Available via license: CC BY-NC 4.0
Content may be subject to copyright.
PNAS Nexus, 2024, 3, pgae393
https://doi.org/10.1093/pnasnexus/pgae393
Advance access publication 15 October 2024
Research Report
How rational inference about authority debunking
can curtail, sustain, or spread belief polarization
Setayesh Radkani
a
, Marika Landau-Wells
b
and Rebecca Saxe
a,c,
*
a
Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
b
Travers Department of Political Science, University of California, Berkeley, CA 94705, USA
c
McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
*To whom correspondence should be addressed: Email: saxe@mit.edu
Edited By Eugen Dimant
Abstract
In polarized societies, divided subgroups of people have different perspectives on a range of topics. Aiming to reduce polarization,
authorities may use debunking to lend support to one perspective over another. Debunking by authorities gives all observers shared
information, which could reduce disagreement. In practice, however, debunking may have no effect or could even contribute to
further polarization of beliefs. We developed a cognitively inspired model of observers’ rational inferences from an authority’s
debunking. After observing each debunking attempt, simulated observers simultaneously update their beliefs about the perspective
underlying the debunked claims and about the authority’s motives, using an intuitive causal model of the authority’s decision-
making process. We varied the observers’ prior beliefs and uncertainty systematically. Simulations generated a range of outcomes,
from belief convergence (less common) to persistent divergence (more common). In many simulations, observers who initially held
shared beliefs about the authority later acquired polarized beliefs about the authority’s biases and commitment to truth. These
polarized beliefs constrained the authority’s inuence on new topics, making it possible for belief polarization to spread. We discuss
the implications of the model with respect to beliefs about elections.
Keywords: polarization, Bayesian inference, credibility, misinformation, inverse planning
Signicance Statement
Polarized beliefs about election fairness put democracy at risk. Debunking is one of only a few tools for combating the polarization of
beliefs in a divided society. We develop a cognitively inspired model of how observers interpret an authority’s debunking actions. The
act of debunking can lead rational observers’ beliefs to converge or remain polarized, and also affects the reputation of the authority.
An authority’s acquired reputation in turn can spread polarization to new topics. Applied to the case of beliefs about election fraud,
our model provides insights into when and how debunking fails to reduce polarization, and delineates conditions under which author-
ities perceived as independent can offer an antidote to divided societies.
Competing Interest: The authors declare no competing interests.
Received: April 8, 2024. Accepted: August 19, 2024
© The Author(s) 2024. Published by Oxford University Press on behalf of National Academy of Sciences. This is an Open Access article
distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-
nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly
cited. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions
can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please
contact journals.permissions@oup.com.
Introduction
A functioning democracy requires that citizens accept election re-
sults when the elections are administered fairly (1–3). One of the
most notable indicators of partisan polarization in the United
States has been the divergence in beliefs between Democrats
and Republicans about the legitimacy of the 2020 presidential
election (4–6). Belief in “the big lie” among Republicans has per-
sisted despite a wave of debunking efforts by public ofcials of
both parties and by the media (7, 8). Why have debunking efforts
in this domain failed? And under what conditions are debunking
efforts likely to be successful in general?
Prior research has shown that condence in election outcomes
can be enhanced by the presence of independent observers who
certify that electoral procedures were respected and followed
(9–11). These observers also debunk false claims regarding elec-
tion manipulation (e.g. (12)). Authority gures in other domains
such as public health and climate science also act to debunk
harmful forms of misinformation (13–15).
However, an authority’s debunking efforts are not always ef-
fective, and the changes in beliefs induced by authorities’ efforts
are both variable and hard to predict (16–21). Perceptions of parti-
san bias (e.g. (22–24)) or less-than-scrupulous motives (e.g. (25))
can reduce the impact of factually accurate debunking. Of course,
not all authority debunking is consistent with the truth, particu-
larly when the authority derives a benet from a piece of misinfor-
mation, e.g. (26–28). Moreover, people may be initially uncertain
about their prior beliefs (29–32). Many beliefs pertain to domains
where knowledge accumulates slowly and lacks epistemic cer-
tainty at the time debunking occurs (e.g. public health, climate
change, election fairness monitoring in real-time and claims
about election results prior to nal vote tabulation, for example
the 2024 Venezuelan election (33)). Fundamental uncertainty
(as opposed to measurement error) can inuence the extent to
which debunking results in converging or diverging beliefs (34–36).
In this article, we construct a formal Bayesian model of the ef-
fects of debunking on observers’ beliefs to synthesize these di-
verse observations. The unwillingness to update beliefs given
disconrming information has sometimes been characterized as
evidence of irrationality (e.g. (37)). However, following recent
work in Bayesian network models (e.g. (38)), our model reveals
many conditions under which debunking rationally fails to reduce
condence in a debunked perspective and instead engenders di-
vergent beliefs about the authority (i.e. new polarization). Our
model also shows how polarization may rationally spread into
additional topics.
Bayesian models posit that people interpret and explain evi-
dence by updating their mental causal model of how the evidence
was generated (38–42). A Bayesian approach highlights that after
observing evidence, people jointly update graded beliefs probabil-
istically across the whole network. Thus, even when two groups of
people see the same evidence, and share the same mental model
of potential causal pathways leading to that evidence, they may
reach different conclusions about the most likely cause of the
evidence if they hold different prior beliefs about the causal
pathways. In some cases, the same piece of evidence can even
strengthen directly opposing beliefs (38). In this sense, we use
the term “rational” to refer to the process by which beliefs are up-
dated, rather than the truth of the resulting beliefs, consistent
with common practice in cognitive science, e.g. (43, 44).
In contrast to prior Bayesian models designed specically to
capture political polarization (e.g. (45)), we use a generic cognitive
framework to model observers’ inferences from others’ actions. In
this framework, the underlying generative model posits that peo-
ple plan actions to achieve their desires given their beliefs, or put
differently, that people choose the action that will maximize their
own expected utility (the principle of rational action (46, 47)). By
inverting this generative model, known as inverse planning (46),
observers estimate the unobservable internal states (i.e. the ac-
tor’s beliefs and desires) that most likely generated the observed
actions. Inverse planning models provide detailed quantitative
ts to human judgements in many settings (46–49). The model ap-
plied here was initially developed and validated to capture infer-
ences from observing an authority’s choice to punish, or not
punish, a potentially harmful action (Radkani et al. (50)).
We propose that observers interpret authority debunking, like
other actions, by inverse planning. To simulate the effects of au-
thority debunking, we start with minimal assumptions: two sub-
groups of observers within a society differ only in their beliefs
about one topic (e.g. election security). Then, an authority faces
the choice of whether to debunk one claim, drawn from one per-
spective on the disputed topic (e.g. perspective: the election was
stolen; claim: voter rolls in a specic district were manipulated).
Observers assume that the authority chooses between debunking
and not debunking the claim based on the authority’s own ex-
pected utility. All observers share an intuitive theory of the author-
ity’s decision-making process: authorities may be motivated to
debunk a claim because they believe it is likely to be false (if the au-
thority gains utility from the accuracy of accepted claims) and/or
because the claim harms their own interests (if the authority gains
utility from undermining the perspective or source of the claim).
Given this intuitive theory, all observers then rationally update
their beliefs about the authority based on the observed choices of
that authority over time, here a sequence of debunking ve claims,
all of which originate from the same perspective (e.g. state that
dead people did not vote; state that illegal immigrants did not vote).
Using simulations, we illustrate the effects of debunking on the
evolution of beliefs about both the topic and the authority. In each
simulation, the two groups initially share beliefs on all dimensions
except about their perspective on the focal topic. This allows us to
(i) identify the conditions leading to convergence or continued po-
larization of beliefs about the topic; (ii) demonstrate that debunking
can lead to divergent and polarized beliefs about the authority’s
epistemic motivation and bias, despite identical initial beliefs;
and (iii) investigate how inferences about the authority can spill
over and cause a rational divergence of beliefs in a new topic
area where observers are initially uncertain and the authority at-
tempts to engage in debunking.
This approach yields several contributions. First, we establish a
set of minimal conditions for belief convergence or continued po-
larization among observers. Our model adopts a neutral stance re-
garding the underlying truth of the claim evaluated by observers
and does not assume group differences in either rationality or
ability. Instead, the two groups in our model differ only in their
perspectives on one initial topic, which may be correlated with
any other descriptive difference (e.g. gender, religion, political
partisanship). Though differences between groups in their epi-
stemic values or abilities could contribute to belief convergence
or polarization (e.g. (51)), our simulations show that they are not
necessary for either outcome. These minimal conditions allow
our model to speak to a wide variety of real-world divisions, not
limited to those where belief accuracy and group membership
are correlated, e.g. (52).
Second, our model provides insight into the cognitive processes
associated with belief change. Experimental and observational
studies have demonstrated the signicance of a source’s credibil-
ity for persuasion (53–55) and debunking (52, 56–59). An inverse
planning approach allows us to unbundle two components of
credibility from an observer’s perspective (epistemic motivation
and bias) and then demonstrate how these components interact
with belief uncertainty to produce nuanced effects of the author-
ity’s reputation on belief updating. This approach also extends the
formal literature on the processing of political information (e.g.
(45, 60, 61)) and contributes to the integration of cognitive science
into the study of political behavior, e.g. (62–64).
Computational framework
To adapt the inverse planning framework to the context of de-
bunking, we must characterize how authorities are perceived to
make debunking decisions. That is, we must dene the observers’
mental model of the authority’s expected consequences of de-
bunking or not debunking (i.e. each option’s utilities), and how
much the authority values and pursues those consequences (i.e.
their motives), in choosing whether or not to debunk a claim.
The inverse planning model (of observers’ inferences) thus begins
with a generative planning model (of authority’s choices) (Fig. 1A).
Note that for this approach, the planning model need not be a cor-
rect or complete model of the authority’s actual planning process;
it only has to capture the planning process as imagined by the ob-
servers (48).
The generative model treats debunking as causing overall
harmful consequences for the perspective from which the claims
2 | PNAS Nexus, 2024, Vol. 3, No. 10
are drawn. This harm (negative utility) could be understood as
making the claims less likely to be transmitted, or the perspective
less likely to be accepted, or as imposing a reputational cost on the
source of the claims.
Authorities consider two kinds of utility when planning
whether to debunk a claim and thus cause harm to the perspec-
tive, or not. First, different authorities place different weights on
this negative utility. These weights, here called bias, range from
bias against the perspective (a negative weight, i.e. debunking
harms the perspective and thus benets the authority) to bias in
favor of the perspective (a positive weight, i.e. debunking harms
the perspective and thus also harms the authority). The bias could
also be understood as towards the source of the claim, for ex-
ample gaining utility from harming political competitors or from
protecting political allies. Bias creates a motivation to debunk
claims if the authority is biased against the claims or their source,
and not to debunk claims if the authority is biased in favor of the
claims or their source.
Second, the consequences of debunking can be balanced
against whether the perspective is likely true or false. This can
be understood as an epistemic value, creating higher utility
if the debunked claims are likely to be false, and lower utility if
the debunked claims are likely to be true. The epistemic value
of not debunking is the converse: a higher utility if the claims
are likely true and a lower utility if the claims are likely false.
Different authorities weigh the epistemic value of debunking or
not debunking to different degrees, depending on how much
they care about transmitted claims being accurate and truthful,
here called their accuracy motive.
To formalize these ideas, we write the authority’s expected util-
ity over each action “a” (i.e. debunking or not) as:
Utotal(a)=αtarget Utarget +αaccuracyUaccuracy , (1)
where Utarget is the utility associated with the consequences of
each possible action “a” for the target. Debunking is harmful, so
Utarget is negative for debunking and zero for doing nothing.
Uaccuracy denotes the epistemic value of “a,” and is dened as
the balance between the consequences of “a” and the claim’s
truth likelihood; i.e. Uaccuracy is highest when the response is
proportional to the likelihood of the claim’s truth (varying con-
tinuously between 0 and 1), and decreases the more the response
is either too lenient (doing nothing when the claim is likely false)
or too harsh (debunking when the claim is likely true):
proportionality(a)=(Lclaim truth −1) −Utarget (a)
Uaccuracy(a)=‖proportionality(a)‖(2)
Authorities can differ in the weight placed on each of these util-
ities. An authority’s accuracy motive (αaccuracy) is sampled con-
tinuously between 0 and 1, and bias towards the perspective
(αtarget), is sampled between −0.5 and 0.5 to denote bias against
and in favor of the perspective, respectively.
An authority plans to debunk a claim, or not, by comparing the
overall utilities of these two options using the softmax decision
rule, whereby the β parameter controls the noisiness, or the de-
gree of rationality of the authority in choosing the decision with
highest utility.
P(a∣Lclaim truth,αaccuracy ,αtarget,Utarget )∝eβUtotal(a).(3)
By inverting this generative model of an authority’s debunking de-
cision, observers simultaneously update their beliefs about the
truth of the claims’ perspective (or source), and the authority’s
motives. That is, an observer with prior beliefs about the truthful-
ness of the perspective and authority’s motives, P(Lclaim truth,α), as
well as their appraisal of the cost of debunking for the target,
Utarget, uses Bayesian inference to update their beliefs about the
perspective and authority’s motives, simultaneously.
P(Lclaim truth,α∣a,Utarget )
∝P(a∣Lclaim truth,α,Utarget )×P(Lclaim truth,α), (4)
where P(a|Lclaim truth,α,Utarget ) is the authority’s policy derived in
Eq. 3.
These equations determine observers’ belief updates after ob-
serving one debunking decision by one authority. To simulate the
evolution of beliefs given evidence of a series of debunking decisions
within one domain, we iterate Bayesian inferences; that is, the pos-
terior belief after observing one decision becomes the prior belief for
the next observation, and this sequence is repeated ve times.
Fig. 1. A) Conceptual depiction of the inverse planning model and B) schematic of model simulations. In each pair of simulations, two subgroups initially
hold different beliefs about a topic, but hold shared beliefs about the authority’s accuracy motive and bias. After observing each of the authority’s
debunking decisions (i.e. debunking the claims from one perspective), observers use their mental model of the authority’s decision-making to
simultaneously update their beliefs about the perspective (i.e. its truth) and the authority’s accuracy motive and bias. Observers’ posterior beliefs after
each debunking decision then serve as the prior beliefs for judging the next decision.
Radkani et al. | 3
Results
We simulated societies in which two subgroups of observers have
different initial perspectives on a topic: a proponent subgroup
who believe claims drawn from one perspective to be on average
likely true, and an opponent subgroup who believe claims drawn
from that perspective to be on average likely false. Critically, ob-
servers in both subgroups initially have shared beliefs about an
authority. Everyone then observes the authority sequentially de-
bunk ve claims from the same perspective. After each time the
authority chooses to debunk a claim, observers in both subgroups
rationally update their beliefs about the causes of the authority’s
choice (Fig. 1B). We simulate and study the resulting evolution of
observers’ beliefs about the topic (i.e. the distribution of likeli-
hoods that any given claim from that perspective is true) and
about the authority’s accuracy motive and bias.
Note that our model is agnostic as to the ground-truth of the
claim. Indeed, our model applies equally to observations of de-
bunking a claim (1) when the claim is false (e.g. debunking the
claim that a fair election was unfair); (2) despite the claim being
true (e.g. debunking the claim that a fair election was fair); and
(3) when there is genuine uncertainty about the claim (e.g. de-
bunking a claim that an election was fair when fairness cannot
be established).
We ran 243 pairs of simulations (i.e. one pair contains a propon-
ent and an opponent subgroup, with the same initial beliefs about
the authority, making the same observations), testing 35 varia-
tions of prior belief parameters. Across all simulation pairs, the
two subgroups differed in their initial perspectives on the topic
by the same amount; the proponent subgroup initially believed
that claims from one perspective on the topic are on average
80% likely to be true, while the opponent subgroup believed that
claims are on average 20% likely to be true. We systematically var-
ied the subgroups’ certainty in their perspective (symmetrically;
both subgroups had the same level of uncertainty in a given
pair of simulations), as well as the value and uncertainty of
their shared beliefs about the authority’s motive to be accurate,
and bias for or against the perspective (see “Simulations” in
Materials and methods).
These simulations support a set of general principles about po-
larization in these settings. Debunking led to convergence of per-
spectives on the topic in a minority of simulations. The modal
outcome, however, was that perspectives on the topic remained
divergent between the two groups, with beliefs about the author-
ity’s accuracy and bias additionally becoming polarized in some of
those simulations.
Belief dynamics
We demonstrate the evolution of beliefs in two contrasting ex-
ample settings (Fig. 2). In the rst example, both subgroups share
priors that the authority’s motives include a high motive for ac-
curacy, no bias in favor or against the claims’ perspective, and
both subgroups are uncertain about their initial beliefs about
the topic. In this situation, debunking is effective: after ve de-
bunking acts, the proponent subgroup strongly updates their be-
liefs about the topic so both subgroups eventually consider the
claims more likely false than true, and still view the authority as
highly motivated by accuracy and quite impartial. This happens
because both subgroups believe that the debunking choices are
highly motivated by accuracy and not motivated by bias against
the claims’ perspective or source; therefore, debunking carries a
lot of information about the truthfulness of the claims (i.e. that
the claims are likely false). Therefore, especially when initial
beliefs about the topic are uncertain, debunking causes most of
those who believed in the debunked perspective to change
their mind. Note that for the proponent subgroup, debunking is
initially evidence against the authority’s accuracy motive and im-
partiality. However, beliefs about the authority’s motives are hard
to change because of their high certainty. Maintaining this high
certainty in authority’s accuracy and impartiality contributes to
the convergence of perspectives on the topic.
By contrast, in the second example, shared observations of the
authority’s debunking lead to divergence of the subgroups’ be-
liefs. When the subgroups are very condent in their differing be-
liefs about the topic, and uncertain in their shared beliefs about
the authority, the debunking is not effective. The subgroups do
not change their perspectives on the topic, and at the same time
they acquire very different beliefs about the authority. The sub-
group that initially believed the claims still does so, but addition-
ally believes that the authority is not motivated by accuracy and is
highly biased against the source. By contrast, the opponent sub-
group believes the authority cares a lot about accuracy and is at
most slightly biased against the claims’ perspective or source.
These two examples demonstrate how the value and uncer-
tainty of initial beliefs about the topic and the authority affect
the evolution of these beliefs after observing debunking. Using
this formal model, we can go beyond these two examples and study
the evolution of subgroup beliefs in a continuous space of shared
and differing priors that can vary in both value and condence.
Drivers of belief polarization
Results from the full set of 243 simulations are shown in (Fig. 3).
Focusing rst on the convergence of beliefs about the topic, three
prominent patterns were evident (Figs. S3 and S4). First, debunk-
ing induced convergence of beliefs about the topic when those be-
liefs were initially uncertain. The more the two groups were
certain about their initial beliefs about the topic, the more these
beliefs remained different despite debunking. Second, debunking
was more effective when both groups initially believed the author-
ity was highly motivated to give accurate information. The more
the groups initially suspected the authority of low accuracy mo-
tives, the more their beliefs remained polarized despite debunk-
ing. Third, debunking was more effective when the authority
was initially believed to be impartial or biased in favor of the de-
bunked perspective. The more the groups initially suspected the
authority of bias against the debunked perspective, the more
their beliefs remained polarized despite debunking. In a small
subset of simulations, authority’s debunking led to an increase
in disagreement about the topic. In these simulations, both sub-
groups updated their beliefs rationally by decreasing their be-
liefs in the truth of the claims’ perspective, but the opponent
subgroup updated their beliefs more strongly than the propon-
ent subgroup.
However, all of these prominent patterns were modulated by
systematic interactions, based on the uncertainty as well as the
value of the other beliefs. For example, consider the interactions
of beliefs about the topic and the authority’s accuracy (Fig. 4).
When the authority was initially believed to be highly motivated
by accuracy with some uncertainty, then the effect of debunking
was very sensitive to the groups’ condence in their differing be-
liefs about the topics. If those beliefs were certain, debunking
failed and the groups quickly acquired very polarized beliefs
about the authority’s accuracy motives. If beliefs about the topic
were uncertain, debunking was successful, the perspectives con-
verged and condence in the authority’s accuracy motive
4 | PNAS Nexus, 2024, Vol. 3, No. 10
remained high. The inuence of uncertainty was itself modulated
by the value of beliefs about authority’s accuracy. The inuence of
uncertainty about the topic disappeared if, holding everything
else constant, the authority was initially believed to have low ac-
curacy motive. In that case, beliefs about the topic remained po-
larized, and beliefs about the authority’s accuracy slowly
became partially polarized, regardless of the initial certainty of
the beliefs about the topic.
Another systematic interaction arose between beliefs about the
topic and the authority’s bias (Fig. 5). For example, when the au-
thority was initially believed with high certainty to be biased in fa-
vor of the debunked perspective (and moderately motivated to be
accurate), then the effect of debunking was very sensitive to the
groups’ certainty in their initial beliefs about the topic. If those
beliefs were uncertain, debunking was successful, the beliefs
about the topic converged and both groups’ condence in the au-
thority’s accuracy motive increased. If the beliefs about the topic
were certain, debunking failed and the groups acquired polarized
beliefs about the authority’s accuracy motive. However, if holding
everything else constant, initial beliefs about the authority’s bias
in favor of the content were uncertain, then beliefs about the topic
remained polarized regardless of their initial certainty, and the
proponent group instead quickly came to believe that the author-
ity was actually biased against the debunked perspective. If the
groups initially suspected that the authority was biased against
the debunked perspective, then debunking also failed regardless
of certainty about the differing beliefs about the topic or the au-
thority’s bias (Fig. S2).
Fig. 2. Model simulations for evolution of beliefs in two example settings. The y-axis represents the mean of the belief distribution, and the x-axis shows
the number of observed debunking actions; debunking actions 0 represents the prior belief on each variable. Truth of the perspective varies between 0
and 1, with larger values representing a higher likelihood of truth. Accuracy varies between 0 and 1, with larger values representing a higher motivation to
respond in proportion to the truthfulness of the claims—debunk likely false content and do nothing in response to likely true content. Bias varies between
−0.5 and 0.5, with positive values representing bias in favor and negative values representing bias against the perspective—e.g. based on the political
orientation of the perspective. The shaded ribbon shows the standard deviation of belief distribution. The solid line represents the subgroup who a priori
believes the perspective of the claims is likely true (i.e. the proponent subgroup), and the dashed line represents the subgroup who a priori believes the
perspective of the claims is likely false (i.e. the opponent subgroup). Left column: A setting in which the subgroups’ prior differing beliefs about the topic
are uncertain, and their shared prior beliefs about authority’s legitimacy are condent—high accuracy and impartiality, leads to effective debunking of
false content. Right column: A setting in which the subgroups’ prior differing beliefs about the topic are condent and their shared prior beliefs about the
authority’s motivations are uncertain, leads to polarization of beliefs about the authority in addition to beliefs about the topic remaining polarized.
Radkani et al. | 5
Overall these simulations suggest that the effects of debunking
on observers’ beliefs could be enormously variable, depending
sensitively on the value and uncertainty of prior beliefs about
both the topic and the authority’s motives (see also Figs. S3 and
S4 for a larger set of simulations).
Polarization in new topic areas
The acquired polarization in beliefs about the authority’s motives
has implications beyond a single topic and can propagate belief
divergence into new topic areas. To explore this effect, we used
the nal beliefs about authority’s motives at the end of the simu-
lations described above (243 pairs of simulations) as prior beliefs
for a new topic area where the two subgroups start with shared un-
certain beliefs (both groups believe the claims from one perspective
are 50% likely to be true but are very uncertain about the probabil-
ity that any given claim from this perspective is true). We ran the
simulations for a perspective on a new topic that is related to the
previous one, such that beliefs about the authority’s bias would
be generalizable across the two topics. We studied the resulting
polarization in beliefs, as the authority debunks claims about a
perspective in this new topic ve times (Fig. 6A).
Divergence in beliefs about the new topic after 5 debunking ac-
tions ranged from about 0 to 0.3 (rst Qu. = 0.0389, median =
0.0771, third Qu. = 0.134), creating at most about half the magni-
tude of divergence as in the original domain, or 60% of the max-
imum possible divergence in the new topic. The polarization in
beliefs about the authority’s motives (accuracy and bias) was a
major determinant of how much beliefs about the new topic
would polarize. If the two groups had acquired a larger difference
in their beliefs about the authority’s accuracy, and/or their beliefs
about the authority’s bias, they ended up more polarized in their
beliefs about the new topic (Fig. 6B). Differences in beliefs about
accuracy or bias alone can lead to divergence in beliefs about
the new topic (e.g. simulations 62 and 76).
Beliefs about the new topic polarize particularly when the pro-
ponent subgroup (in the original domain) have acquired either
certain beliefs that the authority does not care about accuracy
at all (e.g. simulation 106), or relatively certain beliefs that the au-
thority is highly biased against such perspectives (e.g. simulation
Fig. 3. Relationship between the nal belief polarization about the topic
(perspective), the authority’s accuracy and bias, after ve debunking
actions. Each data point represents one pair of simulations; therefore,
there are 243 points shown in the plot (see Fig. S5 for labels). Belief
polarization is dened as the absolute value of the difference between the
two subgroups’ beliefs (i.e. mean of belief distributions). The x- and y-axis
represent polarization in beliefs about the perspective on the topic and
the authority’s accuracy, respectively; the points are color-coded by
polarization in beliefs about the authority’s bias. The vertical dashed line
represents the initial level of perspective belief polarization.
Fig. 4. Interaction between accuracy belief value and perspective belief uncertainty in determining the polarization of beliefs. In all four simulations,
both subgroups are initially somewhat certain that the authority is impartial (mean = 0, std = 0.15), and both subgroups are quite uncertain about their
accuracy beliefs as well. Low and high accuracy correspond to belief values (i.e. distribution mean) of 0.2 and 0.8, respectively. Certain and uncertain
perspective beliefs correspond to belief distributions with standard deviation of 0.05 and 0.15, respectively.
6 | PNAS Nexus, 2024, Vol. 3, No. 10
80). However, if the proponent group’s acquired beliefs about the
authority’s accuracy motive are slightly higher and more uncer-
tain, especially when the authority is believed to be biased in favor
of the perspective (e.g. simulations 52, 70, and 133), the authority
could be successful in debunking in the new domain; and the au-
thority may even be able to gain back the proponent subgroup’s
trust, through recovering their beliefs about authority’s accuracy
(e.g. simulation 70 and 133).
Discussion
Using an inverse planning approach to model observers’ infer-
ences from an authority’s debunking, we showed how observers’
perspectives and their beliefs about the authority interact to
shape the observers’ beliefs. The results of our simulations offer
several insights. First, we show that it is possible for debunking
to work as intended by shifting beliefs. The relative rarity of this
outcome across our simulations reveals important constraints,
however. The certainty of initial beliefs about the topic constrains
belief change, even in the presence of an authority who is seen as
truth-motivated and unbiased. When there is some initial uncer-
tainty about the topic, several factors can independently inu-
ence whether two groups ultimately converge on the same
beliefs. When beliefs do not converge, polarization can spread to
beliefs about the authority’s commitment to the truth and to per-
ceptions of their biases. These acquired polarized perceptions can
spread to new topics as well, coloring the effects of future debunk-
ing by that same authority.
Our simulations identied three congurations of initial beliefs
that facilitated successful debunking. Initial belief polarization
was reduced by debunking when: (i) initially differing beliefs
about the topic were held with greater uncertainty; (ii) the author-
ity was believed to be highly motivated by accuracy (especially
with greater certainty); and (iii) the authority was initially believed
to be biased in favor of the debunked claims or their source (espe-
cially with greater certainty). The current model thus contributes
to the formal literature by synthesizing these ndings into a single
cognitive framework, and by emphasizing the importance of esti-
mating the certainty of beliefs (both beliefs about the topic and be-
liefs about the authority; (35)), as well as distinguishing between
the authority’s perceived accuracy and bias (i.e. the dimensions
of credibility), instead of measuring a single variable of trust or
credibility.
These three congurations are consistent with the existing ex-
perimental and observational literature on belief change. First,
uncertainty about a claim’s truthfulness can be correlated with
successful persuasion (35, 65). For example, in domains where sci-
entic knowledge accumulates slowly and is contested (e.g. public
health), debunking can have limited impact, e.g. (66, 67). In the
case of US elections, uncertainty (rather than certainty) about
fairness characterizes views about future election security (68),
which suggests that our simulations capturing this uncertainty
are relevant.
Second, perceptions of a commitment to accuracy can enhance
the persuasiveness of a source (69, 70). In the case of elections out-
side the US context, some monitors commit signicant resources
to accurately characterizing election processes (27, 71), which
suggests they are aware that perceived accuracy is distinct from
perceived bias.
Third, our nding that a presumed bias in favor of debunked
claims (or their source) heightens the persuasiveness of the au-
thority’s debunking actions also has precedent in the literature
on political persuasion. Just as “only Nixon could go to China,”
so have studies found that debunking efforts and other persuasive
claims carry more weight when the identity of the person making
the correction or claim is a surprise (e.g. (16, 52, 55)), although this
effect is not always found among the strongest partisans (e.g.
(54)). In the context of elections, information provided by
Fig. 5. Interaction between bias belief uncertainty and perspective belief uncertainty in determining the polarization of beliefs. In all four simulations,
both subgroups are initially uncertain that the authority is somewhat accurate (mean = 0.5, std = 0.25), and both subgroups believe the authority is biased
in favor of the target. Certain and uncertain perspective beliefs correspond to standard deviations of 0.05 and 0.15, respectively. Certain and uncertain
bias beliefs correspond to standard deviations of 0.05 and 0.25, respectively.
Radkani et al. | 7
authorities perceived as biased (e.g. Fox News calling the state of
Arizona for Joe Biden in the 2020 election) (26) or debunking efforts
by authorities perceived as biased (e.g. Arizona’s Republican gov-
ernor later denying voter fraud claims in that state) (72) also ap-
pear to be more persuasive (57).
Yet, in many settings, debunking did not reduce the initial
disagreement, and instead induced polarization in beliefs
about the authorities’ motivations. At the start of our simulations,
all observers had shared beliefs about the authority. This situ-
ation is not far-fetched and could arise for several reasons. First,
knowledge about well-established authorities might simply be
low, as it is across many aspects of politics (e.g. (73, 74)). Second,
nonpartisan or newly formed authorities may not have established
reputations (e.g. (75, 76)). Third, well-known authorities may enter
Fig. 6. Acquired polarized beliefs about the authority in one domain can propagate to and polarize initially shared beliefs in new domains. A) Simulation
procedures in the original and new topic area for each pair of subgroups. In the original domain, two subgroups start with differing beliefs about a
perspective on a topic area (i.e. proponent and opponent), but shared beliefs about the authority’s accuracy motive and bias. After observing ve
debunking actions by an authority, the two subgroups update their perspective beliefs as well as their beliefs about the authority, referred to as acquired
beliefs about the authority’s accuracy and bias. The same authority (i.e. the same person or organization) then chooses to enter and debunk claims about
a related perspective in a new topic area, therefore the acquired beliefs about the authority transfer to the new domain. We assume the two subgroups
initially have shared beliefs about the new topic, but they are quite uncertain in their perspective beliefs (mean = 0.5, std = 0.25). We simulate the
evolution of beliefs and quantify the potential acquired polarization in perspective beliefs in the new topic area, as a result of their acquired polarized
beliefs about the authority from the original domain. B) The scatter plot shows the results of 243 pairs of simulations corresponding to the 243 pairs of
simulations with various prior belief settings in the original domain. Each data point represents one pair of simulations; therefore, there are 243 points
shown in the plot (see Fig. S6for labels). Belief polarization is dened as the absolute value of the difference between the two subgroups’ beliefs (i.e. mean
of belief distributions). The x- and y-axis represent the acquired polarization in beliefs about authority’s accuracy and bias, respectively, before observing
the authority’s decisions in the new topic domain; darker points indicate larger polarization in beliefs about the new topic after ve debunking actions by
the same authority. The side panels show the evolution of beliefs in seven example pairs of simulations.
8 | PNAS Nexus, 2024, Vol. 3, No. 10
a new domain where priors about them are not yet established,
such as when celebrities aim to translate their popularity to polit-
ical inuence (e.g. (77, 78)). Finally, authorities may have estab-
lished reputations within the domain based on characteristics
that do not correlate with the source of group differences in belief
about the claim. For example, outside the US context, it is not un-
common for multiple organizations to be invited to monitor elec-
tions (10) and there is evidence that the monitor’s sponsor
(EU, United States, regional states) inuences prior beliefs about
the authority’s capability and biases, rather than the observers’
preferred election outcomes (e.g. Tunisia as discussed in
Ref. (79)). Despite these initially shared beliefs, however, the nal
state of our simulations frequently included polarized beliefs
about the authorities, following the same societal fault lines as
the initial disagreement.
Given the frequent reputational damage to debunking author-
ities in our simulations, authorities could rationally anticipate
these consequences, and thus be reluctant to engage in debunking
(80). Yet our model reects only the perspective of observers; the
expected consequences for the authority’s reputation from the au-
thority’s own perspective might be different. Observably, authorities
often do engage in debunking, and do experience reputational
damage as a consequence. These authorities may have underesti-
mated the likelihood of reputational damage (e.g. because they
overestimated observers’ prior beliefs in their epistemic motives),
or may have deliberately accepted this risk because of very strong
epistemic motivations (e.g. the public health ofcials surveyed in
Ref. (13), or the Republicans who denied voter fraud claims after
the 2020 elections). To capture the authority’s perspective, future
work could embed the current model of observers, recursively, in
a model of authority choice to better understand this dynamic and
study how authorities value the trade-off between correcting be-
liefs and managing their reputation. This type of recursive model
is standard in cognitive science for studying language and com-
munication (81, 82), and has been recently used to model an au-
thority’s punitive decision-making (e.g. see Ref. (83)).
A primary contribution of this work is to illustrate how a set of
minimal conditions can give rise to both belief convergence and
divergence. The initial conditions in our model were minimal:
two groups with differing beliefs in one domain. The source of
this initial divergence could itself be either rational (e.g. exposure
to different evidence) (60, 84) or irrational (e.g. motivated beliefs
that preserve self-image) (85, 86). In either case, the process of be-
lief updating using an intuitive causal model can entrench these
initial differences into a robust structure of polarized supporting
beliefs.
We see several additional extensions for our minimal condi-
tions framework. In addition to modeling authorities’ decision-
making processes, this model could be extended to capture add-
itional cognitive processes among observers. For example, our
model does not predict one form of polarization often taken as
the best evidence for irrationality, known as backring. In our
model, debunking could reduce observers’ belief in the debunked
claims, or not reduce their belief, but debunking never backred
by directly increasing beliefs in the debunked claims (17, 38, 59).
To predict backring, the current model could include observers
who have a generative mental model that an authority may gain
utility specically by spreading false claims (40, 59). Another ex-
tension could incorporate alternative updating mechanisms
through which belief polarization can occur, including differences
in goals and utilities (e.g. motivated reasoning) and differences in
belief updating mechanisms (e.g. ignoring/lapses in considering
the evidence, cognitive ability).
So far, we have explored the effects of a xed series of ve ac-
tions, always debunking claims from the same perspective (e.g.
the election was stolen). Five debunked claims provides enough
information in our model to generate convergence or polarization
using a wide variety of priors. However, our simulations hint
that a major effect of beliefs about the authority’s accuracy is
on the speed of convergence between the two groups, and the un-
certainty of their nal beliefs. Considering these other features of
belief distributions in dening and measuring polarization could
prove useful in certain contexts, such as when the authority has
limited opportunities for debunking, so that the speed of conver-
gence would be more important.
Formal simulations and empirical studies could also investi-
gate the effect of observing a mixture of decisions to debunk
and decisions to say nothing about the truth of the claim, i.e. ab-
stain. The same model developed here can be used to simulate the
evolution of beliefs in these mixed situations. For example, Fig. S7
illustrates the effect of a single debunking (in round 3) and four ab-
stentions using the same initial priors as Fig. 2. When observers
have high condence in either their views about the authority
(left) or about the perspective’s truth (right), choices to abstain
from debunking do not affect these certain beliefs. However,
when observers have higher levels of uncertainty, the confusion
introduced by the mixed debunking causes beliefs to evolve non-
monotonically. Indeed, in the case where convergence occurs, the
combination of four abstentions and one debunking yields con-
vergence towards the debunked perspective, contrary to the pat-
tern observed in Fig. 2. While fully evaluating the complexity
introduced by mixed cases is outside the scope of this manuscript,
the extreme case of one debunking action and four abstentions il-
lustrates that the impact of failing to debunk all claims from a
particular perspective can both undermine the opportunity to
converge on true beliefs, and inuence the impressions of the au-
thority’s epistemic motives and biases.
The most signicant limitation of the current work is the lack of
validation by direct comparison to human inferences. The intui-
tive causal model of authority’s decision-making process used
here is adapted from a generic framework for modeling human
action understanding, known as inverse planning, that has been
empirically validated in many contexts (46–49). In particular, the
current model is derived directly from a model of human observ-
ers’ inferences from an authority’s punitive decisions (50). Similar
experimental approaches could be used to calibrate the instanti-
ation of our modeling framework, to test alternative specications
of the authorities’ utilities, and to test the predictions of this mod-
el in hypothetical or actual scenarios, for political as well as non-
political beliefs.
Belief polarization arose in our simulations through entirely ra-
tional belief updating. Applied to the case of election-related de-
bunking in a polarized political environment, our results provide
useful if sobering insights. First, while independent observers
have persuasively debunked claims of election fraud in non-US
contexts (9, 11, 75), the chance that such efforts will succeed
among those who have extreme condence in their beliefs is
small. However, there is hope for the type of nonpartisan election
monitoring that has been trialed on a small scale in the United
States. Our model shows that organizations able to establish a
clear reputation across partisan groups for commitment to the
truth and unbiasedness can engage in successful debunking.
This debunking can achieve its purpose: enhancing condence
in election outcomes. Nevertheless, as our simulations show,
the act of debunking itself will affect the reputation of those or-
ganizations. When organizations with reputations derived from
Radkani et al. | 9
other domains move into election monitoring and debunking (e.g.
former Democratic president Jimmy Carter’s foundation the
Carter Center) their reputations come with them, and the percep-
tions of bias in particular can reduce the impact of their debunk-
ing efforts in the elections domain.
Materials and methods
Belief distributions
We modeled observers’ beliefs about the topic area and the au-
thority’s motives as random variables with beta distributions.
The beta distribution is a family of continuous probability distri-
butions dened on the interval [0, 1]. We modeled observers as as-
signing a truth likelihood (i.e. probability) to a set of claims drawn
from a perspective on a topic (continuous value between 0 and 1).
Choosing to model beliefs about claims as the truth likelihood of
the claim (rather than a binary belief about whether the claim is
true or false) is inspired by empirical studies which nd people’s
beliefs in a claims’ truth vary continuously (e.g. between 0 and
100, (59)). We then modeled their beliefs about the topic as a dis-
tribution of truth likelihood values for various claims drawn from
one perspective on that topic. For modeling beliefs that follow oth-
er distributions (e.g. binary beliefs) one could simply change the
class of belief distribution, without changing any other computa-
tional machinery in the model. Although we primarily concep-
tualized the belief distribution as implementing the hierarchical
relationship between claims and the topic (i.e. claims are sampled
from one perspective on the topic, and the belief distribution is the
distribution of claim truth likelihoods from that perspective), the
topic belief distribution could alternatively be conceptualized as
the reliability of beliefs about the perspective, especially in topics
where the perspective could be represented as a continuous vari-
able (e.g. estimates of economic growth).
We modeled beliefs about the authority’s motives as beta dis-
tributions, over [0, 1] for accuracy and [−0.5, 0.5] for bias (by rst
sampling from a beta distribution and then shifting the samples
by −0.5). We primarily conceptualized the distribution over au-
thority’s motives as the certainty of beliefs about authority.
However, the belief distribution could also be conceptualized as
implementing a hierarchical relationship between a single au-
thority (observed in each action) drawn from a set of authorities,
for example if the debunking is done by a different individual jour-
nalist from the same newspaper, scientist in the same discipline,
or observer working with the same NGO.
We model beliefs using the beta distribution because it is ex-
ible and can model a wide variety of distribution shapes over a
bounded interval. Moreover, the shape of the distribution can be
controlled and uniquely specied (i.e. “a” and “b” shape parame-
ters) by the mean (to represent belief value) and standard devi-
ation (to represent belief uncertainty) of the distribution.
Utilities
We modeled the authority as considering a binary decision be-
tween doing nothing in response to a claim, or debunking it. The
utility of each response, associated with the consequences of
the response for the target Utarget, is modeled as a deterministic
value: Utarget is set to −1 for debunking and zero for doing nothing.
By setting Utarget of debunking to −1, we assumed that debunking
is a proportional response to a claim that is surely false (truth like-
lihood = 0; see Eq. 2); that is, debunking a surely false claim would
maximize Uaccuracy. Similarly, by setting Utarget of doing nothing to
0, we assumed that doing nothing is a proportional response to a
claim that is surely true (truth likelihood = 1); that is, doing noth-
ing in response a surely true claim would maximize Uaccuracy. For a
claim with truth likelihood of 0.5, the authority would be agnostic
between doing nothing and debunking, in terms of the epistemic
value of their response. In that case, the authority’s bias towards
the target would be the main driver of their response.
Implementation
The probabilistic programming language WebPPL (the local instal-
lation from https://webppl.org) was used to code the inverse plan-
ning model. Python (3.7.6) and R (4.1.1) were used for processing
the simulation results and generating the plots.
We used Markov chain Monte Carlo (MCMC) sampling, as im-
plemented within WebPPL, to estimate the observer’s posterior be-
lief distributions after observing each response by the authority.
The program is structured such that the observer has a generative
model of the authority, who decides whether to do nothing or de-
bunk the claim, given their accuracy motive, bias, claim truth like-
lihood and Utarget of each possible response. The observer has prior
belief distributions over authority’s accuracy, bias, and the
claims’ truth likelihood. In each iteration of the MCMC algorithm,
the observer samples accuracy motive, bias, and claim truth from
their belief distribution and then simulates how this specic
hypothetical authority (with the sampled accuracy motive and
bias) would behave in response to this specic claim (with the
sampled truth likelihood). The observer’s posterior belief distribu-
tion is estimated by conditioning the program on the observed au-
thority’s response (debunking in our simulations). We used
400,000 MCMC iterations for estimating observers’ posterior
beliefs.
The estimated posterior belief distributions are saved after
each time the authority responds to a claim by debunking in the
form of samples from posterior distributions and their corre-
sponding probabilities. Then, three beta distributions are t to
the posterior data (accuracy, bias, and perspective truth), and
are input to the same WebPPL program to serve as the prior belief
distributions for the next authority’s decision.
Simulations
We ran two series of simulations, with each series containing 243
pairs of simulations. Each simulation pair consists of two simula-
tions with IDs “i” and “i”+243 (in the code base); the simulation pair
is referenced by ID = “i” in the manuscript.
In series 1 (Within-Topic), each pair of simulations featured
two subgroups who initially had different belief values about
one perspective on the topic (mean of belief distribution about
the perspective in the opponent subgroup = 0.2, proponent sub-
group = 0.8), but every other aspect of their beliefs was shared (un-
certainty of belief distribution, mean, and uncertainty of beliefs
about authority). The 243 simulations systematically sampled dif-
ferent settings of the shared beliefs (ve dimensions, each taking
three possible values, hence all the combinations would amount
to 35=243 pairs). Note the value and uncertainty of beliefs can
be continuously varied, however for the purpose of illustration
and tractability we used three values to denote low, medium,
and high levels for each variable. The standard deviation (i.e. un-
certainty) of topic beliefs varied in [0.05, 0.1, 0.15]. The value and
uncertainty of accuracy motive prior beliefs varied in [0.2, 0.5, 0.8]
and [0.05, 0.15, 0.25], respectively; similarly, the value and uncer-
tainty of bias prior beliefs varied in [−0.3, 0, 0.3] and [0.05, 0.15,
0.25], respectively. Figure S1 shows the belief distributions with
these parameters. These initial belief distributions served as the
10 | PNAS Nexus, 2024, Vol. 3, No. 10
prior beliefs for the rst debunking action (in the gures, they are
depicted as beliefs when debunking actions = 0). Each pair of sim-
ulations consist of ve iterated epochs, that is observations of ve
debunking actions by the authority. The posterior beliefs from
each epoch serve as the prior beliefs for the next. There is no inde-
pendent effect of trial within the model, so all trials are computa-
tionally identical.
In series 2 (Cross-Topic), each pair of simulations featured two
subgroups who initially had shared beliefs about a new topic area.
Both subgroups believed that the claims from the new topic area
are somewhat likely to be true (mean of perspective belief distri-
bution in both subgroups = 0.5), but they were quite uncertain
(standard deviation of perspective belief distribution in both sub-
groups = 0.25). We assumed the same authority is debunking
claims in the new topic area, and the new topic is related to the
original topic enough that the subgroups’ acquired beliefs about
authority’s accuracy and bias would generalize to this new do-
main. So, for each pair of simulations, the two subgroups’ beliefs
about authority were set to be the posterior beliefs after epoch 5 of
the corresponding pair from the Within-Topic simulation.
Across all simulations, the rationality parameter (beta) was set
to 10.
Acknowledgments
We thank David Rand and Emily Falk for helpful suggestions in
the development of the project, and Andrew Little for feedback
on the earlier versions of the manuscript.
Supplementary Material
Supplementary material is available at PNAS Nexus online.
Funding
This work is supported in part by the Patrick J. McGovern
Foundation and by the Guggenheim Foundation.
Author Contributions
S.R. (Conceptualization, Data curation, Methodology, Formal ana-
lysis, Visualization, Writing—original draft, Writing—review &
editing), M.L.W. (Conceptualization, Writing—original draft,
Writing—review & editing), R.S. (Conceptualization, Methodology,
Project administration, Supervision, Writing—original draft,
Writing—review & editing).
Preprints
A preprint of this article is published at https://doi.org/10.31234/
osf.io/8dq9x.
Data Availability
The code to simulate the model and generate the gures, as well
as the simulation data are available on https://github.com/
sradkani/inverse-planning-polarization and https://osf.io/jg87r/.
References
1 Fearon JD. 2011. Self-enforcing democracy. Q J Econ. 126(4):
1661–1708.
2 Przeworski A. 2005. Democracy as an equilibrium. Public Choice.
123(3):253–273.
3 Weingast BR. 1997. The political foundations of democracy and
the rule of the law. Am Polit Sci Rev. 91(2):245–263.
4 Holliday D, Grimmer J, Lelkes Y, Westwood S. 2023. Who are the
election skeptics? Evidence from the 2022 midterm elections.
Election Law J. Forthcoming.
5 Fahey JJ. 2023. The big lie: expressive responding and mispercep-
tions in the united states. J Exp Polit Sci. 10(2):267–278.
6 McCarthy J. 2022. Confidence in election integrity hides deep par-
tisan divide. Gallup News. https://news.gallup.com/poll/404675/
confidence-election-integrity-hides-deep-partisan-divide.aspx
7 Arceneaux K, Truex R. 2023. Donald trump and the lie. Perspect
Polit. 21(3):863–879.
8 Canon DT, Sherman O. 2021. Debunking the “big lie”: election ad-
ministration in the 2020 presidential election. Pres Stud Q. 51(3):
546–581.
9 Bush SS, Prather L. 2017. The promise and limits of election ob-
servers in building election credibility. J Polit. 79(3):921–935.
10 Hyde SD, Marinov N. 2014. Information and self-enforcing dem-
ocracy: the role of international election observation. Int Organ.
68(2):329–359.
11 Nevitte N, Canton SA. 1997. The rise of election monitoring: the
role of domestic observers. J Democracy. 8(3):47–61.
12 Organization for Security and Cooperation in Europe. 2020.
International Election Observation Mission, United States of
America General Elections, 3 November 2020: Statement of
Preliminary Findings and Conclusions. Technical Report.
13 Ittefaq M. 2023. “It frustrates me beyond words that I can’t fix
that”: health misinformation correction on Facebook during
COVID-19. Health Commun. https://doi.org/10.1080/10410236.
2023.2282279
14 Turney C, van der Linden S. 2024. Can we be inoculated against
climate misinformation? Yes – if we prebunk rather than de-
bunk. The Conversation.
15 World Health Organization. 2022. COVID-19 Mythbusters –
World Health Organization.
16 Berinsky AJ. 2017. Rumors and health care reform: experiments
in political misinformation. Br J Polit Sci. 47(2):241–262.
17 Nyhan B, Reifler J. 2010. When corrections fail: the persistence of
political misperceptions. Polit Behav. 32(2):303–330.
18 Swire-Thompson B, Miklaucic N, Wihbey JP, Lazer D, DeGutis J.
2022. The backfire effect after correcting misinformation is strong-
ly associated with reliability. J Exp Psychol: Gen. 151(7):1655–1665.
19 Gelfand M, et al. 2022. Persuading republicans and democrats to
comply with mask wearing: an intervention tournament. J Exp
Soc Psychol. 101:104299.
20 Kozyreva A, et al. 2024. Toolbox of individual-level interventions
against online misinformation. Nat Hum Behav. 8(6):1044–1052.
21 Zhang FJ. 2023. Political endorsement by nature and trust in sci-
entific expertise during covid-19. Nat Hum Behav. 7(5):696–706.
22 Cohen MJ, Sheagley G. 2021. Partisan poll watchers and
Americans’ perceptions of electoral fairness. Public Opin Q.
88(SI):536–560.
23 Swire B, Berinsky AJ, Lewandowsky S, Ecker UKH. 2017.
Processing political misinformation: comprehending the trump
phenomenon. R Soc Open Sci. 4(3):160802.
24 Painter DL, Fernandes J. 2024. “The big lie:” how fact checking in-
fluences support for insurrection. Am Behav Sci. 68(7):892–912.
25 Lewandowsky S, Oberauer K, Gignac GE. 2013. NASA faked the
moon landing—therefore, (climate) science is a hoax: an anat-
omy of the motivated rejection of science. Psychol Sci. 24(5):
622–633.
26 Baker P. 2023 Mar. Inside the panic at Fox News after the 2020
election. The New York Times.
Radkani et al. | 11
27 Bush SS, Cottiero C, Prather L. 2024. Zombies ahead: explaining
the rise of low-quality election monitoring. Rev Int Organ.
https://doi.org/10.1007/s11558-024-09554-3
28 López A. 2023. Gaslighting: fake climate news and big Carbon’s
network of Denial. In: Fowler-Watt K, McDougall J, editors. The
Palgrave handbook of media misinformation. Cham: Springer
International Publishing. p. 159–177.
29 Bullock JG. 2011. Elite influence on public opinion in an informed
electorate. Am Polit Sci Rev. 105(3):496–515.
30 Freeder S, Lenz GS, Turney S. 2019. The importance of knowing
“what goes with what”: reinterpreting the evidence on policy at-
titude stability. J Polit. 81(1):274–290.
31 Goldstein DAN, Wiedemann J. 2022. Who do you trust? The con-
sequences of partisanship and trust for public responsiveness to
COVID-19 orders. Perspect Polit. 20(2):412–438.
32 Zaller JR. 1992. The nature and origins of mass opinion. Cambridge
(UK): Cambridge University Press.
33 Gunson P. 2024. Venezuela: what next after its election uproar?
International Crisis Group Q&A.
34 Graham MH. 2023. Measuring misperceptions? Am Polit Sci Rev.
117(1):80–102.
35 Li J, Wagner MW. 2020. The value of not knowing: partisan cue-
taking and belief updating of the uninformed, the ambiguous,
and the misinformed. J Commun. 70(5):646–669.
36 Tormala ZL. 2016. The role of certainty (and uncertainty) in atti-
tudes and persuasion. Curr Opin Psychol. 10:6–11.
37 Lord CG, Ross L, Lepper MR. 1979. Biased assimilation and atti-
tude polarization: the effects of prior theories on subsequently
considered evidence. J Pers Soc Psychol. 37(11):2098.
38 Jern A, Chang K-MK, Kemp C. 2014. Belief polarization is not al-
ways irrational. Psychol Rev. 121(2):206.
39 Bhui R, Gershman SJ. 2020. Paradoxical effects of persuasive
messages. Decision. 7(4):239.
40 Cook J, Lewandowsky S. 2016. Rational irrationality: modeling
climate change belief polarization using Bayesian networks.
Top Cogn Sci. 8(1):160–179.
41 Botvinik-Nezer R, Jones M, Wager TD. 2023. A belief systems ana-
lysis of fraud beliefs following the 2020 US election. Nat Hum
Behav. 7(7):1106–1119.
42 Powell D, Weisman K, Markman EM. 2023. Modeling and lever-
aging intuitive theories to improve vaccine attitudes. J Exp
Psychol: Gen. 152(5):1379.
43 Griffiths TL, Tenenbaum JB. 2006. Optimal predictions in every-
day cognition. Psychol Sci. 17(9):767–773.
44 Griffiths TL, Chater N, Kemp C, Perfors A, Tenenbaum JB. 2010.
Probabilistic models of cognition: exploring representations
and inductive biases. Trends Cogn Sci (Regul Ed). 14(8):357–364.
45 Bullock JG. 2009. Partisan bias and the Bayesian ideal in the study
of public opinion. J Polit. 71(3):1109–1124.
46 Baker CL, Saxe R, Tenenbaum JB. 2009. Action understanding as
inverse planning. Cognition. 113(3):329–349.
47 Jara-Ettinger J, Gweon H, Schulz LE, Tenenbaum JB. 2016.
The naïve utility calculus: computational principles underlying
commonsense psychology. Trends Cogn Sci (Regul Ed). 20(8):
589–604.
48 Houlihan SD, Kleiman-Weiner M, Hewitt LB, Tenenbaum JB, Saxe
R. 2023. Emotion prediction as computation over a generative
theory of mind. Philos Trans R Soc A. 381(2251):20220047.
49 Strouse DJ, McKee K, Botvinick M, Hughes E, Everett R. 2021.
Collaborating with humans without human data. Adv Neural Inf
Process Syst. 34:14502–14515.
50 Radkani S, Saxe R. 2023. What people learn from punishment:
joint inference of wrongness and punisher’s motivations from
observation of punitive choices. In: Goldwater M, Anggoro FK,
Hayes BK, Ong DC, editors. Proceedings of the 45th Annual
Conference of the Cognitive Science Society. Vol. 45. p. 1027–1034.
51 Jost JT, Baldassarri DS, Druckman JN. 2022. Cognitive–
motivational mechanisms of political polarization in social-
communicative contexts. Nat Rev Psychol. 1(10):560–576.
52 Benegal SD, Scruggs LA. 2018. Correcting misinformation about
climate change: the impact of partisanship in an experimental
setting. Clim Change. 148(1):61–80.
53 Flanagin A, Metzger MJ. 2017. Digital media and perceptions of
source credibility in political communication. In: Kenski K,
Jamieson KH, editors. The Oxford handbook of political communica-
tion. Oxford: Oxford University Press. p. 417–436.
54 Grossman G, Kim S, Rexer JM, Thirumurthy H. 2020. Political par-
tisanship influences behavioral responses to governors’ recom-
mendations for COVID-19 prevention in the united states. Proc
Natl Acad Sci. 117(39):24144–24153.
55 Mattes M, Weeks JLP. 2019. Hawks, doves, and peace: an experi-
mental approach. Am J Pol Sci. 63(1):53–66.
56 Berinsky AJ. 2017. Rumors and health care reform: experiments
in political misinformation. Br J Polit Sci. 47(2):241–262.
57 Carey J, Fogergty B, Gehrke M, Nyhan B, Reifler J. 2024.
Prebunking and credible source corrections increase election
credibility: evidence from the U.S. and Brazil. Working Paper.
58 Martel C, Rand DG. 2024. Fact-checker warning labels are effect-
ive even for those who distrust fact-checkers. Nat Hum Behav.
https://doi.org/10.1038/s41562-024-01973-x
59 Reinero DA, Harris EA, Rathje S, Duke A, Van Bavel JJ. 2023.
Partisans are more likely to entrench their beliefs in misinforma-
tion when political outgroup members fact-check claims.
Working Paper.
60 Gerber A, Green D. 1999. Misperceptions about perceptual biases.
Ann Rev Polit Sci. 2:189–210.
61 Little AT. 2023. Bayesian explanations for persuasion. J Theor
Polit. 35(3):147–181.
62 Bendor J. 2020. Bounded rationality in political science and polit-
ics. In: Mintz A, Terris L, editors. Oxford handbook of behavioral pol-
itical science. Oxford: Oxford University Press. p. 37–68.
63 Landau-Wells M, Saxe R. 2020. Political preferences and threat
perception: opportunities for neuroimaging and developmental
research. Curr Opin Behav Sci. 34:58–63.
64 McGraw KM. 2000. Contributions of the cognitive approach to
political psychology. Polit Psychol. 21(4):805–832.
65 Druckman JN. 2022. A framework for the study of persuasion.
Ann Rev Polit Sci. 25: 65–88.
66 Cappella JN, Maloney E, Ophir Y, Brennan E. 2015. Interventions
to correct misinformation about tobacco products. Tob Regul Sci.
1(2):186.
67 Smith P, et al. 2011. Correcting over 50 years of tobacco industry
misinformation. Am J Prev Med. 40(6):690–698.
68 Allen J, Orey R, Sanchez T. 2024 Feb. Who voters trust for election
information in 2024. Technical Report, Bipartisan Policy Center,
Washington, D.C.
69 Buczel KA, Szyszka PD, Siwiak A, Szpitalak M, Polczyk R.
2022. Vaccination against misinformation: the inoculation tech-
nique reduces the continued influence effect. PLoS One. 17(4):
e0267463.
70 Walter N, Brooks JJ, Saucier CJ, Suresh S. 2021. Evaluating the im-
pact of attempts to correct health misinformation on social me-
dia: a meta-analysis. Health Commun. 36(13):1776–1784.
71 Hyde SD. 2012. Why believe international election monitors? In:
Lake DA, Stein JG, Gourevitch PA, editors. The credibility of
12 | PNAS Nexus, 2024, Vol. 3, No. 10
transnational NGOs: when virtue is not enough. Cambridge:
Cambridge University Press. p. 37–61.
72 Caldwell LA, Dawsey J, Sanchez YW. 2023 Jul. Trump pressured
Arizona Gov. Doug Ducey to overturn 2020 election. Washington
Post.
73 Angelucci C, Prat A. 2024. Is journalistic truth dead? Measuring
how informed voters are about political news. Am Econ Rev.
114(4):887–925.
74 Borelli G, Gracia S. 2023. What Americans know about their govern-
ment. Pew Research Center.
75 Barker J, Samet O, Hyde SD. 2024. Citizen election observation
and public confidence in U.S. elections. Working Paper.
76 Darnall N, Ji H, Vázquez-Brust DA. 2018. Third-party certifica-
tion, sponsorship, and consumers’ ecolabel use. J Bus Ethics.
150(4):953–969.
77 Grynbaum MM. 2024 Jan. Fox News to Taylor swift: ‘don’t get in-
volved in politics!’. The New York Times.
78 Towler CC, Crawford NN, Bennett RA. 2020. Shut up and play:
black athletes, protest politics, and black political action. Perspect
Polit. 18(1):111–127.
79 Bush SS, Prather L. 2018. Who’s there? Election observer identity
and the local credibility of elections. Int Organ. 72(3):659–692.
80 Choshen-Hillel S, Shaw A, Caruso EM. 2020. Lying to appear hon-
est. J Exp Psychol: Gen. 149(9):1719.
81 Frank MC, Goodman ND. 2012. Predicting pragmatic reasoning in
language games. Science. 336(6084):998–998.
82 Goodman ND, Frank MC. 2016. Pragmatic language interpret-
ation as probabilistic inference. Trends Cogn Sci (Regul Ed).
20(11):818–829.
83 Radkani S, Tenenbaum J, Saxe R. 2022. Modeling punishment as a
rational communicative social action. In: Culbertson J, Perfors A,
Rabagliati H, Ramenzoni V, editors. Proceedings of the 44th Annual
Conference of the Cognitive Science Society. Vol. 44. p. 1040–1047.
84 Achen CH. 2002. Parental socialization and rational party identi-
fication. Polit Behav. 24(2):151–170.
85 Kim S-y, Taber CS, Lodge M. 2010. A computational model of the
citizen as motivated reasoner: modeling the dynamics of the
2000 presidential election. Polit Behav. 32(1):1–28.
86 Little AT. 2019. The distortion of related beliefs. Am J Pol Sci. 63(3):
675–689.
Radkani et al. | 13
