[Wittrs] Wittgenstein, Judgment & Statistics
- From: Sean Wilson <whoooo26505@xxxxxxxxx>
- To: "LAWCOURT-L@xxxxxxxxxxxxxxxxxxx" <LAWCOURT-L@xxxxxxxxxxxxxxxxxxx>
- Date: Thu, 26 Jan 2012 10:33:37 -0800 (PST)
Greetings Jeff.
First, thank you for those references. It will take me a few days to digest
them. I'm trying to finish up my book, for good, because I have manuscript
submission deadlines approaching. But I would enjoy a discussion in here about
Wittgenstein's idea of "connoisseur judgment" and how it compares with those
who base judgment purely upon statistical inference -- and what, in fact, that
even means. My guess is that each camp has information deficits relative to one
another. It would be a cutting edge discussion: one that could deserve its own
conference panel. Let me digest these sources of yours and offer some thoughts
about how a proper understanding of connoisseur judgment could help these
conversations, if it can.
I'll try to get some thoughts together by next week.
But thank you once again for the references.
(P.S. Sent to Wittrs)
Regards and thanks.
Dr. Sean Wilson, Esq.
Assistant Professor
Wright State University
Personal Website: http://seanwilson.org
SSRN papers: http://papers.ssrn.com/sol3/cf_dev/AbsByAuth.cfm?per_id=596860
Wittgenstein Discussion: http://seanwilson.org/wittgenstein.discussion.html
________________________________
From: Jeffrey Segal <jeffrey.segal@xxxxxxxxxxxxxx>
To: LAWCOURT-L@xxxxxxxxxxxxxxxxxxx
Sent: Thursday, January 26, 2012 8:46 AM
Subject: polls and elections
Sean Wilson wrote yesterday that he prefers "connoisseur judgments" over linear
regression models. Unfortunately for Sean's argument, a substantial volume of
literature shows that quantitative assessments clearly outperform qualitative
judgments. See most notably, Philip Tetlock's "Expert Political Judgment: How
Good is it? How Can we Know?" demonstrating across the board that simple
algorithms outperform expert judgments and Daniel Kahneman's "Thinking, Fast
and Slow" on the same point.
Closer to home, Robyn Dawes demonstrates that even improper linear models
outperform qualitative faculty judgments on prospective graduate students
(Dawes, Robyn M. "The robust beauty of improper linear models in decision
making, 34 American Psychologist 571 (1979) (showing that even improperly
(i.e., evenly) weighted linear models outperform expert judgments of an
admissions committee over the eventual quality of graduate students), and
really close to home, Andrew Martin's computer out-predicted law professors in
the law professors' areas of expertise in a series of Supreme Court decisions.
Andrew D. Martin, Kevin M. Quinn, Pauline T. Kim, and Theodore W. Ruger. 2004.
"Competing Approaches to Predicting Supreme Court Decisionmaking."2
Perspectives on Politics761.
On the merits of Sean's point, my Stony Brook colleague Helmut Norpoth has a
linear prediction model based on the New Hampshire primary showing that
incumbents with challengers lose--Truman '52 (who dropped out after a weak New
Hampshire showing), Johnson '68 (ditto), Ford '76, Carter '80, and Bush '92)--
whereas incumbents without strong primary challengers win (Ike 56, LBJ 64, RMN
72, RWR 84, GWB 04, and a prediction for BHO in 12). See
http://www.huffingtonpost.com/helmut-norpoth/new-hampshire-primary-for_b_1200199.html
Sincerely,
Jeff
Jeffrey Segal
Distinguished Professor and Chair
Department of Political Science
Stony Brook University
Stony Brook, NY 11794
phone 631-632-7662
fax 631-632-4116
jeffrey.segal@xxxxxxxxxxxxxx
http://www.sunysb.edu/polsci/jsegal/
2011-2012 Contact Information
Senior Visiting Research Scholar
Center for the Study of Democratic Politics
314 Robertson Hall
Princeton University 08544
phone 609-258-7941
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