elliot anshelevich department of computer science interests: design and analysis of algorithms,...
Post on 21-Dec-2015
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![Page 1: Elliot Anshelevich Department of Computer Science Interests: Design and analysis of algorithms, especially for large decentralized networks. Strategic](https://reader036.vdocuments.site/reader036/viewer/2022081519/56649d6c5503460f94a4c41d/html5/thumbnails/1.jpg)
Elliot AnshelevichDepartment of Computer Science
Interests:• Design and analysis of algorithms,
especially for large decentralized networks.
• Strategic agents in networks and algorithmic game theory.
• Approximation algorithms.
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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• . . .
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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
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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
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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?
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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
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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
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Agents in Network Design
peer 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
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Diffusion and Epidemiology
Graph : social network (or computer network) Nodes: people/computers Edges: relationships/links
Diffusive network process: disease, idea, computer virus, forest fire
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Diffusion and Epidemiology
Graph : social network (or computer network) Nodes: people/computers Edges: relationships/links
Diffusive network process: disease, idea, computer virus, forest fire
![Page 11: Elliot Anshelevich Department of Computer Science Interests: Design and analysis of algorithms, especially for large decentralized networks. Strategic](https://reader036.vdocuments.site/reader036/viewer/2022081519/56649d6c5503460f94a4c41d/html5/thumbnails/11.jpg)
Diffusion and Epidemiology
Graph : social network (or computer network) Nodes: people/computers Edges: relationships/links
Diffusive network process: disease, idea, computer virus, forest fire
![Page 12: Elliot Anshelevich Department of Computer Science Interests: Design and analysis of algorithms, especially for large decentralized networks. Strategic](https://reader036.vdocuments.site/reader036/viewer/2022081519/56649d6c5503460f94a4c41d/html5/thumbnails/12.jpg)
Diffusion and Epidemiology
Graph : social network (or computer network) Nodes: people/computers Edges: relationships/links
Diffusive network process: disease, idea, computer virus, forest fire
![Page 13: Elliot Anshelevich Department of Computer Science Interests: Design and analysis of algorithms, especially for large decentralized networks. Strategic](https://reader036.vdocuments.site/reader036/viewer/2022081519/56649d6c5503460f94a4c41d/html5/thumbnails/13.jpg)
Immunization
Stop the spread by immunizing/protecting nodes/edges Goal: immunize few, protect many from infection
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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?
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Thank you.
If want to learn more, take
Algorithmic Game Theory Spring 09