Comments on Diamond and Sekhon

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Comments on Diamond and Sekhon. Philip Schrodt University of Kansas 21st Summer Meeting of the Society for Political Methodology. or APSA Organized Section on Political Methodology. or “those !*$%#$^s who reviewed my article”. Disclaimer. - PowerPoint PPT Presentation

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Comments on Diamond and Sekhon

Philip SchrodtUniversity of Kansas

21st Summer Meeting of the Society for Political Methodology orAPSA Organized Section on Political Methodologyor“those !*$%#$^s who reviewed my article”

Comments on Philip SchrodtDiamond and Sekhon U. of Kansas

Disclaimer

The Board of Education of the State of Kansas is likely to determine that the theory of evolutionis of questionable scientific merit and consequentlyshould be viewed with skepticism.

See Genesis 1:1-31

Or Genesis 2:4-9 for a different version

whatever…

Comments on Philip SchrodtDiamond and Sekhon U. of Kansas

Disclaimer

The Board of Education of the State of Kansas is likely to determine that heliocentric astronomyis of questionable scientific merit and consequentlyshould be viewed with skepticism.

See Joshua 10:13

Comments on Philip SchrodtDiamond and Sekhon U. of Kansas

Disclaimer

The Board of Education of the State of Kansas is likely to determine that round-earth geographyis of questionable scientific merit and consequentlyshould be viewed with skepticism.

See Daniel 4:10-11, Matthew 4:8

Comments on Philip SchrodtDiamond and Sekhon U. of Kansas

Disclaimer

The Board of Education of the State of Kansas is likely to determine that the assertion π = 3.14159… is of questionable scientific merit and consequentlyshould be viewed with skepticism.

See 1 Kings 7:23

Comments on Philip SchrodtDiamond and Sekhon U. of Kansas

Creeping Artificial Intelligence:Primary Tools of AI from 1980s

Expert Systems/ID3 Neural Networks Genetic Algorithms Nearest neighbor clustering

Diamond-Sekhon: Use a GA to optimize a nearest neighbor metric

Comments on Philip SchrodtDiamond and Sekhon U. of Kansas

Why is this important?

SWWC

“So what? Who cares?

Comments on Philip SchrodtDiamond and Sekhon U. of Kansas

Henry Brady on statistical pedagogy

Theological seminaries distinguish between theology, or the systematic study of religious beliefs, and homiletics, the art of preaching the gospel convincingly. Theologians ask hard questions, and often espouse opinions that would shock and horrify the practicing members of the religion’s congregations. Homiletics is about homilies: sermons that are practical, down to earth, simple and reliable interpretations of the faith.

The social sciences have a great deal of theology, but very little homiletics.

Brady and Collier, eds. Rethinking Social Inquiry 2004) pg. 53

[WRONG] [well, maybe] You decide…

Comments on Philip SchrodtDiamond and Sekhon U. of Kansas

Gary King on statistical pedagogy

“I have found lying to be one of the most effective techniques when teaching statistics”

Comment from floor at some previous PolMeth Summer Meeting, probably ca. late Pleistocene

Comments on Philip SchrodtDiamond and Sekhon U. of Kansas

“Why they hate us”Students, colleagues, random social encounters, etc.

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PoliticalMethodologist

Comments on Philip SchrodtDiamond and Sekhon U. of Kansas

“Why they hate us” What they want:

“If X is applied to case J in situation Z, what difference will it make in Y?”

What we give them:

“The estimate bi of the population coefficient ßi is significantly different from zero at the p = 0.043591 level”

[unless Xi is co-linear with some other independent variables—and in a sufficiently large sample, it almost certainly is—in which case the sign of bi may be reversed, and if bi isn’t significant, don’t pay attention to that either]

[or maybe Xi has no causal effect whatsoever but happens to correlate with something not in the model that does]

Comments on Philip SchrodtDiamond and Sekhon U. of Kansas

Phascinating Phacts: State Nicknames

Illinois Land of Lincoln

Alaska Land of the Midnight Sun

Minnesota Land of 10,000 Lakes

Massachusetts Land of Propensity Functions

Comments on Philip SchrodtDiamond and Sekhon U. of Kansas

Matching Cases on CovariatesVariable Mass. Calif.

Traffic problems

High tech economy

Marriage Equality for a while

GOP governor in liberal state

Outrageous housing costs

Kansas

Only for opossums

Not with current Board of Education

Passed anti-gayamendment

Democratic governorin conservative state

Assistant professorscan afford houses

Treatment:Sekhon

From here…

To here…

Comments on Philip SchrodtDiamond and Sekhon U. of Kansas

Genetic Algorithms

Originally proposed in John Holland's Adaptation in Natural and Artificial Systems (1975). The problems of interest to Holland are characterized by:

The impossibility of enumerating all possible devices for solving the problem.

The performance of a device had a large number of local minima: i.e. the initial introduction of a component might initially degrade performance even if it ultimately improved performance.

The structure of the problem-solving device was sufficiently complicated that it was not always obvious which components were responsible for improvements in performance

Comments on Philip SchrodtDiamond and Sekhon U. of Kansas

Optimization surfaces assumed in OLS and theoretical econometrics

Faculty meetings as

described during job interview

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Comments on Philip SchrodtDiamond and Sekhon U. of Kansas

Optimization surfaces in the real world

Actual faculty meeting

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Your office

Your Dean

APSA JobPlacement

Service

Comments on Philip SchrodtDiamond and Sekhon U. of Kansas

Fundamental Principle of Genetic Algorithms

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Comments on Philip SchrodtDiamond and Sekhon U. of Kansas

“Intelligent Design”

Choice of the fitness/loss function Method of encoding the solution Probabilities of selection, mutation, recombination and

transposition

Comments on Philip SchrodtDiamond and Sekhon U. of Kansas

Queries: Pragmatic

How long does this take to run? Does running it in parallel provide a significant increase in

speed? GA’s are inherently parallel at the point where fitness/loss functions are

evaluated, but the communications costs are only justified if these functions are computationally intensive

How consistently does the method converge to a particular optimum matching? Is it sensitive to the initial values of the simulation

Comments on Philip SchrodtDiamond and Sekhon U. of Kansas

Queries: Comparative Advantage

How does the GA compare to a conventional numerical optimization algorithm?

Hill-climbing Simulated annealing

How does the parameterized formulation compare with simply selecting matching cases directly?

Note that this scales with N2

Directly selecting the subset would be completely nonparametric and well-suited to the strengths of genetic algorithms

“We are all agreed that your theory is crazy. The question which divides us is whether it is crazy enough to have a chance of being correct. My own feeling is that it is not crazy enough”—Niels Bohr

Comments on Philip SchrodtDiamond and Sekhon U. of Kansas