“Competition in Persuasion,” M. Gentzkow & E. Kamenica (2012)

How’s this for fortuitous timing: I’d literally just gone through this paper by Gentzkow and Kamenica yesterday, and this morning it was announced that Gentzkow is the winner of the 2014 Clark Medal! More on the Clark in a bit, but first, let’s do some theory.

This paper is essentially the multiple sender version of the great Bayesian Persuasion paper by the same authors (discussed on this site a couple years ago). There are a group of experts who can (under commitment to only sending true signals) send costless signals about the realization of the state. Given the information received, the agent makes a decision, and each expert gets some utility depending on that decision. For example, the senders might be a prosecutor and a defense attorney who know the guilt of a suspect, and the agent a judge. The judge convicts if p(guilty)>=.5, the prosecutor wants to maximize convictions regardless of underlying guilt, and vice versa for the defense attorney. Here’s the question: if we have more experts, or less collusive experts, or experts with less aligned interests, is more information revealed?

A lot of our political philosophy is predicated on more competition in information revelation leading to more information actually being revealed, but this is actually a fairly subtle theoretical question! For one, John Stuart Mill and others of his persuasion would need some way of discussing how people competing to reveal information strategically interact, and to the extent that this strategic interaction is non-unique, they would need a way for “ordering” sets of potentially revealed information. We are lucky in 2014, thanks to our friends Nash and Topkis, to be able to nicely deal with each of those concerns.

The trick to solving this model (basically every proof in the paper comes down to algebra and some simple results from set theory; they are clever but not technically challenging) is the main result from the Bayesian Persuasion paper. Draw a graph with the agent’s posterior belief on the X-axis, and the utility (call this u) the sender gets from actions based on each posterior on the y-axis. Now draw the smallest concave function (call it V) that is everywhere greater than u. If V is strictly greater than u at the prior p, then a sender can improve her payoff by revealing information. Take the case of the judge and the prosecutor. If the judge has the prior that everyone brought before them is guilty with probability .6, then the prosecutor never reveals information about any suspect, and the judge always convicts (giving the prosecutor utility 1 rather than 0 from an acquittal). If, however, the judge’s prior is that everyone is guilty with .4, then the prosecutor can mix such that 80 percent of criminals are convicted by judiciously revealing information. How? Just take 2/3 of the innocent people, and all of the guilty people, and send signals that each of these people is guilty with p=.5, and give the judge information on the other 1/3 of innocent people that they are innocent with probability 1. This is plausible in a Bayesian sense. The judge will convict all of the folks where p(guilty)=.5, meaning 80 percent of all suspects are convicted. If you draw the graph described above with u=1 when the judge convicts and u=0 otherwise, it is clear that V>u if and only if p<.5, hence information is only revealed in that case.

What about when there are multiple senders with different utilities u? It is somewhat intuitive: more information is always, almost by definition, informative for the agent (remember Blackwell!). If there is any sender who can improve their payoff by revealing information given what has been revealed thus far, then we are not in equilibrium, and some sender has the incentive to deviate by revealing more information. Therefore, adding more senders increases the amount of information revealed and “shrinks” the set of beliefs that the agent might wind up holding (and, further, the authors show that any Bayesian plausible beliefs where no sender can further reveal information to improve their payoff is an equilibrium). We still have a number of technical details concerning multiplicity of equilibria to deal with, but the authors show that these results hold in a set order sense as well. This theorem is actually great: to check equilibrium information revelation, I only need to check where V and u diverge sender by sender, without worrying about complex strategic interactions. Because of that simplicity, it ends up being very easy to show that removing collusion among senders, or increasing the number of senders, will improve information revelation in equilibrium.

September 2012 working paper (IDEAS version). A brief word on the Clark medal. Gentzkow is a fine choice, particularly for his Bayesian persuasion papers, which are already very influential. I have no doubt that 30 years from now, you will still see the 2011 paper on many PhD syllabi. That said, the Clark medal announcement is very strange. It focuses very heavily on his empirical work on newspapers and TV, and mentions his hugely influential theory as a small aside! This means that five of the last six Clark medal winners, everyone but Levin and his relational incentive contracts, have been cited primarily for MIT/QJE-style theory-light empirical microeconomics. Even though I personally am primarily an applied microeconomist, I still see this as a very odd trend: no prizes for Chernozhukov or Tamer in metrics, or Sannikov in theory, or Farhi and Werning in macro, or Melitz and Costinot in trade, or Donaldson and Nunn in history? I understand these papers are harder to explain to the media, but it is not a good thing when the second most prominent prize in our profession is essentially ignoring 90% of what economists actually do.