Opinion dynamics with backfire effect and biased assimilation.
Ontology highlight
ABSTRACT:
SUBMITTER: Chen X
PROVIDER: S-EPMC8409649 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:We study the issue of polarization in society through a model of opinion formation. We say an opinion formation process is polarizing if it results in increased divergence of opinions. Empirical studies have shown that homophily, i.e., greater interaction between like-minded individuals, results in polarization. However, we show that DeGroot's well-known model of opinion formation based on repeated averaging can never be polarizing, even if individuals are arbitrarily homophilous. We generalize DeGroot's model to account for a phenomenon well known in social psychology as biased assimilation: When presented with mixed or inconclusive evidence on a complex issue, individuals draw undue support for their initial position, thereby arriving at a more extreme opinion. We show that in a simple model of homophilous networks, our biased opinion formation process results in polarization if individuals are sufficiently biased. In other words, homophily alone, without biased assimilation, is not sufficient to polarize society. Quite interestingly, biased assimilation also provides a framework to analyze the polarizing effect of Internet-based recommender systems that show us personalized content.
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Project description:The backfire effect is when a correction increases belief in the very misconception it is attempting to correct, and it is often used as a reason not to correct misinformation. The current study aimed to test whether correcting misinformation increases belief more than a no-correction control. Furthermore, we aimed to examine whether item-level differences in backfire rates were associated with test-retest reliability or theoretically meaningful factors. These factors included worldview-related attributes, including perceived importance and strength of precorrection belief, and familiarity-related attributes, including perceived novelty and the illusory truth effect. In 2 nearly identical experiments, we conducted a longitudinal pre/post design with N = 388 and 532 participants. Participants rated 21 misinformation items and were assigned to a correction condition or test-retest control. We found that no items backfired more in the correction condition compared to test-retest control or initial belief ratings. Item backfire rates were strongly negatively correlated with item reliability (ρ = -.61/-.73) and did not correlate with worldview-related attributes. Familiarity-related attributes were significantly correlated with backfire rate, though they did not consistently account for unique variance beyond reliability. While there have been previous papers highlighting the nonreplicable nature of backfire effects, the current findings provide a potential mechanism for this poor replicability. It is crucial for future research into backfire effects to use reliable measures, report the reliability of their measures, and take reliability into account in analyses. Furthermore, fact-checkers and communicators should not avoid giving corrective information due to backfire concerns. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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Project description:Confirmation bias is the tendency to acquire or evaluate new information in a way that is consistent with one's preexisting beliefs. It is omnipresent in psychology, economics, and even scientific practices. Prior theoretical research of this phenomenon has mainly focused on its economic implications possibly missing its potential connections with broader notions of cognitive science.We formulate a (non-Bayesian) model for revising subjective probabilistic opinion of a confirmationally-biased agent in the light of a persuasive opinion. The revision rule ensures that the agent does not react to persuasion that is either far from his current opinion or coincides with it. We demonstrate that the model accounts for the basic phenomenology of the social judgment theory, and allows to study various phenomena such as cognitive dissonance and boomerang effect. The model also displays the order of presentation effect-when consecutively exposed to two opinions, the preference is given to the last opinion (recency) or the first opinion (primacy) -and relates recency to confirmation bias. Finally, we study the model in the case of repeated persuasion and analyze its convergence properties.The standard Bayesian approach to probabilistic opinion revision is inadequate for describing the observed phenomenology of persuasion process. The simple non-Bayesian model proposed here does agree with this phenomenology and is capable of reproducing a spectrum of effects observed in psychology: primacy-recency phenomenon, boomerang effect and cognitive dissonance. We point out several limitations of the model that should motivate its future development.
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Project description:A typical assumption made in the existing opinion formation models is that two individuals can communicate with each other only if the distance between their opinions is less than a threshold called bound of confidence. However, in the real world it is quite possible that people may also have a few friends with quite different opinions. To model this situation, we propose a bounded confidence plus random selection model, in which each agent has several long-range neighbors outside the bound who are selected according to a similarity-based probability rule. We find that the opinions of all agents can reach a consensus in bounded time. We further consider the situation when agents ignore the bound of confidence and select all their neighbors randomly according to the similarity-based probability rule. We prove that in this scenario the whole group could also reach a consensus but in the probability sense.
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Project description:We investigate the two-word Naming Game on two-dimensional random geometric graphs. Studying this model advances our understanding of the spatial distribution and propagation of opinions in social dynamics. A main feature of this model is the spontaneous emergence of spatial structures called opinion domains which are geographic regions with clear boundaries within which all individuals share the same opinion. We provide the mean-field equation for the underlying dynamics and discuss several properties of the equation such as the stationary solutions and two-time-scale separation. For the evolution of the opinion domains we find that the opinion domain boundary propagates at a speed proportional to its curvature. Finally we investigate the impact of committed agents on opinion domains and find the scaling of consensus time.
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Project description:Time is a fundamental dimension, but millisecond-level judgments sometimes lead to perceptual illusions. We previously introduced a "time-shrinking illusion" using a psychological paradigm that induces auditory temporal assimilation (ATA). In ATA, the duration of two successive intervals (T1 and T2), marked by three auditory stimuli, can be perceived as equal when they are not. Here, we investigate the spatiotemporal profile of human temporal judgments using magnetoencephalography (MEG). Behavioural results showed typical ATA: participants judged T1 and T2 as equal when T2 - T1???+80 ms. MEG source-localisation analysis demonstrated that regional activity differences between judgment and no-judgment conditions emerged in the temporoparietal junction (TPJ) during T2. This observation in the TPJ may indicate its involvement in the encoding process when T1???T2. Activation in the inferior frontal gyrus (IFG) was enhanced irrespective of the stimulus patterns when participants engaged in temporal judgment. Furthermore, just after the final marker, activity in the IFG was enhanced specifically for the time-shrinking pattern. This indicates that activity in the IFG is also related to the illusory perception of time-interval equality. Based on these observations, we propose neural signatures for judgments of temporal equality in the human brain.
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Project description:BACKGROUND:Descriptive norms messaging interventions are used to motivate people to adopt or maintain desirable behaviors. Such interventions provide people with information that describes an undesirable behavior as uncommon or a desirable behavior as prevalent within a relevant social group. Descriptive norms messaging interventions have shown promise in increasing individual and social benefit for a broad range of health and sustainability programs. However, evidence suggests that people who have adopted desirable behaviors sometimes regress to undesirable behaviors after receiving descriptive norms messages due to the type of information provided in the messages. This phenomenon is called the boomerang effect. We aim to conduct a systematic review of boomerang effects on health and environmental sustainability behaviors resulting from exposure to descriptive norms messaging interventions. METHODS:We will employ our search strategy to identify studies of descriptive norms messaging interventions published prior to December 31, 2020. We will search the Cochrane Library, Campbell Library, PsycINFO, PubMed, Social Science Research Network (SSRN), and Web of Science to retrieve peer-reviewed articles published in English. We will restrict inclusion to studies (e.g., randomized and non-randomized controlled trials, quasi-experimental studies, and observational studies) of health and environmental sustainability interventions that assess behaviors before and after exposure to descriptive norms messaging. Two reviewers will independently extract data about study populations and design, intervention components, and behavioral measures. We will use the revised Cochrane Risk of Bias assessment tool (RoB2) and Risk Of Bias in Non-randomized Studies-of Intervention (ROBINS-I) to assess the risk of bias, and the Liverpool Quality Assessment Tool (LQAT) to assess the quality of evidence. We will conduct thematic analyses to codify interventions, and examine intervention effects across subgroups of individuals based on their behavior prior to intervention exposure (e.g., those practicing desirable behaviors vs. undesirable behaviors). We will also conduct moderator analyses to determine whether boomerang effects are contingent upon other factors including intervention framing and delivery modality. DISCUSSION:This systematic review will provide information about descriptive norms messaging intervention effects across subgroups of individuals and elucidate factors that potentially moderate boomerang effects. The review will yield evidence-based recommendations for the structure and content of descriptive norms messages that can be employed to avoid unintended boomerang effects within the context of health and sustainability programming. SYSTEMATIC REVIEW REGISTRATION:PROSPERO CRD42020156989.
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Project description:A structurally balanced social network is a social community that splits into two antagonistic factions (typical example being a two-party political system). The process of opinion forming on such a community is most often highly predictable, with polarized opinions reflecting the bipartition of the network. The aim of this paper is to suggest a class of dynamical systems, called monotone systems, as natural models for the dynamics of opinion forming on structurally balanced social networks. The high predictability of the outcome of a decision process is explained in terms of the order-preserving character of the solutions of this class of dynamical systems. If we represent a social network as a signed graph in which individuals are the nodes and the signs of the edges represent friendly or hostile relationships, then the property of structural balance corresponds to the social community being splittable into two antagonistic factions, each containing only friends.
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Project description:Modern technology has drastically changed the way we interact and consume information. For example, online social platforms allow for seamless communication exchanges at an unprecedented scale. However, we are still bounded by cognitive and temporal constraints. Our attention is limited and extremely valuable. Algorithmic personalisation has become a standard approach to tackle the information overload problem. As result, the exposure to our friends' opinions and our perception about important issues might be distorted. However, the effects of algorithmic gatekeeping on our hyper-connected society are poorly understood. Here, we devise an opinion dynamics model where individuals are connected through a social network and adopt opinions as function of the view points they are exposed to. We apply various filtering algorithms that select the opinions shown to each user (i) at random (ii) considering time ordering or (iii) its current opinion. Furthermore, we investigate the interplay between such mechanisms and crucial features of real networks. We found that algorithmic filtering might influence opinions' share and distributions, especially in case information is biased towards the current opinion of each user. These effects are reinforced in networks featuring topological and spatial correlations where echo chambers and polarisation emerge. Conversely, heterogeneity in connectivity patterns reduces such tendency. We consider also a scenario where one opinion, through nudging, is centrally pushed to all users. Interestingly, even minimal nudging is able to change the status quo moving it towards the desired view point. Our findings suggest that simple filtering algorithms might be powerful tools to regulate opinion dynamics taking place on social networks.
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Project description:Jury deliberations provide a quintessential example of collective decision-making, but few studies have probed the available data to explore how juries reach verdicts. We examine how features of jury dynamics can be better understood from the joint distribution of final votes and deliberation time. To do this, we fit several different decision-making models to jury datasets from different places and times. In our best-fit model, jurors influence each other and have an increasing tendency to stick to their opinion of the defendant's guilt or innocence. We also show that this model can explain spikes in mean deliberation times when juries are hung, sub-linear scaling between mean deliberation times and trial duration, and unexpected final vote and deliberation time distributions. Our findings suggest that both stubbornness and herding play an important role in collective decision-making, providing a nuanced insight into how juries reach verdicts, and more generally, how group decisions emerge.
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