural selection favors strategies that emphasize forgiveness over flight in the spatial iterated prisoner’s dilemma. Our interesting results are best explained by considering how population density, error, and offspring dispersal affect the opportunity cost associated with walking away from an error-prone partner.

Decision-makers often have to act before critical times to avoid the collapse of ecosystems using knowledge \textcolor{red}{that can be incomplete or biased}. Adaptive management may help managers tackle such issues. However, because the knowledge infrastructure required for adaptive management may be mobilized in several ways, we study the quality and the quantity of knowledge provided by this knowledge infrastructure. In order to analyze the influence of mobilized knowledge, we study how the following typology of knowledge and its use may impact the safe operating space of exploited ecosystems: 1) knowledge of the past based on a time series distorted by measurement errors; 2) knowledge of the current systems’ dynamics based on the representativeness of the decision-makers’ mental models of the exploited ecosystem; 3) knowledge of future events based on decision-makers’ likelihood estimates of extreme events based on modeling infrastructure (models and experts to interpret them) they have at their disposal. We consider different adaptive management strategies of a general regulated exploited ecosystem model and we characterize the robustness of these strategies to biased knowledge. Our results show that even with significant mobilized knowledge and optimal strategies, imperfect knowledge may still shrink the safe operating space of the system leading to the collapse of the system. However, and perhaps more interestingly, we also show that in some cases imperfect knowledge may unexpectedly increase the safe operating space by suggesting cautious strategies.
The code enables to calculate the safe operating spaces of different managers in the case of biased and unbiased knowledge.

Spatial model of the noisy Prisoner's Dilemma with reward shift

Matus Halas | Published Thu Mar 5 16:17:54 2015 | Last modified Tue May 29 09:09:01 2018

Interactions of players embedded in a closed square lattice are determined by distance and overall gains and they lead to shifts of reward payoff between temptation and punishment. A new winner balancing against threats is ultimately discovered.

Auroa Shooting Model

Roy Lee Hayes Reginald Lee Hayes | Published Fri Oct 25 01:46:40 2013

On July 20th, James Holmes committed a mass shooting in a midnight showing of The Dark Knight Rises. The Aurora Colorado shooting was used as a test case to validate this framework for modeling mass shootings.

InnovationGame

Madeline Tyson | Published Thu Aug 24 19:11:30 2017

This model includes an innovation search environment. Agents search and can share their findings. It’s implemented in Netlogo-Hubnet & a parallel Netlogo model. This allows for validation of search strategies against experimental findings.

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