The marginal utility of money: A modern Marshallian approach to consumer choice

We reformulate neoclassical consumer choice by focusing on lambda, the marginal utility of money. As the opportunity cost of current expenditure, lambda is approximated by the slope of the indirect utility function of the continuation. We argue that lambda can largely supplant the role of an arbitrary budget constraint in partial equilibrium analysis. The result is a better grounded, more flexible and more intuitive approach to consumer choice.

A Behavioural Model of Choice in the Presence of Decision Conflict

This paper proposes a model of choice that does not assume completeness of the decision maker’s preferences. The model explains in a natural way, and within a unified framework of choice when preference-incomparable options are present, four behavioural phenomena: the attraction effect, choice deferral, the strengthening of the attraction effect when deferral is per-missible, and status quo bias. The key element in the proposed decision rule is that an individual chooses an alternative from a menu if it is worse than no other alternative in that menu and is also better than at least one. Utility-maximising behaviour is included as a special case when preferences are complete. The relevance of the partial dominance idea underlying the proposed choice procedure is illustrated with an intuitive generalisation of weakly dominated strategies and their iterated deletion in games with vector payoffs.

Tractable Consumer Choice

We derive a rational model of separable consumer choice which can also serve as a behavioral model. The central construct is [lambda] , the marginal utility of money, derived from the consumer's rest-of-life problem. We present a robust approximation of [lambda], and show how to incorporate liquidity constraints, indivisibilities and adaptation to a changing environment. We fi nd connections with numerous historical and recent constructs, both behavioral and neoclassical, and draw contrasts with standard partial equilibrium analysis. The result is a better grounded, more flexible and more intuitive description of consumer choice.

Tractable valuations under uncertainty

I put forward a concise and intuitive formula for the calculation of the valuation for a good in the presence of the expectation that further, related, goods will soon become available. This valuation is tractable in the sense that it does not require the explicit resolution of the consumerís life-time problem.

von Economo neurons in autism: A stereologic study of the frontoinsular cortex in children.

The presence of von Economo neurons (VENs) in the frontoinsular cortex (FI) has been linked to a possible role in the integration of bodily feelings, emotional regulation, and goal-directed behaviors. They have also been implicated in fast intuitive evaluation of complex social situations. Several studies reported a decreased number of VENs in neuropsychiatric diseases in which the "embodied" dimension of social cognition is markedly affected. Neuropathological analyses of VENs in patients with autism are few and did not report alterations in VEN numbers. In this study we re-evaluated the possible presence of changes in VEN numbers and their relationship with the diagnosis of autism. Using a stereologic approach we quantified VENs and pyramidal neurons in layer V of FI in postmortem brains of four young patients with autism and three comparably aged controls. We also investigated possible autism-related differences in FI layer V volume. Patients with autism consistently had a significantly higher ratio of VENs to pyramidal neurons (p=0.020) than control subjects. This result may reflect the presence of neuronal overgrowth in young patients with autism and may also be related to alterations in migration, cortical lamination, and apoptosis. Higher numbers of VENs in the FI of patients with autism may also underlie a heightened interoception, described in some clinical observations.

Parental education and family characteristics: educational opportunities across cohorts in Italy and Spain

Drawing on data contained in the 2005 EU-SILC, this paper investigates the disparities in educational opportunities in Italy and Spain. Its main objective is to analyse the predicted probabilities of successfully completing upper-secondary and tertiary education for individuals with different parental backgrounds, and the changes in these probabilities across birth cohorts extending from 1940 to 1980. The results suggest that the disparities in tertiary education opportunities in Italy tend to increase over time. By contrast, the gap in educational opportunity in Spain shows a marked decrease across the cohorts. Moreover, by using an intuitive decomposition strategy, the paper shows that a large part of the educational gap between individuals of different backgrounds is “composed” of the difference in the endowment of family characteristics. Specifically, it seems that more highly educated parents are more able to endow their children with a better composition of family characteristics, which accounts for a significant proportion of the disparities in educational opportunity.

Inflation dynamics and the New Keynesian Phillips curve in EU-4

The paper seeks to shed light on inflation dynamics of four new EU member states: the Czech Republic, Hungary, Poland and Slovakia. To this end, the New Keynesian Phillips curve augmented for open economies is estimated and additional statistical tests applied. We find the following. (1) The claim of New Keynesians that the real marginal cost is the main inflation-forcing variable is fragile. (2) Inflation seems to be driven by external factors. (3) Although inflation holds a forward-looking component, the backward-looking component is substantial. An intuitive explanation for higher inflation persistence may be rather adaptive than rational price setting of local firms.

DNA evidence, probabilistic evaluation and collaborative tests.

Forensic scientists working in 12 state or private laboratories participated in collaborative tests to improve the reliability of the presentation of DNA data at trial. These tests were motivated in response to the growing criticism of the power of DNA evidence. The experts' conclusions in the tests are presented and discussed in the context of the Bayesian approach to interpretation. The use of a Bayesian approach and subjective probabilities in trace evaluation permits, in an easy and intuitive manner, the integration into the decision procedure of any revision of the measure of uncertainty in the light of new information. Such an integration is especially useful with forensic evidence. Furthermore, we believe that this probabilistic model is a useful tool (a) to assist scientists in the assessment of the value of scientific evidence, (b) to help jurists in the interpretation of judicial facts and (c) to clarify the respective roles of scientists and of members of the court. Respondents to the survey were reluctant to apply this methodology in the assessment of DNA evidence.

Evolution of decision-making and learning under fluctuating social environments

The capacity to learn to associate sensory perceptions with appropriate motor actions underlies the success of many animal species, from insects to humans. The evolutionary significance of learning has long been a subject of interest for evolutionary biologists who emphasize the bene¬fit yielded by learning under changing environmental conditions, where it is required to flexibly switch from one behavior to another. However, two unsolved questions are particularly impor¬tant for improving our knowledge of the evolutionary advantages provided by learning, and are addressed in the present work. First, because it is possible to learn the wrong behavior when a task is too complex, the learning rules and their underlying psychological characteristics that generate truly adaptive behavior must be identified with greater precision, and must be linked to the specific ecological problems faced by each species. A framework for predicting behavior from the definition of a learning rule is developed here. Learning rules capture cognitive features such as the tendency to explore, or the ability to infer rewards associated to unchosen actions. It is shown that these features interact in a non-intuitive way to generate adaptive behavior in social interactions where individuals affect each other's fitness. Such behavioral predictions are used in an evolutionary model to demonstrate that, surprisingly, simple trial-and-error learn¬ing is not always outcompeted by more computationally demanding inference-based learning, when population members interact in pairwise social interactions. A second question in the evolution of learning is its link with and relative advantage compared to other simpler forms of phenotypic plasticity. After providing a conceptual clarification on the distinction between genetically determined vs. learned responses to environmental stimuli, a new factor in the evo¬lution of learning is proposed: environmental complexity. A simple mathematical model shows that a measure of environmental complexity, the number of possible stimuli in one's environ¬ment, is critical for the evolution of learning. In conclusion, this work opens roads for modeling interactions between evolving species and their environment in order to predict how natural se¬lection shapes animals' cognitive abilities. - La capacité d'apprendre à associer des sensations perceptives à des actions motrices appropriées est sous-jacente au succès évolutif de nombreuses espèces, depuis les insectes jusqu'aux êtres hu¬mains. L'importance évolutive de l'apprentissage est depuis longtemps un sujet d'intérêt pour les biologistes de l'évolution, et ces derniers mettent l'accent sur le bénéfice de l'apprentissage lorsque les conditions environnementales sont changeantes, car dans ce cas il est nécessaire de passer de manière flexible d'un comportement à l'autre. Cependant, deux questions non résolues sont importantes afin d'améliorer notre savoir quant aux avantages évolutifs procurés par l'apprentissage. Premièrement, puisqu'il est possible d'apprendre un comportement incorrect quand une tâche est trop complexe, les règles d'apprentissage qui permettent d'atteindre un com¬portement réellement adaptatif doivent être identifiées avec une plus grande précision, et doivent être mises en relation avec les problèmes écologiques spécifiques rencontrés par chaque espèce. Un cadre théorique ayant pour but de prédire le comportement à partir de la définition d'une règle d'apprentissage est développé ici. Il est démontré que les caractéristiques cognitives, telles que la tendance à explorer ou la capacité d'inférer les récompenses liées à des actions non ex¬périmentées, interagissent de manière non-intuitive dans les interactions sociales pour produire des comportements adaptatifs. Ces prédictions comportementales sont utilisées dans un modèle évolutif afin de démontrer que, de manière surprenante, l'apprentissage simple par essai-et-erreur n'est pas toujours battu par l'apprentissage basé sur l'inférence qui est pourtant plus exigeant en puissance de calcul, lorsque les membres d'une population interagissent socialement par pair. Une deuxième question quant à l'évolution de l'apprentissage concerne son lien et son avantage relatif vis-à-vis d'autres formes plus simples de plasticité phénotypique. Après avoir clarifié la distinction entre réponses aux stimuli génétiquement déterminées ou apprises, un nouveau fac¬teur favorisant l'évolution de l'apprentissage est proposé : la complexité environnementale. Un modèle mathématique permet de montrer qu'une mesure de la complexité environnementale - le nombre de stimuli rencontrés dans l'environnement - a un rôle fondamental pour l'évolution de l'apprentissage. En conclusion, ce travail ouvre de nombreuses perspectives quant à la mo¬délisation des interactions entre les espèces en évolution et leur environnement, dans le but de comprendre comment la sélection naturelle façonne les capacités cognitives des animaux.

Dynamic simulation of regulatory networks using SQUAD.

BACKGROUND: The ambition of most molecular biologists is the understanding of the intricate network of molecular interactions that control biological systems. As scientists uncover the components and the connectivity of these networks, it becomes possible to study their dynamical behavior as a whole and discover what is the specific role of each of their components. Since the behavior of a network is by no means intuitive, it becomes necessary to use computational models to understand its behavior and to be able to make predictions about it. Unfortunately, most current computational models describe small networks due to the scarcity of kinetic data available. To overcome this problem, we previously published a methodology to convert a signaling network into a dynamical system, even in the total absence of kinetic information. In this paper we present a software implementation of such methodology. RESULTS: We developed SQUAD, a software for the dynamic simulation of signaling networks using the standardized qualitative dynamical systems approach. SQUAD converts the network into a discrete dynamical system, and it uses a binary decision diagram algorithm to identify all the steady states of the system. Then, the software creates a continuous dynamical system and localizes its steady states which are located near the steady states of the discrete system. The software permits to make simulations on the continuous system, allowing for the modification of several parameters. Importantly, SQUAD includes a framework for perturbing networks in a manner similar to what is performed in experimental laboratory protocols, for example by activating receptors or knocking out molecular components. Using this software we have been able to successfully reproduce the behavior of the regulatory network implicated in T-helper cell differentiation. CONCLUSION: The simulation of regulatory networks aims at predicting the behavior of a whole system when subject to stimuli, such as drugs, or determine the role of specific components within the network. The predictions can then be used to interpret and/or drive laboratory experiments. SQUAD provides a user-friendly graphical interface, accessible to both computational and experimental biologists for the fast qualitative simulation of large regulatory networks for which kinetic data is not necessarily available.

Aplicació de gestió web per a la Fundació CIM

Mantenir la informació ordenada i accessible és essencial per qualsevol empresa avui en dia. Es desenvoluparà una aplicació en base web que serveixi per integrar la informació de l’empresa “Fundació CIM” respecte als clients, factures i ofertes (pressupostos). A més, el software permetrà també la creació i el manteniment de factures i albarans d’una manera ràpida i intuïtiva, millorant així el mètode actual basat en fulls de Microsoft Excel i Microsoft Access. S’establiran diferents rangs de permisos als diferents usuaris segons els seus rols.

La théorie décisionnelle appliquée à l'orientation: Le modèle PIC comme outil complémentaire

L'approche décisionnelle met l'accent sur la manière dont l'individu aborde et négocie les décisions à différents moments de son parcours. Le modèle PIC propose un outil qui permet d'accompagner le processus décisionnel systématiquement. Souvent les conseillers ont tendance à le suivre de manière intuitive. Cependant, un suivi structuré pourrait améliorer l'efficacité de leurs interventions.

Diseño de la herramienta G.I.A. (Gestión Integral de Accesibilidad)

Con este PFC se ha pretendido desarrollar una herramienta que permita a organismos encargados de la gestión de la accesibilidad la realización de Planes Integrales de Accesibilidad de forma ágil e intuitiva, consiguiendo con ello mejorar la accesibilidad global dentro de un municipio y por consiguiente a los ciudadanos en general. De esta manera se dispondrá de información actualizada que reduzca las barreras arquitectónicas y mejore la accesibilidad universal de los espacios públicos.

Usabilitat en aplicacions comptables

En aquest projecte es tracta de la facilitat d'ús de les aplicacions comptables, centrada en la definició d'una interfície d'usuari que faci que la utilització d'aquest tipus d'aplicacions sigui com més intuïtiu millor i permeti a l'usuari d'introduir un gran nombre d'apunts comptables en un temps limitat.

Steady-state expression of self-regulated genes.

MOTIVATION: Regulatory gene networks contain generic modules such as feedback loops that are essential for the regulation of many biological functions. The study of the stochastic mechanisms of gene regulation is instrumental for the understanding of how cells maintain their expression at levels commensurate with their biological role, as well as to engineer gene expression switches of appropriate behavior. The lack of precise knowledge on the steady-state distribution of gene expression requires the use of Gillespie algorithms and Monte-Carlo approximations. METHODOLOGY: In this study, we provide new exact formulas and efficient numerical algorithms for computing/modeling the steady-state of a class of self-regulated genes, and we use it to model/compute the stochastic expression of a gene of interest in an engineered network introduced in mammalian cells. The behavior of the genetic network is then analyzed experimentally in living cells. RESULTS: Stochastic models often reveal counter-intuitive experimental behaviors, and we find that this genetic architecture displays a unimodal behavior in mammalian cells, which was unexpected given its known bimodal response in unicellular organisms. We provide a molecular rationale for this behavior, and we implement it in the mathematical picture to explain the experimental results obtained from this network.