Instructor: David Aldous
NEW 9/26. Summary comments on your 7-minute talks; for the record, the schedule of talks is here.
Class time: Tuesday Thursday 2.00 - 3.30 in room 330 Evans.
Office Hours: Wednesdays 9.30 - 11.30 in 351 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. It is limited to 36 students.
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?
The requirements for students are (see the link below for more info).
UPDATE 8/29. THE CLASS IS FULL: no more students except those I have already contacted.
Read here for more about administration and deadlines.
This page is a guide to online resources which may be helpful in choosing projects. It's intended for online browsing.
The links 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.
Th 8/24: Lecture 1: Everyday perception of chance.
Tu 8/29: Lecture 2:
The Kelly criterion for favorable games: stock market investing for individuals.
Th 8/31: Lecture 3: Sports rating models.
Tu 9/5: Lecture 4:
Risk to Individuals: Perception and Reality.
Th 9/7: Lecture 5: Predicting the future: Geopolitics etc.
Tu 9/12: Lecture 6:
Coincidences, near misses and one-in-a-million chances.
Th 9/14 Lecture 7: Game theory.
Tu 9/19 : Student talks
Th 9/21: Student talks
Tu 9/26: Student talks
Th 9/28: Lecture 8: Luck .
Tu 10/3: Lecture 9:
Prediction markets, fair games and martingales.
Th 10/5 Lecture 10: Psychology of probability: predictable irrationality.
Tu 10/10: Lecture 11:
Coding and entropy.
Th 10/12 Lecture 12: Science fiction meets science.
Tu 10/17: Lecture 13:
Physical Randomness and the Local Uniformity Principle.
Th 10/19 Lecture 14: Size-biasing, regression effect and dust-to-dust phenomena.
Tu 10/24: Lecture 15: A glimpse at probability research: spatial networks on random points.
Selected slides from this 2010 talk and
this 2014 talk.
Th 10/26 Lecture 16: Branching processes, tipping points and phase transitions.
Tu 10/31: Lecture 17:
Toy models in population genetics: some mathematical aspects of evolution.
Th 11/2 Lecture 18: Toy models of human interaction: use and abuse.
Tu 11/7: Lecture 19:
Th 11/9 Lecture 20: Miscellany 2.
Tu 11/14: NO CLASS
Th 11/16 Student talks
Tu 11/21: Student talks
Th 11/23 No class
Tu 11/28: Student talks
Th 11/30 Student talks
Tu 12/5: Student talks