Television advertising is a billion-dollar industry in the United States. Currently, advertisers place their messages in television programs that are estimated to have a high proportion of their target demographic viewers. The advertising spots are usually purchased months in advance at set list prices or at negotiated prices. Technologies that can place advertisements at the cable box level, instead of the program level, will provide advertisers with the ability to target any demographic group directly and in real-time. This thesis explores the new decision-making required by this new technology and how the television advertisement space can be sold more effectively. In particular, it compares a list price system to a number of new auction models. The structure of the auctions for the new targeted television advertising system is unique and has not been previously studied in the literature. This thesis explores new auction models that can capture these unique features and lead to desirable results for the seller of the advertisement space. A simplified analytical model shows how these features impact advertisers’ bidding behavior and how a list price system compares to the auction models in the ability to raise revenue for the seller of the advertising space. These issues are then explored under various market settings with differing numbers of advertisers and value distributions that these advertisers have for the advertising space. Since sequential first price auctions have undesirable consequences such as strong price fluctuations, this work focuses on second price auctions. The Vickrey-Clarke-Groves (VCG) mechanism is customized for this problem by developing an optimization formulation that determines the best set of advertisers for a particular advertisement space. Because execution time may be an issue, other auction models are developed that lead to similar outcomes as the VCG mechanism but require less computational effort. This thesis provides guidance on when a list price system will lead to higher expected revenue than an auction model and vice versa in a targeted television advertising system. It also demonstrates why some of the standard auction models cannot be applied to this problem and what type of new models are required to lead to desirable advertising outcomes.

En esta tesis se analizan tres mecanismos de subasta distintos, todos ellos bajo el supuesto de valoraciones privadas e independientes.El primer mecanismo que analizamos es una subasta de múltiples unidades en la que los objetos son vendidos secuencialmente por medio de subastas de precio descendente. La característica que hace a esta subasta diferente de la "estándar", analizada por Weber (1983) es que después de la venta del primer objeto el precio no vuelve a subir, sino que los objetos que quedan son ofrecidos al resto de los compradores al mismo precio. Si los objetos no se venden a ese precio, la subasta continúa dejando que el precio siga descendiendo. Esta subasta se analiza en dos contextos: con un modelo de valoraciones continuas y con uno de valoraciones discretas. Se demuestra que si existe un equilibrio simétrico con pujas monótonas, el resultado de la subasta es ineficiente con probabilidad positiva. Aplicando el teorema de equivalencia de rentas se concluye que la subasta no maximiza los beneficios esperados del vendedor. Para poder comparar los precios medios y las varianzas analizamos un modelo de valoraciones discretas. Demostramos que los precios esperados son menores en nuestra subasta y que también lo es la varianza de los beneficios del vendedor. Damos un ejemplo de una familia de funciones de utilidad von Neumann- Morgenstern tal que la utilidad esperada del vendedor es mayor en una u otra de las subasta dependiendo de los valores del parámetro a.El segundo mecanismo que analizamos es una subasta asimétrica de primer precio donde la valoración de uno de los postores es conocida. Demostramos que no existe ningún equilibrio en estrategias puras y caracterizamos un equilibrio en estrategias mixtas en el que el postor cuya valoración es conocida randomiza su puja, mientras que los demás postores juegan una estrategia pura (y monótona). El resultado de la subasta es ineficiente con probabilidad positiva y el beneficio esperado del postor cuya valoración es conocida es menor que en una subasta estándar. Sin embargo, no es obvio que los demás postores mejoren su situación: el hecho de que uno de los postores juegue una estrategia mixta tiene el mismo efecto en sus rivales que un precio de reserva aleatorio. Esto puede obligarles a pujar más agresivamente de lo que pujarían en una subasta normal. El efecto en los beneficios del vendedor también es ambiguo. Tomando un ejemplo con la función de distribución uniforme y comparando los beneficios esperados del vendedor y de los compradores en las dos subastas, obtenemos que, en nuestro ejemplo (con 2 y con 3 postores) los beneficios esperados del vendedor son mas altos en la subasta asimétrica que en la normal.Para terminar, hacemos un repaso de la literatura en subastas secuenciales cuando los compradores desean más de una unidad del bien que se subasta, y analizamos una subasta secuencial de primer precio con y sin opción de compra. Para ello usamos el mismo modelo que Black y de Meza (1992) usan para analizar la subasta secuencial de segundo precio. Demostramos que cuando las preferencias son unidimensionales no existe ningún equilibrio monótono y simétrico, lo cual implica que el resultado de la subasta no puede ser eficiente. Cuando se introduce una opción de compra que permita comprar la segunda unidad al mismo precio al que se adquirió la primera, existe un equilibrio en estrategias puras para algunos valores de los parámetros del modelo. En este caso la opción siempre se ejerce, lo cual lleva a una asignación de los bienes diferente que la que resulta en la subasta secuencial de segundo precio. Cuando la valoración por la segunda unidad es aleatoria, las subastas de primer y segundo precio sin opción de compra son equivalentes. Por último, exponemos las dificultades de caracterizar un equilibrio cuando cuando se introduce la opción de compra en este modelo. / In this thesis we analyze three different auction mechanisms, all of them under the private and independent valuations assumption. The first auction we analyze is a multi-unit auction where the objects are sold sequentially by descending-price auctions. The feature that makes this auction different from the "standard" one is that after one object has been sold, the price does not return to a high level, but the remaining objects are offered to the rest of the bidders at the same price. If the objects fail to be sold at that price, the auction is resumed letting the price descend again. We analyze this auction in two different contexts: a continuous valuation model, and a discrete valuation one. We show that if a symmetric, monotone bidding functions equilibrium exists, the outcome of the auction is inefficient with positive probability. Applying the revenue equivalence theorem we conclude that the auction cannot maximize the seller's expected revenue. In order to be able to compare the averages expected prices and variances, we analyze a discrete-valuation model. We show that the average expected prices are lower in our auction, and that so is the variance of the seller's expected revenue. We give an example of a family of von Neumann-Morgenstern utility functions under which the seller's expected utility may be higher in each of the auctions depending on the value of a parameter a.The second mechanism we analyze is an asymmetric first-price auction where the valuation of one of the bidders is common knowledge. We show that no pure strategy equilibrium exists and we characterize a mixed strategy equilibrium in which the bidder whose valuation is common knowledge randomizes his bid while the other bidders play a (monotone) pure strategy. The outcome of the auction is inefficient with positive probability, and the expected profit of the bidder whose valuation is common knowledge is lower than in a standard auction in which her valuation is private knowledge. However, it is not obvious that the other bidders are better off: the fact that one of the bidders plays a mixed strategy has the effect of on the other bidders as a random reserve price bidder. This may force all them to bid more aggressively than they would in the standard auction. The effect on the seller's expected revenue is also ambiguous. In an example with the uniform distribution, we compare the expected profits of seller and buyers in this auction with those in a standard symmetric private valuation model. In our example, with 2 and 3 bidders, the seller's expected revenue is higher in the asymmetric auction than in a standard auction.To finish, we survey the literature on sequential auctions with multi-unit demand, and we analyze a sequential first-price auction with and without a buyer's option. To do it we use the same model that Black and de Meza (1992) used to analyze the secuencial second-price caution. We show that when the preferences are unidimensional, no monotone symmetric pure strategy equilibrium exists, which implies that the outcome of the auction cannot be efficient. When an option to buy the second unit at the price paid for first one is introduced, there exists a pure strategy equilibrium for some values of the parameters of the model. In this case the option is always exercised, leading to a different allocation than that of the sequential second-price auction. When the valuations for the second unit is stochastic, the first-price and second-price auctions without a buyer's option are efficient and revenue equivalent. To finish, we give some insights into the difficulties of solving for an equilibrium when the buyer's option is introduced in this model.

Television advertising is a billion-dollar industry in the United States. Currently, advertisers place their messages in television programs that are estimated to have a high proportion of their target demographic viewers. The advertising spots are usually purchased months in advance at set list prices or at negotiated prices. Technologies that can place advertisements at the cable box level, instead of the program level, will provide advertisers with the ability to target any demographic group directly and in real-time. This thesis explores the new decision-making required by this new technology and how the television advertisement space can be sold more effectively. In particular, it compares a list price system to a number of new auction models. The structure of the auctions for the new targeted television advertising system is unique and has not been previously studied in the literature. This thesis explores new auction models that can capture these unique features and lead to desirable results for the seller of the advertisement space. A simplified analytical model shows how these features impact advertisers’ bidding behavior and how a list price system compares to the auction models in the ability to raise revenue for the seller of the advertising space. These issues are then explored under various market settings with differing numbers of advertisers and value distributions that these advertisers have for the advertising space. Since sequential first price auctions have undesirable consequences such as strong price fluctuations, this work focuses on second price auctions. The Vickrey-Clarke-Groves (VCG) mechanism is customized for this problem by developing an optimization formulation that determines the best set of advertisers for a particular advertisement space. Because execution time may be an issue, other auction models are developed that lead to similar outcomes as the VCG mechanism but require less computational effort. This thesis provides guidance on when a list price system will lead to higher expected revenue than an auction model and vice versa in a targeted television advertising system. It also demonstrates why some of the standard auction models cannot be applied to this problem and what type of new models are required to lead to desirable advertising outcomes.

Reverse auction bidding is an online auction system. A purchaser's primary objective in using a reverse auction is to obtain the lowest possible bid for goods and services on a construction project. With this type of bidding, it is normal that the purchaser will only consider price, instead of a bidders' work history and experience. As a reverse auction is an online service, the common misperception is that a purchaser can reach a broader market to obtain the lowest possible price. It is a controversial bidding system. No previous research has been undertaken by the Texas A&M University Reverse Auction Bidding study group into potential owner interference with the bidding system for a reverse auction. Six bidders were asked to participate in the Reverse Auction Bidding process for a series of construction projects in Houston. Each participant was also asked to complete a Keirsey Temperament Sorter Test type I and II to determine each participant's personality. After the tests, the six participants competed in an online reverse auction bidding game. The primary objective of this research is to analyze the impact of an owner's interference in a reverse auction bidding scenario. In this test, one of the six bidders acted as the owner's surrogate to interfere with an ethical process and reduce the owner's costs. The other five bidders were unaware of the surrogate's role in the bidding. The primary directive given to the surrogate bidder was to drive down the cost of the projects. The results for the research study show that the owner's surrogate can affect the bidding process. Interference results in reduced returns for the bidders when compared to an uncompromised bidding scenario. It is clear that the method used is unethical.

Participating in an auction and winning items by placing bids has been in practice since at least 500 B.C. Auctions have evolved since then and anyone can now participate in one online and buy items ranging from clothes, electronics, automobiles and homes using online auction websites, such as eBay. A Reverse Auction varies from the traditional style of Auction where items or services are won by placing successive higher bids until the auction ends. The study of Reverse Auction was first introduced to Texas A & M University in 2004 and continues today, using a SQL based web system. This current research provides a detailed statistical analysis of the fourth case study in this long running work. This fourth case study involved the participation of five bidders who had no prior experience in Reverse Auctions. A Microsoft Access database system and ASP web based user interface was developed and used to conduct these initial studies. However, due to the limited capability of the Access system to handle more than a limited number of connections or bidders, a Microsoft SQL database and web system was developed in 2006 and has been used in all subsequent studies. Case studies have involved up to ten participants. The results from the fourth case study show that a Reverse Auction can result in an increase in the average cost of the job to the owner. Also, there is evidence of game play amongst the bidders and against the purchaser that causes their profits to rise as they gain proficiency in the game. This behavior has been termed as 'tacit collision', but it is considered a byproduct of the system and not illegal behavior. This study analyzes the fourth study data to investigate if the behavior termed "tacit collusion" is evident in the bidding data. This analysis is completed by performing a detailed statistical analysis of the bidding data. Analysis of the profit percentages illustrates the different stages of the game play amongst the bidders. This game play behavior is illustrated by plotting average number of bids to the profit made by each bidder. The data clearly suggests that the players became efficient in their bidding strategy, although some bidders are more efficient than others. This observation negates the common conception that Reverse Auctions will result in lowering average costs for the owners. The individual data of bidders for bids and profit reveal why some players were able to obtain higher than average results and why the others were not. This study can be taken further by analyzing the patterns of the successful and unsuccessful players to determine what causes them to gain or lose profits.

Reverse Auction Bidding (RAB) study into the construction industry commenced at Texas AandM (TAMU) University in 2004 from the work of a graduate student who was interested in the reasons for RAB being considered unethical by some. This thesis is the eleventh study into Reverse Auction Bidding building on the work of the previous researchers. Previous case studies investigated a number of different competitive situations ranging from three to ten players. In the last few studies, the bidding behavior and performance of participants in the RAB process is being observed with respect to their personality. Personality for each player is tested using the Keirsey Temperament Sorter (KTS) test. The KTS describes four major personalities and four role variants in each of the personalities, summing up to sixteen role variants. There appears at this stage a strong correlation between personality type and game performance. This study extends the work on the Guardian personality type to investigate the four sub-types of this personality. This study builds on the previous work by analyzing the four different Guardian role variants being Provider, Protector, Inspector and Supervisor. The aim of the research is to investigate whether there is a difference in game returns between the personality type from within this group. The study involves a game scenario involving a facility manager hiring the contractors who submit the lowest bid for the assumed renovation project. The study also gives the contractor a modified KTS questionnaire that can be used by them for hiring an individual for the position of an estimator with a competent personality. The individuals were selected from undergraduate Construction Science students with limited experience. The game lasted for nine rounds, with the statistical results of the bidding and contract data showing patterns similar to the previous studies. The results show us that the individuals with a role variant of Providers provided the highest return in this case study, although a single case study is insufficient to draw formal conclusions on this matter, the result points to future research.

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The on-line auction has become an important issue in Taiwan, due to the intense competition between e-Bay and Yahoo! auction. However the relative researches in Taiwan hasn¡¦t analyzed the bidding strategies of on-line auction bidders in detail. Therefore after reviewing relative literature, the motivation of using on-line auction, the characteristics of personality, the experience of internet and the involvement of product were saw as independent variables to discuss their relationships with bidding strategies, including the time of entering, the increment of bid, the numbers of bid and the degree of insistence in this research. Discovering the main factors affecting the time of entering are the experience of using on-line auction and the rating of bidder; the degree of exocentric in personality can affect the increment of bid; the motivation of using on-line auction and the involvement of product can affect the numbers of bid and the degree of insistence. Hence we can understand these four strategies were affected by different factors, and the forming of entire bidding strategies is very complicated. Going a step further, these four strategies were used to proceed cluster analysis, dividing the bidders into three groups¡G1. amateur bidders; 2. snipers; 3. impulsive bidders.

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<p>\abstract</p><p>{\em Mechanism design} studies optimization problems taking into accounts of the selfish agents. {\em Network externality} is the effect a consumer receives from other consumers of the same good. This effect can be negative or positive. We first consider several mechanism design problems under the network externality assumption. The externality model used in this dissertation is more general than the widely used cardinality based model. In particular the network we consider in this dissertation is a graph, which is not necessarily complete. Our goal is to design {\em truthful} mechanisms to maximize the seller's revenue. Our main results under the network externality utility model are several optimal or near optimal mechanisms for {\em digital goods auctions}. To do so we invent several novel approximation schemes as well as applying results from the {\em approximation algorithm} literature. In particular when the agents exhibit negative network externality, we first model the problem as a two staged {\em pricing game}. We then show that the pricing game is an exact {\em potential game} which always admits a pure {\em Nash Equilibrium}. We then study the {\em best} and {\em worst} Nash Equilibrium in this game in terms of the revenue. We show two positive results. For the best Nash Equilibrium we show a $2$-approximation to the maximum revenue on bipartite graphs. For the worst Nash Equilibrium we use the notion of a {\em $\delta$-relaxed} equilibrium. In the sense that the prices for the same type of agents are within $\delta$ factor of each other. We accompany our positive results with matching hardness results. On the other hand, when the agents exhibit positive network externality, we take the {\em Myersonian} approach. We first give a complete characterization for all the truthful mechanisms. Using this characterization we present a truthful mechanism which achieves the optimal expected revenue among all the truthful mechanisms when the prior distributions of the agents are {\em independent} and {\em regular}. We also show near optimal mechanisms when the prior distributions are possibly {\em correlated}. </p><p>{\em Prior-free} auctions can approximate meaningful benchmarks for</p><p>non-identical bidders only when sufficient qualitative information</p><p>about the bidder asymmetry is publicly known.</p><p>We consider digital goods auctions where there is a {\em</p><p>total ordering} of the bidders that is known to the seller,</p><p>where earlier bidders are in some sense thought to have higher</p><p>valuations. </p><p>We define</p><p>an appropriate revenue benchmark: the maximum revenue that can be</p><p>obtained from a bid vector using prices that are nonincreasing in the</p><p>bidder ordering and bounded above by the second-highest bid. </p><p>This {\em monotone-price benchmark} is always as large as the well-known</p><p>fixed-price benchmark, so designing prior-free auctions with</p><p>good approximation guarantees is only harder. </p><p>By design, an auction that approximates the monotone-price benchmark</p><p>satisfies a very strong guarantee: it is, in particular, simultaneously</p><p>near-optimal for </p><p>essentially every {\em Bayesian} environment in which bidders'</p><p>valuation distributions have nonincreasing monopoly prices, or in</p><p>which the distribution of each bidder stochastically dominates that</p><p>of the next. Even when there is no distribution over bidders'</p><p>valuations, such an auction still provides a quantifiable</p><p>input-by-input performance guarantee. We design a simple $O(1)$-competitive prior-free</p><p>auction for digital goods with ordered bidders.</p> / Dissertation