Acknowledgments

A book of this scope is not written in isolation. It is the product of countless conversations, debates, code reviews, and shared insights from a community that spans academia, industry, and the open-source world. We owe a deep debt of gratitude to many people and communities whose work made this book possible.

The Prediction Markets Research Community

Our understanding of prediction markets rests on decades of rigorous academic research. We are grateful to the scholars who have shaped the field of information aggregation and market microstructure — those who first demonstrated that markets can serve as powerful mechanisms for eliciting and combining dispersed information, those who developed the mathematical foundations of scoring rules and automated market makers, and those who continue to push the boundaries of mechanism design and incentive-compatible elicitation. The papers, lectures, and textbooks produced by researchers in economics, computer science, statistics, and decision science form the intellectual bedrock on which this book is built. We have tried to cite their work faithfully throughout, and any omissions are entirely our own.

Platform Developers and Engineers

The modern prediction markets ecosystem exists because talented engineers and entrepreneurs turned theoretical ideas into working platforms. We thank the teams behind Polymarket, Kalshi, Metaculus, Manifold Markets, and the many other platforms — past and present — whose work has made prediction markets accessible to a global audience. Their open APIs, public documentation, and willingness to engage with the broader community have been invaluable. Several platform developers generously answered our technical questions about settlement mechanisms, order matching, and market maker design, and their insights have improved the accuracy of our engineering chapters considerably.

The Metaculus Forecasting Community

The forecasters on Metaculus deserve special recognition. Their dedication to calibrated, thoughtful forecasting — often on questions of genuine societal importance — has produced one of the richest public datasets on human judgment under uncertainty. The community's culture of careful reasoning, transparent track records, and constructive feedback has been a source of both data and inspiration for this book. Many of the calibration and modeling examples in Parts III and IV draw on patterns and lessons first observed in the Metaculus forecasting ecosystem.

The Manifold Markets Community

We are grateful to the Manifold Markets community for demonstrating how play-money and hybrid prediction markets can serve as laboratories for innovation. The community's willingness to experiment with novel market structures, resolution criteria, and user experience designs has generated a wealth of practical knowledge about what works and what does not in market design. The open-source ethos of Manifold Markets has also been a gift to the field, making it possible for researchers, students, and builders to study real market implementations in detail.

Open-Source Contributors

This book is built on open-source software. We thank the maintainers and contributors of Python, NumPy, pandas, SciPy, scikit-learn, Matplotlib, Jupyter, and the dozens of other libraries and tools that appear in our code examples. We are equally grateful to the developers who have created and maintained open-source prediction market tools, data scrapers, API wrappers, and analysis frameworks. Their work has lowered the barrier to entry for everyone interested in this field, and it has made our code examples more practical and more honest — we use the same tools that practitioners use every day.

Reviewers and Early Readers

We thank the technical reviewers who read drafts of this manuscript at various stages and offered detailed feedback. Their expertise spans quantitative finance, machine learning, mechanism design, and software engineering, and the book is substantially better for their contributions. They caught errors, challenged assumptions, suggested better examples, and pushed us to be clearer in our explanations. We are also grateful to the early readers — students, practitioners, and self-taught forecasters — who tested the exercises, flagged confusing passages, and confirmed that the difficulty progression works as intended. Their perspective kept us grounded in the experience of the learner.

The Broader Forecasting and Decision Science Community

Prediction markets do not exist in a vacuum. They are part of a larger ecosystem of tools and methods for reasoning about uncertainty, including expert elicitation, survey aggregation, Bayesian modeling, and superforecasting. We thank the researchers and practitioners in these adjacent fields whose work informs and enriches the prediction markets literature. The cross-pollination between these communities — visible at conferences, in journal pages, and in online forums — has made prediction markets stronger, and it has made this book more complete.

Our Families and Colleagues

Finally, we thank our families and colleagues for their patience and support during the long process of writing this book. A project of this size inevitably consumes evenings, weekends, and holidays, and it could not have been completed without their understanding and encouragement. We are grateful for their willingness to listen to impromptu explanations of logarithmic scoring rules over dinner and their good humor when every conversation seemed to circle back to market calibration.

To everyone named here, and to the many others whose contributions we have failed to enumerate: thank you. The errors that remain are ours alone.