Regulating Information With Bayesian Audiences

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Yonathan A. Arbel & Murat C. Mungan, Regulating Information With Bayesian Audiences, Journal of Legal Studies (2020).

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One-paragraph thesis:

Information regulation often overlooks how audiences adjust their beliefs and actions based on the strictness of laws governing statement veracity. His research aims to address this "audience gap" by using a Bayesian game to model interactions between speakers, targets, and audiences, particularly examining how legal strictness impacts their behavior and the resulting information landscape.

What this paper is about:

Information regulation often overlooks how audiences adjust their beliefs and actions based on the strictness of laws governing statement veracity. His research aims to address this "audience gap" by using a Bayesian game to model interactions between speakers, targets, and audiences, particularly examining how legal strictness impacts their behavior and the resulting information landscape.

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This work is relevant to Defamation And Speech, Private Law And Market Institutions. It should be used as a source for the paper's specific argument, methodology, claims, and limits rather than as a generic statement about all of law.

Empirical or methodological contribution:

Information regulation often overlooks how audiences adjust their beliefs and actions based on the strictness of laws governing statement veracity. His research aims to address this "audience gap" by using a Bayesian game to model interactions between speakers, targets, and audiences, particularly examining how legal strictness impacts their behavior and the resulting information landscape.

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This work is relevant when answering questions about Defamation And Speech, Private Law And Market Institutions.

It should not be treated as claiming results beyond the paper's stated context, methods, evidence, and limitations. Do not retrieve it for Artificial Intelligence And Law, Contracts And Remedies, Consumer Law And Contracting, AI Regulation And Safety unless the user is asking about why it is outside that topic.

The most important takeaway is: Information regulation often overlooks how audiences adjust their beliefs and actions based on the strictness of laws governing statement veracity. His research aims to address this "audience gap" by using a Bayesian game to model interactions between speakers, targets, and audiences, particularly examining how legal strictness impacts their behavior and the resulting information landscape.

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Information regulation changes not only speakers’ incentives but also how Bayesian audiences interpret and act on the statements that remain

Professors Yonathan A. Arbel and Murat C. Mungan claim, in “Regulating Information with Bayesian Audiences” on pages 1–3, that analysis of false-statement regulation has neglected the audience. A listener rationally adjusts the credibility assigned to speech when legal sanctions change which speakers are willing to speak, so regulation reshapes the informational environment as well as the conduct of speakers and targets. This is significant because the social harm from false speech depends on belief and action, not falsity alone. It connects to Bayesian updating, information regulation, audience effects, signaling, credibility, and behavioral law and economics.

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A three-party Bayesian game captures how a privately informed speaker influences an audience’s decision about a target under threat of litigation

Professors Yonathan A. Arbel and Murat C. Mungan claim, in “Regulating Information with Bayesian Audiences” on pages 1–2 and 5–10, that a Bayesian game can formalize the interaction among a speaker, a good or bad target, and an audience deciding whether to interact with that target. The speaker observes the target’s type and may disparage; the audience updates and acts; the target may sue after a lost interaction. This is significant because each player anticipates the others, making the credibility of speech an equilibrium product of law. It connects to Perfect Bayesian equilibrium, private information, strategic communication, defamation litigation, reputation, and sequential games.

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The same structural information problem recurs when interested speakers influence audiences and law sanctions false negative information

Professors Yonathan A. Arbel and Murat C. Mungan claim, in “Regulating Information with Bayesian Audiences” on pages 2 and 4–5, that their framework applies where an interested speaker seeks to influence an audience, the speaker’s interest conflicts with a target’s, and law penalizes false negative information. Defamation is the running example, but whistleblower rewards, complaint-driven enforcement, criminal reports, and securities regulation share parts of the structure. This is significant because fragmented doctrines can be compared through a common informational mechanism. It connects to defamation, whistleblowing, law enforcement, securities disclosure, mechanism design, and comparative institutional analysis.

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The level of expected damages is a useful policy lever because it jointly shapes the target’s incentive to sue and the speaker’s incentive to disparage

Professors Yonathan A. Arbel and Murat C. Mungan claim, in “Regulating Information with Bayesian Audiences” on pages 2 and 7–8, that damages operationalize the strictness of information law. A target sues when expected recovery exceeds litigation cost, while a speaker disparages only when the private gain from blocking an interaction exceeds expected liability and cost. Judicial error enters through type-dependent probabilities of plaintiff victory. This is significant because a nominal damages amount works through several strategic thresholds rather than a single deterrence response. It connects to optimal damages, litigation costs, expected liability, private enforcement, judicial error, and deterrence theory.

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Stricter law can make surviving disparagement more credible and therefore more damaging, so even a good target may prefer a laxer regime

Professors Yonathan A. Arbel and Murat C. Mungan claim, in “Regulating Information with Bayesian Audiences” on page 2, that strict defamation law makes a negative statement a costlier signal. A Bayesian audience consequently treats the statement as more reliable; a sufficiently motivated liar can exploit that trust, causing a false accusation that survives deterrence to have greater impact. A good target may therefore prefer lower damages even though they reduce recovery if suit succeeds. This is significant because stronger legal protection can endogenously magnify the credibility of the residual lie. It connects to costly signaling, credibility effects, reputational harm, countervailing incentives, Bayesian persuasion, and defamation damages.

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Both lax cheap talk and strict overpriced talk can deprive audiences of useful information

Professors Yonathan A. Arbel and Murat C. Mungan claim, in “Regulating Information with Bayesian Audiences” on pages 2–3, that the informational consequences of very low and very high sanctions can converge. With low damages, speakers lie frequently and audiences fall back on prior beliefs; with high damages, the risk of erroneous liability can silence truthful negative statements even though truth is formally a defense. This is significant because optimal information regulation is often interior rather than maximally strict. It connects to cheap talk, chilling effects, overpriced talk, judicial error, information loss, and nonmonotonic regulation.

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Judicial accuracy determines whether moderate damages can deter lies without inviting frivolous suits and thereby support a separating equilibrium

Professors Yonathan A. Arbel and Murat C. Mungan claim, in “Regulating Information with Bayesian Audiences” on pages 3 and 13–14, that sufficiently accurate courts create a damages interval in which false disparagement of good targets is deterred while bad targets will not file frivolous claims. Speakers then disparage exactly the bad targets, audiences follow the signal, and no litigation occurs on the equilibrium path. This is significant because adjudicative competence changes which informational equilibria law can implement. It connects to judicial accuracy, separating equilibrium, frivolous litigation, truth detection, deterrence, and institutional competence.

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When courts are inaccurate, optimal damages trade off missed good interactions, harmful bad interactions, and litigation costs rather than monotonically favoring more deterrence

Professors Yonathan A. Arbel and Murat C. Mungan claim, in “Regulating Information with Bayesian Audiences” on pages 3 and 15–18, that inaccurate adjudication prevents perfect separation and forces policy to balance type-one errors, type-two errors, and litigation expense. Higher damages can reduce false attacks on good targets yet also induce frivolous suits or suppress useful warnings about bad targets. This is significant because institutional error makes the welfare effect of severity domain specific. It connects to false positives, false negatives, litigation cost, error-cost analysis, optimal enforcement, and welfare economics.

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Moderate information regulation can increase investment in quality by widening the payoff gap between good and bad targets

Professors Yonathan A. Arbel and Murat C. Mungan claim, in “Regulating Information with Bayesian Audiences” on pages 3 and 18–19, that credible communication changes dynamic incentives as well as current transactions. Moderate damages make audiences more likely to interact with good targets than bad ones, increasing the private return to producing quality, safety, or hygiene. Extreme laws leave audiences relying on priors and therefore eliminate that marginal return in the modeled case. This is significant because information law can affect the underlying quality distribution it is often assumed merely to reveal. It connects to endogenous quality, investment incentives, market discipline, information asymmetry, dynamic efficiency, and product safety.

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Private enforcement can outperform a committed public enforcer because the target knows its own type and can condition litigation on the merits

Professors Yonathan A. Arbel and Murat C. Mungan claim, in “Regulating Information with Bayesian Audiences” on pages 3 and 21–23, that public agencies may commit ex ante to enforcement, but private targets possess information about whether the accusation is true. Moderate damages can use the target’s willingness to sue to separate good from bad targets; a public enforcer choosing an unconditional probability cannot replicate that signal. This is significant because commitment is not the only institutional advantage—delegating to an informed party may produce more accurate case selection. It connects to private enforcement, public enforcement, commitment, decentralized information, case selection, and institutional design.

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Defamation’s effect on reputation cannot be evaluated without the audience because disparagement causes harm only when listeners believe and act on it

Professors Yonathan A. Arbel and Murat C. Mungan claim, in “Regulating Information with Bayesian Audiences” on page 4, that conventional defamation analysis concentrates on speakers’ chilling effects and victims’ recovery while largely treating the public as a passive nonparty. Yet a false statement changes a target’s prospects only if an audience credits it and changes conduct. This is significant because the listener is causally central even though absent from the lawsuit. It connects to reputational torts, causation, audience reliance, victim compensation, free speech, and third-party behavior.

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Whistleblower rewards affect an enforcement agency’s belief in a report, not merely the reporter’s willingness to submit it

Professors Yonathan A. Arbel and Murat C. Mungan claim, in “Regulating Information with Bayesian Audiences” on pages 4–5, that whistleblower analysis is incomplete when it balances reward-induced reporting against false claims but omits the agency’s inference. A large reward may induce more reports while simultaneously making the agency expect a larger share to be opportunistic. This is significant because the same incentive that produces information can reduce its credibility to the decisionmaker. It connects to whistleblower rewards, false reporting, agency screening, Bayesian inference, enforcement priorities, and incentive design.

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Sanctions for false crime reports affect how police allocate scarce attention by changing the perceived credibility of incoming reports

Professors Yonathan A. Arbel and Murat C. Mungan claim, in “Regulating Information with Bayesian Audiences” on pages 4–5, that police do not respond to every allegation identically; they ration investigative resources toward reports thought credible. Penalties for false reporting therefore influence both a would-be reporter and the enforcement audience’s inference about the reports it receives. This is significant because deterrence models that make detection depend only on expenditure miss the informational input to enforcement choice. It connects to criminal reporting, police triage, false alarms, resource allocation, credibility screening, and public enforcement.

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Securities enforcement operates as a credibility mechanism because investors interpret corporate disclosures in light of the legal regime governing them

Professors Yonathan A. Arbel and Murat C. Mungan claim, in “Regulating Information with Bayesian Audiences” on page 5, that a public company reports its own performance under the threat of securities enforcement, while investors decide how much trust and capital to place in the disclosure. Strictness affects not just corporate truth-telling but investor confidence in the statements observed. This is significant because disclosure regulation works through market interpretation as well as agency prosecution. It connects to securities fraud, mandatory disclosure, investor beliefs, SEC enforcement, market credibility, and corporate moral hazard.

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The relevant distinction is whether communication changes audience behavior, not merely whether speaker strategies carry statistical information

Professors Yonathan A. Arbel and Murat C. Mungan claim, in “Regulating Information with Bayesian Audiences” on pages 10–11, that equilibria should be classified as effective or ineffective communication according to whether the statement causes the audience to depart from conduct based on prior beliefs. A speaker may use type-dependent messages that are technically informative yet never cross the listener’s action threshold. This is significant because social value arises from better interaction decisions, not information in the abstract. It connects to informative equilibria, babbling equilibria, decision thresholds, Bayesian action, behavioral relevance, and cheap-talk theory.

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Every damages regime admits an ineffective equilibrium in which audiences follow priors and litigation never occurs

Professors Yonathan A. Arbel and Murat C. Mungan claim, in “Regulating Information with Bayesian Audiences” on pages 11–12, that ineffective communication remains a Perfect Bayesian equilibrium regardless of damages. The audience always interacts or never interacts according to its prior; because disparagement cannot change that decision, a speaker gains nothing from triggering potential litigation, and no suit occurs on the equilibrium path. This is significant because a legal regime may permit useful communication without guaranteeing that players coordinate on it. It connects to equilibrium multiplicity, prior beliefs, coordination, off-path beliefs, no-litigation equilibria, and implementation limits.

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Very low damages fail to motivate meritorious suits, while very high damages deter all behaviorally effective disparagement

Professors Yonathan A. Arbel and Murat C. Mungan claim, in “Regulating Information with Bayesian Audiences” on pages 12–13, that extreme regimes collapse communication for different strategic reasons. Below the lower threshold, even a falsely accused good target will not sue; above the upper threshold, any disparagement capable of changing audience conduct is deterred. In both cases only ineffective communication equilibria remain. This is significant because the legal extremes destroy the feedback that could distinguish truthful warnings from false attacks. It connects to enforcement thresholds, underdeterrence, overdeterrence, chilling, expected recovery, and communication failure.

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Only moderate damages can support an equilibrium in which the audience acts consistently with the speaker’s statement

Professors Yonathan A. Arbel and Murat C. Mungan claim, in “Regulating Information with Bayesian Audiences” on page 13, that effective communication requires an intermediate enforcement range. Within that range, some disparagement remains possible, some targets have incentives to litigate, and audience beliefs can rationally make interaction depend on whether the speaker disparages. This is significant because moderate law expands the equilibrium set to include behaviorally useful information even though ineffective equilibria still coexist. It connects to interior solutions, effective communication, equilibrium selection, moderate sanctions, credible speech, and regulatory calibration.

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A sufficiently accurate court permits damages that induce truthful disparagement of bad targets and silence toward good targets without actual litigation

Professors Yonathan A. Arbel and Murat C. Mungan claim, in “Regulating Information with Bayesian Audiences” on pages 13–14, that when the ratio of correct to erroneous plaintiff victories exceeds a critical level, a moderate damages interval implements separation. Speakers disparage if and only if the target is bad; audiences avoid disparaged targets and interact otherwise; and no claim is filed because equilibrium behavior avoids the actionable falsehood. This is significant because threatened litigation can discipline speech while remaining off path. It connects to separating signals, judicial precision, credible threats, equilibrium deterrence, truthful revelation, and dispute avoidance.

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The separating equilibrium maximizes expected welfare because it permits every beneficial interaction, blocks every harmful interaction, and consumes no litigation resources

Professors Yonathan A. Arbel and Murat C. Mungan claim, in “Regulating Information with Bayesian Audiences” on pages 13–14, that full separation dominates every other equilibrium in the model. Good targets interact with the audience, bad targets do not, and the deterrent threat prevents litigation cost from being incurred. Conditional on either target type, no feasible terminal outcome produces a larger modeled social payoff. This is significant because the informational and cost objectives align when courts are accurate enough. It connects to first-best allocation, social welfare, litigation avoidance, efficient matching, deterrence, and mechanism implementation.

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Even a slightly inaccurate court may support a welfare-improving semi-separating equilibrium under moderate damages

Professors Yonathan A. Arbel and Murat C. Mungan claim, in “Regulating Information with Bayesian Audiences” on pages 13–14, that a small decline below the accuracy required for full separation need not eliminate the value of regulation. A non-disparaging message can still reveal a good target, while good targets face only a small probability of disparagement; by continuity, this outcome can outperform reliance on priors. This is significant because institutional imperfection does not create an all-or-nothing boundary for useful information law. It connects to semi-separating equilibrium, continuity, near-accuracy, residual error, second-best regulation, and welfare comparison.

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In the lower-moderate range, raising damages improves welfare by reducing false disparagement of good targets without provoking suits by bad targets

Professors Yonathan A. Arbel and Murat C. Mungan claim, in “Regulating Information with Bayesian Audiences” on pages 15–16, that damages just high enough to motivate good targets—but not bad targets—to sue create a relatively simple margin. Bad targets are disparaged with certainty, while increasing damages discourages attacks on good targets, permits more beneficial interactions, and lowers expected litigation. This is significant because severity is unambiguously welfare improving within this bounded range despite broader nonmonotonicity. It connects to lower-moderate damages, meritorious claims, frivolous suits, beneficial interaction, marginal deterrence, and litigation savings.

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In the intermediate-moderate range, higher damages simultaneously increase beneficial and harmful interactions while reducing litigation, so welfare is ambiguous

Professors Yonathan A. Arbel and Murat C. Mungan claim, in “Regulating Information with Bayesian Audiences” on pages 15–17, that once damages induce frivolous suits by bad targets but do not fully deter false attacks on good targets, raising damages moves several margins at once. It protects more good targets and saves litigation but also suppresses truthful warnings, allowing more audience interactions with bad targets. This is significant because the optimal direction depends on the comparative value of interactions, suit costs, and the distribution of speaker motivations. It connects to intermediate damages, competing margins, harmful interaction, frivolous litigation, distributional assumptions, and ambiguous welfare.

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In the higher-moderate range, increasing damages suppresses truthful warnings about bad targets while saving litigation and is undesirable when avoided harmful interactions matter more than suit cost

Professors Yonathan A. Arbel and Murat C. Mungan claim, in “Regulating Information with Bayesian Audiences” on pages 16–17, that high-but-not-extreme damages already deter all disparagement of good targets. A further increase therefore cannot improve their treatment; it only deters some warnings about bad targets, increasing harmful interactions, while reducing expected litigation. If the value of blocking those interactions exceeds litigation expense, welfare rises by lowering damages. This is significant because the marginal case for strictness reverses after truthful speakers become the affected group. It connects to higher-moderate damages, truthful warnings, marginal analysis, harmful reliance, litigation-cost tradeoffs, and overdeterrence.

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There is no general reason for higher damages to outperform lower damages when courts are inaccurate, and stronger law may sacrifice some good targets

Professors Yonathan A. Arbel and Murat C. Mungan claim, in “Regulating Information with Bayesian Audiences” on page 17, that inaccurate adjudication defeats any general monotonic prescription for damages. Moving audiences from priors to statements may improve matching, but it creates litigation costs and can reduce the welfare of good targets who are falsely disparaged. Stronger law therefore does not automatically protect the very people invoked to justify it. This is significant because aggregate welfare and target protection can diverge. It connects to nonmonotonic policy, victim welfare, inaccurate adjudication, distributional effects, regulatory humility, and domain-specific calibration.

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Moderate damages can outperform ineffective communication whenever the audience’s gains and losses from correct interaction decisions dominate other payoffs

Professors Yonathan A. Arbel and Murat C. Mungan claim, in “Regulating Information with Bayesian Audiences” on pages 17–18, that some moderate damages always improve the audience’s chance of beneficial interaction or avoidance of harmful interaction. When those audience stakes are large relative to litigation cost and the speaker’s and target’s other interests, an effective-communication equilibrium yields greater total welfare than an equilibrium based only on priors. This is significant because it supplies a sufficient condition for useful regulation even with inaccurate courts. It connects to Proposition 4, audience welfare, sufficient conditions, value of information, second-best policy, and social surplus.

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Extreme laws provide no private return to investing in quality when audiences ignore speech, whereas moderate laws can make quality investment privately valuable

Professors Yonathan A. Arbel and Murat C. Mungan claim, in “Regulating Information with Bayesian Audiences” on pages 18–19, that replacing exogenous type with a costly quality choice reveals a dynamic consequence. Under extreme law, audiences act on priors and the target’s investment does not change interaction payoff. Under moderate law, more reliable messages create a larger payoff for becoming a good type, so the target invests when that expected gain exceeds cost. This is significant because regulatory severity can shape future conduct before any statement is made. It connects to endogenous types, human capital, product quality, ex ante incentives, reputational returns, and dynamic regulation.

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Adding honest speakers and speakers biased toward praise preserves the superiority of moderate damages and can make strict law informationally worse than lax law

Professors Yonathan A. Arbel and Murat C. Mungan claim, in “Regulating Information with Bayesian Audiences” on pages 19–21, that speaker heterogeneity changes but does not reverse the main result. Truth speakers report honestly, disparagers favor lost interactions, and eulogists favor interaction. Under lax law, the mixture leaves positive and negative speech partially informative; under strict law, liability chills both disparagers and honest negative speakers, leaving only uninformative nonnegative speech. This is significant because zero sanctions can preserve more information than maximal sanctions once honest speech exists. It connects to heterogeneous motives, honest reporting, puffery, biased speech, partial revelation, and chilling effects.

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A public enforcer’s ex ante commitment can sustain some informative speech even with low damages, but cannot produce full separation

Professors Yonathan A. Arbel and Murat C. Mungan claim, in “Regulating Information with Bayesian Audiences” on pages 21–23, that a public agency can announce an enforcement probability independent of the target’s hidden type. The persistent chance of suit deters some false speech even when damages are too low to motivate private litigation, so low damages may support effective communication. Yet unconditional enforcement also deters some truthful statements or permits some lies, precluding full separation. This is significant because commitment improves one margin while the agency’s informational disadvantage limits another. It connects to public commitment, random enforcement, low sanctions, agency information, partial deterrence, and semi-separation.

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Delegating suit decisions to privately informed targets enables separation, although real comparisons must also consider investigation costs, evidence, discretion, and capture

Professors Yonathan A. Arbel and Murat C. Mungan claim, in “Regulating Information with Bayesian Audiences” on page 23, that private enforcement delegates litigation to the actor with the best information about a claim’s merits. Properly calibrated damages sort good and bad targets by willingness to sue, making speech more informative than under an uninformed public commitment. The authors nevertheless caution that agencies and private parties may differ in detecting remarks, producing evidence, exercising discretion, or suffering capture. This is significant because institutional choice must combine informational advantage with administrative realities. It connects to decentralized enforcement, screening, evidence costs, agency capture, public choice, and comparative institutional competence.

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Audience effects offer informational rationales for treating opinion as nonactionable and especially harmful per se allegations more strictly

Professors Yonathan A. Arbel and Murat C. Mungan claim, in “Regulating Information with Bayesian Audiences” on pages 23–24, that the fact–opinion and per se–per quod distinctions can be understood through model parameters. Opinions are harder for courts to verify and are already discounted by Bayesian audiences, reducing the value of liability. Allegations of crime, sexual misconduct, contagious disease, or business impropriety may create unusually high target harm and speaker gain, supporting stronger protection. This is significant because categorical doctrine may reflect differences in accuracy and audience response. It connects to opinion privilege, defamation per se, judicial verifiability, presumed damages, audience discounting, and doctrinal tailoring.

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Moderate rules for false crime reports and whistleblowing can preserve credible reporting while avoiding both frivolous allegations and the chilling of costly truthful reports

Professors Yonathan A. Arbel and Murat C. Mungan claim, in “Regulating Information with Bayesian Audiences” on pages 24–25, that law-enforcement agencies are audiences that must screen reports under scarce resources, while informants and whistleblowers possess private information and heterogeneous costs. Punishing false reports raises credibility but excessive penalties chill truthful alerts; rewards elicit information but can invite opportunistic claims and fail high-cost reporters. This is significant because enforcement quality depends on the informational mix reaching the agency. It connects to police prioritization, whistleblower statutes, false reports, verification cost, crime deterrence, and calibrated incentives.

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Moderate penalties for frivolous claims may improve jury accuracy and deterrence, whereas excessive penalties can chill legitimate accusations and weaken both

Professors Yonathan A. Arbel and Murat C. Mungan claim, in “Regulating Information with Bayesian Audiences” on pages 25–26, that malicious-prosecution liability and Rule 11 sanctions affect how judges and juries evaluate the claims that reach them. Reducing frivolous cases can improve the evidentiary pool and expand deterrence, but punishing filings too severely may remove legitimate cases and have the opposite effect. This is significant because procedural sanctions change the factfinder’s prior as well as litigants’ filing behavior. It connects to malicious prosecution, Rule 11, Bayesian juries, frivolous litigation, evidentiary quality, and optimal deterrence.

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Securities enforcement should be calibrated for investor belief formation because both lax and strict regimes can make corporate disclosure less informative

Professors Yonathan A. Arbel and Murat C. Mungan claim, in “Regulating Information with Bayesian Audiences” on page 26, that the model can be relabeled for corporate self-reporting: the company is the interested speaker, investors are the audience, and the regulator decides whether to enforce. Excessively lax rules allow unreliable praise, while overly strict enforcement can suppress actionable disclosure; judicial competence also determines which statements can be regulated well. This is significant because optimal securities law must account for the credibility investors assign to disclosures under the enforcement regime. It connects to issuer disclosure, investor reliance, SEC penalties, corporate self-reporting, judicial competence, and capital-market information.

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