An Agent-Based Dialogical Model with Fuzzy Attitudes

Dykstra, Piter; Jager, Wander; Elsenbroich, Corinna; Verbrugge, Rineke; Lavalette, Gerard R. Renardel de

doi:10.18564/jasss.2813

Cited by 9 publications

(5 citation statements)

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“…Likewise, Axelrod's cultural dissemination model [67] uses spatially local interaction with a regular lattice in which partner selection is proportional to the proportion of opinion dimensions that have the same trait. Another set of models [27][28][29] building on self-categorization theory [62] allow actors to change their opinion according to a prototype in their ingroup as well as take repulsive influence from outgroup members. My model is distinct in that it explicitly models the source of spatial constraints by parameterizing positions and dimensions of a hypothetical population structure and examining the associated group arrangements across multiple dimensions.…”

Section: Social Consolidation and Opinion Polarizationmentioning

confidence: 99%

“…But most opinion models-largely developed from a small group setting with much parsimony-that predict opinion clustering or polarization assume only a single dimension where opinions and interactions determine each other. There are a very few computational models [27][28][29] incorporating social identity theory and intergroup relations, but they have not explicitly considered how the population's correlated structure comes into play in interactions. As a result, we lack a theoretical understanding of how population structures relationally promote or inhibit micro network mechanisms in opinion formation processes.…”

Section: Introductionmentioning

confidence: 99%

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Social Consolidation, Spatial Clustering, and Opinion Polarization

Lee¹

2023

Preprint

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Network models of opinion formation highlight homophily and interpersonal influences as profound micro-mechanisms for opinion polarization. Yet, in these models, individuals are usually presumed to behave in a single dimension where opinions determine interactions and vice versa. Consequently, these models have not explicitly considered how the population’s correlated structure configures mixing opportunities among different groups and, in turn, are incapable of making sense of the alignment-driven polarization processes that have been widely documented in empirical research (e.g., ideological identities and sociodemographic groups). Drawing on ecological approaches of network formation, this study examines how social consolidation—the correlation between positions across multiple social dimensions—interacts with micro-mechanisms to produce opinion polarization. I first construct an agent-based model to show that depending on the level of consolidation, the same set of micro-mechanisms contributing to either consensus or polarization can produce different polarization outcomes due to spatial clustering and opinion alignment along multiple dimensions that this model introduces. Then I empirically test a primary theoretical mechanism that links consolidation to polarization: spatial clustering. Using new spatial estimates drawn from 180M geo-coded voter profiles in the United States (Brown and Enos 2021), I find novel evidence that counties and census tracts of high sociodemographic consolidation (measured by a race-education correlation) tend to present greater partisan isolation (a higher proportion of co-partisans in their nearest neighbors). These findings suggest social consolidation constitutes a strong foundation for opinion polarization.

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Section: Social Consolidation and Opinion Polarizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Social Consolidation, Spatial Clustering, and Opinion Polarization

Lee¹

2023

Preprint

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“…• Assimilative, Friedkin and Johnsen (2011); Groeber et al (2014), based on classic social psychological theory that individuals who are connected by a shared social identity always influence each other towards reducing the differentiation between them (through a process of averaging); • Similarity biased, Sherif and Hovland (1961);Festinger (1962); Axelrod (1997), whereby only individuals who are sufficiently similar can influence each other; and • Repulsive, Festinger (1962); Flache and Macy (2011); Dykstra et al (2015), where it is assumed individuals who are highly different still influence each other, but towards increasing those differences. Thus, clusters may form as maximally oppositional views through a process of bi-polarization Flache et al (2017).…”

Section: Psycho-social Models Of Influence and Bondingmentioning

confidence: 99%

The Effect of Influencers on Societal Polarization

Betts

Bliuc

2022

2022 Winter Simulation Conference (WSC)

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Societies can polarize when there is disagreement on important issues. The rise of social media in recent years has led to the phenomenon of influencers, who are now prominent in public debate, especially online. However, their effect on polarization is not well understood. To address this gap in knowledge, we create an Agent-Based Model of a society into which an influencer is introduced and their effect on polarization observed. Results show that an influencer holding extreme views will always increase the rate of polarization, with this effect increasing in line with their reach and activity level. The effect of a neutral influencer varies with the tolerance for opposing beliefs in the society: slowing the rate of polarization for relatively tolerant societies, but increasing the rate when societies are more conservative, or the influencer has narrow reach. Consequently, these results have implications for the design of influencer campaigns for social good.

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“…It allows the representation of heterogeneous and independent entities, as well as complex interactions and partial connections between objects. Additionally, to represent the diversity of preferences and opinions, as well as the particular knowledge of every single agent into a simulated population, some authors have used fuzzy logic [9]. Table 1 shows works on adopting and decision-making using simulation.…”

Section: Introductionmentioning

confidence: 99%

An agent-based behavioral model of technology consumers in a dynamic social network

Forero¹,

Ceballos²,

Sánchez-Torres³

2017

ces

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This article describes the design of a model to identify the relevant factors in the decision-making process of consumers that adopt technology within a dynamic social network. The proposed model includes specific theories and tools from the psychology of consumer behavior, social networks, and complex dynamical systems. The model has been developed to work with the mobile smartphone market and was able to describe trends similar to those described in the real world market.

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An Agent-Based Dialogical Model with Fuzzy Attitudes

Cited by 9 publications

References 0 publications

Social Consolidation, Spatial Clustering, and Opinion Polarization

Social Consolidation, Spatial Clustering, and Opinion Polarization

The Effect of Influencers on Societal Polarization

An agent-based behavioral model of technology consumers in a dynamic social network

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