This code can be used to analyze the sensitivity of the Deffuant model to different measurement errors. Specifically to:
- Intrinsic stochastic error
- Binning of the measurement scale
- Random measurement noise
- Psychometric distortions

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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.

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.

This generic agent-based model simulates the evolution of agent’s opinions through their exchange of arguments.
The idea behind this model is to explicitly represent the process of mental deliberation of agents from arguments to an opinion, through the use of Dung’s argumentation framework complemented by a structured description of arguments. An application of the model on the diffusion of vegetarian diets is proposed.

The simulation is a variant of the “ToRealSim OD variants - base v2.7” base model, which is based on the standard DW opinion dynamics model (but with the differences that rather than one agent per tick randomly influencing another, all agents randomly influence one other per tick - this seems to make no difference to the outcomes other than to scale simulation time). Influence can be made one-way by turning off the two-way? switch

Various additional variations and sources of noise are possible to test robustness of outcomes to these (compared to DW model).
In this version agent opinions change following the empirical data collected in some experiments (Takács et al 2016).

Such an algorithm leaves no role for the uncertainties in other OD models. [Indeed the data from (Takács et al 2016) indicates that there can be influence even when opinion differences are large - which violates a core assumption of these]. However to allow better comparison with other such models there is a with-un? switch which allows uncertainties to come into play. If this is on, then influence (according to above algorithm) is only calculated if the opinion difference is less than the uncertainty. If an agent is influenced uncertainties are modified in the same way as standard DW models.

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]

Contains python3 code to replicate the opinion dynamics model from our (so far unpublished) JASSS sumbission “A Balance Model of Opinion Hyperpolarization”. The main function is run_model(), which returns a dictionary object containing various outcome metrics.

A model for simulating the evolution of individual’s preferences, incliding adaptive agents “falsifying” -as public opinions- their own preferences. It was builded to describe, explore, experiment and understand how simple heuristics can modulate global opinion dynamics. So far two mechanisms are implemented: a version of Festiguer’s reduction of cognitive disonance, and a version of Goffman’s impression management. In certain social contexts -minority, social rank presure- some models agents can “fake” its public opinion while keeping internally the oposite preference, but after a number of rounds following this falsifying behaviour pattern, a coherence principle can change the real or internal preferences close to that expressed in public.

Aroused public opinion has led to public debates on social responsibility issues in food supply chains. This model based op opinion dynamics and the linkages between involved actors simulates the public debate leading to the transitions.

Project for the course “Introduction to Agent-Based Modeling”.

The NetLogo model implements an Opinion Dynamics model with different confidence distributions, inspired by the Bounded Confidence model presented by Hegselmann and Krause in 2002. Hegselmann and Krause used a model with uniform distribution of confidence, but one could imagine agents that are more confident in their own opinions than others. Confidence with triangular, semi-circular, and Gaussian distributions are implemented. Moreover, network structure is optional and can be taken into account in the agent’s confidence such that agents assign less confidence the further away from them other agents are.