Algorithmic Game Theory and Practice 2015
2015-11-16..2015-11-20
Berkeley, United States
[Optimization and Decision-Making Under Uncertainty (Simons Institute, September 2016), Quantifying Uncertainty: Stochastic, Adversarial, and Beyond, Real-Time Decision Making 2018, Mathematics of Online Decision Making 2020, Complexity and Simplicity in Economics (Simons Institute workshop, 2015), Learning, Algorithm Design and Beyond Worst-Case Analysis]
| title | author | length |
|---|---|---|
| Kidney Exchange: Algorithms and Incentives | Alvin Roth | 59m |
Fifteen years of kidney exchange evolution: algorithmic models move from two-way matchings to non-simultaneous chains because logistics and high-sensitization… | ||
| Designing Online Advertising Markets | Susan Athey | 49m |
Argues that standard user-level A/B tests systematically mislead advertisers because each sees only a tiny fraction of traffic, so short-term revenue can be… | ||
| Outsourcing Tasks Online: Matching Supply and Demand on Peer-to-Peer Internet | Chiara Farronato | 35m |
Analysis of TaskRabbit 2010–2014 uses 336 city‑month markets and a search‑and‑matching model to show markets clear mainly through elastic seller effort rather… | ||
| Business Model Analysis | Kamal Jain | 39m |
Shows that commission vs cost‑per‑click shifts final prices: commissions raise buyer prices and push deals off platform, while CPC raises advertiser risk… | ||
| The Limits of Reputation in Platform Markets | Steve Tadelis | 50m |
Uses eBay internal data on 935,000 new buyers tracked ~3 years (15M purchases, 1.8M sellers) to show that traditional percent-positive scores are biased by… | ||
| Adverse Selection and Auction Design for Internet Display Advertising | Paul Milgrom | 44m |
Paper proposes a concrete auction: if highest/second-highest bid > alpha the highest performance bidder wins and pays alpha·(second bid), otherwise a… | ||
| Machine Learning in an Exchange Environment | R. Preston McAfee | 50m |
Claims that active exploration materially improves exchange revenue prove mostly false: the machine learners curse biases wins toward overpredicted ads, but… | ||
| Online Advertisements and Online Algorithms | Nikhil Devanur | 44m |
Shows that the balanced discount rule gives a 1-1/e competitive guarantee for online budgeted allocation via a primal‑dual LP view, then proves stochastic… | ||
| On How Machine Learning and Auction Theory Power Facebook Advertising | Eric Sodomka | 54m |
Facebook predicts probabilities for dozens of user events (clicks, likes, hides, watch time) with logistic regressions and boosted trees, tests new features… | ||
| On-Line Systems with Long-Range Goals | Jon Kleinberg | 55m |
Presents an empirical-plus-theory package: on Stack Overflow the civic duty badge (300 votes) and Electorate badge (600 question votes) show a measurable… | ||
| Decision Making at Scale: Platforms and Problems | Ashish Goel | 43m |
Ashish Goel, Stanford University
Algorithmic Game Theory and Practice
https://simons.berkeley.edu/talks/ashish-goel-2015-11-18 | ||
| Strong Truthfulness in Multi-signal Peer Prediction with Overlapping Tasks | David Parkes | 50m |
Paper designs score-matrix mechanisms for peer prediction with overlapping tasks and multi-valued signals, proves the output-agreement generalization is… | ||
| The State of Techniques for Solving Large Imperfect-Information Games | Tuomas Sandholm | 44m |
Claims concrete wins and limits: a lossless abstraction reduces Rhode Island Hold ’Em from 3 billion nodes to a solvable LP with 1.2 million rows and columns… | ||
| Security Games: Key Algorithmic Principles, Deployed Applications and Research Challenges(*) | Milind Tambe | 46m |
Overview of concrete claims, methods and results: deployable Stackelberg security-game systems ARMOR, PROTECT, PAWS use mixed-integer programs… | ||
| Robust Inference for Games via Theoretical Guarantees | Denis Nekipelov | 48m |
Defines an empirical price of anarchy that takes the worst-case welfare ratio over all value distributions consistent with the observed bid distribution, uses… | ||
| An Evolutionary Model of Economic Behavior, Bounded Rationality, and Intelligence | Andrew Lo | 52m |
A simple evolutionary model has agents that inherit a fixed mixing probability F for a binary choice and reproduce with random offspring XA or XB; with shared… | ||
| The Design of Financial Exchanges: Some Open Questions at the Intersection of Econ and CS | Eric Budish | 44m |
The continuous limit order book creates sniping rents from symmetric public signals, fueling a never‑ending speed race and reducing displayed liquidity… | ||
| Algorithmic Trading and Machine Learning | Michael Kearns | 55m |
Reinforcement learning over two state variables—inventory remaining and time remaining—learns executable policies that beat an optimally tuned… | ||
| Incentive Auctions and Spectrum Repacking | Kevin Leyton-Brown | 51m |
Reverse auction feasibility testing must answer whether removing one of ~3,000 TV stations still allows a global repacking under ~3 million interference… | ||
| Informational Substitutes and Complements for Prediction | Yiling Chen | 46m |
Formal definition uses Blackwell ordering to call signals substitutes when additional information has decreasing marginal value and complements when it has… | ||
| AGT and Machine Learning | Christos Papadimitriou | 45m |
Three concrete results: strategic classification models data as a Stackelberg game and proves that with a separable cost (or min of few separable functions) a… | ||
| Bitcoin's Incentive Structure | Aviv Zohar | 50m |
Explains why Bitcoin’s security depends on concrete assumptions and shows precise attacks and fixes: miners centralize into warehouses, block rate is… | ||
| Dynamic Pricing in Ride-Sharing Platforms | Ramesh Johari | 41m |
Model couples strategic passengers and drivers, a platform price multiplier p(A) that depends on number of available drivers, and exponential service times so… | ||
| nothing matches | ||