Predicting the Future: From Tarot Cards to Algorithms: A Sociological Introduction
No one can see the future, but everyone must try. We must predict the future every day. We brush teeth predicting fewer cavities, buy ice cream expecting to eat it, choose spouse anticipating happiness. College students choose majors and take classes with an eye on their future career. Loan clerks, college admission officers, stockbrokers, and parole boards and many others predict for a living, betting on future outcomes. Most classes are about the past or the present. In this class, we look at ways people try to peek into the future.
For most classes there will be required readings, all are on ereserves or linked in the syllabus, except the only book you need to read: Yuval Harari’s Homo Deus that you can buy on Amazon. It is a fun book but a long one, so start reading it well in advance of December when we discuss it. Be prepared to discuss the readings in class.
There are also two movies you have to watch: The Minority Report (2002) by Steven Spielberg (2 hours and 26 minutes), and Blade Runner (1982) by Ridley Scott (1 hour 57 min). This is the original version not the sequel. Both you can stream through e-reserves.
Finally, there is one podcast you must listen to, The Sorting Hat, an episode of Hidden Brain by Shankar Vedantam (51 min). You must do the readings, the listening and watch the films before the date they appear on the syllabus. (Further readings or listenings are optional.)
This is a small class, and I expect you to attend all classes and to participate actively. You can miss one class without excuse.
You will have three simple tasks spread through the quarter:
Task 1. Make some predictions (see list)
Task 2. Find your horoscope read it and bring it to class
Task 3. To retrieve your free credit bureau report
You will participate in one of three debates with two or three other students as a team. (In the other two debates you will be a member of the audience, and will have lighter duties.) You can divide the work on your team as you see fit, but I expect every member to be equally involved. Two teams will debate the following propositions:
Debate 1. People should never be held criminally liable for predictions.
Debate 2. We should do predictive policing.
Debate 3. We should make important decisions always using algorithms rather than human judgment whenever that is possible.
The rules of the debate will be as follows. One team will argue for (Affirmative Team or AT), the other against the proposition (Negative Team or NT) but which team gets which side will be determined by a coin toss moments before the debate, so you and your team must prepare to argue both for and against. Your team will have to do your own research.
Round 1. The debate will start with the statement of the AT, followed by a statement by the NT, five minutes each. (10 min)
Round 2. The two teams rebut the other’s points. Starting with NT, the two teams take turns. Each will have three turns and each turn will be 2 minutes. Up to 1 minute for the question and the rest for the answer. (2x3x2=12 min)
Round 3. Questions from the audience and me to each team. (18 min)
Everyone (team members and audience members) vote on the proposition through TritonEd before the class where the proposition is debated. At the end of the debate, the audience votes again on the proposition and on who won the debate again, using TritonEd. The whole debate (with transitions) will take about 45 minutes.
There is a short midterm. You will be given 4 questions about the readings from which you choose three to answer. (If you answer all four, I will count the best three.) You will need a blue book.
There is a final paper that should be 6-10 pages. You can use 1.5 lines paragraphs and 12 point fonts. It should have a reference section that does not count towards the page count.
You can choose from the following topics:
The paper should present a clear argument supported by facts and scholarly literature on the topic. The paper must start with an Abstract, a short summary of the main argument in your paper (about 150 words). You need at least scholarly 5 references (academic articles or books) listed at the end of the paper (called Reference section). Use the MLA format. You submit the final paper through Turnitin on TritonEd. The paper must be entirely your own work. Plagiarism is a serious violation of university rules. You must see me at least once about your paper at my office hour.
The final paper is due on the Thursday, 11:59 pm of finals week (December 13). You may pre-submit your paper by Saturday noon, December 8. I will read it and either send it back with comments or offer you a grade with comments. If you got a grade and accept it, you are done. If you did not get a grade or you don’t like the grade you received, you can improve on the paper and turn it in by the final due date.
There will be three pop quizzes on the readings up to that point. Simple questions to check if you did the readings or watch the movies at all. The best two of the three will be counted in your grade.
Your grade will be determined as follows:
Tasks (4% each) 12% (you get full credit for doing them on time)
Debate 25% (you can get full credit even if your team
loses the debate)
Pop quizzes (best 2 of 3, 4% each) 8%
Final paper 30%
Class participation 15%
I predict that anyone
who takes the course seriously, engages with the material actively and plays by
the rules will get a B+ or better.
Why we care about the future?
Past, present, future
Speed of time
Why the future is different from the past and present
Can we imagine a world with change but without a future?
A). If Tesla stock will be over $300 on December 1.
B). If the Democrats win the House this November.
C). If the Democrats win the Senate this November.
D). If the Democrats win both the House and the Senate this November.
E). The probability that you will ever meet your perfect soulmate. [Give a number between 0 and 100]
F). If something will happen that will have a major impact on your life this October. If so, what would that be?
Adam, Barbara, 2010, History of the future: Paradoxes and challenges, Rethinking History, 14:3, 361-378
Watts, Duncan J. 2011. Everything is Obvious. Once You Know the Answer. Crown Press. Chapters 5-7.
Jens Beckert, 2016. Imagined Futures. Fictional Expectations and Capitalist Dynamics. Princeton University Press
Kristie Miller. 2013. Presentism, Eternalism and the Growing Block. In Heather Dyke and Adrian Bardon. A Companion to the Philosophy of Time.
Further Reading (the Cliffs Notes version):
Sam Woolfe. Presentism and Eternalism: Two Philosophical Theories About Time.
What is randomness?
Tarot cards, Tea Leaves, Astrology, Dreams
TASK 2: Find your horoscope and bring it to class. Answer the questions on TritonEd.
Whitson, Jennifer A. and Adam D. Galinsky. “Lacking Control Increases Illusory Pattern Perception.” Science 322, 115 (2008) (online version at )
Damisch, Lysann, Barbara Stoberock and Thomas Mussweiler. 2010.”Keep Your Fingers Crossed! How Superstition Improves Performance.” Psychological Science, 21(7) 1014–1020 (online version at )
Blog by Ed Yong:
Tiresias in Homer’s Odyssey
Augurs of Delphi
Schutz, Alfred, 1959, Tiresias or Our Knowledge of the Future. Social Research, Vol. 26, No. 1 (SPRING 1959), pp. 71-89
Dawson, Lorne L. 1999. When Prophecy Fails and Faith Persists: A Theoretical Overview. Nova Religio: The Journal of Alternative and Emergent Religions, Vol. 3, No. 1, pp. 60-82
Balch, Robert W. and David Taylor. 1977. American Behavioral Scientist
Forecasting Earthquakes, Weather and Climate Change
Cartlidge, Edwin. 2011. “Quake Experts to Be Tried for Manslaughter.” Science 332 (6034) :1135–1136
Orrell, David. 2007. The Future of Everything. Thunder’s Mouth Press
International Commission on Earthquake Forecasting for Civil Protection. 2011. Operational Earthquake Forecasting. State of Knowledge and Guidelines for Utilization. Annals of Geophysics, 54, 4, pp. 319-391.
How doctors make prognoses
Kondziolka, Douglas et al. 2014. Journal of Neurosurgery, 120:24–30
Orrell, David. The Future of Everything. Chapter 5. It’s in the Genes. Pp. 174-217
Christakis, Nicholas A. 2001. Death Foretold. Prophecy and Prognosis in Medical Care. Chicago: University of Chicago Press
Film: Blade Runner (1982)
Scherker, Amanda. 2014. “11 Visions of the Future That Were Utterly Wrong.” Huffington Post, January 3
Davis, Lauren. How Our Predictions for the Year 2000 Changed Throughout the 20th Century.”
When things don’t change much
David, Paul A. "Clio and the Economics of QWERTY." The American economic review 75.2 (1985): 332-337. JSTOR
Stan J. Liebowitz and Stephen E. Margolis. 1990. Fable of the Keys. Journal of Law and Economics, 33/1:1-25.
Arthur, W. Brian. 1990. "Positive Feedbacks in the Economy." Scientific American
Predicting the outcome of a large number of people’s actions
Economy (prophets and profits)
Congressional Budget Office. 2013. CBO's Economic Forecasting Record: 2013 Update. January 17.
Homa, Ken. Nums: Why’s the Fed so bad at forecasting?
Tetlock, Philip. 2006. Expert Political Judgment. How Good Is It? How Can We Know? Princeton University Press
Sorting people by future potential
Sorting Hat. Hidden Brain Podcast by Shankar Vedantam.
Fourcade, Marion and Kieran Healy. 2013. Classification situations: Life-chances in the neoliberal era. Accounting, Organizations and Society, 38, pp. 559-572
Robert Merton. 1948. “The Self-Fulfilling Prophecy.” Antioch Review, 8/2
Richard L. Henshel. 1982. “The Boundary of the Self-Fulfilling Prophecy and the Dilemma of Social Prediction.” The British Journal of Sociology, Vol. 33, No. 4 (Dec., 1982), pp. 511-528
Estimating future academic performance
Guessing who will default and who will pay up
Rona-Tas, Akos. 2017. “Off-label Use of Consumer Credit Rating’s”, Historical Social Research,
Preventing crime: policing, sentencing and parole
ProPublica. Machine Bias.
Harcourt, Bernard E. 2007. Against Prediction. Profiling, Policing and Punishing in an Actuarial Age. University of Chicago Press
How good are experts at predicting in their area expertise?
Expert judgment vs. statistical calculation
Heuristics vs. algorithms
Dawes, Robyn M., David Faust, and Paul E. Meehl. 1989, "Clinical versus actuarial judgment." Science 243.4899 : 1668-1674.
Tetlock, Philip and Dan Gardener. 2015. Superforecasting. The Art and Science of Prediction. Crown Publisher
Gigerenzer, Gerd. 2007. Gut Feelings: The Intelligence of the Unconscious. Viking
Privacy and prediction
Does Google and Facebook know you better than you know yourself?
Kerr, Ian and Jessica Earle. 2013. Prediction, Preemption and Presumption. How Big Data Threatens Big Picture Privacy. Stanford Law Review, September 3
The Privacy Paradox. Note to Self: The Podcast
How predictable are humans?
Wakefield, Jane. 2011. When Algorithms Control the World.” BBC News, August 22,
Wang, Yilun and Michal Kosinski. 2017. Deep neural networks are more accurate than humans at detecting sexual orientation from facial image. Journal of Personality and Social Psychology.
Yuval Harari. 2015. Homo Deus. A Brief History of Tomorrow. Harper: New York