Persuasion, defined as the act of exploiting an informational advantage in order to effect the decisions of others, is ubiquitous. Indeed, persuasive communication has been estimated to account for almost a third of all economic activity in the US. This paper examines persuasion through a computational lens, focusing on what is perhaps the most basic and fundamental model in this space: the celebrated Bayesian persuasion model of Kamenica and Gentzkow [34]. Here there are two players, a sender and a receiver. The receiver must take one of a number of actions with a-priori unknown payoff, and the sender has access to additional information regarding the payoffs of the various actions for both players. The sender can commit to revealing a noisy signal regarding the realization of the payoffs of various actions, and would like to do so as to maximize her own payoff in expectation assuming that the receiver rationally acts to maximize his own payoff. When the payoffs of various actions follow a joint distribution (the common prior), the sender's problem is nontrivial, and its computational complexity depends on the representation of this prior.We examine the sender's optimization task in three of the most natural input models for this problem, and essentially pin down its computational complexity in each. When the payoff distributions of the different actions are i.i.d. and given explicitly, we exhibit a polynomial-time (exact) algorithmic solution, and a "simple" (1 − 1/e)-approximation algorithm. Our optimal scheme for the i.i.d. setting involves an analogy to auction theory, and makes use of Border's characterization of the space of reduced-forms for single-item auctions. When action payoffs are independent but non-identical with marginal distributions given explicitly, we show that it is #P-hard to compute the optimal expected sender utility. In doing so, we rule out a generalized Border's theorem, as defined by Gopalan et al [30], for this setting. Finally, we consider a general (possibly correlated) joint distribution of action payoffs presented by a black box sampling oracle, and exhibit a fully polynomial-time approximation scheme (FPTAS) with a bi-criteria guarantee. Our FPTAS is based on Monte-Carlo sampling, and its analysis relies on the principle of deferred decisions. Moreover, we show that this result is the best possible in the black-box model for information-theoretic reasons.A somewhat less artificial example of persuasion is in the context of providing financial advice. Here, the receiver is an investor, actions correspond to stocks, and the sender is a stockbroker or financial adviser with access to stock return projections which are a-priori unknown to the investor. When the adviser's commission or return is not aligned with the investor's returns, this is a nontrivial Bayesian persuasion problem. In fact, interesting examples exist when stock returns are independent from each other, or even i.i.d. Consider the following simple example which fits into the i.i.d. model considered in Section 3: the...

We study the algorithmics of information structure design -a.k.a. persuasion or signaling -in a fundamental special case introduced by Arieli and Babichenko: multiple agents, binary actions, and no inter-agent externalities. Unlike prior work on this model, we allow many states of nature. We assume that the principal's objective is a monotone set function, and study the problem both in the public signal and private signal models, drawing a sharp contrast between the two in terms of both efficacy and computational complexity.When private signals are allowed, our results are largely positive and quite general. First, we use linear programming duality and the equivalence of separation and optimization to show polynomial-time equivalence between (exactly) optimal signaling and the problem of maximizing the objective function plus an additive function. This yields an efficient implementation of the optimal scheme when the objective is supermodular or anonymous. Second, we exhibit a (1 − 1/e)-approximation of the optimal private signaling scheme, modulo an additive loss of ǫ, when the objective function is submodular. These two results simplify, unify, and generalize results of [3,4], extending them from a binary state of nature to many states (modulo the additive loss in the latter result). Third, we consider the binary-state case with a submodular objective, and simplify and slightly strengthen the result of [4] to obtain a (1 − 1/e)-approximation via a scheme which (i) signals independently to each receiver and (ii) is "oblivious" in that it does not depend on the objective function so long as it is monotone submodular.When only a public signal is allowed, our results are negative. First, we show that it is NP-hard to approximate the optimal public scheme, within any constant factor, even when the objective is additive. Second, we show that the optimal private scheme can outperform the optimal public scheme, in terms of maximizing the sender's objective, by a polynomial factor.

Formalin-fixed and paraffin-embedded tissues represent the vast majority of archived tissue. Access to such tissue specimens via shotgun-based proteomic analyses may open new avenues for both prospective and retrospective translational research. In this study, we evaluate the effects of fixation time on antigen retrieval for the purposes of shotgun proteomics. For the first time, we demonstrate the capability of a capillary isotachophoresis (CITP)-based proteomic platform for the shotgun proteomic analysis of proteins recovered from FFPE tissues. In comparison to our previous studies utilizing capillary isoelectric focusing, the CITP-based analysis is more robust and increases proteome coverage. In this case, results from three FFPE liver tissues yield a total of 4098 distinct Swiss-Prot identifications at a 1% false-discovery rate. To judge the accuracy of these assignments, immunohistochemistry is performed on a panel of 17 commonly assayed proteins. These proteins span a wide range of protein abundances as inferred from relative quantitation via spectral counting. Among the panel were 4 proteins identified by a single peptide hit, including three clusters of differentiation (CD) markers: CD74, CD117, and CD45. Because single peptide hits are often regarded with skepticism, it is notable that all proteins tested by IHC stained positive.

No abstract