Simulating Value Chain Coordination with Artificial Life Agents1

Simulating Value Chain Coordination with Artificial Life Agents1 Torsten Eymann, Boris Padovan, Detlef Schoder Albert-Ludwigs-Universität Freiburg Institute for Computer Science and Social Studies, Telematics Department Friedrichstrasse 50, D-79098 Freiburg im Breisgau, Germany {eymann|padovan|schoder}@iig.uni-freiburg.de

Abstract The increasing use of information technology within and between companies yields changes in the predominant coordination mechanisms. On one hand it is argued that we witness an overall shift towards market-like coordination of business transactions, whereas others argue that we face more hierarchy-like coordination. In this paper a multi-agent system is described that globally coordinates a multiple step value chain either way by using local optimization rules of self-interested autonomous agents. In further research it will be analyzed how changes in setup parameters (e.g. transaction costs, cooperational behavior) will effect the predominant coordination mechanism among the artificial life agents.

1. Organizational Changes through IT The increasing use of information technology (IT) within and between companies changes their organizational structures as well as their use of coordination mechanisms. The move-to-markethypothesis argues that we will witness an overall shift towards market-like coordination due to lowered transaction costs which relatively favor market over

hierarchy. On the other hand the move-to-hierarchyhypothesis shows empirical evidence that especially through the deployment of IT between firms linkages become more and more intense which yields to an overall shift to hierarchy-like coordination [4][6]. Our explanation starts with a continuum between market-like and hierarchical coordination (Fig. 1) where one can find different mixed forms which apply attributes from both, thus establishing different organizational structures over time in dependence on vertical integration or disintegration of economic activities. The research aim of the simulation described in this article is to investigate if, and to which extent, parameters like transaction costs, transparency of supply and demand, connectivity of market places, co-operational behavior and reputation of the participants influence the mechanism of coordination in either direction. With the use of Artificial Life-like agents, a primitive economic process is simulated - a value chain. Changing organizational patterns which appear when the agents trade with each other are visualized and taken as indication for the direction of the change. This is performed by measuring the change of co-operational trade relations, where frequent switches are interpreted as market-like coordination and infrequent switches as hierarchy-like coordination.

2. Implementing an Artificial Economy

Mark e t-l ik e Coordination H ierarch y-l ik e Coordination

H ie rarch y

Typically, a value chain consists of several interconnected organizational units which use raw materials as input and by adding labor and knowledge enhance the value of their manufactured product. This output is then taken as input by the next organizational unit in line until the product reaches the consumer (see Fig. 2). The organizational structure and the coordination mechanisms applied by the different professions is not

Figure 1: Coordination Mechanisms between Markets and Hierarchies

1 in: Demazeau, Yves (Ed.): Proceedings of the 3rd Intl. Conference

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on Multi-Agent Systems (ICMAS'98), IEEE Computer Society Press: Los Alamitos, CA, 1998, ISBN 0-8186-8500-X. p. 423424.

addressed in this picture, as the final product is always Lum be rjack

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the dynamic and non-linear progression of a simulation run also agents can emerge and rule whose attributes are optimal in the environment given, even if the initial population did not exploit that evolutionary niche.

3. Preliminary Results and Outlook Figure 2: An example of a typical value chain from tree to desk the same, whether all workers along the chain are independent firms or part of one large company. Our clipping of a real economy begins with the raw value chain which only consists of goods and mechanismapplying workers, simulated by agents - currently no management, banks or legal system is applied. Seizing an idea of Jennings et al. to „view the business process as a collection of autonomous, problems solving agents which interact when they have interdependencies“ [3], the approach described here uses autonomous adaptive self-interested software agents [7], driven by local interaction rules, to coordinate the model value chain. Running the simulation, the agents will organize themselves in spatially dispersed trade networks while moving through the experimental environment. The development of higher-order organizational structures (such as markets and firms) is, following Artificial Life concepts, now just a matter of time [2][8]. Technically, our project Avalanche [1] uses publicly available Java "Mobile Agent" libraries [5] and adds just a few methods considered to be economically necessary like the concept of a stock capital and automated negotiation protocols (contract nets). Trading places (locations) are distributed over a TCP/IP network and the mobile agents move autonomously from one location to another in concurrent and parallel search for bargains. All agents use a heuristic negotiation strategy that depends on stochastic variables, whose values are determined at the time of the instantiation of the single agent. A key variable of any agent is the capital, which is not only a means of computing and storing "money" units when buying or selling, but also an indicator of the relative success of the single agent. An agent which is faster and/or fitter in trading as another will develop a relatively higher capital account over time.The success of the population as a whole can then be measured in economic (Pareto efficiency, social welfare) or technical terms (computational efficiency) [7]. To identify those parameter values which lead to an agent's success or demise, Genetic Algorithms are used to rule out agents with inferior parameter settings and to promote superior agents in order to achieve a nearoptimal productivity of the overall system [2]. Because of

Preliminary results of running the software prototype show that the agents disperse over the network while cooperating and maintaining the business process coordination. The next step will be to generate different organizational structures by changing the parameters, and to interpret and control them in accordance with empirical data obtained from case studies of small and medium enterprises. Shortcomings of this approach are that the interpretation of the simulation results is affected by encapsulated aspects of the computational implementation (because of the use of standardized mobile agent libraries and negotiation protocols). The technical openness of the application and the reduced complexity of the single agents counterbalances these shortcomings in the view of the authors, while still allowing useful research statements to be made.

4. References [1] Eymann, T., Schoder, D., Padovan, B. (1998). The Living Value Chain - Coordinating Business Processes with Artificial Life Agents. In Proceedings of PAAM'98, London, UK, 1998, p. 111-122. [2] Holland, J.H., Miller, J. Artificial Adaptive Agents in Economic Theory. American Economic Review, Vol. 81, No. 2, 1991, p. 365-370. [3] Jennings, N.R., Faratin P., Johnson, M.J., O’Brien, P., Wiegand, M.E. Using Intelligent Agents to Manage Business Processes. In Proceedings of PAAM'96, London , UK, 1996, p. 345-360. [4] Klein, S. Interorganisationssysteme und Unternehmensnetzwerke. DUV Verlag, Wiesbaden, 1996. [5] Lange, D.B. Mobile Agents: Environments, Technologies and Applications, in Proceedings of PAAM'98, London, UK, 1998, p. 11-14. [6] Malone, T.W., Yates, J., Benjamin, R.I.: Electronic Markets and Electronic Hierarchies, in Communications of the ACM, Vol. 30, June 1987, p. 484-497. [7] Sandholm, T.W. Negotiation among self-interested computationally limited agents. Dissertation, Amherst MA, 1996. [8] Tesfatsion, L. How Economists can get alife. in: Arthur, W.B., Durlauf, S., Lane, D. (eds.): The Economy as a Complex Evolving System, II. Sciences of Complexity, Proceedings Vol. XXVII, Santa Fe Institute Studies, Addison-Wesley, Redwood City CA, 1997.