Variations on the Theme of Conning in Mathematical Economics

The mathematization of economics is almost exclusively in terms of the mathematics of real analysis which, in turn, is founded on set theory (and the axiom of choice) and orthodox mathematical logic. In this paper I try to point out that this kind of mathematization is replete with economic infelicities. The attempt to extract these infelicities is in terms of three main examples: dynamics, policy and rational expectations and learning. The focus is on the role and reliance on standard xed point theorems in orthodox mathematical economics

The intrinsic dimension of biological data landscapes

Analyzing large volumes of high-dimensional data is an issue of fundamental importance in science and beyond. Several approaches work on the assumption that the important content of a dataset belongs to a manifold whose Intrinsic Dimension (ID) is much lower than the crude large number of coordinates. That manifold however is generally twisted and curved; in addition points on it will be non-uniformly distributed: two factors that make the identification of the ID and its exploitation really hard. Here we propose a new ID estimator using only the distance of the first and the second nearest neighbor of each point in the sample. This extreme minimality enables us to reduce the effects of curvature, of density variation, and the resulting computational cost. The ID estimator is theoretically exact in uniformly distributed data sets, and provides consistent measures in general. When used in combination with block analysis, it allows discriminating the relevant dimensions as a function of the block size. This allows estimating the ID even when the data lie on a manifold perturbed by a high-dimensional noise, a situation often encountered in real world data sets. Upon defining a notion of distance between protein sequences, This tools is used to estimate the ID of protein families, and to assess the consistency of generative models. Moreover, If coupled with a density estimator, our ID allows to measure the density of points by taking into account the space in which they actually lie, thus allowing for a cleaner estimation. Here we move a step further towards an automatic classification of protein sequences by using three new tools: our ID estimator, a density estimator and a clustering algorithm. We present the analysis performed on a Pfam PUA clan, showing that these combined tools allow to successfully separate protein domains into architectures. Finally, we present a generalized model for the estimation of the ID that is able to work in data sets with multiple dimensionalities: taking advantage of Bayesian inference techniques, the method allows discriminating manifolds with different dimensions as well as assigning all the points to the respective manifolds. We test the method on a molecular dynamics trajectory, showing that the folded state has a higher dimension with respect to the unfolded one

Adaptive robust control with statistical learning

In stochastic control problems the agent chooses the optimal strategy to maximise or minimise the performance criterion. The performance criterion can be either the expectation of a reward function for the standard control problem or the non-linear expectation for the robust control problem. In parameterised stochastic control problems, the agent needs to know the value of the model parameters in the stochastic system to specify the optimal strategy correctly. However, it is hardly the case that the agent knows the values of the model parameters. In this thesis, we aim to study a robust stochastic control problem where the agent does not know the values of the parameters of the underlying process. Therefore, we frame the stochastic control problem where we assume that the agent does not know the values of the model parameters. However, the agent uses the observable processes to estimate the values of the model parameters while simultaneously solving the stochastic control problem in a robust framework. There are two key components in this new stochastic control problem. The first component is the parameter estimation part where the agent uses the realisation of the underlying process to estimate the unknown parameters in the stochastic system. We particularly focus on online parameter estimation. The online estimator is an important ingredient for our stochastic control problem because this type of estimator allows the agent to obtain the optimal strategy in feedback form. The second component is the stochastic control part which is the question of how to design a time-consistent stochastic control problem that allows the agent to also estimate the parameters and optimise her strategy simultaneously. In this thesis, we address each component of the problem above in the continuous-time setting and then the utility maximisation problem under this framework is studied carefully

ISIPTA'07: Proceedings of the Fifth International Symposium on Imprecise Probability: Theories and Applications

B

Stochastic unfolding and homogenization of evolutionary gradient systems

The mathematical theory of homogenization deals with the rigorous derivation of effective models from partial differential equations with rapidly-oscillating coefficients. In this thesis we deal with modeling and homogenization of random heterogeneous media. Namely, we obtain stochastic homogenization results for certain evolutionary gradient systems. In particular, we derive continuum effective models from discrete networks consisting of elasto-plastic springs with random coefficients in the setting of evolutionary rate-independent systems. Also, we treat a discrete counterpart of gradient plasticity. The second type of problems that we consider are gradient flows. Specifically, we study continuum gradient flows driven by λ-convex energy functionals. In stochastic homogenization the derived deterministic effective equations are typically hardly-accessible for standard numerical methods. For this reason, we study approximation schemes for the effective equations that we obtain, which are well-suited for numerical analysis. For the sake of a simple treatment of these problems, we introduce a general procedure for stochastic homogenization – the stochastic unfolding method. This method presents a stochastic counterpart of the well-established periodic unfolding procedure which is well-suited for homogenization of media with periodic microstructure. The stochastic unfolding method is convenient for the treatment of equations driven by integral functionals with random integrands. The advantage of this strategy in regard to other methods in homogenization is its simplicity and the elementary analysis that mostly relies on basic functional analysis concepts, which makes it an easily accessible method for a wide audience. In particular, we develop this strategy in the setting that is suited for problems involving discrete-to-continuum transition as well as for equations defined on a continuum physical space. We believe that the stochastic unfolding method may also be useful for problems outside of the scope of this work