Predicting Aggregate Choice
Psych 4N, 3 credits, Rm. 120, Bldg. 160 (Wallenberg Hall)
M 3:30-6:00 PM, 01/04/15-03/15/15


Brian Knutson, Ph.D.
Phone: 650.724.2965
Office Hours: W 1:30-3:00 PM (email to book a slot)


Is prediction of group choice possible, and if so, how can it best be implemented? We'll consider interdisciplinary approaches to predicting group choice with an eye towards linking individual to the group levels of analysis. This seminar is ideal for students who aim to connect levels of predictive analysis and who plan to apply what they learn in future research. Readings will primarily be drawn from Silver's book "The Signal and the Noise" and auxiliary scientific publications. Course requirements include participation, questions from and quizzes on assigned readings, an assignment, in-class exercises, a presentation, and a report. Students should hope to learn basic concepts and skills that will improve their predictions.


Silver, N. (2012). The signal and the noise. New York: Penguin Books. (SATN)
Additional scientific readings below (TBD)


Date Topic Readings
01/04/16 The possibility of prediction
01/11/16 Neuroforecasting / Analysis Knutson & Huettel, Knutson
01/18/16 Holiday (MLK Day) N/A
01/25/16 Politics and sports SATN Ch. 2-3 + Buttonwood
02/01/16 Growth and finance SATN Ch. 6, Ch. 11, Ch. 8
02/08/16 Chess and poker SATN Ch. 9-10
02/15/16 Holiday (Presidents' Day) (Report Due) N/A
02/22/16 Climate change and terrorism SATN Ch. 12-14
02/29/16 Technique comparison / Presentations (Outline Due) Mellers et al. (2015), Genevsky et al. (2015)
03/07/16 BK out N/A
03/14/16 (Report Due) N/A


  • Participation (20%): Includes attendance, attention, and discussion. This counts for 20% towards the final grade. The goal is for all of us to learn from each other about the topic.
  • Questions / Quizzes (20% or 10%/10%): Each class, you'll submit two questions that the readings sparked in you (collected midnight before each class). You will also answer two (straightforward, multiple-choice) questions based on the readings in quiz format before each class.

  • Assignment (10%): The assigment will involve finding a dataset, analyzing it, and writing it up (2 pages). The goal of this exercise is to hone your prediction skills for the subsequent report.

  • Exercises (20%): For each class, I'll attempt to include at least one in-class exercise to perform (including but not limited to plotting, analyzing, and interpreting data). Engagement is more important than precision. The goal of these exercises is to acquire skills and intuitions that you can apply to prediction analyses.

  • Presentation (5%): You will present an outline of your report to the class in slide format (5-10 slides, 5 min + 2 min discussion). The presentation will count for 5% of the final grade. The purpose of this exercise is to gain experience in presenting your ideas to others, brainstorm about how to improve them, and integrate useful suggestions.

  • Report (25%): Reports should be brief (e.g., 5-15 pages in length) and written in APA style (particularly the bibliography). Reports should include a predictive question and analysis of data (can be secondhand). Reports could be structured as a simple journal article, including Introduction, Methods, Results (projected), and Discussion. You should first turn in a one-page outline of the report on 03/01/15, on which I will provide feedback. Then, you should send me the final report by noon on 03/14/15. The report will count for 25% total of the final grade total (outline 5% + final draft 20%). The goal of this assignment is to familiarize you with the processes of analysis, writing, and editing. If you have to turn in the report late, you should tell me in advance... 5% will be deducted from your notes or report grade for each day the report is late.

  • Guidelines: Plagiarism is considered academic theft and can result in a failing grade. Don't use computers or devices in class, as they have been demonstrated to impair students' learning (unless explicitly called for during exercises).

  • (last updated 02/25/16;