This note provides a survey for the Economics and Computation community of some recent trends in the field of information elicitation. At its core, the field concerns the design of incentives for strategic agents to provide accurate and truthful information. Such incentives are formalized as proper scoring rules , and turn out to be the same object as loss functions in machine-learning settings, providing many connections. More broadly, the field concerns the design of mechanisms to obtain information from groups of agents and aggregate it or use it for decision making. Recently, work on information elicitation has expanded and been connected to online no-regret learning, mechanism design, fair division, and more.

In 1979, Weitzman introduced Pandora's box problem as a framework for sequential search with costly inspections. Recently, there has been a surge of interest in Pandora's box problem, particularly among researchers working at the intersection of economics and computation. This survey provides an overview of the recent literature on Pandora's box problem, including its latest extensions and applications in areas such as market design, decision theory, and machine learning.

Many studies and online platforms rely on data from individuals to learn properties of a population or train data-driven services and recommendation systems. However, this data is often sensitive or private, and individuals may be unwilling to share personal information. At the same time, the introduction of Differential Privacy has given us a formal and principled way to protect the privacy of individuals. Differential Privacy adds noise to the data or the learner's computation to "hide" the data of any specific individual, while still permitting to learn properties at the level of a population. In this letter, we discuss our recent work on how one may use differential privacy to increase individuals' willingness to share their personal information. We are particularly interested in understanding the following trade-off: on the one hand, providing more privacy requires adding more noise, which leads to less accurate models; on the other hand, providing more privacy allows more individuals to share their personal data, leading to better models. The main challenges are two-fold: i) we may need to provide different levels of privacy protections to individuals with differing privacy preferences and ii) we aim to incentivize individuals to share their data without payments, but instead through the utility they obtain from the data-driven service offered by the learner.

We discuss the exclusion core, a solution concept for object-allocation and object-exchange problems. The exclusion core is based on the right of exclusion and is especially useful for the analysis of economies with complicated property arrangements, such as those with shared ownership. The exclusion core coincides with the (strong) core in classic settings, and is closely related to the celebrated Top Trading Cycles algorithm.

We present a report on the inaugural SIGecom Winter Meeting which was held online on February 25, 2021. We highlight the goals of the workshop and summarize the format and content.

In this letter, we summarize our recent work examining the incentives produced by algorithmic decision-making. Drawing upon principal-agent models in the mechanism design literature, we construct and analyze a model of strategic behavior under algorithmic evaluation. We characterize which behaviors can be incentivized by any reasonable mechanism, showing that simple linear mechanisms are sufficient to incentivize desired behavior. However, we find that it is computationally hard to optimize even simple objectives subject to the constraint that the resulting linear mechanism induces the desired incentives.

This issue of SIGecom Exchanges contains a letter from the new SIGecom Diversity and Inclusion Chair, three research letters, two brief book announcements, and a workshop summary.

Already in 1981 Myerson gave a characterization of the optimal mechanism for a single parameter Bayesian mechanism design. However, till today we have no idea for how such a characterization for the multi-dimensional setting could even look like. Moreover, it wasn't until no that long time ago that we could not develop mechanisms for such setting with any reasonable and provable performance guarantees. The seminal work of [Chawla et al. 2009] on sequential posted pricing mechanisms gave us an approach for approximately solving the Bayesian multi-parameter unit-demand mechanism design problem (BMUMD). The paper left open the question on how to obtain a constant approximation for the matroid setting. Two mathematically beautiful results from combinatorial optimization under uncertainty where devised in order to answer this question. First, Kleinberg and Weinberg in 2011 extended the classical Prophet Inequality result into the matroid setting to give a 2-approximation for BMUMD for a single matroid setting. Second, Feldman, Svensson and Zenklusen in 2016 adapted the Contention Resolution Scheme framework for online settings. We add to this line of work by considering the Contention Resolution Scheme framework in the random order setting. The most impressive implication of this research are the new algorithms for BMUMD which improve the previous results in the multimatroid setting. Although the range of implications of the CR Scheme framework in the random order is reasonably wide, we shall focus in this letter on presenting only the single matroid setting and how it is connected to BMUMD.

This issue of SIGEcom Exchanges contains one brief announcement followed by four research letters. The 2019 SIGEcom Job Candidates Profiles, compiled last year again by Jason Hartline and Vasilis Gkatzelis, were published separately and are now officially included in the issue.

We propose definitions for when signals are "substitutes" and "complements". These give a characterization of equilibria of prediction markets and are relevant to the complexity of information acquisition under constraints.