Challenges for Software Agents Supporting Decision-Makers in Trading Flowers Worldwide

  • Eric van Heck
  • Wolfgang Ketter
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 101)


High performing firms are working in business networks with advanced decision making capabilities. Decision making in business networks is a new research area that provides knowledge and insight about how decision rights are allocated and how decision processes are designed and implemented in evolving business networks [22]. In this article we focus on a particular type of support: software agents. Software agents are software programs that act on behalf of users or other programs. Software agents can be autonomous (capable of modifying the way in which they achieve their objectives), intelligent (capable of learning and reasoning), and distributed (capable to being executed on physically distinct computers). Software agents can act in multi-agent systems (e.g. distributed agents that do not have the capabilities to achieve an objective alone and thus must be able to communicate) and as mobile agents (e.g. these relocate their execution onto different processors). Recent research shows that software agents are able to act as a decision support tool or a training tool for negotiations with people. For example, [16] Lin and Kraus (2010) identified several types of agents in several variations of negotiation settings. These agents differ in the number of negotiators, encounters, and attributes they can handle. The identified agents are: Diplomat, AutONA, Cliff-Edge, Colored-Trails, Guessing Heuristic, QOAgent, and Virtual Human. Although software agents are popular in scientific research programs, the use of software agents in real life business situations is limited. We will explore the use of software agents in the flower industry with its complex logistics, commercial, and financial processes on a global scale.


Supply Chain Management Mobile Agent Software Agent Business Network Combinatorial Auction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Adomavicius, G., Tuzhilin, A.: Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Transactions on Knowledge and Data Engineering 17(6), 734–749 (2005)CrossRefGoogle Scholar
  2. 2.
    Carare, O., Rothkopf, M.H.: Slow Dutch Auctions. Management Science 51(3), 365–373 (2005)CrossRefzbMATHGoogle Scholar
  3. 3.
    Collins, J., Ketter, W., Gini, M.: A Multi-Agent Negotiation Testbed for Contracting Tasks with Temporal and Precedence Constraints. International Journal of Electronic Commerce 7(1), 35–57 (2002)Google Scholar
  4. 4.
    Collins, J., Ketter, W., Gini, M.: Flexible decision support in a dynamic business network. In: Vervest, P., van Liere, D., Zheng, L. (eds.) The Network Experience – New Value from Smart Business Networks, pp. 233–246. Springer, Heidelberg (2008)Google Scholar
  5. 5.
    Collins, J., Ketter, W., Gini, M.: Flexible decision support in dynamic interorganizational networks. European Journal of Information Systems 19(4) (September 2010a)Google Scholar
  6. 6.
    Collins, J., Ketter, W., Gini, M.: Flexible decision control in an autonomous trading agent. Electronic Commerce Research and Applications 8(2), 91–105 (2009)CrossRefGoogle Scholar
  7. 7.
    Collins, J., Ketter, W., Sadeh, N.: Pushing the limits of rational agents: the trading agent competition for supply chain management. AI Magazine 31(2), 63–80 (2010b)Google Scholar
  8. 8.
    Economist, The, A Life of Slime: Railways and Slime Moulds, p. 71 (January 23, 2010)Google Scholar
  9. 9.
    Goldberg, D., et al.: Using collaborative filtering to weave an information tapestry. Communications of the ACM, 61–70 (1992)Google Scholar
  10. 10.
    Haeubl, G., Trifts, V.: Consumer Decision Making in Online Shopping Environments: The Effects of Interactive Decision Aids. Marketing Science 19(1), 4–21 (2000)CrossRefGoogle Scholar
  11. 11.
    Kambil, A., Van Heck, E.: Reengineering the Dutch Flower Auctions: A Framework for Analyzing Exchange Organizations. Information Systems Research 9(1), 1–19 (1998)CrossRefGoogle Scholar
  12. 12.
    Kambil, A., van Heck, E.: Making Markets: How firms can design and profit from online auctions and exchanges. Harvard Business School Press (June 2002)Google Scholar
  13. 13.
    Ketter, W., Collins, J., Gini, M., Gupta, A., Schrater, P.: Detecting and Forecasting Economic Regimes in Multi-Agent Automated Exchanges. Decision Support Systems 47(4), 307–318 (2009)CrossRefGoogle Scholar
  14. 14.
    Koppius, O.R.: Information Architecture and Electronic Market Performance. 2002: Erasmus Research Institute of Management (ERIM, PhD Dissertation), Erasmus University RotterdamGoogle Scholar
  15. 15.
    Koppius, O., van Heck, E., Wolters, M.: The importance of product representation online: Empirical results and implications for electronic markets. Decision Support Systems 38, 161–169 (2004)CrossRefGoogle Scholar
  16. 16.
    Lin, R., Kraus, S.: Can automated agents proficiently negotiate with humans? Communications of the ACM 53(1), 78–88 (2010)CrossRefGoogle Scholar
  17. 17.
    Maes, P.: Agents that reduce work and information overload. Communications of the ACM 37(7), 30–40 (1994)CrossRefGoogle Scholar
  18. 18.
    Myers, K., et al.: An Intelligent Personal Assistant for Task and Time Management. AI Magazine, 47 (2007)Google Scholar
  19. 19.
    Rich, C., Sidner, C.L.: COLLAGEN: A Collaboration Manager for Software Interface Agents. User Modeling and User-Adapted Interaction 8(3), 315–350 (1998)CrossRefGoogle Scholar
  20. 20.
    Sandholm, T., et al.: CABOB: A Fast Optimal Algorithm for Winner Determination in Combinatorial Auctions. Management Science 51(3), 374–390 (2005)MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Tero, A., Takagi, S., Saigusa, T., Ito, K., Bebber, D.P., Fricker, M.D., Yumiki, K., Kobayashi, R., Nakagaki, T.: Rules for Biologically Inspired Adaptive Network Design. Science 327(5964), 439–442 (2010)MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    Van Heck, E., Vervest, P.: Smart business networks: how the network wins. Communications of the ACM 50(6), 29–37 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Eric van Heck
    • 1
  • Wolfgang Ketter
    • 1
  1. 1.Department of Decision and Information Sciences Rotterdam School of ManagementErasmus UniversityRotterdamThe Netherlands

Personalised recommendations