Persuasion by Parts

Classical Bayesian persuasion assumes that an information designer can condition signals on the entire state. We propose persuasion by parts. In this model, the receiver's payoff from acting is X = ∑ᵢ₌₁ⁿ Xᵢ for independent coordinates X₁,…,Xₙ observed by separate sources. The designer commits in advance to a product experiment, in which each source i can condition its signal only on Xᵢ. Given the signals, the receiver computes her posterior mean y of X and acts with probability $g(y)$, where g R → [0,1] is a nondecreasing function. The designer aims to maximize the probability of action. Let Vprod(g) and Vⱼₒᵢₙₜ(g) be the supremum probabilities of action under persuasion by parts and under joint persuasion, respectively. When the designer knows g, we show Vprod(g) \;≥\; c\,Vⱼₒᵢₙₜ(g)² every nondecreasing g R → [0,1], where $c > 0$ is an absolute constant independent of n and the prior; the exponent $2$ is tight. When g is unknown, we construct a product experiment $Π$ from the prior alone, whose probability of action V_Π(g) satisfies V_Π(g) \;≥\; c_ε\,Vⱼₒᵢₙₜ(g)2 + ε every ε > 0 and every nondecreasing g R → [0,1], where c_ε > 0 depends only on ε.