Analyzing the performance of multiple agents with varying bidding behaviors and standard bidders in online auctions

Online auctions have provided an alternative trading method to exchange items without the geographical and time constraints. However, buyers would face difficulties in searching, monitoring, and selecting an auction to participate in. As a consequence, agent technology is introduced to overcome these pitfalls. In this paper, heterogeneous intelligent agents and heterogeneous standard bidders are generated in a simulated auction market and their performances are measured. By doing so, it would further simulate the real online auction marketplace where bidders may have different bidding behaviors or implement different bidder agents. From the simulated results, the average winner's utility, the average number of winning auctions, the average closing price and the average median consumer surplus ratio are used to evaluate the winners' performances. From the results obtained, it is generalized that by using intelligent bidder agents to participate in online auctions, it benefits the bidders. Besides that, market economy is reviewed based on the results obtained

A Proficient and Dynamic Bidding Agent for Online Auctions

E-consumers face biggest challenge of opting for the best bidding strategies for competing in an environment of multiple and simultaneous online auctions for same or similar items. It becomes very complicated for the bidders to make decisions of selecting which auction to participate in, place single or multiple bids, early or late bidding and how much to bid. In this paper, we present the design of an autonomous dynamic bidding agent (ADBA) that makes these decisions on behalf of the buyers according to their bidding behaviors. The agent develops a comprehensive method for initial price prediction and an integrated model for bid forecasting. The initial price prediction method selects an auction to participate in and then predicts its closing price (initial price). Then the bid forecasting model forecasts the bid amount by designing different bidding strategies followed by the late bidders. The experimental results demonstrated improved initial price prediction outcomes by proposing a clustering based approach. Also, the results show the proficiency of the bidding strategies amongst the late bidders with desire for bargai

The BARISTA: A model for bid arrivals in online auctions

The arrival process of bidders and bids in online auctions is important for studying and modeling supply and demand in the online marketplace. A popular assumption in the online auction literature is that a Poisson bidder arrival process is a reasonable approximation. This approximation underlies theoretical derivations, statistical models and simulations used in field studies. However, when it comes to the bid arrivals, empirical research has shown that the process is far from Poisson, with early bidding and last-moment bids taking place. An additional feature that has been reported by various authors is an apparent self-similarity in the bid arrival process. Despite the wide evidence for the changing bidding intensities and the self-similarity, there has been no rigorous attempt at developing a model that adequately approximates bid arrivals and accounts for these features. The goal of this paper is to introduce a family of distributions that well-approximate the bid time distribution in hard-close auctions. We call this the BARISTA process (Bid ARrivals In STAges) because of its ability to generate different intensities at different stages. We describe the properties of this model, show how to simulate bid arrivals from it, and how to use it for estimation and inference. We illustrate its power and usefulness by fitting simulated and real data from eBay.com. Finally, we show how a Poisson bidder arrival process relates to a BARISTA bid arrival process.Comment: Published in at http://dx.doi.org/10.1214/07-AOAS117 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Bidder Behavior in Complex Trading Environments: Modeling, Simulations, and Agent-Enabled Experiments

University of Minnesota Ph.D. dissertation. January 2018. Major: Business Administration. Advisor: Gediminas Adomavicius. 1 computer file (PDF); vi, 90 pages.Combinatorial auctions represent sophisticated market mechanisms that are becoming increasingly important in various business applications due to their ability to improve economic efficiency and auction revenue, especially in settings where participants tend to exhibit more complex user preferences and valuations. While recent studies on such auctions have found heterogeneity in bidder behavior and its varying effect on auction outcomes, the area of bidder behavior and its impact on economic outcomes in combinatorial auctions is still largely underexplored. One of the main reasons is that it is nearly impossible to control for the type of bidder behavior in real world or experimental auction setups. In my dissertation I propose two data-driven approaches (heuristic-based in the first part and machine-learning-based in the second part) to design and develop software agents that replicate several canonical types of human behavior observed in this complex trading mechanism. Leveraging these agents in an agent-based simulation framework, I examine the effect of different bidder compositions (i.e., competing against bidders with different bidding strategies) on auction outcomes and bidder behavior. I use the case of continuous combinatorial auctions to demonstrate both approaches and provide insights that facilitate the implementation of this combinatorial design for online marketplaces. In the third part of my thesis, I conduct human vs. machine style experiments by integrating the bidding agents into an experimental combinatorial auction platform, where participants play against (human-like) agents with certain pre-determined bidding strategies. This part investigates the impact of different competitive environments on bidder behavior and auction outcomes, the underlying reasons for different behaviors, and how bidders learn under different competitive environments

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

Measuring Emotions in Electronic Auctions

This book develops a structured methodology that allows to systematically analyze emotions in auctions. It provides a unified framework for emotional bidding in auctions, which comprises the bidders\u27 processes of cognitive reasoning and emotional processing, and a methodology for measuring physiological correlates of human emotional processing in economic experiments is proposed: physioeconomics. Finally, an experiment is presented which investigates the impact of clock speeds in Dutch auctions

Emotions in auctions: When and why bidders overbid

The objective of this thesis is to identify situations in which bidders overbid in auctions and to explain overbidding behavior with emotionally motivated bidding behavior. The specific challenge is to identify the comprehensive range of types of emotions that influence bidding behavior based on approaches in emotional psychology. A standard procedure is developed to discuss emotions in auctions and to identify emotional pattern. A special focus is on the design of experiments with real goods

Emotions and cognitive workload in economic decision processes - A NeuroIS Approach

The influence of cognitive and emotions on decision processes have been recently highlighted. Emotions interplay with the process of cognition, and determine decision processes. In this work, the role of external and internal influences on economic decision processes are studied. A NeuroIS method is applied for measuring emotions and cognitive workload. The lack of a suitable experimental platform for performing NeuroIS studies was recognized and the platform Brownie was developed and evaluated