Bayesian Persuasion¶

Model how a sender who cannot lie — committed to a public, truthful signal structure faced by a Bayes-rational receiver — still shifts the receiver's action by choosing how informative the signal is, solved geometrically as the concave closure of the sender's value over posteriors.

Core Idea¶

Bayesian persuasion (Kamenica and Gentzkow, 2011) is the strategic situation in which a sender commits in advance to an information experiment — a publicly binding signal whose distribution depends on the state — and a receiver observes the signal, updates by Bayes' rule, and acts. Even unable to lie and facing a fully rational receiver, the sender can profit by choosing how informative the experiment is, often committing to less than full disclosure. Geometrically, the solution is the concavification of the sender's value function over posteriors.

Scope of Application¶

Bayesian persuasion lives within the information-design field it founded, restaged across committed sender-receiver games with a Bayes-rational receiver and a binding public signal.

Information design in markets — prosecutor evidence design, credit-rating granularity, exam thresholds.
Regulatory disclosure — stress tests, drug labels, calorie counts a regulator tunes for informativeness.
Advertising and recommendation systems — which reviews to surface without saying anything false.
Political communication — which polls or endorsements to commission for rational voters.

Clarity¶

The concept makes legible a lever the lying-versus-truth dichotomy hides: a sender who can say nothing false still holds latitude through the design of the experiment — how informative the public signal is. It relocates a communicator's power from message content to disclosure structure, separating forbidden deception from the informativeness choice that is the whole game. It also makes commitment a load-bearing object to check for: without a binding signal structure the situation collapses to cheap talk.

Manages Complexity¶

The sprawl is the continuum of disclosure strategies — every degree of informativeness, every way of pooling states — recurring anew in each application. The concept collapses the search to one geometric operation: because the receiver acts only on their posterior, the sender's problem becomes forming the concave closure of the value-over-posteriors function and evaluating it at the prior. The analyst then tracks only the belief-to-action map and the prior, reading off whether persuasion has bite and how coarse the optimal signal is.

Abstract Reasoning¶

The concept licenses reasoning backward from the receiver's belief-to-action map and its thresholds, then concavifying the value function to read the optimal experiment off its chord. A decisive binary — is the value function concave at the prior? — predicts both whether persuasion pays and how coarse the signal should be. Bayes-plausibility guards the design as redistribution not manufacture of belief, and commitment is a precondition to verify, separating persuasion from cheap talk.

Knowledge Transfer¶

Within economic theory and information design, Bayesian persuasion transfers as mechanism: the backward reasoning, concavification, the decisive binary, and the two preconditions carry intact across application domains of one model — market information design, regulatory disclosure, recommendation systems, political communication. Beyond committed sender-receiver games the honest account is a shared insight carried by component primes — gatekeeping, framing, information_asymmetry, and commitment — of which this is one formalisation. The concavification-under-commitment machinery stays home-bound.

Relationships to Other Abstractions¶

Current abstraction Bayesian Persuasion Domain-specific

Parents (5) — more general patterns this builds on

Bayesian Persuasion is part of Bayesian Updating Prime

Bayesian Persuasion contains the receiver's prior-to-posterior update on each realized signal, which is the belief-to-action link the sender designs around.
Bayesian Persuasion presupposes Credible Commitment Prime

Bayesian Persuasion presupposes a publicly believable ex-ante commitment to honor the chosen signal structure after the state is realized.
Bayesian Persuasion presupposes Information Asymmetry Prime

Persuasion presupposes that the receiver lacks payoff-relevant state information that the committed experiment can selectively reveal.
Bayesian Persuasion is a decomposition of Framing Prime

Removing the game-theoretic frame leaves truthful presentation structure altering evaluation and action even though no underlying fact is falsified.
Bayesian Persuasion is a decomposition of Gatekeeping Prime

Stripping the posterior-simplex apparatus leaves selective passage control: the sender chooses which distinctions reach the receiver and at what granularity.

Hierarchy paths (10) — routes to 8 parentless roots

Bayesian Persuasion → Credible Commitment → Commitment → Constraint

Neighborhood in Abstraction Space¶

Bayesian Persuasion sits in a crowded region of the domain-specific corpus (28^th percentile for distinctiveness): several abstractions share nearly its structure, so a description that fits it tends to fit its neighbors too.

Family — Unclustered & Miscellaneous (309 abstractions)

Nearest neighbors

Computed from structural-signature embeddings · 2026-07-12