AI and Geoeconomics

Drawing of the Eiffel Tower by Maurice Koechlin Science Classics Robust Statistics , 1981, New York, Wiley by Peter J. Huber. Peter J. Huber Peter J. Huber was born on March 25, 1934, in Wohlen, a small town in the Swiss countryside. He obtained a diploma in mathematics in 1958 and a Ph.D. in mathematics in 1961, both from ETH Zurich. His thesis was in pure mathematics, but he then decided to go into statistics. He spent 1961–1963 as a postdoc at the statistics department in Berkeley where he wrote his first and most famous paper on robust statistics, “Robust Estimation of a Location Parameter.” After a position as a visiting professor at Cornell University, he became a full professor at ETH Zurich. He worked at ETH until 1978, interspersed by visiting positions at Cornell, Yale, Princeton and Harvard. After leaving ETH, he held professor positions at Harvard University 1978–1988, at MIT 1988–1992, and finally at the University of Bayreuth from 1992 unti...

Thomas Sargent - Nobel Prize 2011 Le contexte post-crise financière Suie à la crise financière et systémique de 2008 et ses fortes répliques de 2011 qui se sont manifestées par une défiance vis-à-vis des dettes souveraines européennes, on constate que: ·          l’insatisfaction d’un investisseur qui subit  une perte d’amplitude D est plus élevée que sa satisfaction à percevoir un gain de la même amplitude D (voir la note de Simon Savage de GLG Partners « Skill, luck and human frailty » GLG Views, July 2011), ·          des pertes de confiance des agents économiques en leur modèle de décision peuvent apparaitre subitement. Dans un papier récent, « Robust Control in a Nonlinear DSGE [1] model » (février 2012), Rhys Bidder (Federal Reserve Bank of San Francisco) et Matthew Smith (Federal Reserve Board of Governors) soulignent le fait qu’après une longue période de s...

Decision under uncertainty with imperfect probabilistic description of reality The field of decision-making under uncertainty was pioneered in the 1950s by Dantzig [6] and Charnes and Cooper [5], who set the foundation for, respectively, stochastic programming and optimization under probabilistic constraints. While these classes of problems require very different models and solution techniques, they share the same assumption that the probability distributions of the random variables are known exactly, and despite Scarf's [10] early observation that we may have reason to suspect that the future demand will come from a distribution that differs from that governing past history in an unpredictable way," the majority of the research efforts in decision-making under uncertainty over the past decades have relied on the precise knowledge of the underlying probabilities . Even under this simplifying assumption, a number of computational issues arises, e.g., the need for multi-variate ...