Trading agents competing: performance, progress, and market effectiveness

Wellman, Michael P.; Cheng, Shih-Fen; Reeves, Daniel M.; Lochner, Kevin M.

doi:10.1109/mis.2003.1249169

Cited by 17 publications

(8 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Economic advantage is clearly a significant incentive to user adoption, but not necessarily the only one. Michael Wellman's research in the artificial intelligence field tends to be highly interdisciplinary, building on ideas from computer science, linguistics, psychology, economics, biology, controls, and philosophy (Wellman et al, 2003(Wellman et al, , 2004(Wellman et al, , 2008Cheng et al, 2005;Schvartzman and Wellman, 2007). Other studies have examined other aspects, such as the importance and maintenance of trust (Ba et al, 2003;Riegelsberger et al, 2005) or the factors influencing the final bid price (McDonald and Slawson, 2002;Kamins et al, 2004).…”

Section: Classification Of Online Auctionmentioning

confidence: 99%

Task-technology fit and user acceptance of online auction

Chang

2010

International Journal of Human-Computer Studies

View full text Add to dashboard Cite

Section: Classification Of Online Auctionmentioning

confidence: 99%

Task-technology fit and user acceptance of online auction

Chang

2010

International Journal of Human-Computer Studies

View full text Add to dashboard Cite

“…Trading competitions like cat have been an effective tool in fostering innovative approaches and advocating enthusiasm and exchange among researchers [41,46]. However, the competitions themselves usually cannot provide a complete view of the relative strength and weakness of entries.…”

Section: Strategy Evaluation In Competitive Gamesmentioning

confidence: 99%

What the 2007 TAC Market Design Game tells us about effective auction mechanisms

Niu

Cai

Parsons

et al. 2009

Auton Agent Multi-Agent Syst

View full text Add to dashboard Cite

This paper analyzes the entrants to the 2007 tac Market Design Game. We present a classification of the entries to the competition, and use this classification to compare these entries. The paper also attempts to relate market dynamics to the auction rules adopted by these entries and their adaptive strategies via a set of post-tournament experiments. Based on this analysis, the paper speculates about the design of effective auction mechanisms, both in the setting of this competition and in the more general case.

show abstract

“…We have begun to analyze this issue [20], by comparing actual TAC market allocations with ideal global allocations. Consider the total group of 64 clients, and the set of available resources: 16 hotel rooms of each type per day, plus 8 entertainment tickets of each type per day.…”

Section: Agentmentioning

confidence: 99%

Walverine

Cheng

Leung

Lochner

et al. 2003

Proceedings of the Second International Joint Conference on Autonomous Agents and Multiagent Systems

View full text Add to dashboard Cite

TAC-02 was the third in a series of Trading Agent Competition events fostering research in automating trading strategies by showcasing alternate approaches in an open-invitation market game. TAC presents a challenging travel-shopping scenario where agents must satisfy client preferences for complementary and substitutable goods by interacting through a variety of market types. Michigan's entry, Walverine, attempts to bid optimally based on a competitive analysis of the TAC travel economy. Walverine's approach embodies several techniques not previously employed in TAC:(1) price prediction based on competitive equilibrium analysis, (2) hedged optimization with respect to a model of outlier prices, (3) optimal bidding based on a decision-theoretic calculation of bid actions, and (4) reinforcement learning for CDA trading strategies. Each of these is potentially applicable in a broad class of trading environments.

show abstract

Trading agents competing: performance, progress, and market effectiveness

Abstract: How good are TAC-02 agents? How effective is the TAC travel market?

Cited by 17 publications

References 12 publications

Task-technology fit and user acceptance of online auction

Task-technology fit and user acceptance of online auction

What the 2007 TAC Market Design Game tells us about effective auction mechanisms

Walverine

Contact Info

Product

Resources

About