Kamenica and Gentzkow recently published this gloriously-titled extension of the cheap talk literature in AER. Recall that in Crawford-Sobel cheap talk, a sender and receiver differ in their preferred action, and the sender holds better information about the true state. The receiver cannot commit to an action conditional on the message, so this is not a principal-agent problem. Even though both people know the sender is biased, there are equilibria that are partially informative, where the sender credibly just tells you what set of states the true state is in, and the receiver updates based on that knowledge. These equilibria are not always good for the sender, nor for the receiver – both may be better off in the “babbling equilibrium” where the signal is totally ignored.
Kamenica and Gentzkow consider a problem where the agent picks the signal structure, and then sends a verifiable signal; this avoids worries about the babbling equilibrium. Their example is a prosecutor, who legally cannot lie, but can choose how to collect information. Both judge and prosecutor have a prior of .3 that the defendant is guilty. The prosecutor earns payoff 1 from a guilty conviction, and 0 from innocence. The judge earns payoff 1 for a correct conviction/acquittal, and 0 from an incorrect one. The prosecutor bias is known. Nonetheless, the prosecutor can get the defendant convicted 60% of the time! How? Collect two types of evidence. We want to use signals to generate a posterior that the defendant is guilty with probability of exactly .5 (in which case the expected-utility maximizing judge convicts), and a posterior that the defendant is innocent with probability 1. That is, half of the time that the prosecutor says “guilty”, the defendant is innocent, and always if the prosecutor says “innocent”, the defendant is innocent. This means the prosecutor says guilty 60% of the time, and innocent 40% of the time. These are Bayesian rational given the prior of guilt with p=.5.
Note what’s going on. From the prosecutor’s perspective, it doesn’t matter when the judge thinks the defendant is guilty with probability 1 or p=.8 or p=.55. As long as p(guilty)>=.5, the judge will convict, and the prosecutor will get payoff 1. So collecting evidence that is really strong is worthless. Better to collect evidence that is just strong enough to get a conviction; by doing so, the prosecutor can give the same evidence for lots of innocent people as well as the truly guilty! This principle applies broadly; the authors show that a car dealership will want a buyer to just barely believe the car is a good match for her if she should buy, and tell all others that the car is a terrible match.
The math here is interesting. Subgame perfection, plus a short proposition, means that the agent’s action will be a deterministic function of his posterior. The sender’s payoff is a function of the agent’s action. Hence the sender’s payoff is a function of the induced posterior. Whenever there are convexities in the sender payoff as a function of agent’s action, the sender should choose signals (or randomize) to “smooth out” that convexity. In the example, the prosecutor payoff is 0 if the judge’s posterior of guilt is below .5, and 1 if equal to or above .5. That function has a convexity between 0 and .5. We can choose signals in a verifiable way such that if the prior is some p in (0, .5), the overall probability of conviction rises to 2p; just choose evidence such that the p percent of guilty defendants, plus another p percent of innocent defendants, all have the same just-strong-enough evidence against them.
A Fine Theorem 
Hi, I am a fan of this blog. I would be very interested to hear your thoughts on the Apple vs Samsung case.
On legal points, I don’t have anything to add that hasn’t been said by many others on the internet. But on the economics of software patents, see Bessen and Maskin (RAND, 2009), which I’ve discussed on this site previously. The question, “Are these patents socially useful?” has a simple answer: no. You can see this just by imagining the counterfactual world where Apple has zero protection on software elements, and where trade dress patents are interpreted like trademarks (i.e., anyone can use a bezel and rounded corners, but you can’t make a knockoff and try to fool consumers into thinking your phone is an iPhone). Does Apple still invent the “rubber-band” bounceback when scrolling? Do they still use double-tap for zoom, the obvious analog of double-click on a touchscreen? Clearly, the answer is yes. If that’s the case, then, these patents have purely anticompetitive effects. That’s worrying.
Thanks. That would be my guess, too. I must say, I learned most of what I know about these issues from this blog.
This sounds like something that’s quite common in poker strategies. Like your prosecutor, a poker player can choose what quality hands to bet and how many bad hand they will bluff with.
To get more than your fair share of pots, you pick all your good hands and some percentage of your bad hands to bluff with, so that your opponent slightly loses (below 0 expected value) if he calls every time. As long as the expected value is slightly below 0, he should theoretically fold every time if he cannot distinguish the cases apart.
Like your prosecutor bluffing your judge. He’s mixed in some small percentage of innocent cases, but not so much that the judge can call his bluff profitably. Is that the same dynamic as analyzed in the paper?
I’ve read this paper but didn’t understand that player’s could be worse off by persuasion. The authors also claim there isn’t even a babbling equilibrium. I’m no theorist, did I miss something? In any case, a crucial ingredient of the model that should be mentioned is that Sender can commit to a strategy, which is not the case in the classic literature.
Indeed it is – see the discussion on p. 2598 of precisely this point.