Chain: A Dynamic Double Auction Framework for Matching Patient Agents

In this paper we present and evaluate a general framework for the design of truthful auctions for matching agents in a dynamic, two-sided market. A single commodity, such as a resource or a task, is bought and sold by multiple buyers and sellers that arrive and depart over time. Our algorithm, Chain, provides the first framework that allows a truthful dynamic double auction (DA) to be constructed from a truthful, single-period (i.e. static) double-auction rule. The pricing and matching method of the Chain construction is unique amongst dynamic-auction rules that adopt the same building block. We examine experimentally the allocative efficiency of Chain when instantiated on various single-period rules, including the canonical McAfee double-auction rule. For a baseline we also consider non-truthful double auctions populated with zero-intelligence plus"-style learning agents. Chain-based auctions perform well in comparison with other schemes, especially as arrival intensity falls and agent valuations become more volatile

Dynamic Pricing in the Presence of Social Learning and Strategic Consumers

When a product of uncertain quality is first introduced, consumers may choose to strategically delay their purchasing decisions in anticipation of the product reviews of their peers. This paper investigates how the presence of social learning affects the strategic interaction between a dynamic-pricing monopolist and a forward-looking consumer population, within a simple two-period model. Our analysis yields three main insights. First, we find that the presence of social learning has significant structural implications for optimal pricing policies: In the absence of social learning, decreasing price plans are always preferred by the firm; by contrast, in the presence of social learning we find that (i) if the firm commits to a price path ex ante (preannounced pricing), an increasing price plan is typically announced, whereas (ii) if the firm adjusts price dynamically (responsive pricing), prices are initially low and may either rise or decline over time. Second, we establish that under both preannounced and responsive pricing, even though the social learning process exacerbates strategic consumer behavior (i.e., increases strategic purchasing delays), its presence results in an increase in expected firm profit. Third, we illustrate that, contrary to results reported in existing literature on strategic consumer behavior, in settings where social learning is significantly influential, preannounced pricing policies are generally not beneficial for the firm

Information Exchange, Market Transparency and Dynamic Oligopoly

In the economics literature, various views on the likely (efficiency) effects of information exchange, communication between firms and market transparency present themselves. Often these views on information flows are highly conflicting. On the one hand, it is argued that increased information dissemination improves firm planning to the benefit of society (including customers) and/or allows potential customers to make the right decisions given their preferences. On the other hand, the literature also suggests that increased information dissemination can have significant coordinating or collusive potential to the benefit of firms but at the expense of society at large (mainly, potential customers). In this chapter, we try to make sense of these views, with the aim of presenting some simple lessons for antitrust practice. In addition, the chapter presents some cases, from both sides of the Atlantic, where informational issues have played a significant role.

Price formation in a sequential selling mechanism

This paper analyzes the trade of an indivisible good within a two-stage mechanism, where a seller first negotiates with one potential buyer about the price of the good. If the negotiation fails to produce a sale, a second–price sealed–bid auction with an additional buyer is conducted. The theoretical model predicts that with risk neutral agents all sales take place in the auction rendering the negotiation prior to the auction obsolete. An experimental test of the model provides evidence that average prices and profits are quite precisely predicted by the theoretical benchmark. However, a significant large amount of sales occurs already during the negotiation stage. We show that risk preferences can theoretically account for the existence of sales during the negotiation stage, improve the fit for buyers’ behavior, but is not sufficient to explain sellers’ decisions. We discuss other behavioral explanations that could account for the observed deviations

Incentive-aware Contextual Pricing with Non-parametric Market Noise

We consider a dynamic pricing problem for repeated contextual second-price auctions with strategic buyers whose goals are to maximize their long-term time discounted utility. The seller has very limited information about buyers' overall demand curves, which depends on $d$-dimensional context vectors characterizing auctioned items, and a non-parametric market noise distribution that captures buyers' idiosyncratic tastes. The noise distribution and the relationship between the context vectors and buyers' demand curves are both unknown to the seller. We focus on designing the seller's learning policy to set contextual reserve prices where the seller's goal is to minimize his regret for revenue. We first propose a pricing policy when buyers are truthful and show that it achieves a $T$-period regret bound of $\tilde{\mathcal{O}}(\sqrt{dT})$ against a clairvoyant policy that has full information of the buyers' demand. Next, under the setting where buyers bid strategically to maximize their long-term discounted utility, we develop a variant of our first policy that is robust to strategic (corrupted) bids. This policy incorporates randomized "isolation" periods, during which a buyer is randomly chosen to solely participate in the auction. We show that this design allows the seller to control the number of periods in which buyers significantly corrupt their bids. Because of this nice property, our robust policy enjoys a $T$-period regret of $\tilde{\mathcal{O}}(\sqrt{dT})$, matching that under the truthful setting up to a constant factor that depends on the utility discount factor

Promising Payment Reform: Risk-Sharing With Accountable Care Organizations

Describes the implementation of shared payer-provider risk payment models at eight private accountable care organizations. Analyzes challenges for providers, purchasers, and payers, including securing the infrastructure for successful risk management