Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Autonomous Agents and Multi-Agent Systems
Published in: Autonomous Agents and Multi-Agent Systems 3/2017

29-07-2016

Enhancing comparison shopping agents through ordering and gradual information disclosure

Authors: Chen Hajaj, Noam Hazon, David Sarne

Published in: Autonomous Agents and Multi-Agent Systems | Issue 3/2017

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

The plethora of comparison shopping agents (CSAs) in today’s markets enables buyers to query more than a single CSA when shopping, thus expanding the list of sellers whose prices they obtain. This potentially decreases the chance of a purchase within any single interaction between a buyer and a CSA, and consequently decreases each CSAs’ expected revenue per-query. Obviously, a CSA can improve its competence in such settings by acquiring more sellers’ prices, potentially resulting in a more attractive “best price”. In this paper we suggest a complementary approach that improves the attractiveness of the best result returned based on intelligently controlling the order according to which they are presented to the user, in a way that utilizes several known cognitive-biases of human buyers. The advantage of this approach is in its ability to affect the buyer’s tendency to terminate her search for a better price, hence avoid querying further CSAs, without spending valuable resources on finding additional prices to present. The effectiveness of our method is demonstrated using real data, collected from four CSAs for five products. Our experiments confirm that the suggested method effectively influence people in a way that is highly advantageous to the CSA compared to the common method for presenting the prices. Furthermore, we experimentally show that all of the components of our method are essential to its success.
previous article A dynamic default revision mechanism for speculative computation
next article The joint movement of pebbles in solving the ()-puzzle suboptimally and its applications in rule-based cooperative path-finding

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
Footnotes
1
As for today, some of the common CSAs are PriceGrabber.com, Bizrate.com and Shopper.com.
 
2
In case the number of prices known to the CSA is odd, the lower part includes one additional price compared to the upper one.
 
3
In case the number of prices in this subset is odd, the anchor phase includes one additional price compared to the effort phase.
 
4
The raw data used for the experiments is available upon request from the corresponding author.
 
5
This figure is a composition of Figs. 2 and 3.
 
6
The videos are available upon request from the corresponding author.
 
Literature
1.
Alkoby, S., Sarne, D., & Das, S. (2015). Strategic free information disclosure for search-based information platforms. In International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS) (pp. 635–643).
2.
3.
Ariely, D., & Zakay, D. (2001). A timely account of the role of duration in decision making. Acta Psychologica, 108(2), 187–207.CrossRef
4.
Azaria, A., Aumann, Y., & Kraus, S. (2014). Automated agents for reward determination for human work in crowdsourcing applications. Autonomous Agents and Multi-Agent Systems, 28(6), 934–955.CrossRef
5.
Azaria, A., Gal, Y., Kraus, S., & Goldman, C.V. (2015). Strategic advice provision in repeated human-agent interactions. In Autonomous Agents and Multi-Agent Systems (pp. 1–26).
6.
Azaria, A., Hassidim, A., Kraus, S., Eshkol, A., Weintraub, O., & Netanely, I. (2013). Movie recommender system for profit maximization. In Proceedings of RecSys, ACM (pp. 121–128).
7.
Azaria, A., Rabinovich, Z., Goldman, C. V., & Kraus, S. (2014). Strategic information disclosure to people with multiple alternatives. ACM Transactions on Intelligent Systems and Technology, 5(4), 64:1–64:21.CrossRef
8.
Azaria, A., Richardson, A., & Kraus, S. (2014). An agent for the prospect presentation problem. In International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS) (pp. 989–996).
9.
Bakos, J. (1997). Reducing buyer search costs: Implications for electronic marketplaces. Management Science, 43(12), 1676–1692.CrossRefMATH
10.
Bar-Hillel, M. (2011). Location, location, location: Position effects in choice among simultaneously presented options. Tech. rep., The Center for the Study of Rationality.
11.
Baumeister, R. (2003). The psychology of irrationality: Why people make foolish, self-defeating choices. The Psychology of Economics Decisions, 1, 3–16.
12.
Bennett, P., Brennan, M., & Kearns, Z. (2003). Psychological aspects of price: An empirical test of order and range effects. Marketing Bulletin, 14, 1–8.
13.
Bonnardel, N., Piolat, A., & Le Bigot, L. (2011). The impact of colour on website appeal and users cognitive processes. Displays, 32(2), 69–80.CrossRef
14.
Buhrmester, M., Kwang, T., & Gosling, S. D. (2011). Amazon’s mechanical turk a new source of inexpensive, yet high-quality, data? Perspectives on Psychological Science, 6(1), 3–5.CrossRef
15.
Clay, K., Krishnan, R., Wolff, E., & Fernandes, D. (2002). Retail strategies on the web: Price and non-price competition in the online book industry. Journal of Industrial Economics, 50, 351–367.CrossRef
16.
Decker, K., Sycara, K., & Williamson, M. (1997). Middle-agents for the internet. In Proceedings of the International Joint Conferences on Artificial Intelligence (IJCAI) (pp. 578–583).
17.
Elmalech, A., Sarne, D., & Agmon, N. (2016). Agent development as a strategy shaper. In International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS) 30(3) (pp. 506–525).
18.
Elmalech, A., Sarne, D., & Grosz, B. J. (2015). Problem restructuring for better decision making in recurring decision situations. Autonomous Agents and Multi-Agent Systems, 29(1), 1–39.CrossRef
19.
Elmalech, A., Sarne, D., Rosenfeld, A., & Erez, E.S. (2015). When suboptimal rules. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) (pp. 1313–1319). Citeseer.
20.
Entin, E. E., & Serfaty, D. (1997). Sequential revision of belief: An application to complex decision making situations. Systems Man and Cybernetics, 27(3), 289–301.CrossRef
21.
Fogg, B.J. (2002). Persuasive technology: Using computers to change what we think and do. In Ubiquity, 2002 December, p. 5.
22.
Garfinkel, R., Gopal, R., Pathak, B., & Yin, F. (2008). Shopbot 2.0: Integrating recommendations and promotions with comparison shopping. Decision Support Systems, 46(1), 61–69.CrossRef
23.
Grewal, D., Krishnan, R., Baker, J., & Borin, N. (1998). The effect of store name, brand name and price discounts on consumers’ evaluations and purchase intentions. Journal of Retailing, 74(3), 331–352.CrossRef
24.
Hajaj, C., Hazon, N., & Sarne, D. (2014). Ordering effects and belief adjustment in the use of comparison shopping agents. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI) (pp. 930–936).
25.
Hajaj, C., Hazon, N., & Sarne, D. (2015). Improving comparison shopping agents? competence through selective price disclosure. Electronic Commerce Research and Applications, 14(6), 563–581.CrossRef
26.
Hajaj, C., & Sarne, D. (2014). Strategic information platforms: Selective disclosure and the price of free. In Proceedings of the ACM Conference on Economics and Computation (EC), ACM (pp. 839–856).
27.
He, M., Jennings, N. R., & Leung, H. (2003). On agent-mediated electronic commerce. IEEE Transaction on Knowledge and Data Engineering, 15(4), 985–1003.CrossRef
28.
Hogarth, R. M., & Einhorn, H. J. (1992). Order effects in belief updating: The belief-adjustment model. Cognitive Psychology, 24(1), 1–55.CrossRef
29.
Icard, T., Pacuit, E., & Shoham, Y. (2010). Joint revision of belief and intention. In Proceedings of Knowledge Representation and Reasoning (pp. 572–574).
30.
Johnson, E. J., Moe, W. W., Fader, P. S., Bellman, S., & Lohse, G. L. (2004). On the depth and dynamics of online search behavior. Management Science, 50(3), 299–308.CrossRef
31.
Kahneman, D. (1992). Reference points, anchors, norms, and mixed feelings. Organizational Behavior and Human Decision Processes, 51(2), 296–312.CrossRef
32.
Karat, C. M., Blom, J. O., & Karat, J. (2004). Designing personalized user experiences in eCommerce., Human-Computer Interaction Series Dordrecht: Kluwer Academic.CrossRef
33.
Kephart, J. O., & Greenwald, A. R. (2002). Shopbot economics. Journal of Autonomous Agents and Multi-Agent Systems, 5(3), 255–287.MathSciNetCrossRefMATH
34.
Knight, E. (2010). The Use of Price Comparison Sites in the UK General Insurance Market.
35.
Krulwich, B. (1996). The bargainfinder agent: Comparison price shopping on the internet. In J. Williams (Ed.), Bots and Other Internet Beasties (pp. 257–263). Indianapolis: Sams Publishing. chap. 13.
36.
Lau, R.Y. (2003). Belief revision for adaptive recommender agents in e-commerce. In Intelligent Data Engineering and Automated Learning, Springer (pp. 99–103).
37.
Levy, P., & Sarne, D. (2016). Intelligent advice provisioning for repeated interaction. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI).
38.
Li, Y. M., Wu, C. T., & Lai, C. Y. (2013). A social recommender mechanism for e-commerce: Combining similarity, trust, and relationship. Decision Support Systems, 55(3), 740–752.CrossRef
39.
Lieto, A., & Vernero, F. (2013). Unveiling the link between logical fallacies and web persuasion. In Proceedings of the 5th Annual ACM Web Science Conference, ACM (pp. 473–478).
40.
Mandel, N., & Johnson, E. (1999). Constructing preferences online: can web pages change what you want? Working paper. University of Pennsylvania.
41.
Markopoulos, P., & Kephart, J. (2002). How valuable are shopbots? In International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS) (pp. 1009–1016).
42.
Markopoulos, P., & Ungar, L. (2001). Pricing price information in e-commerce. In Proceedings of the ACM Conference on Economics and Computation (EC) (pp. 260–263).
43.
Markopoulos, P., & Ungar, L. (2002). Shopbots and pricebots in electronic service markets. In Game Theory and Decision Theory in Agent-Based Systems (pp. 177–195).
44.
Mason, W., & Suri, S. (2012). Conducting behavioral research on amazon’s mechanical turk. Behavior Research Methods, 44(1), 1–23.CrossRef
45.
Menon, S., & Kahn, B. (2002). Cross-category effects of induced arousal and pleasure on the internet shopping experience. Journal of Retailing, 78(1), 31–40.CrossRef
46.
Monroe, K. B. (1990). Pricing: Making profitable decisions. New York: McGraw-Hill.
47.
Moraga-Gonzalez, J. L., & Wildenbeest, M. (2012). Comparison sites. In M. Peitz & J. Waldfogel (Eds.), The oxford handbook of the digital economy. Oxford: Oxford University Press.
48.
Nisbett, R. E., & Wilson, T. D. (1977). Telling more than we can know: Verbal reports on mental processes. Psychological Review, 84(3), 231.CrossRef
49.
Oestreicher-Singer, G., & Sundararajan, A. (2012). The visible hand? demand effects of recommendation networks in electronic markets. Management Science, 58(11), 1963–1981.CrossRef
50.
Paolacci, G., Chandler, J., & Ipeirotis, P. (2010). Running experiments on amazon mechanical turk. Judgment and Decision Making, 5(5), 411–419.
51.
Pathak, B. (2010). A survey of the comparison shopping agent-based decisions support systems. Journal of Electronic Commerce Research, 11(3), 177–192.
52.
Peled, N., Gal, Y. K., & Kraus, S. (2015). A study of computational and human strategies in revelation games. Autonomous Agents and Multi-Agent Systems, 29(1), 73–97.CrossRef
53.
Piercy, N. F., Cravens, D. W., & Lane, N. (2010). Thinking strategically about pricing decisions. Journal of Business Strategy, 31(5), 38–48.CrossRef
54.
Rao, A., & Monroe, K. (1989). The effect of price, brand name, and store name on buyers’ perceptions of product quality: An integrative review. Journal of Marketing Research, 26(3), 351–357.CrossRef
55.
Rochlin, I., & Sarne, D. (2015). Constraining information sharing to improve cooperative information gathering. Journal Artificial Intelligence Research, 54, 437–469.MathSciNetMATH
56.
Rochlin, I., Sarne, D., & Mash, M. (2014). Joint search with self-interested agents and the failure of cooperation enhancers. Artificial Intelligence, 214, 45–65.MathSciNetCrossRefMATH
57.
Rosenfeld, A., Zuckerman, I., Segal-Halevi, E., Drein, O., & Kraus, S. (2016). Negochat-a: A chat-based negotiation agent with bounded rationality. Autonomous Agents and Multi-Agent Systems, 30(1), 60–81.CrossRef
58.
Dupin de Saint-Cyr, F., & Lang, J. (2011). Belief extrapolation (or how to reason about observations and unpredicted change). Artificial Intelligence, 175(2), 760–790.MathSciNetCrossRefMATH
59.
Sarne, D. (2013). Competitive shopbots-mediated markets. ACM Transactions on Economics and Computation, 1(3), 17:1–17:41.CrossRef
60.
Sarne, D., Kraus, S., & Ito, T. (2007). Scaling-up shopbots: a dynamic allocation-based approach. In International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS) (pp. 338–345).
61.
Shapiro, S., Pagnucco, M., Lespérance, Y., & Levesque, H. J. (2011). Iterated belief change in the situation calculus. Artificial Intelligence, 175(1), 165–192.MathSciNetCrossRefMATH
62.
Tan, C. H., Goh, K. Y., & Teo, H. H. (2010). Effects of comparison shopping websites on market performance: Does market structure matter? Journal of Electronic Commerce Research, 11(3), 193–219.
63.
Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185, 1124–1131.CrossRef
64.
Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5(4), 297–323.CrossRefMATH
65.
Ullmann-Margalit, E., & Morgenbesser, S. (1977). Picking and choosing. Social Research, 44(4), 757–785.
66.
Waldeck, R. (2008). Search and price competition. Journal of Economic Behavior and Organization, 66(2), 347–357.CrossRef
67.
Wan, Y., Menon, S., & Ramaprasad, A. (2009). The paradoxical nature of electronic decision aids on comparison-shopping: The experiments and analysis. Journal of Theoretical and Applied Electronic Commerce Research, 4, 80–96.CrossRef
68.
Wan, Y., & Peng, G. (2010). What’s next for shopbots? IEEE Computer, 43, 20–26.CrossRef
69.
Xiao, B., & Benbasat, I. (2007). E-commerce product recommendation agents: Use, characteristics, and impact. MIS Quarterly, 31(1), 137–209.
70.
Xing, X., Yang, Z., & Tang, F. (2006). A comparison of time-varying online price and price dispersion between multichannel and dotcom DVD retailers. Journal of Interactive Marketing, 20(2), 3–20.CrossRef
71.
Yuan, S. T. (2003). A personalized and integrative comparison-shopping engine and its applications. Decision Support Systems, 34(2), 139–156.CrossRef
Metadata
Title
Enhancing comparison shopping agents through ordering and gradual information disclosure
Authors
Chen Hajaj
Noam Hazon
David Sarne
Publication date
29-07-2016
Publisher
Springer US
Published in
Autonomous Agents and Multi-Agent Systems / Issue 3/2017
Print ISSN: 1387-2532
Electronic ISSN: 1573-7454
DOI
https://doi.org/10.1007/s10458-016-9342-8

Other articles of this Issue 3/2017

Autonomous Agents and Multi-Agent Systems 3/2017 Go to the issue

OriginalPaper

A framework for organization-aware agents

OriginalPaper

A dynamic default revision mechanism for speculative computation

OriginalPaper

How to trust a few among many

OriginalPaper

Evolution of commitment and level of participation in public goods games

OriginalPaper

Collaborative privacy preserving multi-agent planning

OriginalPaper

The joint movement of pebbles in solving the ()-puzzle suboptimally and its applications in rule-based cooperative path-finding

Premium Partner

    Image Credits