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Bibliography

MASTER CITATION INDEX

Sources cited across the Future Studies knowledge base, organized by topic. Epistemic markers follow the portfolio standard: [confirmed] indicates peer-reviewed, experimentally validated work; [theoretical] indicates published but not yet independently replicated; [speculative] indicates exploratory or contested claims.

Forecasting & Calibration

  1. [confirmed] Tetlock, P. E. & Gardner, D. Superforecasting: The Art and Science of Prediction. Crown, 2015. ISBN 978-0804136693. The foundational empirical study of forecasting accuracy, establishing that trained individuals can outperform intelligence analysts with classified data. Underpins the Crystal Ball calibration methodology.
  2. [confirmed] Tetlock, P. E. Expert Political Judgment: How Good Is It? How Can We Know? Princeton University Press, 2005. ISBN 978-0691128719. Twenty-year study demonstrating that domain experts perform no better than dart-throwing chimpanzees at long-range political forecasting.
  3. [confirmed] Brier, G. W. "Verification of forecasts expressed in terms of probability." Monthly Weather Review, 78(1), 1–3, 1950. Defines the Brier score, the proper scoring rule used throughout Future Studies calibration.
  4. [confirmed] Gneiting, T. & Raftery, A. E. "Strictly proper scoring rules, prediction, and estimation." Journal of the American Statistical Association, 102(477), 359–378, 2007. Mathematical foundations for proper scoring rules.
  5. [confirmed] Good, I. J. "Rational decisions." Journal of the Royal Statistical Society B, 14(1), 107–114, 1952. Introduces the logarithmic scoring rule used for calibration assessment.

Bayesian Methods

  1. [confirmed] Jaynes, E. T. Probability Theory: The Logic of Science. Cambridge University Press, 2003. ISBN 978-0521592710. The maximum-entropy interpretation of Bayesian inference that informs Crystal Ball's prior construction.
  2. [confirmed] Gelman, A. et al. Bayesian Data Analysis. 3rd ed., CRC Press, 2013. ISBN 978-1439840955. Standard reference for hierarchical Bayesian models used in prediction aggregation.
  3. [confirmed] Pearl, J. Causality: Models, Reasoning, and Inference. 2nd ed., Cambridge University Press, 2009. ISBN 978-0521895606. Causal inference framework underlying the constraint graph methodology.

Simulation & Modeling

  1. [confirmed] Epstein, J. M. & Axtell, R. Growing Artificial Societies: Social Science from the Bottom Up. Brookings/MIT Press, 1996. ISBN 978-0262550253. Foundational work on agent-based modeling for complex systems.
  2. [confirmed] Schelling, T. C. "Dynamic models of segregation." Journal of Mathematical Sociology, 1(2), 143–186, 1971. Demonstrates emergent macro-behavior from simple agent rules.
  3. [theoretical] Grieves, M. & Vickers, J. "Digital twin: mitigating unpredictable, undesirable emergent behavior in complex systems." Transdisciplinary Perspectives on Complex Systems, Springer, 85–113, 2017. Framework for continuous-updating simulation models.

Prediction Markets & Aggregation

  1. [confirmed] Wolfers, J. & Zitzewitz, E. "Prediction markets." Journal of Economic Perspectives, 18(2), 107–126, 2004. Survey of prediction market accuracy and mechanisms.
  2. [confirmed] Arrow, K. J. et al. "The promise of prediction markets." Science, 320(5878), 877–878, 2008. Endorsement of prediction markets as information aggregation tools by leading economists.
  3. [confirmed] Manski, C. F. "Interpreting the predictions of prediction markets." Economics Letters, 91(3), 425–429, 2006. Caution on the interpretation of prediction market prices as probabilities.

Uncertainty & Complexity

  1. [confirmed] Taleb, N. N. The Black Swan: The Impact of the Highly Improbable. Random House, 2007. ISBN 978-1400063512. Framework for understanding fat-tailed distributions and rare events in forecasting.
  2. [confirmed] Lorenz, E. N. "Deterministic nonperiodic flow." Journal of the Atmospheric Sciences, 20(2), 130–141, 1963. Foundational paper on sensitive dependence on initial conditions.
  3. [confirmed] Mandelbrot, B. B. The Fractal Geometry of Nature. W. H. Freeman, 1982. ISBN 978-0716711865. Self-similarity and power-law scaling in natural systems.

Weather & Earth Systems Prediction

  1. [confirmed] Richardson, L. F. Weather Prediction by Numerical Process. Cambridge University Press, 1922. The first numerical weather prediction attempt, establishing the computational framework still used today.
  2. [confirmed] Bauer, P., Thorpe, A. & Brunet, G. "The quiet revolution of numerical weather prediction." Nature, 525, 47–55, 2015. Review of the improvement from 1-day to 10-day useful forecast horizons over five decades.

Financial Prediction & Quantitative Methods

  1. [confirmed] Zuckerman, G. The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution. Portfolio/Penguin, 2019. ISBN 978-0735217980. History of Renaissance Technologies and the Medallion Fund's 66% annual returns.
  2. [confirmed] Patterson, S. The Quants: How a New Breed of Math Whizzes Conquered Wall Street and Nearly Destroyed It. Crown, 2010. ISBN 978-0307453389. Quantitative modeling in financial markets, including failure modes.
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