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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.
QuickTime™ and aTIFF (LZW) decompressor
are needed to see this picture.
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
QuickTime™ and aTIFF (Uncompressed) decompressor
are needed to see this picture.
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