Pranav Dandekar is a PhD candidate in Management Science & Engineering at Stanford. He is broadly interested in algorithmic and economic aspects of online networks and markets. He worked at Amazon in Seattle for 3.5 years before heading back to school to pursue a PhD. He received his B.Eng from SGSITS, Indore, India in 2002 and his MS from the University of Florida, Gainesville in 2004, both in Computer Science.

AG 1, AG 2, AG 3, AG 4, AG 5, SWS, RG1, MMCI   Info

Expert Audience

English

60 Minutes

Saarbrücken

Credit networks represent a way of modeling trust between entities in a network. Nodes in the network print their own currency and trust each other for a certain amount of each other's currency. This allows the network to serve as a decentralized payment infrastructure---arbitrary payments can be routed through the network by passing IOUs along a chain of trusting nodes in their respective currencies---and obviates the need for a common currency. Thus, credit networks are a decentralized approach based on trust to enable interactions between untrusting individuals in internet based networks and markets.

We will first analyze the liquidity, i.e. the ability to route transactions, in credit networks in terms of the long term failure probability of transactions for various network topologies and credit values. We will show that under symmetric transaction rates, the transaction failure probability in a number of credit network topologies is comparable to that in equivalent centralized currency systems; thus we do not lose much liquidity in return for their robustness and decentralized properties. This is based on joint work with Ashish Goel, Ramesh Govindan and Ian Post which appeared in EC'11.

Next we will analyze the formation of credit networks when agents strategically decide how much credit to extend each other under different models of risk. When each agent trusts a fixed set of other agents, and transacts directly only with those it trusts, the formation game is a potential game and all Nash equilibria are social optima. Moreover, the Nash equilibria of this game are equivalent in a very strong sense: the sequences of transactions that can be supported from each equilibrium credit network are identical. However, when we allow transactions over longer paths, the game may not admit a Nash equilibrium, and the price of anarchy is unbounded. When agents have a shared belief about the trustworthiness of each agent, the networks formed in equilibrium have a star-like structure. Though the price of anarchy is unbounded, myopic best response quickly converges to a social optimum. This is based on joint work with Ashish Goel, Michael Wellman and Bryce Wiedenbeck to be presented at WWW'12.

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