Spatial voting

According to standard rational choice theory, as commonly used in political science and economics, an agent’s fundamental preferences are exogenously fixed, and any preference change over decision options is due to Bayesian information learning. Although elegant and parsimonious, such a model fails to account for preference change driven by experiences or psychological changes distinct from information learning. We develop a model of non-informational preference change. Alternatives are modelled as points in some multidimensional space, only some of whose dimensions play a role in shaping the agent’s preferences. Any change in these ‘motivationally salient’ dimensions can change the agent’s preferences. How it does so is described by a new representation theorem. Our model not only captures a wide range of frequently observed phenomena, but also generalizes some standard representations of preferences in political science and economics.
Preprint: http://personal.lse.ac.uk/list/PDF-files/PreferenceChange20July09.pdf

In this paper we provide an application-oriented characterization of a class of distance functions monotonically related to the Euclidean distance in terms of some general properties of distance functions between real-valued vectors. Our analysis hinges upon two fundamental properties of distance functions that we call "value-sensitivity" and "order-sensitivity". We show how these two general properties, combined with natural monotonicity considerations, lead to characterization results that single out several versions of Euclidean distance from the wide class of separable distance functions. We then discuss and motivate our results in two different and apparently unrelated application areas -mobility measurement and spatial voting theory -and propose our characterization as a test for deciding whether Euclidean distance (or some suitable variant) should be used in your favourite application context. JEL Classification Numbers: C0, C53, D63, D72. This is the pre-print authors' version of the paper: M. D'Agostino, V. Dardanoni. What's so special about Euclidean distance? A characterization with applications to mobility and spatial voting. Social Choice and Welfare 33(2009):211-233.

IngenIería e InvestIgacIón vol. 37 n.° 3, december-2017 (37-44) 37 Detection and location of surfaces in a 3D environment through a single transducer and ultrasonic spherical caps Detección y localización de superficies en un ambiente 3D a través de un solo sensor y casquetes esféricos ultrasónicos ABSTRACT In this paper, an ultrasonic arc map method for flat mapping is extended to three-dimensional space replacing the circumference arcs by spherical caps. An enclosed environment is scanned by employing a single ultrasonic device. The range, position, and orientation of the transducer are used to digitize the uncertainty caps and place them in a three-dimensional map. Through the spatial voting method, the generated voxels are elected in order to distinguish those which mark the true position of an obstacle and discard those that are produced by cross talk, noise, fake ranges, and angular resolution. The results show that it is possible to obtain sufficient information to build a three-dimensional map for navigation by employing inexpensive sensors and a low power data processing.

This paper presents a novel algorithm for generating wavelet transform basis functions from given 1-D time series data.
Our method finds an information theory based sub-sample rate to extract a basis function from the data and also defines
the overall sample size. The sub-sampled data is used as the basis function for the wavelet transform. Doing so allows
the basis function to be adaptively driven by the data itself and requires no a priori modeling or assumptions about the
basis function. New features for discrimination are developed and their performance evaluated.