Over the past decade, an exciting connection has developed between the theory of posted-price mechanisms and the prophet inequality, a result from the theory of optimal stopping. This survey provides an overview of this literature, covering extensions and applications of the prophet inequality through the lens of an economic proof of this classic result. We focus on highlighting ways in which the economic perspective drives new advances in the theory of online stochastic optimization, and vice versa.

Recent years have witnessed the rise of many successful e-commerce marketplace platforms like the Amazon marketplace, AirBnB, Uber/Lyft, and Upwork, where a central platform mediates economic transactions between buyers and sellers. A common feature of many of these two-sided marketplaces is that the platform has full control over search and discovery, but prices are determined by the buyers and sellers. We summarize our results from [Banerjee et al. 2017] where, motivated by this application domain, we study the algorithmic aspects of market segmentation via directed discovery in two-sided markets with endogenous prices. We consider a model where an online platform knows each buyer/seller's characteristics, and associated demand/supply elasticities. Moreover, the platform can use discovery mechanisms (search, recommendation, etc.) to control which buyers/sellers are visible to each other. We develop efficient algorithms for throughput (i.e. volume of trade) and welfare maximization with provable guarantees under a variety of assumptions on the demand and supply functions.

This letter describes an exercise in market mechanism refinement that was recently undertaken on a major electronic trading venue: Thomson Reuters Matching. The exercise sought to address problems associated with continuous markets that have been described in recent literature. To this end, the design of the refinement is described, and its consequences are discussed.

In this letter, we briefly summarize two recent works from our group that use observational data to study the mechanisms used by two large markets. First, we examine Uber's surge price algorithm, and observe that its incentive model may not be effective at changing driver behavior. Second, we study the adoption of dynamic pricing strategies by sellers on Amazon Marketplace, and investigate how these strategies interact with Amazon's "Buy Box" matching algorithm. We make our data available to the research community.

Information structure design , also sometimes known as signaling or persuasion , is concerned with understanding the effects of information on the outcomes of strategic interactions (the descriptive question), and in characterizing and computing the information sharing strategies which optimize some design objective (the prescriptive question). Both questions are illuminated through the lens of algorithms and complexity, as evidenced by recent work on the topic in the algorithmic game theory community. This monograph is a biased survey of this work, and paints a picture of the current state of progress and challenges ahead. We divide information structure design into single agent and multiple agent models, and further subdivide the multiple agent case into the public channel and private channel modes of information revelation. In each of these three cases, we describe the most prominent models and applications, survey the associated algorithms and complexity results and their structural implications, and outline directions for future work.

Optimal mechanisms are often prohibitively complicated, leading to serious obstacles both in theory and in bridging theory and practice. Consider the problem of a monopolist seller facing a single additive buyer with independent valuations over n heterogeneous items. Even in this simple setting, it is known that optimal (revenue-maximizing) mechanisms may require randomization [Hart and Reny 2012], use menus of infinite size [Daskalakis et al. 2015], and may be computationally intractable [Daskalakis et al. 2014]. In a letter here last year, Babiaoff et al. [Babaioff et al. 2014a] described their attempt to alleviate the problem by showing that a constant fraction of the optimal revenue can be obtained by a simple mechanism. In this letter we argue in favor of a related research direction: finding the optimal simple mechanism. We survey our recent results in this setting [Rubinstein 2016] and draw attention to the question of what is a "simple" mechanism?

We describe logit dynamics, which are used to model bounded rationality in games, and their related equilibrium concept, the logit equilibrium. We also present some results about the convergence time of these dynamics and introduce a suitable approximation of the logit equilibrium. We conclude by describing some interesting future extensions to logit dynamics.

Issue 12.1 of SIGecom Exchanges includes a letter from the program chairs of EC'13, a survey, and six research letters. The purpose of the first letter is to update the community about some of the decisions and discussions surrounding EC'13. The previous Exchanges issue---11.2---included a letter from the SIG chair David Parkes, and going forward the plan is to alternate between these two types of updates, publishing a letter from the SIG chair in each December issue and a letter from the EC chairs in each June issue.

We outline a recently developed method for analyzing non-cooperative bargaining games in networks, and discuss some applications.

In August I have officially taken over from Yiling Chen as the editor of SIGecom Exchanges. My predecessors have done a wonderful job with Exchanges and I hope that this unusual journal-newsletter hybrid will continue to be an essential source of ...