How Best to Auction Natural Resources

I study the design of auctions of natural resources, such as oil or mineral rights. A good auction design promotes both an efficient assignment of rights and competitive revenues for the seller. The structure of bidder preferences and the degree of competition are key factors in determining the best design. With weak competition and simple value structures, a simultaneous first-price sealed-bid auction may suffice. With more complex value structures, a dynamic auction with package bids likely is needed to promote efficiency and revenue objectives. Bidding on production shares, rather than bonuses, typically increases government take by reducing oil or mining company risk.Auctions, natural resource auctions, oil auctions

How Best to Auction Oil Rights

I study the design of oil rights auctions. A good auction design promotes both an efficient assignment of rights and competitive revenues for the seller. The structure of bidder preferences and the degree of competition are key factors in determining the best design. With weak competition and additive values, a simultaneous first-price sealed-bid auction may suffice. With more complex value structures, a dynamic auction with package bids, such as the clock-proxy auction, likely is needed to promote the efficiency and revenue objectives. Bidding on production shares, rather than bonuses, typically increases government take by reducing oil company risk.Auctions, Oil Auctions, Market Design, Clock Auctions

Waiting for Leviathan: A Note on \u3cem\u3eModern Wo\u27er Trading Co Ltd v Ministry of Finance of the People\u27s Republic of China\u3c/em\u3e

This article analyzes a Chinese bid protest that has taken nearly seven years to adjudicate, yet as of this writing, no institution of the Chinese state has evaluated the substance of the protester’s bid challenge. Instead, the supplier’s complaint has been snared in a grey area between two of China’s multiple bid protest systems, burdening the supplier to push China’s administrative state to respond. The saga of Modern Wo’Er Trading Company Ltd. v The Ministry of Finance of the People’s Republic of China raises compelling questions about the relationship of China’s 1999 Tender and Bidding Law and China’s 2002 Government Procurement Law, the nature of administrative power in China, and the ability of Chinese public procurement law to offer justice to aggrieved supplier

Optimal No-regret Learning in Repeated First-price Auctions

We study online learning in repeated first-price auctions with censored feedback, where a bidder, only observing the winning bid at the end of each auction, learns to adaptively bid in order to maximize her cumulative payoff. To achieve this goal, the bidder faces a challenging dilemma: if she wins the bid--the only way to achieve positive payoffs--then she is not able to observe the highest bid of the other bidders, which we assume is iid drawn from an unknown distribution. This dilemma, despite being reminiscent of the exploration-exploitation trade-off in contextual bandits, cannot directly be addressed by the existing UCB or Thompson sampling algorithms in that literature, mainly because contrary to the standard bandits setting, when a positive reward is obtained here, nothing about the environment can be learned. In this paper, by exploiting the structural properties of first-price auctions, we develop the first learning algorithm that achieves $O(\sqrt{T}\log^2 T)$ regret bound when the bidder's private values are stochastically generated. We do so by providing an algorithm on a general class of problems, which we call monotone group contextual bandits, where the same regret bound is established under stochastically generated contexts. Further, by a novel lower bound argument, we characterize an $\Omega(T^{2/3})$ lower bound for the case where the contexts are adversarially generated, thus highlighting the impact of the contexts generation mechanism on the fundamental learning limit. Despite this, we further exploit the structure of first-price auctions and develop a learning algorithm that operates sample-efficiently (and computationally efficiently) in the presence of adversarially generated private values. We establish an $O(\sqrt{T}\log^3 T)$ regret bound for this algorithm, hence providing a complete characterization of optimal learning guarantees for this problem

Empirical Implications of Equilibrium Bidding in First-Price, Symmetric, Common Value Auctions

This paper studies federal auctions for wildcat leases on the Outer Continental Shelf from 1954 to 1970. These are leases where bidders privately acquire (at some cost) noisy, but equally informative, signals about the amount of oil and gas that may be present. We develop a test of equilibrium bidding in a common values model that is implemented using data on bids and ex post values. We compute bid markups and rents under the alternative hypotheses of private and common values and find that the data are more consistent with the latter hypothesis. Finally, we use data on tract location and ex post values to test the comparative static prediction in common value auctions that bidders may bid less aggressively when they expect more competition.

Hopscotch: Robust Multi-agent Search

The task of searching a space is critical to a wide range of diverse applications such as land mine clearing and planetary exploration. Because applications frequently require searching remote or hazardous locations, and because the task is easily divisible, it is natural to consider the use of multi-robot teams to accomplish the search task. An important topic of research in this area is the division of the task among robot agents. Interrelated with subtask assignment is failure handling, in the sense that, when an agent fails, its part of the task must then be performed by other agents. This thesis describes Hopscotch, a multi-agent search strategy that divides the search area into a grid of lots. Each agent is assigned responsibility to search one lot at a time, and upon completing the search of that lot the agent is assigned a new lot. Assignment occurs in real time using a simple contract net. Because lots that have been previously searched are skipped, the order of search from the point of view of a particular agent is reminiscent of the progression of steps in the playground game of Hopscotch. Decomposition of the search area is a common approach to multi-agent search, and auction-based contract net strategies have appeared in recent literature as a method of task allocation in multi-agent systems. The Hopscotch strategy combines the two, with a strong focus on robust tolerance of agent failures. Contract nets typically divide all known tasks among available resources. In contrast, Hopscotch limits each agent to one assigned lot at a time, so that failure of an agent compels re-allocation of only one lot search task. Furthermore, the contract net is implemented in an unconventional manner that empowers each agent with responsibility for contract management. This novel combination of real-time assignment and decentralized management allows Hopscotch to resiliently cope with agent failures. The Hopscotch strategy was modeled and compared to other multi-agent strate- gies that tackle the search task in a variety of ways. Simulation results show that Hopscotch is failure-tolerant and very effective in comparison to the other approaches in terms of both search time and search efficiency. Although the search task modeled here is a basic one, results from simulations show the promise of using this strategy for more complicated scenarios, and with actual robot agents

Overcoming Managerial Challenges to Realize Growth Spurts: Insights from Cases of Three Enterprises

Organizations face several managerial challenges during their growth period. Growth spurts are realized when organizations overcome these challenges. Though the literature is full of studies on the enterprise growth, the knowledge about how these challenges facilitate or hinder growth is limited. We conceptualize and explain five challenges faced by an enterprise along its growth trajectory. For evidence, we then look at history of three organizations from different sectors and trace their strategies to overcome the challenges faced by them. The firm and the environment interact and make certain strategic choices, which in turn results in growth spurts in the organization. We draw insights from their growth stories and discuss the different strategies and interactions between the firm and the environment.

Information in Mechanism Design

We survey the recent literature on the role of information in mechanism design. First, we discuss an emerging literature on the role of endogenous payoff and strategic information for the design and the efficiency of the mechanism. We specifically consider information management in the form of acquisition of new information or disclosure of existing information. Second, we argue that in the presence of endogenous information, the robustness of the mechanism to the type space and higher order beliefs becomes a natural desideratum. We discuss recent approaches to robust mechanism design and robust implementation.Mechanism Design, Information Acquisition, Ex Post Equilibrium, Robust Mechanism Design, Interdependent Values, Information Management