An opinion diffusion model with deliberation (original) (raw)
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Mixing Dyadic and Deliberative Opinion Dynamics in an Agent-Based Model of Group Decision-Making
Complexity, 2019
In this article, we propose an agent-based model of opinion diffusion and voting where influence among individuals and deliberation in a group are mixed. The model is inspired from social modeling, as it describes an iterative process of collective decision-making that repeats a series of interindividual influences and collective deliberation steps, and studies the evolution of opinions and decisions in a group. It also aims at founding a comprehensive model to describe collective decision-making as a combination of two different paradigms: argumentation theory and ABM-influence models, which are not obvious to combine as a formal link between them is required. In our model, we find that deliberation, through the exchange of arguments, reduces the variance of opinions and the proportion of extremists in a population as long as not too much deliberation takes place in the decision processes. Additionally, if we define the correct collective decisions in the system in terms of the arg...
Swarm Intelligence
We propose an agent-based model of collective opinion formation to study the wisdom of crowds under social influence. The opinion of an agent is a continuous positive value, denoting its subjective answer to a factual question. The wisdom of crowds states that the average of all opinions is close to the truth, i.e., the correct answer. But if agents have the chance to adjust their opinion in response to the opinions of others, this effect can be destroyed. Our model investigates this scenario by evaluating two competing effects: (1) agents tend to keep their own opinion (individual conviction), (2) they tend to adjust their opinion if they have information about the opinions of others (social influence). For the latter, two different regimes (full information vs. aggregated information) are compared. Our simulations show that social influence only in rare cases enhances the wisdom of crowds. Most often, we find that agents converge to a collective opinion that is even farther away f...
Opinion Formation by Informed Agents
Journal of Artificial Societies and Social Simulation, 2010
Opinion formation and innovation diffusion have gained lots of attention in the last decade due to its application in social and political science. Control of the diffusion process usually takes place using the most influential people in the society, called opinion leaders or key players. But the opinion leaders can hardly be accessed or hired for spreading the desired opinion or information. This is where informed agents can play a key role. Informed agents are common people, not distinguishable from the other members of the society that act in coordination. In this paper we show that informed agents are able to gradually shift the public opinion toward a desired goal through microscopic interactions. In order to do so they pretend to have an opinion similar to others, but while interacting with them, gradually and intentionally change their opinion toward the desired direction. In this paper a computational model for opinion formation by the informed agents based on the bounded confidence model is proposed. The effects of different parameter settings including population size of the informed agents, their characteristics, and network structure, are investigated. The results show that social and open-minded informed agents are more efficient than selfish or closed-minded agents in control of the public opinion.
Opinion Dynamics Model Based on Cognitive Biases of Complex Agents
Journal of Artificial Societies and Social Simulation, 2018
We present an introduction to a novel way of simulating individual and group opinion dynamics, taking into account how various sources of information are filtered due to cognitive biases. The agent-based model presented here falls into the 'complex agent' category, in which the agents are described in considerably greater detail than in the simplest 'spinson' model. To describe agents' information processing, we introduced mechanisms of updating individual belief distributions, relying on information processing. The open nature of this proposed model allows us to study the e ects of various static and time-dependent biases and information filters. In particular, the paper compares the e ects of two important psychological mechanisms: confirmation bias and politically motivated reasoning. This comparison has been prompted by recent experimental psychology work by Dan Kahan. Depending on the e ectiveness of information filtering (agent bias), agents confronted with an objective information source can either reach a consensus based on truth, or remain divided despite the evidence. In general, this model might provide understanding into increasingly polarized modern societies, especially as it allows us to mix di erent types of filters: e.g., psychological, social, and algorithmic.
Achieving consensus among agents-an opinion-dynamics model
2008
ABSTRACT The paper considers the problem of how a distributed system of agents (who communicate only via a localised network) might achieve consensus by copying beliefs (copy) from each other and doing some belief pruning themselves (drop). This is explored using a social simulation model, where beliefs interact with each other via a compatibility function, which assigns a level of compatibility (which is a sort of weak consistency) to a set of beliefs.
Polarizing crowds: Consensus and bipolarization in a persuasive arguments model
Chaos: An Interdisciplinary Journal of Nonlinear Science, 2020
Understanding the opinion formation dynamics in social systems is of vast relevance in diverse aspects of society. In particular, it is relevant for political deliberation and other group decision-making processes. Although previous research has reported different approaches to model social dynamics, most of them focused on interaction mechanisms where individuals modify their opinions in line with the opinions of others, without invoking a latent mechanism of argumentation. In this paper, we present a model where changes of opinion are due to explicit exchanges of arguments, and we analyze the emerging collective states in terms of simple dynamic rules. We find that, when interactions are equiprobable and symmetrical, the model only shows consensus solutions. However, when either homophily, confirmation bias, or both are included, we observe the emergence and dominance of bipolarization, which appears due to the fact that individuals are not able to accept the contrary information from their opponents during exchanges of arguments. In all cases, the predominance of each stable state depends on the relation between the number of agents and the number of available arguments in the discussion. Overall, this paper describes the dynamics and shows the conditions wherein deliberative agents are expected to construct polarized societies.
Agent-Based Models for Opinion Formation: A Bibliographic Survey
IEEE Access
Agent-based models are now largely adopted to describe how opinions emerge in a group of people. This survey provides an analysis of the literature on the subject, highlighting the major characteristics of such models. Over the last decade, the number of papers has grown at an overall annual rate of 16%, though not continually. Two communities contribute to the research effort: physics and control systems. However, their mutual awareness and collaboration are rather low. The prevailing mechanism adopted to describe the interaction among the agents is bilateral, but not symmetric. In most cases, the opinion is described by a continuous variable. Just a few papers consider a utility function for the agents. INDEX TERMS Agent-based modeling, decentralized control, multi-agent systems, opinion dynamics.
Diffusion Controlled Model of Opinion Dynamics
Reports in Advances of Physical Sciences, 2017
We have studied the effect of diffusion controlled opinion dynamics on a ring lattice where agents are placed on a fraction of sites. We have chosen the diffusion on a circular ring as a simple model to study emphasizing on the fact that agents approach their nearest neighbor for exchanging opinion. The agents execute simple exclusion process (SEP) on the ring and exchange opinion with neighboring agents according to a fixed rule. Our study shows that as agent density decreases, higher conviction power is necessary to create consensus. We have also investigated the nature of active-to-absorbing state phase transition for various densities and found that there are two universality classes for density [Formula: see text] and [Formula: see text].
A population dynamics model for opinion dynamics with prominent agents and incentives
2013 American Control Conference, 2013
In this paper, the design of a population dynamics model based on both opinion and imitator dynamics is presented. This approach is focused on the analysis of some population behaviors such as the emergence of either consensus or disagreement, information aggregation, and spread of misinformation. The analysis of these properties is made in the context of network populations with or without the presence of prominent agents and environmental incentives. Some simulation results illustrate the ideas presented in this paper.