Digital social networks facilitate the opinion dynamics and idea flow and also provide reliable data to understand these dynamics. Public opinion and cooperation behavior are the key factors to determine the capacity of a successful and effective public policy. In particular, during the crises, such as the Corona virus pandemic, it is necessary to understand the people’s opinion toward a policy and the performance of the governance institutions. The problem of the mathematical explanation of the human behaviors is to simplify and bypass some of the essential process. To tackle this problem, we adopted a data-driven strategy to extract opinion and behavioral patterns from social media content to reflect the dynamics of society’s average beliefs toward different topics. We extracted important subtopics from social media contents and analyze the sentiments of users at each subtopic. Subsequently, we structured a Bayesian belief network to demonstrate the macro patters of the beliefs, opinions, information and emotions which trigger the response toward a prospective policy. We aim to understand the factors and latent factors which influence the opinion formation in the society. Our goal is to enhance the reality of the simulations. To capture the dynamics of opinions at an artificial society we apply agent-based opinion dynamics modeling. We intended to investigate practical implementation scenarios of this framework for policy analysis during Corona Virus Pandemic Crisis. The implemented modular modeling approach could be used as a flexible data-driven policy making tools to investigate public opinion in social media. The core idea is to put the opinion dynamics in the wider contexts of the collective decision-making, data-driven policy-modeling and digital democracy. We intended to use data-driven agent-based modeling as a comprehensive analysis tools to understand the collective opinion dynamics and decision making process on the social networks and uses this knowledge to utilize network-enabled policy modeling and collective intelligence platforms.

This code is for an agent-based model of collective problem solving in which agents with different behavior strategies, explore the NK landscape while they communicate with their peers agents. This model is based on the famous work of Lazer, D., & Friedman, A. (2007), The network structure of exploration and exploitation.

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.

Like many developing countries, Nigeria is faced with a number of tradeoffs that pit rapid economic development against environmental preservation. Environmentally sustainable, “green” economic development is slower, more costly, and more difficult than unrestricted, unregulated economic growth. The mathematical model that we develop in this code suggests that widespread public awareness of environmental issues is insufficient to prevent the tendency towards sacrificing the environment for the sake of growth. Even if people have an understanding of negative impacts and always choose to act in their own self-interest, they may still act collectively in such a way as to bring down the quality of life for the entire society. We conclude that additional actions must be taken besides raising public awareness of the environmental problem.

this agent-based model explores the dynamics of volunteer participation in urban community gardens, by combining behavioral theory and institutional theory

The name of the model, CoComForest, stands for COllaborative COMmunity FOREST management. The purposes of this model are to expose local resource harvesters to the competition with external resource harvesters, called outsiders, and to provide them the opportunity to collectively discuss on resource management. The model, which is made of a set of interconnected entities, including (i) community forest habitat, (ii) resource harvesters, (iii) market, and (iv) firebreak. More details about the CoComForest model are described based on the Overview, Design concept, and Details (ODD) protocol uploaded with the model.

WatASit is an agent-based model implemented in the CORMAS plateform. The model is developped to simulate irrigation situations at the operational level during a collective irrigation campaign.

The O.R.E. (Opinions on Risky Events) model describes how a population of interacting individuals process information about a risk of natural catastrophe. The institutional information gives the official evaluation of the risk; the agents receive this communication, process it and also speak to each other processing further the information. The description of the algorithm (as it appears also in the paper) can be found in the attached file OREmodel_description.pdf.
The code (ORE_model.c), written in C, is commented. Also the datasets (inputFACEBOOK.txt and inputEMAILs.txt) of the real networks utilized with this model are available.

For any questions/requests, please write me at [email protected]

Overview

Purpose

Modeling an economy with stable macro signals, that works as a benchmark for studying the effects of the agent activities, e.g. extortion, at the service of the elaboration of public policies..
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The Garbage Can Model of Organizational Choice (GCM) is a fundamental model of organizational decision-making originally propossed by J.D. Cohen, J.G. March and J.P. Olsen in 1972. In their model, decisions are made out of random meetings of decision-makers, opportunities, solutions and problems within an organization.
With this model, these very same agents are supposed to meet in society at large where they make decisions according to GCM rules. Furthermore, under certain additional conditions decision-makers, opportunities, solutions and problems form stable organizations. In this artificial ecology organizations are born, grow and eventually vanish with time.