Digital Library of the European Council for Modelling and Simulation

Title:	MACSIMA: An Agent Framework For Simulating The Evolution Of Negotiation Strategies In B2B-Networked Economies
Authors:	Christian Russ, Alexander Walz
Published in:	(2009).ECMS 2009 Proceedings edited by J. Otamendi, A. Bargiela, J. L. Montes, L. M. Doncel Pedrera. European Council for Modeling and Simulation. doi:10.7148/2009 ISBN: 978-0-9553018-8-9 23^rd European Conference on Modelling and Simulation, Madrid, June 9-12, 2009
Citation format:	Russ, C., & Walz, A. (2009). MACSIMA: An Agent Framework For Simulating The Evolution Of Negotiation Strategies In B2B-Networked Economies. ECMS 2009 Proceedings edited by J. Otamendi, A. Bargiela, J. L. Montes, L. M. Doncel Pedrera (pp. 173-179). European Council for Modeling and Simulation. doi:10.7148/2009-0173-0179
DOI:	http://dx.doi.org/10.7148/2009-0173-0179
Abstract:	In this paper, we describe the multiagent supply chain simulation framework MACSIMA. This framework allows the design of large-scale supply network topologies consisting of a multitude of autonomous agents representing the companies in the supply network and acting on their behalf. MACSIMA provides all agents with negotiation and learning capabilities so that the co-evolution and adaptation of the price negotiation strategies of the business agents that exchange goods over an electronic business-to-business (B2B) market can be simulated and evaluated. An electronic B2B market is a market run by computers (and often mounted on a website) providing facilities for companies acting as suppliers to post offered materials, products, services together with price ideas on the market and matching these offers with the demand of other companies acting as producers or retailers. Thereby MACSIMA supports a fine-tuning of the parameterization of the learning mechanism of each individual business agent and additionally enables the agents to exchange information about finished negotiations with other cooperating agents. We outline evaluation results with a first focus on the emergence of niche strategies within a group of cooperating agents. After that we center a second focus on the coordination efficiency, i.e. on the effects of the application of different learning mechanism parameterizations on the overall turnover and profit of supply networks. Our simulation results show that depending on the parameter setting of the learning mechanism the outcome (e.g. the overall turnover) of such a supply network can vary significantly.
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