Four puzzles in information and politics: Product bans, informed voters, social insurance, & persistent disagreement

Abstract

In four puzzling areas of information in politics, simple intuition and simple theory seem to conflict, muddling policy choices. This thesis elaborates theory to help resolve these conflicts. The puzzle of product bans is why regulators don’t instead offer the equivalent information, for example through a “would have banned” label. Regulators can want to lie with labels, however, either due to regulatory capture or to correct for market imperfections. Knowing this, consumers discount regulator warnings, and so regulators can prefer bans over the choices of skeptical consumers. But all sides can prefer regulators who are unable to ban products, since then regulator warnings will be taken more seriously. The puzzle of voter information is why voters are not even more poorly informed; press coverage of politics seems out of proportion to its entertainment value. Voters can, however, want to commit to becoming informed, either by learning about issues or by subscribing to sources, to convince candidates to take favorable positions. Voters can also prefer to be in large groups, and to be ignorant in certain ways. This complicates the evaluation of institutions, like voting pools, which reduce ignorance. The puzzle of group insurance as a cure for adverse selection is why this should be less a problem for groups than individuals. The usual argument about reduced variance of types for groups doesn’t work in separating equilibria; what matters is the range, not variance, of types. Democratic group choice can, however, narrow the group type range by failing to represent part of the electorate. Furthermore, random juries can completely eliminate adverse selection losses. The puzzle of persistent political disagreement is that for ideal Bayesians with common priors, the mere fact of a factual disagreement is enough of a clue to induce agreement. But what about agents like humans with severe computational limitations? If such agents agree that they are savvy in being aware of these limitations, then any factual disagreement implies disagreement about their average biases. Yet average bias can in principle be computed without any private information. Thus disagreements seem to be fundamentally about priors or computation, rather than information.