Permis d’émission négociables et commerce international dans des marchés de concurrence imparfaite

Dans cet article, nous considérons des entreprises en concurrence imparfaite sur un marché international de bien. Elles ne sont pas toutes soumises à un marché de permis et les pouvoirs publics fixent le plafond de pollution. Nous montrons qu’une entreprise dominante sur le marché des permis utilise ce dernier pour s’approprier un avantage sur le marché du bien. Ce résultat élargit ainsi les hypothèses de la manipulation par exclusion. Les pouvoirs publics se comportent également de façon stratégique, en utilisant deux instruments – la dotation initiale et le plafond de pollution – pour maximiser le bien-être collectif. Le plafond de pollution qui en résulte est alors plus élevé qu’à l’équilibre concurrentiel.In this article, we consider firms competing in an imperfectly competitive international market. These firms are not all submitted to a pollution permit market. We show that a dominant firm being part of the pollution permit market can use it to obtain an advantage in the product market. This result enlarges assumptions about exclusionary manipulation. We also show that the government acts strategically as well, using two instruments to increase welfare: the pollution cap and the initial allocation. The resulting pollution cap is higher with respect to the one achieved without strategic behavior

Numerical approach of cyclic behavior of 316LN stainless steel based on a polycrystal modeling including strain gradients.

International audienceA non-local polycrystal approach, taking into account strain gradients, is proposed to simulate the 316LN stainless steel fatigue life curve in the hardening stage. Material parameters identification is performed on tensile curves corresponding to several 316LN polycrystals presenting different grain sizes. Applied to an actual 3D aggregate of 316LN stainless steel of 1,200 grains, this model leads to an accurate prediction of cyclic curves. Geometrical Necessary Dislocation densities related to the computed strain gradient are added to the micro-plasticity laws. Compared to standard models, this model predicts a decrease of the local stresses as well as a grain size effect

A computational model of perceptuo-motor processing in speech perception: learning to imitate and categorize synthetic CV syllables

International audienceThis paper presents COSMO, a Bayesian computational model, which is expressive enough to carry out syllable production, perception and imitation tasks using motor, auditory or perceptuo-motor information. An imitation algorithm enables to learn the articulatory-to-acoustic mapping and the link between syllables and correspond- ing articulatory gestures, from acoustic inputs only: syn- thetic CV syllables generated with a human vocal tract model. We compare purely auditory, purely motor and perceptuo-motor syllable categorization under various noise levels

COSMO (“Communicating about Objects using Sensory–Motor Operations”): A Bayesian modeling framework for studying speech communication and the emergence of phonological systems

International audienceWhile the origin of language remains a somewhat mysterious process, understanding how human language takes specific forms appears to be accessible by the experimental method. Languages, despite their wide variety, display obvious regularities. In this paper, we attempt to derive some properties of phonological systems (the sound systems for human languages) from speech communication principles. We introduce a model of the cognitive architecture of a communicating agent, called COSMO (for “Communicating about Objects using Sensory–Motor Operations') that allows a probabilistic expression of the main theoretical trends found in the speech production and perception literature. This enables a computational comparison of these theoretical trends, which helps us to identify the conditions that favor the emergence of linguistic codes. We present realistic simulations of phonological system emergence showing that COSMO is able to predict the main regularities in vowel, stop consonant and syllable systems in human languages

Sensorimotor learning in a Bayesian computational model of speech communication

International audienceAlthough sensorimotor exploration is a basic process within child development, clear views on the underlying computational processes remain challenging. We propose to compare eight algorithms for sensorimotor exploration, based on three components: " accommodation " performing a compromise between goal babbling and social guidance by a master, " local extrapolation " simulating local exploration of the sensorimotor space to achieve motor generalizations and " idiosyncratic babbling " which favors already explored motor commands when they are efficient. We will show that a mix of these three components offers a good compromise enabling efficient learning while reducing exploration as much as possible

Emergence du langage par jeux déictiques dans une société d'agents sensori-moteurs en interaction

International audienceIn this paper, we show how some properties of human language could emerge from the primitive deixis function. For this aim, we model a society of sensori-motor agents able to produce vocalizations and to point to objects in their environnement. We show how principles of the Dispersion Theory [6] and the Quantal Theory [13] could emerge from the interaction between these agents

A Bayesian framework for speech motor control

International audienceThe remarkable capacity of the speech motor system to adapt to various speech conditions is due to an excess of degrees of freedom, which enables producing similar acoustical properties with different sets of control strategies. To explain how the Central Nervous System selects one of the possible strategies, a common approach, in line with optimal motor control theories, is to model speech motor planning as the solution of an optimality problem based on cost functions. Despite the success of this approach, one of its drawbacks is the intrinsic contradiction between the concept of optimality and the observed experimental intra-speaker token-to-token variability. The present paper proposes an alternative approach by formulating feedforward optimal control in a probabilistic Bayesian modeling framework. This is illustrated by controlling a biomechanical model of the vocal tract for speech production and by comparing it with an existing optimal control model (GEPPETO). The essential elements of this optimal control model are presented first. From them the Bayesian model is constructed in a progressive way. Performance of the Bayesian model is evaluated based on computer simulations and compared to the optimal control model. This approach is shown to be appropriate for solving the speech planning problem while accounting for variability in a principled way

Modeling the concurrent development of speech perception and production in a Bayesian framework: COSMO, a Bayesian computational model of speech communication: Assessing the role of sensory vs. motor knowledge in speech perception

International audienceIt is now widely accepted that there is a functional relationship between the speech perception and production systems in the human brain. However, the precise mechanisms and role of this relationship still remain debated. The question of invariance and robustness in categorization are set at the center of the debate: how is stable information extracted from the variable sensory input in order to achieve speech comprehension? In this context, auditory (resp. motor, perceptuo-motor) theories propose that speech is categorized thanks to auditory (resp. motor, perceptuo-motor) processes. However, experimental evidence is still scarce and does not allow to clearly distinguish between the current theories and determine whether invariance in speech perception is of an auditory or motor type. This is why we developed COSMO, a Bayesian model comparing sensory and motor processes in the form of probability distributions which enable both theoretical developments and quantitative simulations. A first significant result in COSMO is an indistinguishability theorem: it is only by simulations of adverse conditions or partial learning that the specificity of sensory vs. motor processing can emerge and provide a basis for evaluation of the specific role of each sub-system. We present the COSMO model, and how its sensory and motor sub-systems are learned, then we describe simulations exploring the way these sub-systems differ during speech categorization. We discuss the experimental results in the light of a “narrowband vs. wideband” interpretation: the sensory sub-system is more precisely tuned to the frequently learned sensory input and hence more efficient in recognizing these inputs, providing a “narrowband” system. Conversely, the motor sub-system is less accurate to recognize learned sensory inputs but it has better generalization properties, making it more robust to unexpected variability which would provide it with “wideband” characteristics

A Unified Theoretical Bayesian Model of Speech Communication

International audienceBased on a review of models and theories in speech communication, this paper proposes an original Bayesian framework able to express each of them in a unified way. This framework allows to selectively incorporate motor processes in perception or auditory representations in production, thus implementing components of a perceptuo-motor link in speech communication processes. This provides a basis for future computational works on the joint study of perception, production and their coupling in speech communication

Assessing Idiosyncrasies in a Bayesian Model of Speech Communication

International audienceAlthough speakers of one specific language share the same phoneme representations, their productions can differ. We propose to investigate the development of these differences in production , called idiosyncrasies, by using a Bayesian model of communication. Supposing that idiosyncrasies appear during the development of the motor system, we present two versions of the motor learning phase, both based on the guidance of an agent master: " a repetition model " where agents try to imitate the sounds produced by the master and " a communication model " where agents try to replicate the phonemes produced by the master. Our experimental results show that only the " communication model " provides production idiosyncrasies, suggesting that idiosyncrasies are a natural output of a motor learning process based on a communicative goal