Introduction: 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.

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doi.org/10.1007/978-3-642-28082-5_3, hdl.handle.net/1765/41212
Lecture Notes in Business Information Processing (LNBIP)
Erasmus School of Economics

van Heck, H.W.G.M, & Ketter, W. (2012). Challenges for software agents supporting decision-makers in trading flowers worldwide. In Lecture Notes in Business Information Processing (LNBIP) (Vol. 101, pp. 32–39). doi:10.1007/978-3-642-28082-5_3