Algorithmic Game Theory and Practice

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source: Simons Institute     2015年11月20日
Algorithmic Game Theory and Practice
Nov. 16 – Nov. 20, 2015
Algorithmic Game Theory (AGT) has made important theoretical contributions benefiting both Economics and Computer Science. It has also had significant practical impact, in a broad range of applications including online, matching and assignment markets, Internet advertising, information diffusion, airport security, etc. This workshop will showcase the impact of AGT on practice, and explore avenues for increasing the field's practical impact, including connections to machine learning, data science, and financial markets.
For more information, please visit https://simons.berkeley.edu/workshops/economics2015-2.
These presentations were supported in part by an award from the Simons Foundation.

Kidney Exchange: Algorithms and Incentives 58:55 Alvin Roth, Stanford University https://simons.berkeley.edu/talks/alv...
Designing Online Advertising Markets 48:53
Outsourcing Tasks Online: Matching Supply and Demand on Peer-to-Peer Internet 34:41
Business Model Analysis 39:08
The Limits of Reputation in Platform Markets 49:41
Adverse Selection and Auction Design for Internet Display Advertising 43:37
Machine Learning in an Exchange Environment 49:38
Online Advertisements and Online Algorithms 44:16
On How Machine Learning and Auction Theory Power Facebook Advertising 54:21
On-Line Systems with Long-Range Goals 54:52
Decision Making at Scale: Platforms and Problems 43:03
Strong Truthfulness in Multi-signal Peer Prediction with Overlapping Tasks 49:44
The State of Techniques for Solving Large Imperfect-Information Games 44:07
Security Games: Key Algorithmic Principles, Deployed Applications and Research Challenges(*) 46:18
Robust Inference for Games via Theoretical Guarantees 47:42
An Evolutionary Model of Economic Behavior, Bounded Rationality, and Intelligence 52:19
The Design of Financial Exchanges: Some Open Questions at the Intersection of Econ and CS 43:43
Algorithmic Trading and Machine Learning 54:50
Incentive Auctions and Spectrum Repacking 50:55
Informational Substitutes and Complements for Prediction 45:30
AGT and Machine Learning 44:48
Bitcoin's Incentive Structure 49:46
Dynamic Pricing in Ride-Sharing Platforms 40:31

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