Computational Game Theory for Markets & Strategy with Python

Game theory has become indispensable for understanding how real markets function, how firms set prices, and how strategic actors negotiate outcomes. This book provides a comprehensive and highly applied introduction to computational game theory for economics, business strategy, and policy analysis, with every concept grounded in executable Python code.
Starting from fundamental models of competition, pricing, and strategic interaction, the book progresses through auctions, bargaining, market design, matching, mechanism design, and multi-agent simulations. Readers learn how to move beyond static analytical solutions and build full computational models that handle complexity, asymmetry, and non-cooperative behavior at scale.
Topics include Nash equilibrium, mixed strategies, dynamic games, auction formats (first-price, second-price, all-pay, English, and Vickrey), optimal bidding, price competition (Bertrand, Cournot, Hotelling), matching markets, bargaining games, social choice, and incentive-compatible mechanism design. Practical chapters integrate microeconomics, algorithmic strategy, and computational tools to model real-world applications in financial markets, supply chains, auctions, platform marketplaces, and strategic corporate competition.
Each chapter includes mathematical foundations, conceptual interpretation, and complete Python implementations, enabling readers to translate theory into simulation, optimization, and empirical experimentation.
This book is written for economists, data scientists, quantitative analysts, and strategy professionals who want to understand and simulate strategic behavior using code. Graduate students, researchers, and policy analysts will also find it useful as a hands-on complement to theoretical coursework in game theory, mechanism design, and industrial organization.
By the end of the book, readers will have a full computational toolkit for analyzing markets, strategies, and incentives, and for designing and testing mechanisms in code rather than relying on abstract models alone.

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