Learning optimization models in the presence of unknown relations

In a sequential auction with multiple bidding agents, it is highly challenging to determine the ordering of the items to sell in order to maximize the revenue due to the fact that the autonomy and private information of the agents heavily influence the outcome of the auction. The main contribution of this paper is two-fold. First, we demonstrate how to apply machine learning techniques to solve the optimal ordering problem in sequential auctions. We learn regression models from historical auctions, which are subsequently used to predict the expected value of orderings for new auctions. Given the learned models, we propose two types of optimization methods: a black-box best-first search approach, and a novel white-box approach that maps learned models to integer linear programs (ILP) which can then be solved by any ILP-solver. Although the studied auction design problem is hard, our proposed optimization methods obtain good orderings with high revenues. Our second main contribution is the insight that the internal structure of regression models can be efficiently evaluated inside an ILP solver for optimization purposes. To this end, we provide efficient encodings of regression trees and linear regression models as ILP constraints. This new way of using learned models for optimization is promising. As the experimental results show, it significantly outperforms the black-box best-first search in nearly all settings.Comment: 37 pages. Working pape

The Hidden Effects of Opening Bids in Online Auctions

Auction opening bid is one of the online auction features that can be manipulated to promote bidding activity. Oftentimes, auction sellers that expect high bidding volume set their opening bids low only to later realize a lower price premium in their auctions. The current study offers explanations to this phenomenon by approaching this situation in a more holistic way. It examines the impacts of auction opening bids on bidding behaviors. Auction data from eBay were collected and separated into two samples, including auctions with low and high opening bids (LOB and HOB auctions). We found that HOB auctions attracted more serious bidders as indicated by their commitment to stay longer in the auctions. We also found that some bidding strategies that were commonly considered undesirable by auction sellers produced positive price premium to HOB auctions but not to its counterparts. Theoretical and pragmatic implications are later offered in the study

Eliminating the Flaws in New England's Reserve Markets

New England’s wholesale electricity market has been in operation, since May 1, 1999. When the market began it was understood that the rules were not perfect (Cramton and Wilson 1998). However, it was decided that it was better to start the market with imperfect rules, rather than postpone the market for an indefinite period. After several months of operation, we now have a sense of the extent market imperfections have resulted in observed problems. Here we study the three reserve markets—ten-minute spinning reserve (TMSR), ten-minute non-spinning reserve (TMNSR), and thirty-minute operating reserve (TMOR); we also discuss the closely related operable capability (OpCap) market. The paper covers the first four months of operation from May 1 to August 31, 1999. It is based on the market rules and their implementation by the ISO, and the market data during this period, including bidding, operating, and settlement information. Since that data are confidential, we have presented only aggregate information in the tables and figures that follow. Although this paper will cover only the reserves markets, we have studied the data from the energy, AGC, and capacity markets as well. Since all of the NEPOOL markets are interrelated, one cannot hope to understand one market without having an understanding of the others.Auctions, Electricity Auctions, Multiple Item Auctions

Emotions and Emotion Regulation in Economic Decision Making

By employing the methodology of experimental economics, the thesis examines the influence of emotions on decision making in electronic auction markets. Subjects\u27 emotional processes are measured by psychophysiological indicators, helping to decipher the coherence of information, emotion (regulation) and decision making. Four chapters build the main body of the thesis and all are constructed similarly: introduction, design, method, results, limitations, theoretical and managerial implications

EXPLORING AND MODELING OF BIDDING BEHAVIOR AND STRATEGIES OF ONLINE AUCTIONS

Internet auctions, as an exemplar of the recent boom in e-commerce, are grow- ing faster than ever in the last decade. Understanding the reasons why bidders be- have a certain way allows invaluable insight into the auction process. This research focuses on methods for modeling, testing and estimation of bidders' behavior and strategies. I start my discussion with bid shading, which is a common strategy bidders believe obtains the lowest possible price. While almost all bidders shade their bids, at least to some degree, it is impossible to infer the degree and volume of shaded bids directly from observed bidding data. In fact, most bidding data only allows researchers to observe the resulting price process, i.e. whether prices increase fast (due to little shading) or whether they slow down (when all bidders shade their bids). In this work, I propose an agent-based model that simulates bidders with different bidding strategies and their interaction with one another. The model is calibrated (and hence properties about the propensity and degree of shaded bids are estimated) by matching the emerging simulated price process with that of the observed auction data using genetic algorithms. From a statistical point of view, this is challenging because it requires matching functional draws from simulated and real price processes. I propose several competing fitness functions and explore how the choice alters the resulting ABM calibration. The method is applied to the context of eBay auctions for digital cameras and show that a balanced fitness function yields the best results. Furthermore, in light of the discrepancy find from the model in bidders' be- havior and optimal strategies proposed from online auction literature. I extract empirical bidding strategies from auction winners and utilize the agent based model to simulate and test the performance of twenty-four different empirical and theo- retical strategies. The experiment results suggest that some empirical strategies perform robustly when compared to theoretical strategies and taking into account other bidders' ability to learn. In addition, I expended the online auction framework from single auction to multiple auction simulation, which acts as a platform for investigating and test- ing more complicated situations that involves the competition among concurrent auctions. This framework facilitates my investigation of bidders' switching behavior and enables me to answer a series questions. For example, is it beneficial for auction website to promote bidders' switching behavior? Will bidders and even sellers get any advantage from bidders' switching? What is the best auction recommendation strategy for online auction website to obtain higher profit and/or a better customer experience? Through careful experiment design, it has been showed that higher switching frequency leads to higher profit for auction website and reduces the price dispersion, which leads to reduced risk for both bidders and sellers. In addition, the best auction recommendation strategy is providing the five earliest closing auctions so that bidders can choose the lowest price auction

The Impact of Computerized Agents on Immediate Emotions, Overall Arousal and Bidding Behavior in Electronic Auctions

The presence of computerized agents has become pervasive in everyday live. In this paper, we examine the impact of agency on human bidders’ affective processes and bidding behavior in an electronic auction environment. In particular, we use skin conductance response and heart rate measurements as proxies for the immediate emotions and overall arousal of human bidders in a lab experiment with human and computerized counterparts. Our results show that computerized agents mitigated 1) the intensity of bidders’ immediate emotions in response to discrete auction events, such as submitting a bid and winning or losing an auction, and 2) the bidders’ overall arousal levels during the auction. Moreover, agency affected bidding behavior and its relation to overall arousal: whereas overall arousal and bids were negatively correlated when competing against human bidders, we did not observe this relationship for computerized agents. In other words, lower levels of agency yield less emotional behavior. The results of our study have implications for the design of electronic auction platforms and markets that include both human and computerized actors

Mining of Business Data

Applying statistical tools to help understand business processes and make informed business decisions has attracted enormous amount of research interests in recent years. In this dissertation, we develop and apply data mining techniques to two sources of data, online bidding data for eBay and offline sales transaction data from a grocery product distributor. We mine online auction data to develop forecasting models and bidding strategies and mine offline sales transaction data to investigate sales people's price formation process. We start with discussing bidders' bidding strategies in online auctions. Conventional bidding strategies do not help bidders select an auction to bid on. We propose an automated and data-driven strategy which consists of a dynamic forecasting model for auction closing price and a bidding framework built around this model to determine the best auction to bid on and the best bid amount. One important component of our bidding strategy is a good forecasting model. We investigate forecasting alternatives in three ways. Firstly, we develop model selection strategies for online auctions (Chapter 3). Secondly, we propose a novel functional K-nearest neighbor (KNN) forecaster for real time forecasting of online auctions (Chapter 4). The forecaster uses information from other auctions and weighs their contribution by their relevance in terms of auction features. It improves the predictive performance compared to several competing models across various levels of data heterogeneity. Thirdly, we develop a Beta model (Chapter 5) for capturing auction price paths and find this model has advantageous forecasting capability. Apart from online transactions, we also employ data mining techniques to understand offline transactions where sales representatives (salesreps) serve as media to interact with customers and quote prices. We investigate the mental models for salesreps' decision making, and find that price recommendation makes salesreps concentrate on cost related information. In summary, the dissertation develops various data mining techniques for business data. Our study is of great importance for understanding auction price formation processes, forecasting auction outcomes, optimizing bidding strategies, and identifying key factors in sales people's decision making. Those techniques not only advance our understanding of business processes, but also help design business infrastructure

Live Biofeedback in Electronic Markets

Decisions in electronic markets are frequently made under time pressure and in competition to others. Both factors can cause the decision maker to experience high levels of arousal. Without sound emotional processing, arousal can have detrimental effects on decision making. In this thesis the use of live biofeedback to support emotion perception and thus, to facilitate emotion regulation during emotionally charged decision making is evaluated. Based on a systematic literature review existing live biofeedback research is analyzed in Chapter 2. A transmission model for live biofeedback is developed that classifies the main components of live biofeedback applications and the flow of information in form of transmission signals. To address the identified research gaps, three experimental studies (study I-III) are designed that investigate the effects of arousal and the use of live biofeedback in electronic markets. Study I in Chapter 3 examines how arousal affects purchasing decisions with and without social interaction to analyze the context dependence of the effects of arousal on decision making. The results reveal that in auctions, where social interaction is a key characteristic, arousal increases final prices. Purchasing decisions without social interaction, however, are not affected by arousal. As social interaction has been identified as an essential factor for arousal to affect decision making, the subsequent studies II and III investigate the effects of live biofeedback in markets experiments that involve social interaction. Study II in Chapter 4 evaluates the effects of live biofeedback on emotional processing in the context of auction bidding. Without prior biofeedback training this novel user interface element alters decision making processes at a cognitive and affective level. Study participants, who suppress emotional expressions, experience higher levels of physiological arousal. When provided with live biofeedback, this effect is mitigated. Furthermore, participants who receive live biofeedback show increased coherence of physiological and perceived arousal. Study III in Chapter 5 examines the use of biofeedback in a game that has frequently been used to model financial markets, that is, the beauty contest game. In this study, participants complete a training in order to familiarize with the live biofeedback prior to the experiment. The analysis reveals that live biofeedback increases arousal perception and reduces suppression of emotional expressions. Importantly, participants who receive live biofeedback yield higher decision making quality. In summary, this thesis provides further insights into the effects of arousal on behavior and how live biofeedback affects emotional processing and decision making in electronic markets. The results of this thesis suggest that live biofeedback is a promising tool to support emotion perception, regulation, and decision making of market participants