Computational Model Library

Studies on the fundamental role of diverse media in the evolution of public opinion can protect us from the spreading brainwashing, extremism, and terrorism. Many fear the information cocoon may result in polarization of the public opinion. The model of opinion dynamics that considers different influences and horizons for every individual, and the simulations are based on a real-world social network.

Bayesian Updating Opinion Shared Uncertainty Model.

Johnathan Adams | Published Mon Nov 16 23:25:02 2020 | Last modified Tue Dec 1 01:27:29 2020

This is an opinion dynamics model which extends the model found in (Martins 2009). The previous model had an unshared uncertainty assumption in agent-to-agent interaction this model relaxes that assumption. The model only supports a fully connect network where every agent has an equal likelihood of interacting with every other agent at any given time step. The model is highly modular so different social network paradigm can easier be implemented.

This model was built to estimate the impacts of exogenous fodder input and credit loans services on livelihood, rangeland health and profits of pastoral production in a small holder pastoral household in the arid steppe rangeland of Inner Mongolia, China. The model simulated the long-term dynamic of herd size and structure, the forage demand and supply, the cash flow, and the situation of loan debt under three different stocking strategies: (1) No external fodder input, (2) fodders were only imported when natural disaster occurred, and (3) frequent import of external fodder, with different amount of available credit loans. Monte-Carlo method was used to address the influence of climate variability.

1984 social computation model

Harun Šiljak | Published Mon Sep 30 16:27:46 2019

A system of nonlinear differential equations, modelled in MATLAB Simulink, simulating the world of George Orwell’s 1984.


Nicholas Magliocca | Published Thu Aug 29 18:35:53 2019

Investigate spatial adaptive behaviors of narco-trafficking networks in response to various counterdrug interdiction strategies within the cocaine transit zone of Central America and associated maritime areas. Through the novel application of the ‘complex adaptive systems’ paradigm, we implement a potentially transformative coupled agent-based and interdiction optimization modeling approach to compellingly demonstrate: (a) how current efforts to disrupt narco-trafficking networks are in fact making them more widespread, resilient, and economically powerful; (b) the potential for alternative interdiction approaches to weaken and contain traffickers.

An economic agent-based model of Coupled Housing and Land Markets (CHALMS) simulates the location choices, insurance purchasing decisions, and risk perceptions of coastal residents, and how coastal risks are capitalized (or not) into coastal housing and land markets.

We establish a double-layer network for China’s financial system, consisting of an interbank lending network and a cross-shareholding network. The loss of diffusion in an interbank lending channel independently, a cross-shareholding channel independently and a double-layer contagion channel after one of the financial institutions goes bankrupt with an initial shock are simulated to explore the nonlinear evolution mechanism of financial risk and impact factors of financial systemic risk in China.

Online Collaboration, Competing for Attention

Miles Manning | Published Wed Jul 19 02:42:20 2017 | Last modified Thu Jan 24 21:24:57 2019

This is a model of a community of online communities. Using mechanisms such as win-stay, lose-shift, and preferential attachment the model can reproduce similar patterns to those of the Stack Exchange network.

Crowd dynamics have important applications in evacuation management systems relevant to organizing safer large scale gatherings. For crowd safety, it is very important to study the evolution of potential crowd behaviours by simulating the crowd evacuation process. Planning crowd control tasks by studying the impact of crowd behaviour evolution towards evacuation could mitigate the possibility of crowd disasters. During a typical emergency evacuation scenario, conflict among agents occurs when agents intend to move to the same location as a result of the interaction with their nearest neighbours. The effect of the agent response towards their neighbourhood is vital in order to understand the effect of variation of crowd behaviour on the whole environment. In this work, we model crowd motion subject to exit congestion under uncertainty conditions in a continuous space via computer simulations. We model best-response, risk-seeking, risk-averse and risk-neutral behaviours of agents via certain game theoretic notions. We perform computer simulations with heterogeneous populations in order to study the effect of the evolution of agent behaviours towards egress flow under threat conditions. Our simulation results show the relation between the local crowd pressure and the number of injured agents. We observe that when the proportion of agents in a population of risk-seeking agents is increased, the average crowd pressure, average local density and the number of injured agents increases. Besides that, based on our simulation results, we can infer that crowd disasters could be prevented if the agent population consists entirely of risk-averse and risk-neutral agents despite circumstances that lead to threats.

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.

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