In a multi-stage contest known as a two-player race, players display two fundamental behaviors: (1) The laggard will make a last stand in order to avoid the cost of losing; and (2) the player who is ahead will defend his lead if it is threatened. Last stand behavior, in particular, contrasts with previous research where the underdog simply gives up. The distinctive results are achieved by introducing losing penalties and discounting into the racing environment. This framework permits the momentum effect, typically ascribed to the winner of early stages, to be more thoroughly examined. I study the likelihood that the underdog will catch up. I find that neck-and-neck races are common when the losing penalty is large relative to the winning prize, while landslide victories occur when the prize is relatively large. Closed-form solutions are given for the case where players have a common winning prize and losing penalty. Chapter 2 then experimentally examines the prediction of last stand behavior in a multi-battle contest with a winning prize and losing penalty, as well as the contrasting prediction of surrendering in the corresponding contest with no penalty. We find varied evidence in support of these hypotheses in the aggregated data, but more conclusive evidence when scrutinizing individual player behavior. Players tend to adopt one of several strategies. We develop a taxonomy to classify player types and study how the different strategies interact. The last stand and surrendering behaviors have implications for winning margins and the likelihood of an upset, which we investigate. Behaviorally, players are typically more aggressive when they reach a state in the contest by winning rather than by losing. The third and final chapter is a distinct departure from the study of multi-battle contests. Using comprehensive census data for Cornwall County, England, I create a panel dataset that spans six censuses (1841--1891)—possibly the largest panel dataset for Victorian England at present. I present the methodology for linking individuals and families across these censuses. This methodology incorporates recent advances in census linking (including the use of machine learning) and introduces new methods for tracking migration and changes in household composition. I achieve a forward matching rate of 43%. The additional inclusion of marriage and death records could allow for well over 60% of the population to be accounted for from one census to the next. Using this new panel, I investigate the frequency with which sons pursue the same occupations that they observed their fathers doing while growing up. For sons that did not follow in their father's footsteps, I identify some correlates that may have contributed to the change.

Australian wool traders and researchers have little knowledge of the incomplete adoption of the price risk management strategies that are available to stabilise wool producers’ incomes. Auction is by far the most popular method of selling wool in Australia with an adoption rate of about 85%. However this system exposes users (wool producers and buyers alike) to highly volatile prices and non-specific knowledge of supply and demand. Furthermore, it places differentiated wool types in the same commodity market as mass produced, homogeneous wool types. In order to address these issues, a mixed-method research design was used to develop and test a behavioural model of wool producers’ intentions to adopt the use of forward contracts; a selling method alternative to auction. In the simplest terms, a forward contract is a binding agreement between a buyer and a seller that stipulates price, quality, quantity and delivery date of a product. The behavioural model developed for this research was based on the Theory of Reasoned Action, Theory of Planned Behaviour and Diffusion of Innovations as well as some farm-level constructs that were raised in focus groups with Western Australian wool producers. The focus groups were pivotal in adding a unique, farm-level decision-making dimension to the behavioural model by the inclusion of various factors external and internal to the farm business. Based on the behavioural model, 28 hypotheses were developed and tested. Data was collected via a telephone survey of 305 Western Australian wool producers and analysis was conducted using the Partial Least Squares (PLS) approach to Structural Equation Modelling (SEM). / A key finding of this analysis, contrary to the initial indications of focus group discussions, is that the current selling and marketing structure of the Australian wool industry, including the dominance of the auction system, is an important but not a limiting factor associated with the adoption of forward contracts for the sale of raw wool. Similarly, some other factors internal to the farm business, such as past experiences with selling wool, level of dependence on wool to earn a living and commitment to producing wool, were also found not to limit the adoption of forward contracts. The main factor limiting the adoption of forward contracts was identified as the wool producers’ perceptions of risk and uncertainty. Farmers’ perceptions of risk and uncertainty and their perceptions and attitudes in general are known to be important influences on farmers’ adoption decisions. While the majority of the hypotheses tested within the model were explained by the data, further data were collected to solve the issues associated with why farmers perceive forward contracting as being subject to risk and uncertainty. Additional research was conducted in the form of four case studies with Western Australian wool producers who had varying commitments to using forward contacts. Results showed that profit-raising, the whole farm system as a basis for decision making, the mass media and social pressures are important behavioural factors that are limiting the adoption of forward contracts by Western Australian wool producers. Overall, the results of the study indicate that the current structure of the Australian wool industry and various factors internal to the farm business account for farmers’ attitudes towards the use of forward contracts to sell their wool. / More importantly, from an agribusiness point of view, it is the perceived risk associated with price that principally accounts for the incomplete adoption of forward contracts in the wool industry. The conclusions of this study resulted in the development of new research questions that focus on the study’s theoretical framework, the impact of supply chain dynamics on the adoption of forward contracts and the empirical testing of additional behavioural determinants such as trust, habit and social cohesion. Based on the results of this study, several contributions have been made to the literature and agribusiness. The study showed that variables from the Diffusion of Innovations model played a significant part in this research. However, the more substantial finding was that the Theory of Reasoned Action is likely to be a superior theoretical framework for modelling wool producers’ adoption behaviours related to forward contracts than the Theory of Planned Behaviour. This claim is based on the finding that perceived behavioural controls are not a significant factor in the intention of wool producers to adopt the use of forward contracts. In terms of the contributions to agribusiness, information and extension initiatives that explain and demonstrate the benefits of forward contracts may be necessary if farmers’ perceptions of the riskiness and uncertainty surrounding these contracts are to be altered.

Marketing research has attempted to shed light on donor responses to a variety of donation appeals and strategies. More recently, research has examined the effect of changing the content of an appeal in both a donation solicitation and a cause-related marketing context. Some charities are highly successful with their marketing and fundraising strategies, while many others struggle to fund their services. This discrepancy in donor support is cause for concern from a public policy perspective, where optimizing the distribution of dollars is a key objective. Particularly in a recessionary economy, with more and more charities appealing to donors for their support, charity choice has become more crowded than ever before. The question of which charity is chosen and how much to spend on that charity can determine which charities succeed and which ones fail, as donors become increasingly concerned with maximizing the impact of their donor dollars. I begin the dissertation with a thorough review of the relevant literature to provide a foundation and backdrop to the issues I study in two sets of studies. In the first set of studies, I examine deservingness of a recipient, where judgments are affected by the donation appeal content. Specifically, I look at how recipient information profiles can affect donor response. In the second set of studies, I examine donor response in a novel cause-related marketing format - online charity auctions where I vary factors related to the auction products, price and the percentage of auction price that is donated to charity. These two papers contribute to the research in donor response to charity appeals by shedding light on the deliberative aspect of the decision process. Public policy and managerial implications are discussed, where an increasingly competitive environment with many comparative options are becoming standard challenges for charity fundraisers. A review of the relevant research areas for both papers precedes the studies to provide a foundation and motivation for our hypotheses and research designs. / Marketing

The allocation of resources over time, referred to as resource scheduling, in large-scale construction environments is a challenging problem. Although traditional network scheduling techniques are the most popular scheduling techniques in the construction industry, they are ineffective in modeling the dynamic nature and resource interactions of large projects. Simulation based modeling or optimization techniques are also time-consuming, complicated and costly to be implemented in large-scale projects. This research is focused on developing a new framework to insert artificial intelligence inside construction simulations for facilitating the resource allocation process. The first stage in this study was developing a framework to solve resource scheduling problems in large scale construction projects. This framework, called the Simulation Based Auction Protocol (SBAP), integrates Multi-Agent Resource Allocation (MARA) in a simulation environment. This hybrid framework deploys centralized MARA (i.e., auction protocols) whereby agents bid on different combinations of resources at the start of a simulation cycle. Agents attempt to improve their individual welfare by acquiring a combination of resources. An auctioneer is designed to allocate resources to the agents by maximizing the overall welfare of the society. Simulation is also employed to track the availability of resources, and manage resource oriented activities. This framework is implemented in two large construction applications of scheduling module assembly yard and multiple heavy lift planning in modular construction. The second objective of this project is to develop a generic resource allocation component for addressing optimized resource allocation in various construction projects. This component is developed in a large scale model using High Level Architecture (HLA), instead of traditional simulation environments. HLA allows splitting a large scale model, known as a federation, into a number of manageable components (i.e., federates), while maintaining interoperability between them. A generic Resource Allocation (RA) federate is designed to act as an auctioneer for federates developed based on the SBAP. Another generic federate is also built to automate the communication with the RA federate. These two generic federates can be reused in various construction federations. This framework is successfully implemented in an industrial construction process that involves different supply chains including spool fabrication, module assembly and heavy crane lifts in site construction. / Construction Engineering and Management

This research addresses the problem of allocating closely spaced targets to multiple autonomous underwater vehicles (AUV) in the presence of constant ocean currents. The main difficulty of this problem is that the non-holonomic vehicles are constrained to move along forward paths with bounded curvatures. The Dubins model is a simple but effective way to handle the kinematic characteristics of AUVs. It gives complete characterization of the optimal paths between two configurations for a vehicle with limited turning radius moving in a plane at constant speed. In the proposed algorithm, Dubins paths are modified to include ocean currents, resulting in paths defined by curves whose radius of curvature is not constant. To determine the time required to follow such paths, an approximate dynamic model of the AUV is queried due to the computational complexity of the full model. The lower order model is built from data obtained from sampling the full model. The full model is used in evaluating the final tour times of the sequences generated by the proposed algorithm to validate the results. The proposed algorithm solves the task allocation problem with market-based auctions that minimize the total travel time to complete the mission. The novelty of the research is the path cost calculation that combines a Dubins model, an AUV dynamic model, and a model of the ocean current. Simulations were conducted in Matlab to illustrate the performance of the proposed algorithm using various number of task points and AUVs. The task points were generated randomly and uniformly close together to highlight the necessity for considering the curvature constraints. For a sufficiently dense set of points, it becomes clear that the ordering of the Euclidean tours are not optimal in the case of the Dubins multiple travelling salesmen problem. This is due to the fact that there is little relationship between the Euclidean and Dubins metrics, especially when the Euclidean distances are small with respect to the turning radius. An algorithm for the Euclidean problem will tend to schedule very close points in a successive order, which can imply long maneuvers for the AUV. This is clearly demonstrated by the numerous loops that become problematic with dense sets of points. The algorithm proposed in this thesis does not rely on the Euclidean solution and therefore, even in the presence of ocean currents, can create paths that are feasible for curvature bound vehicles. Field tests were also conducted on an Iver2 AUV at the Avila Pier in California to validate the performance of the proposed algorithm in real world environments. Missions created based on the sequences generated by the proposed algorithm were conducted to observe the ability of an AUV to follow paths of bounded curvature in the presence of ocean currents. Results show that the proposed algorithm generated paths that were feasible for an AUV to track closely, even in the presence of ocean current.

Thesis is to Introduce an Intelligent cross platform architecture with Multi-agent system in order to equip the simulation Models with agents, having intelligent behavior, reactive and pro-active nature and rational in decision making.

This research addresses the problem of allocating closely spaced targets to multiple autonomous underwater vehicles (AUV) in the presence of constant ocean currents. The main difficulty of this problem is that the non-holonomic vehicles are constrained to move along forward paths with bounded curvatures. The Dubins model is a simple but effective way to handle the kinematic characteristics of AUVs. It gives complete characterization of the optimal paths between two configurations for a vehicle with limited turning radius moving in a plane at constant speed. In the proposed algorithm, Dubins paths are modified to include ocean currents, resulting in paths defined by curves whose radius of curvature is not constant. To determine the time required to follow such paths, an approximate dynamic model of the AUV is queried due to the computational complexity of the full model. The lower order model is built from data obtained from sampling the full model. The full model is used in evaluating the final tour times of the sequences generated by the proposed algorithm to validate the results. The proposed algorithm solves the task allocation problem with market-based auctions that minimize the total travel time to complete the mission. The novelty of the research is the path cost calculation that combines a Dubins model, an AUV dynamic model, and a model of the ocean current. Simulations were conducted in Matlab to illustrate the performance of the proposed algorithm using various number of task points and AUVs. The task points were generated randomly and uniformly close together to highlight the necessity for considering the curvature constraints. For a sufficiently dense set of points, it becomes clear that the ordering of the Euclidean tours are not optimal in the case of the Dubins multiple travelling salesmen problem. This is due to the fact that there is little relationship between the Euclidean and Dubins metrics, especially when the Euclidean distances are small with respect to the turning radius. An algorithm for the Euclidean problem will tend to schedule very close points in a successive order, which can imply long maneuvers for the AUV. This is clearly demonstrated by the numerous loops that become problematic with dense sets of points. The algorithm proposed in this thesis does not rely on the Euclidean solution and therefore, even in the presence of ocean currents, can create paths that are feasible for curvature bound vehicles. Field tests were also conducted on an Iver2 AUV at the Avila Pier in California to validate the performance of the proposed algorithm in real world environments. Missions created based on the sequences generated by the proposed algorithm were conducted to observe the ability of an AUV to follow paths of bounded curvature in the presence of ocean currents. Results show that the proposed algorithm generated paths that were feasible for an AUV to track closely, even in the presence of ocean current.

<p>Central to the explosive growth of the Internet has been the desire</p><p>of dispersed buyers and sellers to interact readily and in a manner</p><p>hitherto impossible. Underpinning these interactions, auction</p><p>pricing mechanisms have enabled Internet transactions in novel ways.</p><p>Despite this massive growth and new medium, empirical work in</p><p>marketing and economics on auction use in Internet contexts remains</p><p>relatively nascent. Accordingly, this dissertation investigates the</p><p>role of online auctions; it is composed of three essays.</p><p>The first essay, ``Online Auction Demand,'' investigates seller and</p><p>buyer interactions via online auction websites, such as eBay. Such</p><p>auction sites are among the earliest prominent transaction sites on</p><p>the Internet (eBay started in 1995, the same year Internet Explorer</p><p>was released) and helped pave the way for e-commerce. Hence, online</p><p>auction demand is the first topic considered in my dissertation. The</p><p>second essay, ``A Dynamic Model of Sponsored Search Advertising,''</p><p>investigates sponsored search advertising auctions, a novel approach</p><p>that allocates premium advertising space to advertisers at popular</p><p>websites, such as search engines. Because sponsored search</p><p>advertising targets buyers in active purchase states, such</p><p>advertising venues have grown very rapidly in recent years and have</p><p>become a highly topical research domain. These two essays form the</p><p>foundation of the empirical research in this dissertation. The third</p><p>essay, ``Sponsored Search Auctions: Research Opportunities in</p><p>Marketing,'' outlines areas of future inquiry that I intend to</p><p>pursue in my research.</p><p>Of note, the problems underpinning the two empirical essays exhibits</p><p>a common form, that of a two-sided network wherein two parties</p><p>interact on a common platform (Rochet and Tirole, 2006). Although</p><p>theoretical research on two-sided markets is abundant, this</p><p>dissertation focuses on their use in e-commerce and adopts an</p><p>empirical orientation. I assume an empirical orientation because I</p><p>seek to guide firm behavior with concrete policy recommendations and</p><p>offer new insights into the actual behavior of the agents who</p><p>interact in these contexts. Although the two empirical essays share</p><p>this common feature, they also exhibit notable differences,</p><p>including the nature of the auction mechanism itself, the</p><p>interactions between the agents, and the dynamic frame of the</p><p>problem, thus making the problems distinct. The following abstracts</p><p>for these two essays as well as the chapter that describes my future</p><p>research serve to summarize these contributions, commonalities and</p><p>differences.</p><p>Online Auction Demand</p><p>With $40B in annual gross merchandise volume, electronic auctions</p><p>comprise a substantial and growing sector of the retail economy. For</p><p>example, eBay alone generated a gross merchandise volume of $14.4B</p><p>during the fourth quarter of 2006. Concurrent with this growth has</p><p>been an attendant increase in empirical research on Internet</p><p>auctions. However, this literature focuses primarily on the bidder;</p><p>I extend this research to consider both seller and bidder behavior</p><p>in an integrated system within a two-sided network of the two</p><p>parties. This extension of the existing literature enables an</p><p>exploration of the implications of the auction house's marketing on</p><p>its revenues as well as the nature of bidder and seller interactions</p><p>on this platform. In the first essay, I use a unique data set of</p><p>Celtic coins online auctions. These data were obtained from an</p><p>anonymous firm and include complete bidding and listing histories.</p><p>In contrast, most existing research relies only on the observed</p><p>website bids. The complete bidding and listing histories provided by</p><p>the data afford additional information that illuminates the insights</p><p>into bidder and seller behavior such as bidder valuations and seller</p><p>costs.</p><p>Using these data from the ancient coins category, I estimate a</p><p>structural model that integrates both bidder and seller behavior.</p><p>Bidders choose coins and sellers list them to maximize their</p><p>respective profits. I then develop a Markov Chain Monte Carlo (MCMC)</p><p>estimation approach that enables me, via data augmentation, to infer</p><p>unobserved bidder and seller characteristics and to account for</p><p>heterogeneity in these characteristics. My findings indicate that:</p><p>i) bidder valuations are affected by item characteristics (e.g., the</p><p>attributes of the coin), seller (e.g. reputation), and auction</p><p>characteristics (e.g., the characteristics of the listing); ii)</p><p>bidder costs are affected by bidding behavior, such as the recency</p><p>of the last purchase and the number of concurrent auctions; and iii)</p><p>seller costs are affected by item characteristics and the number of</p><p>concurrent listings from the seller (because acquisition costs</p><p>evidence increasing marginal values).</p><p>Of special interest, the model enables me to compute fee</p><p>elasticities, even though no variation in historical fees exists in</p><p>these data. I compute fee elasticities by inferring the role of</p><p>seller costs in their historical listing decision and then imputing</p><p>how an increase in these costs (which arises from more fees) would</p><p>affect the seller's subsequent listing behavior. I find that these</p><p>implied commission elasticities exceed per-item fee elasticities</p><p>because commissions target high value sellers, and hence, commission</p><p>reductions enhance their listing likelihood. By targeting commission</p><p>reductions to high value sellers, auction house revenues can be</p><p>increased by 3.9%. Computing customer value, I find that attrition</p><p>of the largest seller would decrease fees paid to the auction house</p><p>by $97. Given that the seller paid $127 in fees, competition</p><p>offsets only 24% of the fees paid by the seller. In contrast,</p><p>competition largely in the form of other bidders offsets 81% of the</p><p>$26 loss from buyer attrition. In both events, the auction house</p><p>would overvalue its customers by neglecting the effects of</p><p>competition.</p><p>A Dynamic Model of Sponsored Search Advertising</p><p>Sponsored search advertising is ascendant. Jupiter Research reports</p><p>that expenditures rose 28% in 2007 to $8.9B and will continue to</p><p>rise at a 26% Compound Annual Growth Rate (CAGR), approaching half</p><p>the level of television advertising and making sponsored search</p><p>advertising one of the major advertising trends affecting the</p><p>marketing landscape. Although empirical studies of sponsored search</p><p>advertising are ascending, little research exists that explores how</p><p>the interactions of various agents (searchers,</p><p>advertisers, and the search engine) in keyword</p><p>markets affect searcher and advertiser behavior, welfare and search</p><p>engine profits. As in the first essay, sponsored search constitutes</p><p>a two-sided network. In this case, bidders (advertisers) and</p><p>searchers interact on a common platform, the search engine. The</p><p>bidder seeks to maximize profits, and the searcher seeks to maximize</p><p>utility.</p><p>The structural model I propose serves as a foundation to explore</p><p>these outcomes and, to my knowledge, is the first structural model</p><p>for keyword search. Not only does the model integrate the behavior</p><p>of advertisers and searchers, it also accounts for advertisers</p><p>competition in a dynamic setting. Prior theoretical research has</p><p>assumed a static orientation to the problem whereas prior empirical</p><p>research, although dynamic, has focused solely on estimating the</p><p>dynamic sales response to a single firm's keyword advertising</p><p>expenditures.</p><p>To estimate the proposed model, I have developed a two-step Bayesian</p><p>estimator for dynamic games. This approach does not rely on</p><p>asymptotics and also facilitates a more flexible model</p><p>specification.</p><p>I fit this model to a proprietary data set provided by an anonymous</p><p>search engine. These data include a complete history of consumer</p><p>search behavior from the site's web log files and a complete history</p><p>of advertiser bidding behavior across all advertisers. In addition,</p><p>the data include search engine information, such as keyword pricing</p><p>and website design.</p><p>With respect to advertisers, I find evidence of dynamic</p><p>bidding behavior. Advertiser valuation for clicks on their sponsored</p><p>links averages about $0.27. Given the typical $22 retail price of</p><p>the software products advertised on the considered search engine,</p><p>this figure implies a conversion rate (sales per click) of about</p><p>1.2%, well within common estimates of 1-2% (gamedaily.com). With</p><p>respect to consumers, I find that frequent clickers place a</p><p>greater emphasis on the position of the sponsored advertising link.</p><p>I further find that 10% of consumers perform 90% of the clicks.</p><p>I then conduct several policy simulations to illustrate the effects</p><p>of change in search engine policy. First, I find that the</p><p>search engine obtains revenue gains of nearly 1.4% by sharing</p><p>individual level information with advertisers and enabling them to</p><p>vary their bids by consumer segment. This strategy also improves</p><p>advertiser profits by 11% and consumer welfare by 2.9%. Second, I</p><p>find that a switch from a first to second price auction results in</p><p>truth telling (advertiser bids rise to advertiser valuations), which</p><p>is consistent with economic theory. However, the second price</p><p>auction has little impact on search engine profits. Third, consumer</p><p>search tools lead to a platform revenue increase of 3.7% and an</p><p>increase of consumer welfare of 5.6%. However, these tools, by</p><p>reducing advertising exposure, lower advertiser profits by 4.1%.</p><p>Sponsored Search Auctions: Research Opportunities in Marketing</p><p>In the final chapter, I systematically review the literature on</p><p>keyword search and propose several promising research directions.</p><p>The chapter is organized according to each agent in the search</p><p>process, i.e., searchers, advertisers and the search engine, and</p><p>reviews the key research issues for each. For each group, I outline</p><p>the decision process involved in keyword search. For searchers, this</p><p>process involves what to search, where to search, which results to</p><p>click, and when to exit the search. For advertisers, this process</p><p>involves where to bid, which word or words to bid on, how much to</p><p>bid, and how searchers and auction mechanisms moderate these</p><p>behaviors. The search engine faces choices on mechanism design,</p><p>website design, and how much information to share with its</p><p>advertisers and searchers. These choices have implications for</p><p>customer lifetime value and the nature of competition among</p><p>advertisers. Overall, I provide a number of potential areas of</p><p>future research that arise from the decision processes of these</p><p>various agents.</p><p>Foremost among these potential areas of future research are i) the</p><p>role of alternative consumer search strategies for information</p><p>acquisition and clicking behavior, ii) the effect of advertiser</p><p>placement alternatives on long-term profits, and iii) the measure of</p><p>customer lifetime value for search engines. Regarding the first</p><p>area, a consumer's search strategy (i.e., sequential search and</p><p>non-sequential search) affects which sponsored links are more likely</p><p>to be clicked. The search pattern of a consumer is likely to be</p><p>affected by the nature of the product (experience product vs. search</p><p>product), the design of the website, the dynamic orientation of the</p><p>consumer (e.g., myopic or forward-looking), and so on. This search</p><p>pattern will, in turn, affect advertisers payments, online traffic,</p><p>sales, as well as the search engine's revenue. With respect to the</p><p>second area, advertisers must ascertain the economic value of</p><p>advertising, conditioned on the slot in which it appears, before</p><p>making decisions such as which keywords to bid on and how much to</p><p>bid. This area of possible research suggests opportunities to</p><p>examine how advertising click-through and the number of impressions</p><p>differentially affect the value of appearing in a particular</p><p>sponsored slot on a webpage, and how this value is moderated by an</p><p>appearance in a non-sponsored slot (i.e., a slot in the organic</p><p>search results section). With respect to the third area of future</p><p>research, customer value is central to the profitability and</p><p>long-term growth of a search engine and affects how the firm should</p><p>allocate resources for customer acquisition and retention.</p><p>Organization</p><p>This dissertation is organized as follows. After this brief</p><p>introduction, the essay, ``Online Auction Demand,'' serves as a</p><p>basis that introduces some concepts of auctions as two-sided</p><p>markets. Next, the second essay, ``A Dynamic Model of Sponsored</p><p>Search Advertising,'' extends the first essay by considering a</p><p>richer context of bidder competition and consumer choice behavior.</p><p>Finally, the concluding chapter, which outlines my future research</p><p>interests, considers potential extensions that pertain especially to</p><p>sponsored search advertising.</p> / Dissertation

This thesis studies the impact of sequential auction design on participant behavior from both a theoretical and an empirical viewpoint. In the first of the two analyses, three sequential auction designs are characterized and compared based on expected profitability to the participants. The optimal bid strategy is derived as well. One of the designs, the alternating design, is a new auction design and is a blend of the other two. It assumes that the ability to bid in or initiate an auction is given to each side of the market in an alternating fashion to simulate seasonal markets. The conditions for an equilibrium auction design are derived and characteristics of the equilibrium are outlined. The primary result is that the alternating auction is a viable compromise auction design when buyers and suppliers disagree on whether to hold a sequence of forward or reverse auctions. We also found the value of information on future private value for a strategic supplier in a two-period case of the alternating and reverse auction designs. The empirical work studies the cause of low aggregation of timber supply in reverse auctions of an online timber exchange. Unlike previous research results regarding timber auctions, which focus on offline public auctions held by the U.S. Forest Service, we study online private auctions between logging companies and mills. A limited survey of the online auction data revealed that the auctions were successful less than 50% of the time. Regression analysis is used to determine which internal and external factors to the auction affect the aggregation of timber in an effort to determine the reason that so few auctions succeeded. The analysis revealed that the number of bidders, the description of the good and the volume demanded had a significant influence on the amount of timber supplied through the online auction exchange. A plausible explanation for the low aggregation is that the exchange was better suited to check the availability for custom cuts of timber and to transact standard timber.

Satellite constellations are an increasingly attractive option for many commercial and military applications. They provide a robust and distributed method of accomplishing the goals of expensive monolithic satellites. Among the many challenges that satellite constellations engender (challenges in control, coordination, disposal, and other areas), refueling is of particular interest because of the many methods one can use to refuel a constellation and the lifetime implications on the satellites. The present work presents a methodology for carrying out peer-to-peer refueling maneuvers within a constellation. Peer-to-peer (P2P) refueling can be of great value both in cases where a satellite unexpectedly consumes more fuel than it was alloted, and as part of a mixed refueling strategy that will include an outside tanker bringing fuel to the constellation. Without considering mixed-refueling, we formulate the peer-to-peer refueling problem as an assignment problem that seeks to guarantee that all satellites will have the fuel they need to be functional until the next refueling, while concurrently minimizing the cost in fuel that the refueling maneuvers entail. The assignment problem is then solved via auctions, which, by virtue of their distributed nature, can easily and effectively be implemented on a constellation without jeopardizing any robustness properties. Taking as a given that the P2P assignment problem has been solved, and that it has produced some matching among fuel deficient and fuel sufficient satellites, we then seek to sequence those prescribed maneuvers in the most effective manner. The idea is that while a constellation can be expected to have some redundancy, enough satellites leaving their assigned orbital slots will eventually make it impossible for the constellation to function. To tackle this problem, we define a wide class of operability conditions, and present three algorithms that intelligently schedule the maneuvers. We then briefly show how combining the matching and scheduling problems yields a complete methodology for organizing P2P satellite refueling operations.