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Quantifying Agent Strategies Under Reputation

Marti, Sergio and Garcia-Molina, Hector (2004) Quantifying Agent Strategies Under Reputation. Technical Report. Stanford.




Our research proposes a simple game model that captures the incentives dictating the interaction between buyers and sellers and reveals the strategies that evolve in different scenarios, such as eBay auctions. In particular, we find seller history has a significant effect on player strategy. We prove that for simple reputation-based buyer strategies, a seller's decision whether to cheat or not is dependent only on the length of history, not on the particular actions committed. Given a finite number of transactions, a seller can compute a utility optimal sequence of cooperations and defections. As more advanced buyer/seller strategies evolve, equilibrium is reached when players predominantly cooperate.

Item Type:Techreport (Technical Report)
Uncontrolled Keywords:reputation, game theory, e-commerce
Subjects:Computer Science > E-Commerce
Related URLs:Project Homepage
ID Code:666
Deposited By:Import Account
Deposited On:06 Dec 2004 16:00
Last Modified:23 Dec 2008 09:37

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