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• W1914322500 abstract "In this thesis I offer new results on how we can acquire, reward, and use accurate predictions of future events. Some of these results are entirely theoretical, improving our understanding of strictly proper scoring rules (Chapter 3), and expanding strict properness to include cost functions (Chapter 4). Others are more practical, like developing a practical cost function for the [0, 1] interval (Chapter 5), exploring how to design simple and informative prediction markets (Chapter 6), and using predictions to make decisions (Chapter 7). Strict properness is the essential property of interest when acquiring and rewarding predictions. It ensures more accurate predictions are assigned higher scores than less accurate ones, and incentivizes self-interested experts to be as accurate as possible. It is a property of associations between predictions and the scoring functions used to score them, and Chapters 3 and 4 are developed using convex analysis and a focus on these associations; the relevant mathematical background appears in Chapter 2, which offers a relevant synthesis of measure theory, functional analysis, and convex analysis. Chapters 5–7 discuss prediction markets that are more than strictly proper. Chapter 5 develops a market for the [0, 1] interval that provides a natural interface, is computable, and has bounded worst-case loss. Chapter 6 offers a framework to understand how we can design markets that are as simple as possible while still providing" @default.
• W1914322500 created "2016-06-24" @default.
• W1914322500 creator A5049270519 @default.
• W1914322500 date "2013-10-18" @default.
• W1914322500 modified "2023-09-26" @default.
• W1914322500 title "Prediction Markets: Theory and Applications" @default.
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