Agent-Mediated Electronic Commerce V. Designing Mechanisms by D. J. A. Somefun, E. H. Gerding, S. Bohte, J. A. La Poutré

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By D. J. A. Somefun, E. H. Gerding, S. Bohte, J. A. La Poutré (auth.), Peyman Faratin, David C. Parkes, Juan A. Rodríguez-Aguilar, William E. Walsh (eds.)

This booklet constitutes the completely refereed post-proceedings of the fifth overseas Workshop on Agent-Mediated digital trade, AMEC 2003, held in Melbourne, Australia in July 2003 as a part of AAMAS 2003.

The nine revised complete papers provided have been rigorously chosen from 22 submissions in the course of rounds of reviewing and revision. The papers are prepared in topical sections on computerized negotiation, structures and mechanism layout, and multi-agent markets.

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Extra resources for Agent-Mediated Electronic Commerce V. Designing Mechanisms and Systems: AAMAS 2003 Workshop, AMEC 2003, Melbourne, Australia, July 15, 2003, Revised Selected Papers

Example text

The pseudo-code of the price control method is given in Table 3. The currentPosition, which can be a positive or negative integer, records the current holdings of the agent. 0001; currentPosition ← getAgentCurrentPosition() if currentPosition < 0 buyReferencePrice ← buyReferencePrice - currentPosition * priceEncouragement elseif currentPosition > 0 sellReferencePrice ← sellReferencePrice - currentPosition * priceEncouragement placeOrder(BUY, buyReferencePrice, volume); placeOrder(SELL, sellReferencePrice, volume); Table 3.

The start price is not crucial to the analysis since if it’s not p − δ, we can ignore the first several ticks until we have the price p − δ. Tables 7 and 8 show the calculations of the changes in holdings, cash and value for the basic and reverse strategy in one cycle. Each time we place a sell order or buy order, the volume size is v. time t0 t1 t2 t3 t4 price p−δ p p+δ p p−δ action — buy buy sell sell holding 0 v 2v v 0 cash 0 −vp −2vp − vδ −vp − vδ −2vδ value 0 0 vδ −vδ −2vδ Table 7. The Basic Strategy over One Cycle.

Cloete. ” In Proc. Artificial Intelligence and Applications, pp. 121-125, 2001. 9. J. Kingdon. Intelligent Systems and Financial Forecasting. New York, NY: Springer, 1997. 10. R. Donaldson and M. Kamstra, “Forcast Combining With Neural Networks”, Journal of Forecasting, vol. 15, No. 49-61. 1996. 11. M. Sheimo. , 1991. 12. P. Stone and A. Greenwald, “The First International Trading Agent Competition: Autonomous Bidding Agents”. Electronic Commerce Research 5(1), 2005 (To appear). 13. html Acquiring Tradeoff Preferences for Automated Negotiations: A Case Study Xudong Luo, Nicholas R.

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