This dissertation develops Bayesian methods to analyze data from auctions and produce policy recommendations for auction design. The essay, "Auction Design Using Bayesian Methods," proposes a decision theoretic method to choose a reserve price in an auction using data from past auctions. Our method formally incorporates parameter uncertainty and the payoff structure into the decision procedure. When the sample size is modest, it produces higher expected revenue than the plug-in methods. Monte Carlo evidence for this is provided. The second essay, "Flexible Bayesian Analysis of First Price Auctions Using Simulated Likelihood," develops an empirical framework that fully exploits all the shape restrictions arising from economic theory: bidding monotonicity and density affiliation. We directly model the valuation density so that bidding monotonicity is automatically satisfied, and restrict the parameter space to rule out all the nonaffiliated densities. Our method uses a simulated likelihood to allow for a very exible specification, but the posterior analysis is exact for the chosen likelihood. Our method controls the smoothness and tail behavior of the valuation density and provides a decision theoretic framework for auction design. We reanalyze a dataset of auctions for drilling rights in the Outer Continental Shelf that has been widely used in past studies. Our approach gives significantly different policy prescriptions on the choice of reserve price than previous methods, suggesting the importance of the theoretical shape restrictions. Lastly, in the essay, "Simple Approximation Methods for Bayesian Auction Design," we propose simple approximation methods for Bayesian decision making in auction design problems. Asymptotic posterior distributions replace the true posteriors in the Bayesian decision framework, which are typically a Gaussian model (second price auction) or a shifted exponential model (first price auction). Our method first approximates the posterior payoff using the limiting models and then maximizes the approximate posterior payoff. Both the approximate and exact Bayes rules converge to the true revenue maximizing reserve price under certain conditions. Monte Carlo studies show that my method closely approximates the exact procedure even for fairly small samples.

Šiame magistriniame darbe autorius gvildena iki šiol teisinėje literatūroje mažai nagrinėtą elektroninių aukcionų temą. Ši tema nagrinėjama trimis aspektais. Pirmiausia, išnagrinėjami tradiciniai aukcionai, kaip e. aukciono pagrindas. Ištiriama aukcionų teorija, aukcionų rūšys, atsižvelgiama į tai kaip jie reglamentuojami Lietuvoje, išskiriami esminiai požymiai ir pagal juos atskleidžiama aukciono sąvoka. Sekančiame etape išnagrinėjamas e. aukciono, tradicinio aukciono ir e. komercijos santykis. Ištiriama, kuo pasižymi ir kokių ypatumu turi e. aukcionai, kokią įtaką jie daro teisiniams santykiams ir kaip juos keičia. Daug dėmesio skiriama ne tik teisės aktų analizei, bet ir teismų praktikai. Atsižvelgiama kokios bylos buvo nagrinėjamos pasaulyje, kokie sprendimai buvo priimti ir kas išdėstyta bylos argumentuojamoje dalyje. Pagal tai pateikiamos autoriaus rekomendacijos, kaip galėtų būti reguliuojama aukcionų veikla Lietuvoje. Galiausiai, nagrinėjamos teisės pažeidimų e. aukcionuose problemos. Darbe pateikiama teisės pažeidimų teorinė medžiaga, problemos aktualumas, bei pasaulyje vyraujantys įvairių įstaigų, įmonių ir organizacijų sprendimai šiai problemai spręsti. Atsižvelgiama į tai ar šie sprendimai galėtų būti pritaikyti Lietuvoje, kokios kliūtys iškyla jų įgyvendinimui ir kaip jas reikėtų panaikinti, koks turėtų būti valstybinis reguliavimas neteisėtoms veikoms užkirsti. Taigi, šis darbas yra vienas pirmųjų žingsnių į Lietuvoje dar naują ekonominio gyvenimo sritį... [toliau žr. visą tekstą] / This Master’s thesis is a research on the online auctions law. The paper consists of three parts. In the first part the author introduces traditional auctions as the basis of online auctions. There is the analysis of the auctions theory, regulation, essential features and clear definitions. In the second part comparative analysis is made on the traditional auctions and online auctions. A closer look reveals that there exist considerable differences that may cause legal controversy. Much of attention is paid to the court ruling in different law systems and countries such as Australia, Germany, UK, USA and etc. The last chapter of the work is designed for fraudulent activities in online auctions, and research of legal regulation solutions to dissolve it.

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We first present a non-cooperative auction game to solve the bandwidth allocation problem for non-cooperative channel access in a wireless network. The Nash equilibrium is obtained as a solution of the game. To address this problem of bandwidth sharing under unknown information, we further develop a Bayesian auction game model and then Bayesian Nash equilibrium is then obtained. Next, we present a framework based on coalitional game for cooperative channel access for carry-and-forward-based data delivery. Each mobile node helps others to carry and then forward their data. A coalitional game is proposed to find a stable coalition structure for this cooperative data delivery. We next present static and dynamic coalitional games for carry-and-forward-based data delivery when the behavior of each mobile node is unknown by others. In the dynamic game, each mobile node can update its beliefs about other mobile nodes’ types when the static coalitional game is played repeatedly.

We first present a non-cooperative auction game to solve the bandwidth allocation problem for non-cooperative channel access in a wireless network. The Nash equilibrium is obtained as a solution of the game. To address this problem of bandwidth sharing under unknown information, we further develop a Bayesian auction game model and then Bayesian Nash equilibrium is then obtained. Next, we present a framework based on coalitional game for cooperative channel access for carry-and-forward-based data delivery. Each mobile node helps others to carry and then forward their data. A coalitional game is proposed to find a stable coalition structure for this cooperative data delivery. We next present static and dynamic coalitional games for carry-and-forward-based data delivery when the behavior of each mobile node is unknown by others. In the dynamic game, each mobile node can update its beliefs about other mobile nodes’ types when the static coalitional game is played repeatedly.

MBA Professional Report / Aproved for public release; distribution is unlimited. / In light of the limited recognition of electronic reverse auctions (e-RA) in the acquisition field, the purpose of this research paper is to further the use of e-RAs throughout the federal government and the Department of Defense (D0D). By exploring a multitude of auction types and designs that have been or are currently being used in the private sector, these practices set a basis for researching the auction types being used in the public sector. This paper further explores the regulations guiding the use of e-RAs and investigates the federal government's application of reverse auctions through studies, reports, and interviews with users of e-RAs. Details as to what attributes are prevalent in these e-RAs, what features are best suited for e-RAs, and what benefits have been derived from the use of e-RAs in the federal government are also included. Finally, the researchers found that e-RAs have increased transparency, cost savings, and efficiencies in the acquisition process. Although reverse auctions are being used effectively, the researchers found that there are still opportunities for improvement including incorporating a fully functional best-value tool into e-RA technology and developing more thorough federal guidance on e-RAs as an alternative pricing method.

Die Analyse von online vermitteltem Tausch, insbesondere derjenige bei Onlineauktionen, erfreut sich einiger Beliebtheit. Dafür gibt es neben der ökonomischen Bedeutung dieser Tauschform auch soziologische Gründe. Inzwischen zeigt eine beträchtliche Anzahl von Untersuchungen, dass online vermittelte Transaktionen durch ein Reputationssystem stabilisiert werden können. Dabei bewerten sich die beiden Transaktionspartner nach erfolgtem Tausch gegenseitig mittels eines von der Auktionsplattform bereitgestellten Bewertungssystems. Beide Tauschpartner haben die Möglichkeit, eine negative Bewertung mit einer ebensolchen zu vergelten, bzw. im Vorgriff darauf mit Vergeltung zu drohen, falls eine negative Bewertung erfolgen sollte. Es besteht hier also ein Kollektivgutproblem zweiter Ordnung, dessen Lösung Bedingung für eine stabile Gütertransaktion ist. In diesem Aufsatz wird analysiert, wie diese Stabilisierung theoretisch geschehen kann und welche empirischen Belege sich dafür anführen lassen. Dazu wird im nächsten Abschnitt vorerst der gesamte Tauschvorgang als ein zweifaches Vertrauensspiel modelliert und zwei einschlägige Lösungsansätze in Hypothesenform präsentiert. Dabei wird einmal von vollständig rationalen Akteuren und einmal von reziprok rationalen Akteuren ausgegangen. Die generierten Hypothesen werden anschließend anhand eines selbst erhobenen Datensatzes überprüft. Dieser enthält Angaben zu insgesamt 1800 Transaktionen und entsprechend 3600 potentiellen Bewertungen, die über eBay Deutschland getätigt wurden. Dabei wird neben der Analyse der Standardbewertungen insbesondere eine Analyse der dazugehörigen Freitextkommentare vorgenommen. Im vierten Abschnitt werden die Resultate theoretisch diskutiert und es wird auf der Basis der empirischen Ergebnisse zur Handlungsmotivation der Bewertenden ein Vorschlag gemacht, mit welchen institutionellen Maßnahmen der Bewertungsmechanismus verbessert werden könnte.

In this thesis, we examine stochastic techniques for overcoming game theoretic and computational issues in the collective decision making process of self-interested individuals. In particular, we examine truthful, stochastic mechanisms, for settings with a strong budget balance constraint (i.e. there is no net flow of money into or away from the agents). Building on past results in AI and computational social choice, we characterise affine-maximising social choice functions that are implementable in truthful mechanisms for the setting of heterogeneous item allocation with unit demand agents. We further provide a characterisation of affine maximisers with the strong budget balance constraint. These mechanisms reveal impossibility results and poor worst-case performance that motivates us to examine stochastic solutions. To adequately compare stochastic mechanisms, we introduce and discuss measures that capture the behaviour of stochastic mechanisms, based on techniques used in stochastic algorithm design. When applied to deterministic mechanisms, these measures correspond directly to existing deterministic measures. While these approaches have more general applicability, in this work we assess mechanisms based on overall agent utility (efficiency and social surplus ratio) as well as fairness (envy and envy-freeness). We observe that mechanisms can (and typically must) achieve truthfulness and strong budget balance using one of two techniques: labelling a subset of agents as ``auctioneers'' who cannot affect the outcome, but collect any surplus; and partitioning agents into disjoint groups, such that each partition solves a subproblem of the overall decision making process. Worst-case analysis of random-auctioneer and random-partition stochastic mechanisms show large improvements over deterministic mechanisms for heterogeneous item allocation. In addition to this allocation problem, we apply our techniques to envy-freeness in the room assignment-rent division problem, for which no truthful deterministic mechanism is possible. We show how stochastic mechanisms give an improved probability of envy-freeness and low expected level of envy for a truthful mechanism. The random-auctioneer technique also improves the worst-case performance of the public good (or public project) problem. Communication and computational complexity are two other important concerns of computational social choice. Both the random-auctioneer and random-partition approaches offer a flexible trade-off between low complexity of the mechanism, and high overall outcome quality measured, for example, by total agent utility. They enable truthful and feasible solutions to be incrementally improved on as the mechanism receives more information and is allowed more processing time. The majority of our results are based on optimising worst-case performance, since this provides guarantees on how a mechanism will perform, regardless of the agents that use it. To complement these results, we perform empirical, average-case analyses on our mechanisms. Finally, while strong budget balance is a fixed constraint in our particular social choice problems, we show empirically that this can improve the overall utility of agents compared to a utility-maximising assignment that requires a budget imbalanced mechanism.

In this thesis, we examine stochastic techniques for overcoming game theoretic and computational issues in the collective decision making process of self-interested individuals. In particular, we examine truthful, stochastic mechanisms, for settings with a strong budget balance constraint (i.e. there is no net flow of money into or away from the agents). Building on past results in AI and computational social choice, we characterise affine-maximising social choice functions that are implementable in truthful mechanisms for the setting of heterogeneous item allocation with unit demand agents. We further provide a characterisation of affine maximisers with the strong budget balance constraint. These mechanisms reveal impossibility results and poor worst-case performance that motivates us to examine stochastic solutions. To adequately compare stochastic mechanisms, we introduce and discuss measures that capture the behaviour of stochastic mechanisms, based on techniques used in stochastic algorithm design. When applied to deterministic mechanisms, these measures correspond directly to existing deterministic measures. While these approaches have more general applicability, in this work we assess mechanisms based on overall agent utility (efficiency and social surplus ratio) as well as fairness (envy and envy-freeness). We observe that mechanisms can (and typically must) achieve truthfulness and strong budget balance using one of two techniques: labelling a subset of agents as ``auctioneers'' who cannot affect the outcome, but collect any surplus; and partitioning agents into disjoint groups, such that each partition solves a subproblem of the overall decision making process. Worst-case analysis of random-auctioneer and random-partition stochastic mechanisms show large improvements over deterministic mechanisms for heterogeneous item allocation. In addition to this allocation problem, we apply our techniques to envy-freeness in the room assignment-rent division problem, for which no truthful deterministic mechanism is possible. We show how stochastic mechanisms give an improved probability of envy-freeness and low expected level of envy for a truthful mechanism. The random-auctioneer technique also improves the worst-case performance of the public good (or public project) problem. Communication and computational complexity are two other important concerns of computational social choice. Both the random-auctioneer and random-partition approaches offer a flexible trade-off between low complexity of the mechanism, and high overall outcome quality measured, for example, by total agent utility. They enable truthful and feasible solutions to be incrementally improved on as the mechanism receives more information and is allowed more processing time. The majority of our results are based on optimising worst-case performance, since this provides guarantees on how a mechanism will perform, regardless of the agents that use it. To complement these results, we perform empirical, average-case analyses on our mechanisms. Finally, while strong budget balance is a fixed constraint in our particular social choice problems, we show empirically that this can improve the overall utility of agents compared to a utility-maximising assignment that requires a budget imbalanced mechanism.