- Introduction: The Game You're Already Playing
- 1. Players, Strategies, and Payoffs
- 2. Nash Equilibrium and Why Your GAN Won't Converge
- 3. Shapley Values and the Game Theory Inside SHAP
- 4. Mechanism Design and Incentive-Compatible Systems
- 5. Auctions, Ads, and Differentiable Economics
- 6. Strategic Classification and Adversarial Users
- 7. Multi-Agent Reinforcement Learning
- 8. Federated Learning and Coalition Games
- 9. LLM Alignment Through a Game-Theoretic Lens
- 10. Designing for Strategic Environments
- Conclusion: Seeing the Board
- Resources
- References
Game Theory for ML Engineers
How Strategic Thinking Makes You a Better Builder of Intelligent Systems.
The game theory already inside your ML toolkit, explained for working engineers. Covers GANs, SHAP, auctions, strategic classification, MARL, federated learning, and LLM alignment.
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About
About the Book
Every ML system you deploy operates in a world of strategic agents. Users game your classifiers. Annotators shape your reward models. Federated learning participants free-ride on others' computation. And the tools you already use daily — SHAP, GANs, RLHF — are built on game-theoretic foundations you may not recognize.
This book bridges the gap between game theory and ML engineering. Across 10 chapters, you'll learn to diagnose GAN training failures through Nash equilibrium theory, understand why SHAP values are Shapley values from cooperative game theory, design incentive-compatible mechanisms for data markets and federated learning, build classifiers that survive contact with strategic users, and see LLM alignment as the multi-player game it is.
Every chapter includes a concrete diagnostic, framework, or checklist you can apply on Monday morning.
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Author
About the Author
Kaushik Rajan
Kaushik Rajan is an applied scientist and engineer with nearly a decade of experience in the tech industry. He spent four years at Amazon (US) building production-grade ML models that directly impacted billions of dollars in revenue, and has shipped production systems across deep learning, computer vision, time-series forecasting, and econometric modeling in multiple industry verticals. His peer-reviewed research spans multi-agent AI, behavioral game theory, and cooperative systems, published in the Theoretical Computer Science and Games & Political Behavior eJournals. He has written 74+ technical articles (26K+ reads) and holds a Master's in Computer Science from NC State University. He currently runs Origin Labs, a solo startup that builds AI-powered consumer applications through agent-orchestrated workflows. He writes from a practitioner's perspective: every framework in his books comes from building real systems.
Contents
Table of Contents
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