Preface

Prediction markets are changing the way the world thinks about the future. For decades, forecasting was the province of expert panels, statistical bureaus, and proprietary models locked inside institutional walls. Today, a quiet revolution is underway. Platforms like Polymarket, Kalshi, and Metaculus have opened the doors to anyone willing to stake money, reputation, or simply attention on the outcome of real-world events — from election results and central bank decisions to pandemic trajectories and technological breakthroughs. The prices that emerge from these markets carry a remarkable property: they are, on average, among the most accurate and well-calibrated probability estimates available anywhere.

We wrote this book because we believe prediction markets deserve a thorough, unified treatment that bridges the many disciplines they draw upon. The existing literature is fragmented. Excellent papers on mechanism design live in computer science journals. Foundational work on information aggregation sits in economics departments. Practical trading wisdom circulates in blog posts and Discord channels. Calibration research appears in psychology and decision science. Platform engineering knowledge is scattered across codebases and technical talks. No single resource ties it all together and presents it in a form that a motivated learner — someone with basic programming skills and a willingness to engage with mathematics — can follow from start to finish. This book is our attempt to fill that gap.

Who This Book Is For

We have written for a broad audience united by curiosity about how markets can quantify uncertainty. If you are a data scientist looking for new applications of probabilistic modeling, you will find prediction markets to be a rich and rewarding domain. If you are a trader exploring a nascent asset class with distinctive microstructure, you will find strategies grounded in both theory and empirical evidence. If you are a software engineer interested in building platforms that aggregate collective intelligence, you will find detailed architectural guidance and working code. If you are a researcher in economics, political science, or decision science, you will find a self-contained reference that connects theory to the operational reality of modern markets. And if you are simply a curious learner who wants to understand why a contract trading at 73 cents might be telling you something important about tomorrow, this book is for you too.

We assume you can write basic Python — variables, functions, loops, and classes — and that you have encountered introductory statistics and probability. We do not assume backgrounds in finance, game theory, or mechanism design. When we need results from those fields, we develop them from first principles.

How This Book Is Organized

The book is divided into seven parts that follow a deliberate progression.

Part I: Foundations (Chapters 1--6) lays the groundwork. We define prediction markets, trace their history from eighteenth-century election betting to modern digital platforms, and build the probabilistic and statistical toolkit you will use throughout the rest of the book. By the end of Part I, you will understand what prediction markets are, why they work, and how to reason quantitatively about uncertain outcomes.

Part II: Market Mechanics and Microstructure (Chapters 7--12) dives into how prediction markets actually operate. We cover order books, automated market makers, the mechanics of binary and multi-outcome contracts, liquidity, and the cost-function approach pioneered by Hanson. You will build a working market maker from scratch in Python.

Part III: Modeling and Forecasting (Chapters 13--18) is where data science meets prediction markets. We develop models for estimating event probabilities from historical data, news, polls, and other signals. We cover calibration, ensembles, and the art of translating model output into actionable trading signals.

Part IV: Trading Strategies and Portfolio Management (Chapters 19--24) turns forecasts into positions. We cover Kelly criterion sizing, portfolio construction across correlated contracts, arbitrage detection, execution tactics, and rigorous backtesting. We treat risk management not as an afterthought but as a discipline woven into every decision.

Part V: Platform Design and Engineering (Chapters 25--30) addresses the supply side: how to design, build, and operate a prediction market platform. We cover smart contract architectures, matching engines, settlement and oracle design, regulatory compliance frameworks, and the user experience considerations unique to prediction markets.

Part VI: Advanced Topics (Chapters 31--36) explores the frontier. Combinatorial markets, conditional prediction markets, decision markets, mechanism design for incentive compatibility, manipulation resistance, and the integration of prediction markets with machine learning pipelines are all examined in depth.

Part VII: The Broader Landscape (Chapters 37--42) places prediction markets in their wider context. We examine regulation across jurisdictions, the ethics of betting on sensitive events, the use of prediction markets inside organizations for decision support, comparisons with traditional forecasting methods, and the future trajectory of the field. The final chapter is a capstone project that synthesizes the skills developed throughout the book.

A Field in Motion

We want to be transparent about a challenge inherent in writing this book: the field is moving fast. New platforms launch regularly. Regulatory frameworks are evolving in the United States, the European Union, and beyond. Academic research continues to refine our understanding of when and why prediction markets succeed or fail. Trading volumes on platforms like Polymarket surged during recent election cycles, drawing mainstream attention and raising new questions about market design, manipulation, and the boundary between prediction and gambling.

We have done our best to present principles and frameworks that will remain useful even as specific platforms and regulations change. Where we discuss particular platforms or regulatory regimes, we have noted the date of the information and encouraged you to verify the current state of affairs. The conceptual foundations — probability theory, information economics, market microstructure, Bayesian inference — are durable. The applications will continue to evolve, and we hope this book gives you the tools to evolve with them.

How to Read This Book

The book is designed to be read sequentially, but we recognize that different readers have different goals. We have provided a detailed guide in the "How to Use This Book" section that follows, including recommended learning paths tailored to specific backgrounds and objectives. Each chapter includes exercises at multiple difficulty levels, quizzes for self-assessment, and case studies drawn from real prediction market events.

We encourage you to write code as you read. The examples are not decoration — they are an integral part of the exposition. Prediction markets are a domain where theory and practice are deeply intertwined, and the act of implementing an idea will deepen your understanding in ways that reading alone cannot.

Welcome to the world of prediction markets. We are glad you are here.