Exploring and Modeling Online Auctions Using Functional Data Analysis

In recent years, the increasing popularity of eCommerce, and particularly online auctions has stirred a great amount of scholarly research, especially in information systems, economics, and marketing, but little or no attention has been received from statistics. ECommerce arrives with enormous amounts of rich and clean data as well as statistical challenges. eCommerce not only creates new data challenges, it also motivates the need for innovative models. While there exist many theories about economic behavior of participants in market exchanges, many of these theories have been developed before the appearance of the world wide web and often are not appropriate to be used in explaining modern economic behavior in eCommerce. This calls for new models that describe not only the evolution of a process, but also its dynamics. This research takes a different look at online auctions and proposes to study an auction's price evolution and associated price dynamics from different points of view using functional data analysis techniques. In this dissertation, we develop novel dynamic modeling procedures applicable to online auctions. First, we develop a dynamic forecasting system to predict the price of an ongoing auction. By dynamic we mean that the model can predict the price of an auction ``in-progress" and can update its prediction based on newly arriving information. Our dynamic forecasting model accounts for the special features of online auction data by using modern functional data analysis techniques. We also use the functional context to systematically describe the empirical regularities of auction dynamics. Second, we propose a family of differential equation models to capture the dynamics in online auctions. A novel multiple comparisons test is proposed to compare dynamics models of auction sub-populations. We accomplish the modeling task within the framework of principal differential analysis and functional models. Third, we propose Model-based Functional Differential Equation Trees to better incorporate the different characteristics of the auction, item, bidders and seller into the differential equation. We compare this new tree-method with trees either based on high-dimensional multivariate responses or functional responses. We apply our methods to a novel set of Harry Potter and Microsoft Xbox data for model validation and comparison of method

Collusion through Communication in Auctions

We study the extent to which communication can serve as a collusion device in one-shot first- and second-price sealed-bid auctions. Theoretically, second-price auctions are more fragile to collusion through communication than first-price auctions. In an array of laboratory experiments we vary the amount of interactions (communication and/or transfers without commitment) available to bidders. We find that the auctioneer's revenues decrease significantly when bidders can communicate. When, in addition, bidders can make transfer promises, revenues decline substantially, with 70% of our experimental auctions culminating in the object being sold for approximately the minimal price. Furthermore, the effects of communication and transfers are similar across auction formats

Improving Data Quality, Model Functionalities and Optimizing User Interfaces in Decision Support Systems

This dissertation contributes to the research on three core elements of decision support systems for managers and consumers: data management, model management and user interface. With respect to data management this dissertation proposes an approach for reducing unobserved product heterogeneity in online transaction data sets. The example of an online auction data set is used to investigate the approach’s ability to improve data quality. In the area of model management this dissertation contributes an approach to elicit consumer product preferences for exponential (beside linear) utility functions aiming at predicting consumers’ utilities and willingness-to-pay for individual products. The question which utility function (linear or exponential) is better suited for predicting product utilities and the willingness to pay is evaluated using a laboratory experiment. Further, in the area of user interfaces this dissertation deals with information visualization. Focusing on coordinate systems, a laboratory experiment is used to investigate which visualization format (two or three dimensional) is better suited for supporting simple vs. complex decision making scenarios and which criteria matter when choosing a visualization format for a particular level of decision making complexity

Market Design for the Transition to Renewable Electricity Systems

The research carried out in this thesis aims to shed light on the role of the European electricity market design in the transition to a target electricity system that combines sustainability, affordability, and reliability. While the ongoing expansion of fluctuating renewable electricity sources challenges the established structures and market mechanisms, governments across Europe have decided to phase-out certain conventional technologies like coal or nuclear power. Since traditional electricity systems rely on flexibility provided by controllable generation capacity, other flexibility options are needed to compensate for the decommissioned conventional power plants and support the system integration of renewables. Against this background, the dissertation extends an established large-scale agent-based electricity market model in order to account for the developments towards an integrated European electricity market and the characteristics of storage technologies. In particular, the representation of cross-border effects is enhanced by integrating approaches from the fields of operations research, non-cooperative game theory, and artificial intelligence in the simulation framework. The extended model is then applied in three case studies to analyze the diffusion of different flexibility options under varying regulatory settings. These case studies cover some central aspects of the European electricity market, most importantly capacity remuneration mechanisms, the interaction of day-ahead market and congestion management, and the role of regulation for residential self-consumption. Results of the case studies confirm that by designing the regulatory framework, policymakers and regulators can substantially affect quantity, composition, location, and operation of technologies – both, on the supply side and the demand side. At the same time, changes and amendments to market design are frequent and will continue to be so in the years ahead. Moreover, given the increasing level of market integration in Europe, the role of cross-border effects of national market designs will gain further in importance. In this context, agent-based simulation models are a valuable tool to better understand potential long-term effects of market designs in the interconnected European electricity system and can therefore support the European energy transition

Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting

The most significant progress in recent years in online display advertising is what is known as the Real-Time Bidding (RTB) mechanism to buy and sell ads. RTB essentially facilitates buying an individual ad impression in real time while it is still being generated from a user’s visit. RTB not only scales up the buying process by aggregating a large amount of available inventories across publishers but, most importantly, enables direct targeting of individual users. As such, RTB has fundamentally changed the landscape of digital marketing. Scientifically, the demand for automation, integration and optimisation in RTB also brings new research opportunities in information retrieval, data mining, machine learning and other related fields. In this monograph, an overview is given of the fundamental infrastructure, algorithms, and technical solutions of this new frontier of computational advertising. The covered topics include user response prediction, bid landscape forecasting, bidding algorithms, revenue optimisation, statistical arbitrage, dynamic pricing, and ad fraud detection

Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting

The most significant progress in recent years in online display advertising is what is known as the Real-Time Bidding (RTB) mechanism to buy and sell ads. RTB essentially facilitates buying an individual ad impression in real time while it is still being generated from a user’s visit. RTB not only scales up the buying process by aggregating a large amount of available inventories across publishers but, most importantly, enables direct targeting of individual users. As such, RTB has fundamentally changed the landscape of digital marketing. Scientifically, the demand for automation, integration and optimisation in RTB also brings new research opportunities in information retrieval, data mining, machine learning and other related fields. In this monograph, an overview is given of the fundamental infrastructure, algorithms, and technical solutions of this new frontier of computational advertising. The covered topics include user response prediction, bid landscape forecasting, bidding algorithms, revenue optimisation, statistical arbitrage, dynamic pricing, and ad fraud detection

Predicting Temporal Aspects of Movement for Predictive Replication in Fog Environments

To fully exploit the benefits of the fog environment, efficient management of data locality is crucial. Blind or reactive data replication falls short in harnessing the potential of fog computing, necessitating more advanced techniques for predicting where and when clients will connect. While spatial prediction has received considerable attention, temporal prediction remains understudied. Our paper addresses this gap by examining the advantages of incorporating temporal prediction into existing spatial prediction models. We also provide a comprehensive analysis of spatio-temporal prediction models, such as Deep Neural Networks and Markov models, in the context of predictive replication. We propose a novel model using Holt-Winter's Exponential Smoothing for temporal prediction, leveraging sequential and periodical user movement patterns. In a fog network simulation with real user trajectories our model achieves a 15% reduction in excess data with a marginal 1% decrease in data availability

Market Segmentation Trees

Problem Definition: We seek to provide an interpretable framework for segmenting users in a population for personalized decision-making. Methodology / Results: We propose a general methodology, Market Segmentation Trees (MSTs), for learning market segmentations explicitly driven by identifying differences in user response patterns. To demonstrate the versatility of our methodology, we design two new, specialized MST algorithms: (i) Choice Model Trees (CMTs), which can be used to predict a user’s choice amongst multiple options and (ii) Isotonic Regression Trees (IRTs), which can be used to solve the bid landscape forecasting problem. We provide a theoretical analysis of the asymptotic running times of our algorithmic methods, which validates their computational tractability on large datasets. We also provide a customizable, open-source code base for training MSTs in Python which employs several strategies for scalability, including parallel processing and warm starts. Finally, we assess the practical performance of MSTs on several synthetic and real world datasets, showing that our method reliably finds market segmentations which accurately model response behavior. Managerial Implications: The standard approach to conduct market segmentation for personalized decision-making is to first perform market segmentation by clustering users according to similarities in their contextual features, and then fit a “response model” to each segment in order to model how users respond to decisions. However, this approach may not be ideal if the contextual features prominent in distinguishing clusters are not key drivers of response behavior. Our approach addresses this issue by integrating market segmentation and response modeling, which consistently leads to improvements in response prediction accuracy, thereby aiding personalization. We find that such an integrated approach can be computationally tractable and effective even on large-scale datasets. Moreover, MSTs are interpretable since the market segments can easily be described by a decision tree and often require only a fraction of the number of market segments generated by traditional approaches