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 publication constitutes the completely refereed post-proceedings of the fifth foreign 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 offered have been rigorously chosen from 22 submissions in the course of rounds of reviewing and revision. The papers are equipped in topical sections on computerized negotiation, structures and mechanism layout, and multi-agent markets.
Read or Download Agent-Mediated Electronic Commerce V. Designing Mechanisms and Systems: AAMAS 2003 Workshop, AMEC 2003, Melbourne, Australia, July 15, 2003, Revised Selected Papers PDF
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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
Table 9 shows the calculation of the reverse strategy. In the simulation, δ is always positive, while a can be positive or negative. Since 28 Y. Feng, R. Yu, and P. Stone the basic strategy does the exact opposite of the reverse strategy, we omit the calculation. δ t2 t1 t0 δ t2 t3 t3 δ t1 a δ t4 t4 t0 (a) (b) Fig. 5. Diﬀerent price trend models: (a) shows the initial model in which the price oscillates consistently in a wave pattern around a constant price. (b) extends the model to allow for the price to oscillate around a line with non-zero slope.
Experiments on Variations of Market Making Strategy 34 Y. Feng, R. Yu, and P. 05 23 0 500 1000 1500 2000 2500 ÙÖ ¿ ÅË Ì Ë Ö ÈÖ Fig. 8. Market Making Strategy in PLAT Live-data Competition Round 2, Mar. 06, 2003 From Table 15, we can see that normally all the variations of market making strategies behave similarly – either all ending in red or all ending in black. ½ and 28th do exist. Overall, no strategy However, exceptions such as Mar 27th dominates all the others. They oﬀer a good variety when we trade with market making strategies.
This is the volume control method we propose to alleviate the huge position problem. We introduce a positive ﬂoating point parameter, volumeAlteration, to tune the trade volume of both orders in a continuous way. Table 4 shows the pseudo-code for the volume control method. 0001; currentPosition ← getAgentCurrentPosition() buyVolume ← volume * (1 - currentPosition * volumeAlteration) sellVolume ← volume * (1 + currentPosition * volumeAlteration) placeOrder(BUY, buyReferencePrice, buyVolume); placeOrder(SELL, sellReferencePrice, sellVolume); Table 4.
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