In many auction environments sellers are better informed about bidders' valuations than the bidders themselves. For such environments we derive a sharp and general optimal policy of information transmission in the case of independent private values. Under this policy bidders whose (ex-post) valuation is below a certain threshold are provided with all the information (about their valuations), but those bidders whose valuation lies below the threshold receive no information whatsoever. Surprisingly, the threshold expressed in percentiles is independent of the probability distribution over bidders' ex-post valuations; it depends solely on the number of bidders. Similar results are also derived for the bidder-optimal policy. Our analysis builds on the approach of “Bayesian persuasion” and on a linearity of sellers' revenues as a function of the inverse distribution. This latter property allows us to use important results on stochastic comparisons.

Emotions—specially desire and the objects of desire, like enjoyment and satisfaction—drive much of what we do; indeed they drive all we do that is not recurrent. They are thus indispensable to human life. Inter alia, emotions enable the operation of incentives—like hunger for eating—that motivate us to perform tasks that are vital to our lives. We suggest that the adaptive function of consciousness is to enable emotions to operate.

We provide an elementary mathematical description of the spread of the coronavirus. We explain two fundamental relationships: How the rate of growth in new infections is determined by the “effective reproductive number”; and how the effective reproductive number is affected by social distancing. By making a key approximation, we are able to formulate these relationships very simply and thereby avoid complicated mathematics. The same approximation leads to an elementary method for estimating the effective reproductive number.

A set of sensors is used to identify which of the users, from a pre-specified set of users, is currently using a device. Each sensor provides a name of a user and a real number representing its level of confidence in the assessment. However, the sensors measure different signals for different traits that are largely unrelated. To be able to implement a policy based on these measurements, one needs to aggregate the information provided by all sensors. We use an axiomatic approach to provide several reasonable trust functions. We show that by providing a few desirable properties we can derive several solutions that are characterized by these properties. Our analysis makes use of an important result by Kolmogorov (1930).

In the standard Bayesian framework the data are assumed to be generated by a distribution parametrized by θ in a parameter space Θ, over which a prior distribution π is defined. A Bayesian statistician quantifies the belief that the true parameter is θ_0 in Θ by its posterior probability given the observed data. We investigate the behavior of the posterior belief in θ_0 when the data are generated under some parameter θ_1, which may or may not be be the same as θ_0. Starting from stochastic orders, specifically, likelihood ratio dominance, that obtain for resulting distributions of posteriors, we consider monotonicity properties of the posterior probabilities as a function of the sample size when data arrive sequentially. While the θ_0-posterior is monotonically increasing (i.e., it is a submartingale) when the data are generated under that same θ_0, it need not be monotonically decreasing in general, not even in terms of its overall expectation, when the data are generated under a different θ_1; in fact, it may keep going up and down many times. In the framework of simple iid coin tosses, we show that under certain conditions the overall expected posterior of θ_0 eventually becomes monotonically decreasing when the data are generated under θ_1≠θ_0. Moreover, we prove that when the prior is uniform this expected posterior is a log-concave function of the sample size, by developing an inequality that is related to Turán's inequality for Legendre polynomials.

A stumper is a riddle whose solution is typically so elusive that it does not come to mind, at least initially - leaving the responder stumped. Stumpers work by eliciting a (typically visual) representation of the narrative, in which the solution is not to be found. In order to solve the stumper, the blocking representation must be changed, which does not happen to most respondents. I have collected all the riddles I know at this time that qualify, in my opinion, as stumpers. I have composed a few, and tested many. Whenever rates of correct solutions were available, they are included, giving a rough proxy for difficulty.

Reports in the 1970s of cognitive illusions in judgments of uncertainty had been anticipated by Laplace 150 years earlier. We discuss Miller and Gelman's remark that Laplace's anticipation of the main ideas of the heuristics and biases approach "gives us a new perspective on these ideas as more universal and less contingent on particular developments [that came much] later."