## Probability and the Real World (Spring 2016). Redirect to the page for the current, Fall 2017, course

NEWS Here is the Schedule of student project presentations.

(2/23) Here are the summary comments on your 6-minute talks; for the record, the schedule of talks is here.

Instructor: David Aldous

GSI None.

Class time: MW 4.00-5.30 in room 9 Evans.

Prerequisite: Upper division probability (STAT 134 or equivalent). The course emphasizes student participation and initiative while offering students the opportunity to pursue intellectual curiosity in directions of their individual choice.

Courses in mathematical probability teach you to do certain mathematical calculations, but these are often far removed from broader questions about the the role of randomness in the "real world" of science or of human affairs. In contrast, this junior/senior seminar course seeks to engage such questions in two ways.

1. In lectures I will treat about 20 different topics, one each lecture, chosen to illustrate the diversity of contexts where probability arises. Some idea of this diversity can be gleaned from my list of 100 contexts where we perceive chance.

2. A recurrent theme is to adopt a classical science paradigm: can we use probability theory to make predictions about the real world which can be verified or falsified by experiment or observation?

• To attend class (attendance will be taken)
• To do a (small) reading/talk project (talks start February 10 -- 3 weeks after first class).
• To do a (big) course project.
There are no other homeworks or exams. There is no course textbook, but to get into the spirit of the course it's helpful to read one of the ``popular science" books on the book list.

### Resources for projects

This page is a guide to online resources which may be helpful in choosing projects. It's intended for online browsing.

## Class-by-class schedule

The link below go to slides of the lectures, which will be posted after the lecture. The link here goes to extended write-ups of about half of the lectures. The link here goes to links, books and papers mentioned in lectures -- implicit suggestions for further reading.

W 1/20: Lecture 1: Everyday perception of chance.

M 1/25: Lecture 2: The Kelly criterion for favorable games: stock market investing for individuals.
W 1/27: Lecture 3: Sports rating models.

M 2/1: Lecture 4: Risk to Individuals: perception and reality.
W 2/3: Lecture 5: Short and Medium term predictions and risks in politics and economics.

M 2/8: Lecture 6: Coincidences, near misses and one-in-a-million chances.
W 2/10 : Student talks

M 2/15: [holiday]
W 2/17: Student talks

M 2/22: Student talks
W 2/24: Lecture 7: Game theory. And here is an extended write-up.

M 2/29: Lecture 8: Luck.
W 3/2: Lecture 9: Prediction markets, fair games and martingales. And here is a published extended write-up of the 2011 lecture.

M 3/7: Lecture 10: Psychology of probability: predictable irrationality.
W 3/9: Lecture 11: Coding and entropy.

M 3/14: Lecture 12: Science fiction meets science.
W 3/16: Lecture 13: Physical randomness and the local uniformity principle.

[Spring Break]

M 3/28: Lecture 14: Size-biasing, regression effect and dust-to-dust phenomena.
W 3/30: Lecture 15: A glimpse at probability research: spatial networks on random points. Selected slides from this 2010 talk and this 2014 talk.

M 4/4: Lecture 16: Branching processes, tipping points and phase transitions.
W 4/6: Lecture 17: Toy models in Population Genetics: some mathematical aspects of evolution.

M 4/11: Lecture 18: Toy models of human interaction: use and abuse.
W 4/13: Lecture 19: Miscellany.

M 4/18: No class.
W 4/20: Student talks.

M 4/25: Student talks.
W 4/27: Student talks.

M 5/2: Student talks.
W 5/4: Student talks.