Bidding strategies for real-life small loan auctions

We define and identify bidding strategies in real-life small loan auctions (Prosper.com). In such auctions, lenders bid for borrowers' loan listings and the winners get to fund the loan at an interest rate determined by the auction. The exceptionally large empirical database provided by Prosper.com offers a unique opportunity to test and further develop the theory of online auctions. This study shows that bidding behavior is not homogeneous among bidders, as the traditional auction theory suggests. Instead, bidders use many different bidding strategies. Moreover, learning and bidders' consistency over time in different auctions is studied.     

Bidding agents for online auctions with hidden bids

There is much active research into the design of automated bidding agents, particularly for environments that involve multiple decoupled auctions. These settings are complex partly because an agent’s strategy depends on information about other bidders’interests. When bidders’ valuation distributions are not known ex ante, machine learning techniques can be used to approximate them from historical data. It is a characteristic feature of auctions, however, that information about some bidders’valuations is systematically concealed. This occurs in the sense that some bidders may fail to bid at all because the asking price exceeds their valuations, and also in the sense that a high bidder may not be compelled to reveal her valuation. Ignoring these “hidden bids” can introduce bias into the estimation of valuation distributions. To overcome this problem, we propose an EM-based algorithm. We validate the algorithm experimentally using agents that react to their environments both decision-theoretically and game-theoretically, using both synthetic and real-world (eBay) datasets. We show that our approach estimates bidders’ valuation distributions and the distribution over the true number of bidders significantly more accurately than more straightforward density estimation techniques. Editors: Amy Greenwald and Michael Littman An earlier version of this work was presented at the Workshop on Game-Theoretic and Decision-Theoretic Agents (GTDT) 2005, Edinburgh, Scotland.     

An interactive approach for multi-attribute auctions

Auction processes are commonly employed in many environments. With rapid advances in Internet and computing technologies, electronic auctions have become very popular. People sell and buy a wide range of goods and services online. There is a growing need for the proper management of online auctions and for providing support to parties involved. In this paper, we develop an interactive approach supporting both the buyer and the bidders in a multi-attribute, single-item, multi-round, reverse auction environment. We demonstrate the algorithm on a number of problems.     

Reactivity based model to study online auctions dynamics

Online auctions have challenged many assumptions and results from the traditional economic auction theory. Observed bidder behavior in online auctions often deviates from equilibrium strategies postulated by economic theory. In this research, we consider an online auction as an information system that provides a long-duration, information-rich, dynamic application environment in which users (bidders) interact with the system in a feedback loop, in what we term reactivity. Bidders react to the observed conditions of the auction and events triggered by actions of other bidders. In this work we propose a new characterization model with the purpose of isolating the segments of the auction in which users react to the auction conditions and events. Through this model, it is possible to enrich the auction characterization. Despite the existence of other bidding characterization models, none of them is enough for understanding the factors that characterize and explain the auction dynamics. We present results which demonstrate the advantages of applying our methodology. The final objective is to gain an understanding of what drives the dynamics of online auctions, the role of reactivity in the auction dynamics, and how the outcome of the auction is affected by the particular dynamics of the system.

An adaptive attitude bidding strategy for agents in continuous double auctions

A continuous double auction (CDA) is an efficient market institution for real-world trading of commodities and electronic marketplaces. In this paper, we present the design and analysis of a new bidding strategy for buyer and seller agents participating in agent-based CDAs. The strategy employs heuristic rules and a reasoning mechanism based on a two-level adaptive bid-determination method, including short-term and long-term attitudes. Agents adopting the strategy dynamically adjust their behaviors in response to the changes of the supply and demand relationships in the market. Experimental results show that agents adopting the strategy outperform agents using other strategies reported in the literature.     

New Algorithms for Mining the Reputation of Participants of Online Auctions

The assessment of credibility and reputation of contractors in online auctions is the key issue in providing reliable environment for customer-to-customer e-commerce. Confident reputation rating system is an important factor in managing risk and building customer satisfaction. Unfortunately, most online auction sites employ a very simple reputation rating scheme that utilizes user feedbacks and comments issued after committed auctions. Such schemes are easy to deceive and do not provide satisfactory protection against several types of fraud. In this paper we propose two novel measures of trustworthiness, namely, credibility and density. We draw inspiration from social network analysis and present two algorithms for reputation rating estimation. Our first algorithm computes the credibility of participants by an iterative search of inter-participant connections. Our second algorithm discovers clusters of participants who are densely connected through committed auctions. We test both measures on real-world data and we experimentally compare them with existing solutions.     

网上拍卖的风险防范机制及其发展研究

网络经济的发展促使电子商务得到快速应用,而网上拍卖是电子商务中最成功的一种形式。网上拍卖有着传统拍卖所没有的一些优点。随着计算机技术和网络技术的发展,网上拍卖日益广泛与深入,同时也使得网上拍卖系统的风险问题日益突出。本文考虑的就是通过建立一些风险防范机制来降低网上拍卖的风险。     

Bidding strategies in online single-unit auctions: Their impact and satisfaction

Online bidders use different strategies in their biddings. However, we are unsure of the bidders strategies and their effectiveness in achieving auction goals. Our study was an attempt to identify bidding strategies empirically in online single-unit auctions and evaluate their outcome in terms of cost saving, perceived bidder enjoyment, and bidder satisfaction. A research model was developed and used to examine the relationships. Both objective and subjective data of online single-unit auctions were collected from Taobao.com to validate our postulated hypotheses. We found three main bidding strategies: early, snipe, and agent-supported ratchet bidding based on their behavior patterns. These resulted in different levels of winning outcome and cost saving, but had no significant effect on perceived enjoyment. The hypotheses of the relationship between auction outcomes and bidder satisfaction of their bidding strategies were confirmed.     

Should we collude? Analyzing the benefits of bidder cooperation in online group-buying auctions

Cooperation among bidders in traditional auctions is typically forbidden. This is because it is viewed as being harmful to the interests of sellers, who hope to obtain fair prices for their sale items. It also may be harmful to other bidders who are not able to take advantage of any cooperation that is occurring. In online group-buying auctions, in contrast to traditional auctions, cooperation results in higher welfare, leading to market expansion that benefits buyers and sellers, as well as the auction intermediary. This has not been well understood in prior research, however. In this article, we show how the online group-buying auction mechanism on the Internet can be effectively enhanced to produce higher welfare for the participants. The key to achieving this, we find, is for the auction intermediary to provide a means for bidders to cooperate, so as to collectively express greater demand. Such cooperation, it turns out, permits the group-buying auction mechanism to dominate the fixed-price mechanism from the seller’s point of view under some circumstances. Through an analytical modeling analysis, we offer insights into how sellers can set their group-buying auction price curves more effectively, so as to take advantage of bidder cooperation to improve auction performance. We further argue that the goal of the auction intermediary should be to offer an information sharing mechanism to facilitate bidding ring formation, as a means to maximize the value of this market mechanism.     

Coordination of purchasing and bidding activities across posted offer and auction markets

In both consumer purchasing and industrial procurement, combinatorial interdependencies among the items to be purchased are commonplace. E-commerce compounds the problem by providing more opportunities for switching suppliers at low costs, but also potentially eases the problem by enabling automated market decision-making systems, commonly referred to as trading agents, to make purchasing decisions in an integrated manner across markets. We are investigating a new approach to deal with the combinatorial interdependency challenges for online markets. This approach relies on existing commercial online market institutions such as posted-offer markets and various online auctions that sell single items. It uses trading agents to coordinate a buyer’s purchasing and bidding activities across multiple online markets simultaneously to achieve the best overall procurement effectiveness. This paper presents two sets of models related to this approach. The first set of models formalizes optimal purchasing decisions across posted-offer markets with fixed transaction costs. The second set of models is concerned with the coordination of bidding activities across multiple online auctions.Research reported in this paper was partially supported by a Proposition 301 Electronic Commerce Grant, University of Arizona Eller College of Management. The first author is also affiliated with The Key Lab of Complex Systems and Intelligence Science, Chinese Academy of Sciences (CAS), Beijing, and was supported in part by a grant for open research projects (ORP-0303) from CAS. Earlier versions of this paper have appeared in the Proceedings of the Hawai’i International Conference on System Sciences (HICSS-37) and the Proceedings of the 13th Annual Workshop on Information Technology and Systems (WITS ’03).     

Internet环境下在线评标系统的研究

为了避免评标过程中一些人为因素的干扰,维护招投标的公正、公平的原则,探求用Web技术实现在线评标。文章设计了基于Web的在线评标系统,介绍了系统的主要功能和开发模式,给出了系统实现过程中一些关键问题的处理算法。     

Online behavior change detection in computer games

Player Modelling has been receiving much attention from the game community in the recent years. The ability to build accurate models of player behavior can be useful in many aspects of a game. One important aspect is the tracking of a player’s behavior along time, informing every time a change is perceived. This way, the game Artificial Intelligence can adapt itself to better respond to this new behavior. In order to build models of player behavior, researchers frequently resort to Machine Learning techniques. Such methods work on previously recorded game metrics representing player’s interactions with the game environment. However, if the player changes styles over time, the constructed models get out of date. In order to address this drawback, this work proposes the use of and incremental learning technique to track a player’s behavior during his/her interaction with the game environment. Our approach attempts to automatically detect the moments in time when the player changes behavior. We apply a change detection technique from the area of Data Stream Mining that is based on incremental clustering and novelty detection. We also propose three modifications to the original technique, in order to formalize change detection, improve detection rate and reduce detection delay. Simulations were performed considering data produced by the Unreal Tournament game, showing the applicability of the method to online tracking of a player’s behavior and informing whenever behavior changes occur.     

浅析我国网络教育在线辅导的现状

随着信息时代和网络时代的到来,以信息技术为核心的知识经济占据着主导地位。教育终身化、人才科技化,导致了传统的课堂教学模式已经不能完全满足社会对人才的需求。网络教学凭借灵活性、自主性和资源共享等特点,为社会各层次的学习需求者提供了终身学习的机会和便利条件,降低了学习成本,提高了社会效益,较好解决了成人学习工学矛盾这一突出问题。为了帮助学生更好的学习,各网络教育学院提供了多种类型的学习参考资料,但这并不能满足学习者的需要,还应提供与学习过程密切相关的辅导、答疑、导学、促学等非学术性学习服务,对每一个学习者提供个性化的指导和帮助。目前,我国远程教育学生规模不断上升,如何为学生提供多方位、全过程的在线学习辅导是提高我国远程教育教学质量的关键问题。     

Winner's curse and parallel sales channels—Online auctions linked within e-tail websites

A sample of 416 online auctions was examined to determine the extent of overpayment (winner's curse) where online auctions and e-tail websites were linked together to form a parallel sales channel. The results indicated that 8.7% of the highest winning online auction bidders exceeded e-tail posted reference prices of identical retail merchandise found at the same website. Significantly, such bids exceeded the reference prices by a mean percentage dollar amount of 14.1% thus suggesting the existence of a winner's curse. The results also indicated that (1) there was a significant negative association between reference price and mean percentage dollar amount overbid; (2) there was a significant negative association between auction lot size and mean percentage dollar amount overbid; and (3) there was no significant association between overtime auctions and mean percentage dollar amount overbid. While manipulation of reference price and auction lot size might minimize winner's curse, erratic or irrational behavior (by online auction and/or e-tail websites) may lead to disinformation.     

认知负荷理论在中职计算机教学中的应用

认知负荷理论是一种先进的教育理论,主张通过优化大脑资源,来对学生的记忆力进行充分的利用。中职计算机教学是中职教育中一门重要的课程,对于提高中职学生的计算机水平以及就业能力有着重要的作用。中职计算机教学不仅要加强学生对计算机技能的掌握,更要充分应用认知负荷理论,减少学生的认知负荷,提高中职计算机教学的质量和效率。     

When Online Dating Partners Meet Offline: The Effect of Modality Switching on Relational Communication Between Online Daters

Despite the popularity of online dating sites, little is known about what occurs when online dating partners choose to communicate offline. Drawing upon the modality switching perspective, the present study assessed a national sample of online daters to determine whether face‐to‐face (FtF) relational outcomes could be predicted by the amount of online communication prior to the initial FtF meeting. Results were consistent with the hypothesized curvilinear relationship between the amount of online communication and perceptions of relational messages (intimacy, composure, informality, social orientation), forecasts of the future of the relationship, and information seeking behavior when meeting their partner FtF. The results provide support for the modality switching perspective, and offer important insight for online daters.     

Interacting with computer technology in secondary schools

In this article I reflect on my teaching and scholarship with computers in secondary schools during the past 20 years. I chart how changing technology and changing expectations for technology shaped my teaching and student learning in the writing centers and classrooms where I have worked.     

Online recreation: The relationship between loneliness,Internet self-efficacy and the use of the Internet for entertainment purposes

In this study, 150 undergraduates answered questions about their Internet usage and completed a loneliness and an Internet self-efficacy questionnaire. A factor analysis of the Internet usage items revealed three facets of online recreation, including, using the Internet for: computer-based entertainment, to facilitate offline entertainment, and for information about the entertainment world. Those who scored higher on loneliness were more likely to use the Internet for computer-based entertainment, as well as, use the Internet to obtain information about the entertainment world. Individuals higher in Internet self-efficacy were more likely to use the Internet for computer-based entertainment and to facilitate offline entertainment. Implications for the study of the psychological influences of the Internet are discussed in this paper.