Polarized Extremes and the Confused Centre: Campaign Targeting of Voters with Correlation Neglect

Levy, Gilat; Barreda, Inés Moreno de; Razin, Ronny

doi:10.1561/100.00019125

Cited by 7 publications

(5 citation statements)

References 22 publications

(30 reference statements)

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“…Such expectations are matched by the data [10]. Assessing American National Election Studies (ANES) and General Social Survey (GSS) data from the last five decades demonstrates strong differences of opinion between liberals and conservatives on a vast variety of issues.…”

Section: Confused Centrists and Polarised Extremes: Empirical Evidencementioning

confidence: 83%

“…In a recent paper, Levy and colleagues analyse a model of targeted and coordinated campaigns [10]. The model assumes that voters' opinions can be manipulated and that such manipulation is possible in part because voters are unaware of the correlation between the sources of information.…”

Section: Correlation Neglect and Targeted Campaignsmentioning

confidence: 99%

“…As these voters are able to pull moderates to their side, they then also enter the 'echo chamber' , cutting them off from other perspectives. The success of such campaigns can thus result in increased polarisation [10].…”

Section: Correlation Neglect and Targeted Campaignsmentioning

confidence: 99%

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Social Media and Political Polarisation

Levy¹,

Razin²

2022

Populism: Origins and Alternative Policy Responses

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The rise in populism in the Western world, most evident in the results of the 2016 Brexit referendum and the 2016 United States presidential election, has often been connected with the rise of social media. The unique character of social media has allowed extreme and polarised beliefs, two of the most identifiable features of populism, to emerge and spread in society through permitting the creation of echo chambers on a new larger scale, and providing new means for political campaigners and interested third parties to influence voter opinion. The abundance of information on social media might trigger voters to use simple heuristics to aggregate multiple sources of information. In this chapter we report on several studies that focus on the implications of one such documented bias: ‘correlation neglect’, the propensity to treat information sources as if they are (conditionally) independent. We discuss the relation between correlation neglect and polarisation in opinions and party platforms. We also discuss how targeted political campaigns in the presence of correlation neglect may bias voters from different groups in different directions. Specifically, competition in targeted social media campaigns increases polarisation among extreme voters but at the same time increases the randomness and unpredictability of moderates’ voting behaviour. These findings are consistent with new data on the evolution of US voters’ opinions in the last five decades. The data show a significant change in the trajectory of the opinions of moderates versus extreme voters starting from the mid-1990s, which is consistent with the rise in the ability of campaigns more effectively to target and bombard voters with information through social media.

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Section: Confused Centrists and Polarised Extremes: Empirical Evidencementioning

confidence: 83%

Section: Correlation Neglect and Targeted Campaignsmentioning

confidence: 99%

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Social Media and Political Polarisation

Levy¹,

Razin²

2022

Populism: Origins and Alternative Policy Responses

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“…11 Subsequent papers include Fudenberg, Romanyuk, and Strack (2017), Molavi (2019), Bohren and Hauser (2021), Fudenberg and Lanzani (2023), He and Libgober (2021), Esponda, Pouzo, and Yamamoto (2021), Heidhues, Kőszegi, and Strack (2021), Levy, Moreno de Barreda, and Razin (2021), He (2022), and Frick, Iijima, and Ishii (2023). Before this, Arrow and Green (1973) gave the first general framework for this problem, and Nyarko (1991) pointed out that the combination of misspecification and endogenous observations can lead to cycles.…”

mentioning

confidence: 99%

Pathwise concentration bounds for Bayesian beliefs

Fudenberg,

Lanzani,

Strack

2023

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We show that Bayesian posteriors concentrate on the outcome distributions that approximately minimize the Kullback–Leibler divergence from the empirical distribution, uniformly over sample paths, even when the prior does not have full support. This generalizes Diaconis and Freedman's (1990) uniform convergence result to, e.g., priors that have finite support, are constrained by independence assumptions, or have a parametric form that cannot match some probability distributions. The concentration result lets us provide a rate of convergence for Berk's (1966) result on the limiting behavior of posterior beliefs when the prior is misspecified. We provide a bound on approximation errors in “anticipated‐utility” models, and extend our analysis to outcomes that are perceived to follow a Markov process.

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“…In Levy, Moreno de Barreda and Razin (2021b) we consider a related model that is applied to political competition.…”

mentioning

confidence: 99%

Persuasion with Correlation Neglect: A Full Manipulation Result

Levy

Barreda

Razin

2022

American Economic Review: Insights

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We consider an information design problem in which a sender tries to persuade a receiver that has “correlation neglect,” i.e., fails to understand that signals might be correlated. We show that a sender with unlimited number of signals can fully manipulate the receiver. Specifically, the sender can induce the receiver to hold any state-dependent posterior she wishes to. If the sender only wishes to induce a state-independent posterior, she can use fully correlated signals, but generally she needs to design more involved correlation structures. (JEL D82, D83)

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Polarized Extremes and the Confused Centre: Campaign Targeting of Voters with Correlation Neglect

Cited by 7 publications

References 22 publications

Social Media and Political Polarisation

Social Media and Political Polarisation

Pathwise concentration bounds for Bayesian beliefs

Persuasion with Correlation Neglect: A Full Manipulation Result

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