This thesis studies issues in auctions and contests. The seller of an object and the organizer of a contest have many instruments to improve the revenue of the auction or the efficiency of the contest. The three essays in this dissertation shed light on these issues. Chapter 2 investigates how a refund policy affects a buyer's strategic behavior by characterizing the equilibria of a second-price auction with a linear refund policy. I find that a generous refund policy induces buyers to bid aggressively. I also examine the optimal mechanism design problem when buyers only have private initial estimates of their valuations and may privately learn of shocks that affect their valuations later. When all buyers are \emph{ex-ante} symmetric, this optimal selling mechanism can be implemented by a first-price or second-price auction with a refund policy. Chapter 3 investigates how information revelation rules affect the existence and the efficiency of equilibria in two-round elimination contests. I establish that there exists no symmetric separating equilibrium under the full revelation rule and find that the non-existence result is very robust. I then characterize a partially efficient separating equilibrium under the partial revelation rule when players' valuations are uniformly distributed. I finally investigate the no revelation rule and find that it is both most efficient and optimal in maximizing the total efforts from the contestants. Within my framework, more information revelation leads to less efficient outcomes. Chapter 4 analyzes the signaling effect of bidding in a two-round elimination contest. Before the final round, bids in the preliminary round are revealed and act as signals of the contestants' private valuations. Compared to the benchmark model, in which private valuations are revealed automatically before the final round and thus no signaling of bids takes place, I find that strong contestants bluff and weak contestants sandbag. In a separating equilibrium, bids in the preliminary round fully reveal the contestants' private valuations. However, this signaling effect makes the equilibrium bidding strategy in the preliminary round steeper for high valuations and flatter for low valuations compared to the benchmark model. / Thesis (Ph.D, Economics) -- Queen's University, 2010-04-20 21:34:12.295

<p>The primary research question of this dissertation is, \textit{How do contestants make sequential design decisions under the influence of competition?} To address this question, I study the influence of three factors, that can be controlled by the contest organizers, on the contestants' sequential information acquisition and decision-making behaviors. These factors are (i) a contestant's domain knowledge, (ii) framing of a design problem, and (iii) information about historical contests. The \textit{central hypothesis} is that by conducting controlled behavioral experiments we can acquire data of contestant behaviors that can be used to calibrate computational models of contestants' sequential decision-making behaviors, thereby, enabling predictions about the design outcomes. The behavioral results suggest that (i) contestants better understand problem constraints and generate more feasible design solutions when a design problem is framed in a domain-specific context as compared to a domain-independent context, (ii) contestants' efforts to acquire information about a design artifact to make design improvements are significantly affected by the information provided to them about their opponent who is competing to achieve the same objectives, and (iii) contestants make information acquisition decisions such as when to stop acquiring information, based on various criteria such as the number of resources, the target objective value, and the observed amount of improvement in their design quality. Moreover, the threshold values of such criteria are influenced by the information the contestants have about their opponent. The results imply that (i) by understanding the influence of an individual's domain knowledge and framing of a problem we can provide decision-support tools to the contestants in engineering design contexts to better acquire problem-specific information (ii) we can enable contest designers to decide what information to share to improve the quality of the design outcomes of design contest, and (iii) from an educational standpoint, we can enable instructors to provide students with accurate assessments of their domain knowledge by understanding students' information acquisition and decision making behaviors in their design projects. The \textit{primary contribution} of this dissertation is the computational models of an individual's sequential decision-making process that incorporate the behavioral results discussed above in competitive design scenarios. Moreover, a framework to conduct factorial investigations of human decision making through a combination of theory and behavioral experimentation is illustrated. <br></p>