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Group testing increases efficiency by pooling patient specimens and clearing the entire group with one negative test. Optimal grouping strategy is well studied in one-off testing scenarios with reasonably well-known prevalence rates and no correlations in risk. We discuss how the strategy changes in a pandemic environment with repeated testing, rapid local infection spread, and highly uncertain risk. First, repeated testing mechanically lowers prevalence at the time of the next test. This increases testing efficiency, such that increasing frequency by x times only increases expected tests by around vx rather than x. However, this calculation omits a further benefit of frequent testing: infected people are quickly removed from the population, which lowers prevalence and generates further e?ciency. Accounting for this decline in intra-group spread, we show that increasing frequency can paradoxically reduce the total testing cost. Second, we show that group size and e?ciency increases with intra-group risk correlation, which is expected in natural test groupings based on proximity. Third, because optimal groupings depend on uncertain risk and correlation, we show how better estimates from machine learning can drive large efficiency gains. We conclude that frequent group testing, aided by machine learning, is a promising and inexpensive surveillance strategy.

We model two agents who wish to determine if their types match, but who also desire to reveal as little information as possible to non-matching types. For example, firms considering a merger must determine the merger's profitability, but would prefer to keep their information private if the deal fails. In the model, agents with different traits reveal information to a potential partner to determine if they share the same type, but face a penalty depending on the accuracy of their partner's posterior beliefs. With limited signaling, there is a universally-preferred dynamic communication protocol in which traits are sequentially revealed depending on the sensitivity of the trait. Interestingly, the rarity of an agent's traits plays no role due to the balance of opposing effects: although revealing a rare trait reveals more information immediately, it also screens more partners from later learning information about other traits.

We model religious faith as a “demand for beliefs,” following the logic of the Pascalian wager. We show how standard experimental interventions linking financial consequences to falsifiable religious statements can elicit and characterize beliefs. We implemented this approach with members of a group that expected the “End of the World” to occur on May 21, 2011 by varying monetary prizes payable before and after May 21st. To our knowledge, this is the first incentivized elicitation of religious beliefs ever conducted. The results suggest that the members held extreme, sincere beliefs that were unresponsive to experimental manipulations in price.

The article of record as published may be found at https://doi.org/10.1111/ecin.12105 We explore the effects of competitive and cooperative motivations on contributions in a field experiment. A total of 10,000 potential political donors received solicitations referencing past contribution behavior of members of the competing party (competition treatment), the same party (cooperative treatment), or no past contribution information (control). We first theoretically analyze the effect of these treatments on the contribution behavior of agents with different social preferences in a modified intergroup public good (IPG) game. Then, we report the empirical results: Contribution rates in the competitive, cooperative, and control treatments were 1.45%, 1.08%, and 0.78%, respectively. With the exception of one large contribution, the distribution of contributions in the compet- itive treatment first order stochastically dominates that of the cooperative treatment. Qualitatively, it appears that the cooperative treatment induced more contributions around the common monetary reference point, while the competitive treatment led to more contributions at twice this amount. These results suggest that eliciting competitive rather than cooperative motivations can lead to higher contributions in IPG settings. George P. Shultz Fund and the B.F. Haley and E.S. Shaw Fellowship