Elliot Anshelevich Department of Computer Science Interests: Design and analysis of algorithms, especially for large decentralized networks. Strategic agents in networks and algorithmic game theory. Approximation algorithms.

Networks in Theoretical CS A major focus of Theoretical Computer Science is the study of networks Networks arise in many contexts, with many different properties The Internet Networks of processors Distributed Databases Social networks Control-Flow Networks Biological networks...

Networks with Independent Agents Internet is not centrally controlled Transportation Networks Social Networks Peer-to-peer Networks Business relationships To understand these, cannot assume centralized control Algorithmic Game Theory studies such agents

Transportation Networks  Traffic patterns are not centrally controlled  Behavior can be very different from centrally controlled traffic  Braess’ Paradox: sometimes building new roads can increase congestion

Transportation Networks  Traffic patterns are not centrally controlled  “Price of anarchy” = quality lost because of agents being self-interested  What do equilibria look like? How to improve them?

Agents in Network Design What if network is built by many self-interested agents? Properties of resulting network may be very different from the globally optimum one Connection Game (e.g. construction of roads and bus stations) Autonomous Systems and Contracts

Agents in Network Design What if network is built by many self-interested agents? Properties of resulting network may be very different from the globally optimum one Connection Game –In general, converges to solution within log of optimal –In multicast (single-source) case, can form a good solution –True even for survivable networks

Agents in Network Design peer customer provider What if network is built by many self-interested agents? Properties of resulting network may be very different from the globally optimum one Connection Game Autonomous Systems and Contracts –Characterize stable systems of contracts –Can get the AS’s to agree on a solution within factor 2 of optimal

Diffusion and Epidemiology  Graph : social network (or computer network)  Nodes: people/computers  Edges: relationships/links  Diffusive network process: disease, idea, computer virus, forest fire

Diffusion and Epidemiology  Graph : social network (or computer network)  Nodes: people/computers  Edges: relationships/links  Diffusive network process: disease, idea, computer virus, forest fire

Diffusion and Epidemiology  Graph : social network (or computer network)  Nodes: people/computers  Edges: relationships/links  Diffusive network process: disease, idea, computer virus, forest fire

Diffusion and Epidemiology  Graph : social network (or computer network)  Nodes: people/computers  Edges: relationships/links  Diffusive network process: disease, idea, computer virus, forest fire

Immunization  Stop the spread by immunizing/protecting nodes/edges  Goal: immunize few, protect many from infection

Immunization  Stop the spread by immunizing/protecting nodes/edges  Goal: immunize few, protect many from infection  Somewhat know what to do if immunizing in advance  What if immunizing in real-time?

Thank you. If want to learn more, take Algorithmic Game Theory Spring 09