Win-Vector Blog

SIGACT News review of: Combinatorics the Rota Way. Also found on Professor Gasarch’s page and ACM SIGACT News Volume 41, Issue 2 (paywall)

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This is an elementary mathematical finance article. This means if you know some math (linear algebra, differential calculus) you can find a quick solution to a simple finance question. The topic was inspired by a recent article in The American Mathematical Monthly (Volume 117, Number 1 January 2010, pp. 3-26): “Find Good Bets in the Lottery, and Why You Shouldn’t Take Them” by Aaron Abrams and Skip Garibaldi which said optimal asset allocation is now an undergraduate exercise. That may well be, but there are a lot of people with very deep mathematical backgrounds that have yet to have seen this. We will fill in the details here. The style is terse, but the content should be about what you would expect from one day of lecture in a mathematical finance course.

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We describe the “the local to global principle.” It is a principle used to break algorithmic problem solving into two distinct phases (local criticism followed by global solution) and is an aid both in the design and in the application of algorithms. Instead of giving a formal definition of the principle we quickly define it and discuss a few examples and methods. We have produced both a stand-alone PDF (more legible) and a HTML/blog form (more skimable).
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“Comparing Apples and Oranges: Two Examples of the Limits of Statistical Inference, With an Application to Google Advertising Markets” is our analysis of Google AdSense Channel IDs and our use of the Cramer Rao bound to show that these IDs fundamentally limit what participants in the Google online advertising market can measure (and therefore in turn limit what these players can do).
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New paper: A Discrete Model Gauging Market Efficiency PDF

We highly recommend reading the PDF version, but please find below a HTML translation of the paper.

We follow up on some interesting work from the literature and explore some conditions that allow large predatory traders to dominate markets.

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What makes a good graph? When faced with a slew of numeric data, graphical visualization can be a more efficient way of getting a feel for the data than going through the rows of a spreadsheet. But do we know if we are getting an accurate or useful picture? How do we pick an effective visualization that neither obscures important details, or drowns us in confusing clutter? In 1968, William Cleveland published a text called The Elements of Graphing Data, inspired by Strunk and White’s classic writing handbook The Elements of Style . The Elements of Graphing Data puts forward Cleveland’s philosophy about how to produce good, clear graphs — not only for presenting one’s experimental results to peers, but also for the purposes of data analysis and exploration. Cleveland’s approach is based on a theory of graphical perception: how well the human perceptual system accomplishes certain tasks involved in reading a graph. For a given data analysis task, the goal is to align the information being presented with the perceptual tasks the viewer accomplishes the best. Read more…

REPOST (now in HTML in addition to the original PDF).

This paper demonstrates and explains some of the basic techniques used in data mining. It also serves as an example of some of the kinds of analyses and projects Win Vector LLC engages in. Read more…

What is “genetic art?” My answer to this is http://www.geneticart.org (redirects to http://www.mzlabs.com), but this requires some explanation. Read more…

I recently had the pleasure of finding a copy of the manual for my favorite calculator. I know it is incredibly nerdy to have a favorite calculator (and even more nerdy to read the manual), but it really got me thinking. Read more…

I have just posted a new write-up: Volunteers in Large Clubs: The Theorist’s View. This paper describes some interesting issues in organizing volunteers in a large club and tries to show (without math) how a theoretical computer scientist attacks such problems. Read more…