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      2017: An Unlikely World

      Social Studies

      Predicting the Future

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        Introductory Questions

          • When did people first start thinking that the future would be different than the past?
          • Who benefits the most from accurate predictions of the future?
          • Are predictions of the future too rooted in the present?
          • Can a prediction ever be a certainty?
          • Can predictions affect outcomes? If so, are they more likely to be self-fulfilling or self-destructive?
          • As humans, are we able to comprehend truly large numbers (such as “1 in a million”) when we consider risk-related questions? If not, should designing policies related to risk management be taken out of human hands?
          • What might lead you to trust certain predictions over others?
          • How often do unforeseeable events obstruct otherwise accurate predictions? Have you ever made a prediction only to have something completely unexpected interrupt it?

        Where will Tomorrowland Land? | Understanding Prediction

          • The Supernatural and the Scientific: Foundations of Prediction
            • From Oracle to Actuary: A History of Prediction
            • Insights from Mathematics: Probability and Statistics
            • Appointments in Samara: The Impacts of Prediction on Behavior

          • Key Terms and Concepts
            • Omens | Auguries | Self-Fulfilling Prophecy | Luck
            • Prediction vs. Projection vs. Forecast | Causation vs. Correlation
            • Sampling Error | Standard Deviation | Confidence Interval | Outliers
            • Prediction Markets | Black Swans
          • Notable Methods and Tools
            • Sabermetrics | Actuarial Science | Predictive Modeling | Big Data
            • Horoscope | Zodiac | I Ching | Tarot | Ouija | Tea Leaves | Palmistry
            • Retrodiction | Nowcasting | Persistence Forecasting

        Money Talks, but What is it Saying? | The Challenges of Economic Forecasting

          • Uses, Purposes, and Applications
          • Measuring Economic Conditions
          • Analyzing Economic Indicators
          • Issues with Economic Accuracy

        Trumping the Pundits | Political Predictions and Election Forecasts

          • Different Predictive Models
          • The Primacy of Fundamentals vs. The Value of Polling
          • Confounding Factors and Variables

        How Off Are All the Bets? | Statistics and Predictions in Sports

          • Sports Analytics and Forecasting
          • Horsing Around: Betting and Bookmaking
          • Mapping the Field: The Science of Bracketology

        This Spells Disaster | Forecasting Natural Forces

          • Predicting the Weather and Climate
          • Understanding Natural Disasters:
            • Earthquake | Tsunami | Volcano
            • Hurricane

          • Disaster Prevention and Mitigation

        Specific Topics for Exploration

          • Notable “Prophets”
            • Nostradamus | Rasputin | Cassandra | Robert Fitzroy
            • Michio Kaku | Ray Kurzweil | Nate Silver | Nate Cohn
            • Hari Seldon | Paul the Octopus | Yoda | Sybill Trelawney
            • Pāora Te Potangaroa | John Elfreth Watkins, Jr.

          • Notable Predictions, Statements, and Laws
            • Y2K Problem | 2012 Doomsday Phenomenon | Final Anthropic Principle
            • The Called Shot | AIMA Prophecy | Benford’s Law | Schrodinger’s Cat
            • Law of Truly Large Numbers | Littlewood’s Law | The Birthday Problem

        Guided Cases

          • Explore the idea of "big data" and its relationship to prediction. Then discuss with your team: why might "big data" be described as the comeback of correlation over causation? How might it affect government policy, or the choices of a company such as Netflix?
          • Explore the idea of a "black swan event" - a term popularized by the author Nassim Nicholas Taleb. Discuss with your team: what are some examples of black swan events in real life? Should we try to prepare for them (can we?) or is the concept overrated?
          • Does eating cheese make someone more likely to die of bedsheet entanglement? If not, what else could possibly explain this graph? Discuss as a team: what is the difference between correlation and causation, and how can you tell one from the other?
          • Consider the so-called Doomsday Clock, which attempts to measure how close we are to the end of human civilization. Discuss with your team: is it accurately measuring the likelihood of the end of the world? Should scientists also create a "Bestdayever" Clock to balance it? If so, what factors might this more optimistic clock consider? And is there any way in which something like the Doomsday Clock can itself be dangerous?
          • Some of our white-collar criminals from last year might have done well to understand Benford's Law. Do you think it applies to World Scholar's Cup team scores?
          • Use the linked summary as a starting point from which to explore the science of (and policies related to) disaster prediction. To what degree can potentially catastrophic events such as earthquakes and tsunamis be forecast ahead of time? Discuss with your team: should all credible forecasts be made available to the public, and should those who ignore them be held responsible for their own safety?
          • Discuss with your team: what makes psychohistory different from any other form of forecasting the future? What are its supposed limitations? Do you think it (or something like it) could be developed in the real world? If so, what would be the implications, and would we want to limit access to its findings, or spread them far and wide?
          • Are you a lucky person? In recent years, there has been substantial research into whether luck is a real factor in human affairs. Read this interview with psychologist Richard Wiseman, then discuss with your team: if luck is real, where does it come from, and is there a way to become luckier (other than Felix Felicis)? Should lucky people receive less credit for their achievements?
          • Will a team in yellow jackets win the Global Round? Will the Cavaliers repeat as NBA champions? A whole industry exists around predicting the relative performances of sports teams, match by match and year by year. Discuss with your team: to what extent can (and should) sports outcomes be modeled and/or predicted? Should sports teams use modern analytics to guide their strategies? Or is the best way to forecast outcomes, in sports and beyond, through prediction markets (such as the defunct intrade.com) that harness the wisdom of crowds?
          • In the run-up to the 2016 United States presidential election, near-legendary statistician and election forecaster Nate Silver was widely criticized for giving Donald Trump too high a chance of defeating Hillary Clinton. Learn more about Nate Silver's history and approach, and explore his wider body of work at fivethirtyeight.com. Then discuss with your team: how do election predictions work, and how *well* do they work? How can different models based on similar data lead to such different predictions? And why were outcomes such as President Trump and Brexit considered such monumental surprises despite each being one of only two choices in a binary system?
          • Consider these forecasts by professional "futurists" of the world just a few years from now. Which seem most plausible? On what factors are their predictions based? How far out in the future is it possible to make meaningful predictions? Do any of these futurists seem to have a political or ideological agenda, and, if so, does it detract from the value of their predictions?
          • Insurance companies depend on predictions of the future - or at least on the best possible measures of risk and uncertainty. They rely on actuaries to make those measurements. Read this linked article, then consider new developments in predictive modeling in the context of actuarial science. Discuss with your team: should companies such as Netflix be allowed to use data on our lifestyle and habits to predict our future behavior? How about insurance companies, or governments? Should any information about us that might help with modeling our future behavior and/or health be off limits to external analysis?
          • Is anyone out there? Discuss with your team: how likely is it that other intelligent life exists in the universe? Why does it matter—or does it? Be sure to consider possible explanations for the Fermi Paradox. Are any convincing?
          • Consider a group of women who have survived against great odds, and even against the wishes of their government - the babushkas of Chernobyl. Discuss with your team: are these women brave or foolish? Have they chosen the certainty of radiation exposure over the uncertainty of starting a new life? Should their government allow them to assess and bear their own risk, or should it intervene to ensure their continued well-being?

        Concluding Questions

          • What is the most unlikely thing that you have ever witnessed?
          • What is the most unlikely thing that has ever happened to you?
          • What do you think the world will be like in 10 years? In 100 years?
          • Is it ethical to determine an athlete’s salary based on how well they are predicted to perform?
          • Would you want to know the various likelihoods in your future—for instance, the likelihood that you have children, or live past sixty, or succeed in your career?
          • Do you believe in (even a little bit) any superstitions related to fortune-telling, such as zodiac signs?
          • Whose predictions of the future do you trust the most?

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