The role of source reliability in belief polarisation

Henderson, Leah; Gebharter, Alexander

doi:10.1007/s11229-021-03244-y

Cited by 6 publications

(9 citation statements)

References 31 publications

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“…Bayesian updating including source reliability (Koehler, 1993) Bayesian networks (Cook & Lewandowsky, 2016;Henderson & Gebharter, 2021;Jern et al, 2014) Biased evaluation prior to assimilation (Gerber & Green, 1999;Lord et al, 1979) Belief-based sequential updating with source reliability (Bovens et al, 2003;Hahn et al, 2018;Merdes et al, 2020;Olsson, 2011) BIASR. Bayesian updating with an independence approximation and source reliability Biased evaluation…”

Section: Simple Version Of Bayes' Theoremmentioning

confidence: 99%

“…Jern et al (2014) generalise this idea by describing the set of Bayesian networks that can lead rational agents to attitude polarisation -crucially this set of networks share the property that upon receiving some data, beliefs about more than one hypothesis are updated simultaneously. In order to generate rational attitude polarisation, individuals require differences in prior beliefs about the ''central'' hypothesis in question, and importantly also some difference in other ''auxiliary'' prior beliefs (Gerber & Green, 1999;Henderson & Gebharter, 2021). For example, those with strong views about the dangers of climate change may also believe that scientific evidence is more reliable than those who are less worried about climate change (Cook & Lewandowsky, 2016).…”

Section: Simple Version Of Bayes' Theoremmentioning

confidence: 99%

“…Other recent work explaining polarisation has proposed additional mechanisms such as relying on participants optionally stopping evidence accumulation when they are polarised (Kvam, Alaukik, Mims, Martemyanova, & Baldwin, 2022) or having an additional private source of affective information (Melnikoff & Strohminger, 2023). The rational basis for attitude polarisation was explored further by Henderson and Gebharter (2021) using a Bayesian network where evidence is influenced by the true values of the central hypothesis and source reliability, as in the BIASR model. They conclude that attitude polarisation can arise only if the individuals have different prior beliefs in both the central hypothesis and source reliability (Henderson & Gebharter, 2021).…”

Section: Attitude Polarisationmentioning

confidence: 99%

“…Explanations for confirmation bias have been put forward at the social (Mercier & Sperber, 2011;Norman, 2016;Peters, 2020), individual (Festinger, 1962;Friedrich, 1993;Kunda, 1990;Nickerson, 1998) and information processing levels (Cook & Lewandowsky, 2016;Gerber & Green, 1999;Henderson & Gebharter, 2021;Jern, Chang, & https://doi.org/10.1016/j.cognition.2023.105693 Received 25 April 2023; Received in revised form 30 October 2023; Accepted 11 December 2023 Kemp, 2014;Koehler, 1993). These levels of analysis are qualitatively different but are nonetheless connected.…”

Section: Introductionmentioning

confidence: 99%

“…This definition introduces difficulties because behaviour that is irrational given one belief-updating model may be rational given a different belief-updating model. This has been the case for biased evaluation (Koehler, 1993) and attitude polarisation (Cook & Lewandowsky, 2016;Henderson & Gebharter, 2021;Jern et al, 2014). We aim to sidestep the issue by not claiming that the behaviours are fundamentally biased under all possible belief-updating models.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Confirmation Bias Emerges from an Approximation to Bayesian Reasoning

Pilgrim¹,

Sanborn²,

Malthouse³

et al. 2022

Preprint

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Confirmation bias is defined as searching for and assimilating information in a way that favours existing beliefs. We show that confirmation bias is a natural consequence of boundedly rational belief updating by presenting the BIASR model (Bayesian updating with an Independence Approximation and Source Reliability). Upon receiving information, an individual updates beliefs about the hypothesis in question and the reliability of the information source simultaneously. In this model, an individual's beliefs about a hypothesis and the source reliability form a Bayesian network. This rational updating introduces numerous dependencies between beliefs, the tracking of which represents an unrealistic demand on memory. Our model takes this into account by including realistic cognitive limitations proposed in prior research. Specifically, human cognition can overcome this memory limitation by assuming independence between beliefs. We show how a Bayesian belief updating model incorporating this independence approximation generates many types of confirmation bias, including biased evaluation, biased assimilation, attitude polarisation, belief perseverance and confirmation bias in the selection of sources.

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Section: Simple Version Of Bayes' Theoremmentioning

confidence: 99%

Section: Simple Version Of Bayes' Theoremmentioning

confidence: 99%

Section: Attitude Polarisationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Confirmation Bias Emerges from an Approximation to Bayesian Reasoning

Pilgrim¹,

Sanborn²,

Malthouse³

et al. 2022

Preprint

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From Belief Polarization to Echo Chambers: A Rationalizing Account

Begby

2022

Episteme

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Belief polarization (BP) is widely seen to threaten havoc on our shared political lives. It is often assumed that BP is the product of epistemically irrational behaviors at the individual level. After distinguishing between BP as it occurs in intra-group and inter-group settings, this paper argues that neither process necessarily reflects individual epistemic irrationality. It is true that these processes can work in tandem to produce so-called “echo chambers.” But while echo chambers are often problematic from the point of view of collective rationality, it doesn't follow that individuals are doing anything wrong, epistemically speaking, in seeking them out. In non-ideal socio-epistemic contexts, echo chamber construction might provide one's best defense against systematic misinformation and deception.

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Rational Polarization

Dorst

2023

Philosophical Review

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Predictable polarization is everywhere: we can often predict how people’s opinions, including our own, will shift over time. Extant theories either neglect the fact that we can predict our own polarization, or explain it through irrational mechanisms. They needn’t. Empirical studies suggest that polarization is predictable when evidence is ambiguous, that is, when the rational response is not obvious. I show how Bayesians should model such ambiguity and then prove that—assuming rational updates are those which obey the value of evidence—ambiguity is necessary and sufficient for the rationality of predictable polarization. The main theoretical result is that there can be a series of such updates, each of which is individually expected to make you more accurate, but which together will predictably polarize you. Polarization results from asymmetric increases in accuracy. This mechanism is not only theoretically possible, but empirically plausible. I argue that cognitive search—searching a cognitively accessible space for a particular item—often yields asymmetrically ambiguous evidence, I present an experiment supporting its polarizing effects, and I use simulations to show how it can explain two of the core causes of polarization: confirmation bias and the group polarization effect.

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The role of source reliability in belief polarisation

Cited by 6 publications

References 31 publications

Confirmation Bias Emerges from an Approximation to Bayesian Reasoning

Confirmation Bias Emerges from an Approximation to Bayesian Reasoning

From Belief Polarization to Echo Chambers: A Rationalizing Account

Rational Polarization

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