Politics as statistical learner

Greedy algorithms as pragmatism
Game theory
Learning agents
Federated learning
Machines of loving grace
Loss functions and public risk aversion
Legibility and labelled data sets
Evolutionary learning in institutions
Dimension reduction of politics
References

A Centrifugal Governor, the origin point both of cybernetic theory and signal processing thanks to James Clerk Maxwell’s analysis.

In mind-as-ml I wondered about whether statistical learning models can give us insight into the individual human mind. Here I wonder if we can learn anything about humans en masse from statistical analogies. Probably, since this spans Herbert Simon’s interests, I should be ransacking his work more thoroughly.

Greedy algorithms as pragmatism

Contrast with utopian ideals as analytic solutions.

Game theory

Institutions and adversarial robustness.

Learning agents

See sociology of information.

Federated learning

Machines of loving grace

The cybernetics folks. Politics as operations research. Insert Adam Curtis documentary here, and a brief history of Operations Research.

TODO: Mention Pigs for the Ancestors, and the Water Temples (Lansing 2000; Rappaport 1967; Sage 2013).

Loss functions and public risk aversion

Which experiments do we permit the state to do?

Legibility and labelled data sets

Which census category do you fit in and which benefits do you reap thereby? Affirmative action, Trans-exclusionary Radical Feminists etc.

Evolutionary learning in institutions

Dimension reduction of politics

See political aces

References

Apicella, Coren L., and Joan B. Silk. 2019. “The Evolution of Human Cooperation.” Current Biology 29 (11): R447–50.

Barkow, Jerome H., Leda Cosmides, and John Tooby. 1995. The adapted mind: evolutionary psychology and the generation of culture. [2nd. ed.], 1srt issued as an Oxford university press paperback, cop. 1992. New York (N.Y.): Oxford university press.

Bieniawski, Stefan, and David H. Wolpert. 2004. “Adaptive, Distributed Control of Constrained Multi-Agent Systems.” In Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems-Volume 3, 4:1230–31. IEEE Computer Society.

Lansing, J Stephen. 2000. “Foucault and the Water Temples: A Reply to Helmreich.” Critique of Anthropology 20: 309–18.

Leibon, Gregory, Scott Pauls, Dan Rockmore, and Robert Savell. 2010. “Statistical Learning for Complex Systems an Example-Driven Introduction.”

Newell, Barry, and Robert Wasson. 2002. “Social System Vs Solar System: Why Policy Makers Need History.” In. Grenoble.

Rappaport, Roy A. 1967. Pigs for the Ancestors : Ritual in the Ecology of a New Guinea People. New Haven: Yale Y.P.

Rosicky, Anton’ın. 2001. “Information and Social Systems Evolution.” In.

Sage, Andrew P. 2013. “Cybernetics and Complex Adaptive Systems.” In Encyclopedia of Operations Research and Management Science, edited by Saul I. Gass and Michael C. Fu, 339–48. Boston, MA: Springer US.

Simon, Herbert A. 1962. “The Architecture of Complexity.” Proceedings of the American Philosophical Society 106 (6): 467–82.

———. 1996. The Sciences of the Artificial. The MIT Press.

Sornette, Didier. 2009. “Dragon-Kings, Black Swans and the Prediction of Crises.” arXiv:0907.4290 [Physics] 2 (1).

Sornette, Didier, and Peter Cauwels. 2015. “Managing Risk in a Creepy World.” Journal of Risk Management in Financial Institutions 8 (1): 83–108.

Sterman, John D. 2009. Business dynamics: systems thinking and modeling for a complex world. Nachdr. Boston: Irwin/McGraw-Hill.

Walker, Brian H, and Marco A Janssen. 2002. “Rangelands, Pastoralists and Governments: Interlinked Systems of People and Nature.” Philosophical Transactions of the Royal Society B: Biological Sciences 357 (1421): 719.