Good Predictions and Bad Policies

Relatively little has been said on economic policy by participants in the debate on the realisticness of assumptions in economic models. What has been said is that a `Friedmanian´ methodology which accepts unrealistic assumptions and is only concerned with correct predictions is appropriate from the perspective of a practical economist who is in charge of designing policy. This paper tries to show that this is not true. Even if a model provides very accurate predictions of an event, its ability to provide valid explanations is determined by the realisticness of its underlying assumptions. Different assumptions yield different explanations and unrealistic assumptions tend to provide no explanation at all. There is a strong relation between the way a phenomenon is explained and understood and the actions that are consequently recommended. Therefore, a model based on unrealistic assumptions is not a reliable source of advice on policy.Milton Friedman, unrealistic assumptions, economic policy, economic models, instrumentalism.

Relaxation oscillations, pulses, and travelling waves in the diffusive Volterra delay-differential equation

The diffusive Volterra equation with discrete or continuous delay is studied in the limit of long delays using matched asymptotic expansions. In the case of continuous delay, the procedure was explicitly carried out for general normalized kernels of the form Sigma/sub n=p//sup N/ g/sub n/(t/sup n//T/sup n+1/)e/sup -t/T/, pges2, in the limit in which the strength of the delayed regulation is much greater than that of the instantaneous one, and also for g/sub n/=delta/sub n2/ and any strength ratio. Solutions include homogeneous relaxation oscillations and travelling waves such as pulses, periodic wavetrains, pacemakers and leading centers, so that the diffusive Volterra equation presents the main features of excitable media

Perceptions and Acceptance of Desalinated Seawater for Irrigation: A Case Study in the Níjar District (Southeast Spain)

In the context of increasing demand for irrigation water—but, at the same time, with the constraints in the supply from traditional resources—desalinated seawater has been recognized as one of the alternative sources of water to increase the supply for agricultural irrigation. However, its use among farmers has not yet started to expand. Policy makers need to understand what is causing the low acceptance levels of farmers, and how their attitudes could be improved. This is the first study that has conducted an analysis of farmers’ perceptions and acceptance of the use of desalinated seawater for irrigation. The study is based on collected data from a survey completed by farmers in southeastern Spain who do not use desalinated seawater. The main results indicate that desalinated seawater as a water supply source has the lowest acceptance level for farmers. Barriers for its use are price, the need for additional fertilization, and the perception that it would negatively affect the yield and crop quality. The farmers’ general level of knowledge about the impact of using desalinated seawater in agriculture is extremely low. Furthermore, farmers consider it a priority that their startup investment should be subsidized and that water prices should be reduced. Based on the study findings, this paper makes recommendations for the decision-making process in order to improve farmers’ acceptance levels

Codimension two and three Kneser Transversals

Let $k,d,\lambda \geqslant 1$ be integers with $d\geqslant \lambda$ and let $X$ be a finite set of points in $\mathbb{R}^{d}$. A $(d-\lambda)$-plane $L$ transversal to the convex hulls of all $k$-sets of $X$ is called Kneser transversal. If in addition $L$ contains $(d-\lambda)+1$ points of $X$, then $L$ is called complete Kneser transversal.In this paper, we present various results on the existence of (complete) Kneser transversals for $\lambda =2,3$. In order to do this, we introduce the notions of stability and instability for (complete) Kneser transversals. We first give a stability result for collections of $d+2(k-\lambda)$ points in $\mathbb{R}^d$ with $k-\lambda\geqslant 2$ and $\lambda =2,3$. We then present a description of Kneser transversals $L$ of collections of $d+2(k-\lambda)$ points in $\mathbb{R}^d$ with $k-\lambda\geqslant 2$ for $\lambda =2,3$. We show that either $L$ is a complete Kneser transversal or it contains $d-2(\lambda-1)$ points and the remaining $2(k-1)$ points of $X$ are matched in $k-1$ pairs in such a way that $L$ intersects the corresponding closed segments determined by them. The latter leads to new upper and lower bounds (in the case when $\lambda =2$ and $3$) for $m(k,d,\lambda)$ defined as the maximum positive integer $n$ such that every set of $n$ points (not necessarily in general position) in $\mathbb{R}^{d}$ admit a Kneser transversal.Finally, by using oriented matroid machinery, we present some computational results (closely related to the stability and unstability notions). We determine the existence of (complete) Kneser transversals for each of the $246$ different order types of configurations of $7$ points in $\mathbb{R}^3$

BRAIN COMPUTER INTERFACE - Application of an Adaptive Bi-stage Classifier based on RBF-HMM

Brain Computer Interface is an emerging technology that allows new output paths to communicate the users intentions without the use of normal output paths, such as muscles or nerves. In order to obtain their objective, BCI devices make use of classifiers which translate inputs from the users brain signals into commands for external devices. This paper describes an adaptive bi-stage classifier. The first stage is based on Radial Basis Function neural networks, which provides sequences of pre-assignations to the second stage, that it is based on three different Hidden Markov Models, each one trained with pre-assignation sequences from the cognitive activities between classifying. The segment of EEG signal is assigned to the HMMwith the highest probability of generating the pre-assignation sequence. The algorithm is tested with real samples of electroencephalografic signal, from five healthy volunteers using the cross-validation method. The results allow to conclude that it is possible to implement this algorithm in an on-line BCI device. The results also shown the huge dependency of the percentage of the correct classification from the user and the setup parameters of the classifier

Fine structure in the large n limit of the non-hermitian Penner matrix model

In this paper we apply results on the asymptotic zero distribution of the Laguerre polynomials to discuss generalizations of the standard large $n$ limit in the non-hermitian Penner matrix model. In these generalizations $g_n n\to t$, but the product $g_n n$ is not necessarily fixed to the value of the 't Hooft coupling $t$. If $t>1$ and the limit $l = \lim_{n\rightarrow \infty} |\sin(\pi/g_n)|^{1/n}$ exists, then the large $n$ limit is well-defined but depends both on $t$ and on $l$. This result implies that for $t>1$ the standard large $n$ limit with $g_n n=t$ fixed is not well-defined. The parameter $l$ determines a fine structure of the asymptotic eigenvalue support: for $l\neq 0$ the support consists of an interval on the real axis with charge fraction $Q=1-1/t$ and an $l$-dependent oval around the origin with charge fraction $1/t$. For $l=1$ these two components meet, and for $l=0$ the oval collapses to the origin. We also calculate the total electrostatic energy $\mathcal{E}$, which turns out to be independent of $l$, and the free energy $\mathcal{F}=\mathcal{E}-Q\ln l$, which does depend of the fine structure parameter $l$. The existence of large $n$ asymptotic expansions of $\mathcal{F}$ beyond the planar limit as well as the double-scaling limit are also discussed