The Myth of the Rational Market: Nudging Each Other Away from Fool’s Gold

A flurry of media commentary and several new books are focused on the recent financial crisis and near economic collapse. A Newsweek article by Zakaria (2009), “Greed is Good (To a Point),” suggests reconsidering the role of greed in capitalism. This is also the theme in Fools Gold (Tett, 2009), a story about the way derivatives markets have evolved: showing greed at its worst. In many ways this is the core source of the current set of problems. In some sense, these perspectives are integrated in The Myth of the Rational Market by Fox (2009), who traces the thinking on the efficient market hypothesis, now understood for what it is: a myth. Both books are based in large part on interviews with major players in the crisis. There are also books drawing mainly on science, but still quite accessible to general readers, as represented in Nudge by Thaler and Sunstein (2008). Both have done extensive research on human foibles in economic choice. There is also Animal Spirits (Akerlof and Schiller, 2009), a book about what Keynesian economics is really about, a look at human forces at work. Akerlof is a Nobel prize winner in economics, who before this has pointed to the problems with presuming rationality in real markets. Schiller is one of the few economists who predicted these events.

Applications of Linear Programming to Coding Theory

Maximum-likelihood decoding is often the optimal decoding rule one can use, but it is very costly to implement in a general setting. Much effort has therefore been dedicated to find efficient decoding algorithms that either achieve or approximate the error-correcting performance of the maximum-likelihood decoder. This dissertation examines two approaches to this problem. In 2003 Feldman and his collaborators defined the linear programming decoder, which operates by solving a linear programming relaxation of the maximum-likelihood decoding problem. As with many modern decoding algorithms, is possible for the linear programming decoder to output vectors that do not correspond to codewords; such vectors are known as pseudocodewords. In this work, we completely classify the set of linear programming pseudocodewords for the family of cycle codes. For the case of the binary symmetric channel, another approximation of maximum-likelihood decoding was introduced by Omura in 1972. This decoder employs an iterative algorithm whose behavior closely mimics that of the simplex algorithm. We generalize Omura's decoder to operate on any binary-input memoryless channel, thus obtaining a soft-decision decoding algorithm. Further, we prove that the probability of the generalized algorithm returning the maximum-likelihood codeword approaches 1 as the number of iterations goes to infinity.