Improving comparison shopping agents' competence through selective price disclosure

A new approach for increasing the attractiveness of comparison shopping agents.The approach, selective price-disclosure, affects the buyer's decision-making.Two methods for selective price-disclosure for fully-rational agents are given.The effectiveness and efficiency of the methods are demonstrated using real data.A third method is experimentally shown to be highly effective with people. The plethora of comparison shopping agents (CSAs) in today's markets enables buyers to query more than a single CSA when shopping, and an inter-CSAs competition naturally arises. We suggest a new approach, termed "selective price disclosure", which improves the attractiveness of a CSA by removing some of the prices in the outputted list. The underlying idea behind this approach is to affect the buyer's beliefs regarding the chance of obtaining more attractive prices. The paper presents two methods, which are suitable for fully-rational buyers, for deciding which prices among those known to the CSA should be disclosed. The effectiveness and efficiency of the methods are evaluated using real data collected from five CSAs. The methods are also evaluated with human subjects, showing that selective price disclosure can be highly effective in this case as well; however, the disclosed subset of prices should be extracted in a different (simplistic) manner.

[1]  Zhulei Tang,et al.  The Impact of Shopbot Use on Prices and Price Dispersion: Evidence from Online Book Retailing , 2010 .

[2]  Chuan-Hoo Tan,et al.  EFFECTS OF COMPARISON SHOPPING WEBSITES ON MARKET PERFORMANCE: DOES MARKET STRUCTURE MATTER? , 2010 .

[3]  Y. Shoham Reasoning About Change: Time and Causation from the Standpoint of Artificial Intelligence , 1987 .

[4]  Amos Azaria,et al.  Giving Advice to People in Path Selection Problems , 2012, Interactive Decision Theory and Game Theory.

[5]  David Clark,et al.  Shopbots Become Agents for Business Change , 2000, Computer.

[6]  Ramayya Krishnan,et al.  Retail Strategies on the Web: Price and Non-Price Competition in the Online Book Industry , 2003 .

[7]  G. Schwarz Estimating the Dimension of a Model , 1978 .

[8]  John Morgan,et al.  Persistent Price Dispersion in Online Markets , 2006 .

[9]  M. Wildenbeest,et al.  Comparison Sites , 2011 .

[10]  Jérôme Lang,et al.  Belief extrapolation (or how to reason about observations and unpredicted change) , 2002, Artif. Intell..

[11]  David Sarne,et al.  Strategic information platforms: selective disclosure and the price of "free" , 2014, EC.

[12]  Yoav Shoham,et al.  Joint revision of belief and intention , 2010, KR 2010.

[13]  Maarten C. W. Janssen,et al.  Truly costly sequential search and oligopolistic pricing , 2005 .

[14]  Joshua Grass Reasoning about computational resource allocation , 1996, CROS.

[15]  Erik Brynjolfsson,et al.  Consumer Surplus in the Digital Economy: Estimating the Value of Increased Product Variety at Online Booksellers , 2003, Manag. Sci..

[16]  John Morgan,et al.  Temporal price dispersion: Evidence from an online consumer electronics market , 2004 .

[17]  Paolo Pin,et al.  Growth effects of inflation in Europe : How low is too low , how high is too high ? , 2004 .

[18]  Jeffrey O. Kephart,et al.  Shopbot Economics , 1999, AGENTS '99.

[19]  Michael F. Gorman,et al.  Who wins when price information is more ubiquitous? An experiment to assess how infomediaries influence price , 2009, Electron. Mark..

[20]  Jeffrey O. Kephart,et al.  Dynamic pricing by software agents , 2000, Comput. Networks.

[21]  Gang Peng,et al.  What's Next for Shopbots? , 2010, Computer.

[22]  Soe-Tsyr Yuan,et al.  A personalized and integrative comparison-shopping engine and its applications , 2003, Decis. Support Syst..

[23]  Roger Waldeck,et al.  Search and price competition , 2008 .

[24]  Amos Azaria,et al.  Strategic advice provision in repeated human-agent interactions , 2012, Autonomous Agents and Multi-Agent Systems.

[25]  Peter S. Fader,et al.  On the Depth and Dynamics of Online Search Behavior , 2004, Manag. Sci..

[26]  B. Pathak,et al.  A Survey of the Comparison Shopping Agent-Based Decision Support Systems , 2010 .

[27]  Avshalom Elmalech,et al.  Search More, Disclose Less , 2013, AAAI.

[28]  Zhenlin Yang,et al.  A comparison of time-varying online price and price dispersion between multichannel and dotcom DVD retailers , 2006 .

[29]  Justine Cassell,et al.  Negotiated Collusion: Modeling Social Language and its Relationship Effects in Intelligent Agents , 2003, User Modeling and User-Adapted Interaction.

[30]  J. Bakos Reducing buyer search costs: implications for electronic marketplaces , 1997 .

[31]  H. Varian A Model of Sales , 1980 .

[32]  Avshalom Elmalech,et al.  Less is more: restructuring decisions to improve agent search , 2011, AAMAS.

[33]  Katia P. Sycara,et al.  Middle-Agents for the Internet , 1997, IJCAI.

[34]  Ram D. Gopal,et al.  Shopbot 2.0: Integrating Recommendations and Promotions with Comparison Shopping , 2006, Decis. Support Syst..

[35]  David Sarne,et al.  Ordering Effects and Belief Adjustment in the Use of Comparison Shopping Agents , 2014, AAAI.

[36]  Siddharth Suri,et al.  Conducting behavioral research on Amazon’s Mechanical Turk , 2010, Behavior research methods.

[37]  Prithviraj Dasgupta,et al.  Firefly-Inspired Synchronization for Improved Dynamic Pricing in Online Markets , 2008, 2008 Second IEEE International Conference on Self-Adaptive and Self-Organizing Systems.

[38]  Shaul Markovitch,et al.  Information Filtering: Selection Mechanisms in Learning Systems , 1993, Machine Learning.

[39]  Panagiotis G. Ipeirotis Analyzing the Amazon Mechanical Turk marketplace , 2010, XRDS.

[40]  Glenn Ellison,et al.  Search, Obfuscation, and Price Elasticities on the Internet , 2004 .

[41]  Adrian Micu,et al.  Strategic Pricing , 2010 .

[42]  Kristin Diehl,et al.  When Two Rights Make a Wrong: Searching Too Much in Ordered Environments , 2005 .

[43]  Sulin Ba,et al.  Understanding Online Purchase Decision Making: The Effects of Unconscious Thought, Information Quality, and Information Quantity , 2012, Decis. Support Syst..

[44]  Clare-Marie Karat,et al.  Designing Personalized User Experiences in eCommerce , 2004, Human-Computer Interaction Series.

[45]  S. Bikhchandani,et al.  Optimal Search with Learning , 2011 .

[46]  M. Janssen,et al.  Strategic Pricing, Consumer Search and the Number of Firms , 2004 .

[47]  Arkalgud Ramaprasad,et al.  The Paradoxical Nature of Electronic Decision Aids on Comparison-Shopping: The Experiments and Analysis , 2009, J. Theor. Appl. Electron. Commer. Res..

[48]  Ed Hopkins,et al.  Price Dispersion , 2006 .

[49]  A. Rao,et al.  The Effect of Price, Brand Name, and Store Name on Buyers’ Perceptions of Product Quality: An Integrative Review , 1989 .

[50]  Chris Arney Nudge: Improving Decisions about Health, Wealth, and Happiness , 2015 .

[51]  Alexander Serenko,et al.  Investigating the functionality and performance of online shopping bots for electronic commerce: a follow-up study , 2010, Int. J. Electron. Bus..

[52]  Avshalom Elmalech,et al.  Problem restructuring for better decision making in recurring decision situations , 2014, Autonomous Agents and Multi-Agent Systems.

[53]  Paolo Pin,et al.  The informational divide , 2013, Games Econ. Behav..

[54]  Israel Spiegler,et al.  Modeling the search for the least costly opportunity , 2009, Eur. J. Oper. Res..

[55]  Michael P. Wellman,et al.  The Michigan Internet AuctionBot: a configurable auction server for human and software agents , 1998, AGENTS '98.

[56]  Byung-Kook Kang Optimal stopping problem with double reservation value property , 2005, Eur. J. Oper. Res..

[57]  S. Iyengar The Art of Choosing , 2010 .

[58]  Michael C. Horsch,et al.  An Anytime Algorithm for Decision Making under Uncertainty , 1998, UAI.

[59]  Sarit Kraus,et al.  Scaling-up shopbots: a dynamic allocation-based approach , 2007, AAMAS '07.

[60]  Byung-Kook Kang Optimal stopping problem with recall cost , 1999, Eur. J. Oper. Res..

[61]  Hector J. Levesque,et al.  Iterated belief change in the situation calculus , 2000, Artif. Intell..

[62]  Nicholas R. Jennings,et al.  On Agent-Mediated Electronic Commerce , 2003, IEEE Trans. Knowl. Data Eng..

[63]  Pedro Pereira,et al.  Do lower search costs reduce prices and price dispersion? , 2005, Inf. Econ. Policy.

[64]  Jeffrey O. Kephart,et al.  How valuable are shopbots? , 2002, AAMAS '02.

[65]  Shlomo Zilberstein,et al.  Using Anytime Algorithms in Intelligent Systems , 1996, AI Mag..

[66]  Lyle H. Ungar,et al.  Shopbots and Pricebots in Electronic Service Markets , 2002 .

[67]  Dale O. Stahl,et al.  Oligopolistic Pricing with Sequential Consumer Search , 1989 .

[68]  Izak Benbasat,et al.  E-Commerce Product Recommendation Agents: Use, Characteristics, and Impact , 2007, MIS Q..

[69]  Ke Wang,et al.  Understanding online purchase decision making: The effects of unconscious thought, information quality, and information quantity , 2012, Decis. Support Syst..

[70]  Panagiotis G. Ipeirotis,et al.  Running Experiments on Amazon Mechanical Turk , 2010, Judgment and Decision Making.

[71]  Amos Azaria,et al.  Strategic Information Disclosure to People with Multiple Alternatives , 2011, AAAI.

[72]  M. Peitz,et al.  The Oxford Handbook of the Digital Economy , 2012 .

[73]  Lyle H. Ungar,et al.  Pricing price information in e-commerce , 2001, EC '01.

[74]  E. Parzen On Estimation of a Probability Density Function and Mode , 1962 .