Corporate Management of Highly Dynamic Risks: The Case of Terrorism Insurance in Germany

This article extends the theory of corporate risk management to encompass highly dynamic risks. Taking Viscusi'�s (1989) prospective reference from the context of individual decision making and applying it to a corporate context we propose a theory of how corporations process new information. Using unique data on all terrorism insurance policies sold in Germany we find support for this concept of risk-updating by showing that the demand for terrorism insurance is strongly determined by the recent occurrence of terrorist attacks.Corporate Insurance, Risk Management, Terrorism Insurance, Expected Utility, Prospect Theory

Learning agent's spatial configuration from sensorimotor invariants

The design of robotic systems is largely dictated by our purely human intuition about how we perceive the world. This intuition has been proven incorrect with regard to a number of critical issues, such as visual change blindness. In order to develop truly autonomous robots, we must step away from this intuition and let robotic agents develop their own way of perceiving. The robot should start from scratch and gradually develop perceptual notions, under no prior assumptions, exclusively by looking into its sensorimotor experience and identifying repetitive patterns and invariants. One of the most fundamental perceptual notions, space, cannot be an exception to this requirement. In this paper we look into the prerequisites for the emergence of simplified spatial notions on the basis of a robot's sensorimotor flow. We show that the notion of space as environment-independent cannot be deduced solely from exteroceptive information, which is highly variable and is mainly determined by the contents of the environment. The environment-independent definition of space can be approached by looking into the functions that link the motor commands to changes in exteroceptive inputs. In a sufficiently rich environment, the kernels of these functions correspond uniquely to the spatial configuration of the agent's exteroceptors. We simulate a redundant robotic arm with a retina installed at its end-point and show how this agent can learn the configuration space of its retina. The resulting manifold has the topology of the Cartesian product of a plane and a circle, and corresponds to the planar position and orientation of the retina.Comment: 26 pages, 5 images, published in Robotics and Autonomous System

The Head Turning Modulation System: An Active Multimodal Paradigm for Intrinsically Motivated Exploration of Unknown Environments

Over the last 20 years, a significant part of the research in exploratory robotics partially switches from looking for the most efficient way of exploring an unknown environment to finding what could motivate a robot to autonomously explore it. Moreover, a growing literature focuses not only on the topological description of a space (dimensions, obstacles, usable paths, etc.) but rather on more semantic components, such as multimodal objects present in it. In the search of designing robots that behave autonomously by embedding life-long learning abilities, the inclusion of mechanisms of attention is of importance. Indeed, be it endogenous or exogenous, attention constitutes a form of intrinsic motivation for it can trigger motor command toward specific stimuli, thus leading to an exploration of the space. The Head Turning Modulation model presented in this paper is composed of two modules providing a robot with two different forms of intrinsic motivations leading to triggering head movements toward audiovisual sources appearing in unknown environments. First, the Dynamic Weighting module implements a motivation by the concept of Congruence, a concept defined as an adaptive form of semantic saliency specific for each explored environment. Then, the Multimodal Fusion and Inference module implements a motivation by the reduction of Uncertainty through a self-supervised online learning algorithm that can autonomously determine local consistencies. One of the novelty of the proposed model is to solely rely on semantic inputs (namely audio and visual labels the sources belong to), in opposition to the traditional analysis of the low-level characteristics of the perceived data. Another contribution is found in the way the exploration is exploited to actively learn the relationship between the visual and auditory modalities. Importantly, the robot—endowed with binocular vision, binaural audition and a rotating head—does not have access to prior information about the different environments it will explore. Consequently, it will have to learn in real-time what audiovisual objects are of “importance” in order to rotate its head toward them. Results presented in this paper have been obtained in simulated environments as well as with a real robot in realistic experimental conditions

Codage discriminant appliqué à la reconnaissance de phonèmes

Nous proposons dans cet article une nouvelle méthode de codage appliquée à la reconnaissance de phonèmes. Le modèle en question est une extension au domaine non linéaire des méthodes de codage adaptatives habituellement utilisées en reconnaissance de la parole. Il est basé sur l'utilisation d'un réseau de neurones perceptron multicouches en prédiction. Nous montrons qu'il est possible d'introduire des informations de classe d'appartenance des signaux dès l'étape de codage, ce qui permet d'améliorer significativement les résultats en reconnaissance. Afin d'évaluer les performances du codeur NPC (Codeur Prédictif Neuronal), nous présentons une étude expérimentale à partir de phonèmes issus de la base Darpa-Ntimit. Les simulations présentées mettent en évidence une amélioration des taux de classification relativement aux codages classiques