Naïve, Resolute or Sophisticated? A Study of Dynamic Decision Making

Dynamically inconsistent decision makers have to decide, implicitly or explicitly, what to do about their dynamic inconsistency. Economic theorists have identified three possible responses – to act naively (thus ignoring the dynamic inconsistency), to act resolutely (not letting their inconsistency affect their behaviour) or to act sophisticatedly (hence taking into account their inconsistency). We use data from a unique experiment (which observes both decisions and evaluations) in order to distinguish these three possibilities. We find that the majority of subjects are either naïve or resolute (with slightly more being naïve) but very few are sophisticated. These results have important implications for predicting the behaviour of people in dynamic situations.Dynamic decision making, naivety, sophistication, resoluteness, dynamic inconsistencies

Choquet OK?

There is a large theoretical literature in both economics and psychology on decision making under ambiguity (as distinct from risk) and many preference functionals proposed in this literature for describing behaviour in such contexts. However, the empirical literature is scarce and largely confined to testing between various proposed functionals. Using a new design, in which we create genuine ambiguity in the laboratory and can control the amount of ambiguity, we generate data which enables us to estimate several of the proposed preference functionals. In particular, we fit Subjective Expected Utility, Prospect Theory, Choquet Expected Utility, Maximin, Maximax, and Minimum Regret preference functionals, and examine how the fit changes when we vary the ambiguity. We find that the Choquet formulation performs best overall, though it is clear that different decision makers have different functionals. We also identify new decision rules which are not explicitly modelled in the literature.Ambiguity, Subjective Expected Utility, Prospect Theory, Choquet Expected Utility, Decision Making, Maximin, Maximax, Minimum Regret, Bingo Blower

Tunable fragmentation of organic molecules in laser ablation glow discharge time-of-flight mass spectrometry

A DC-pulsed glow discharge (GD) has distinct temporal regimes which are characterized by "softer” or "harder” ionization of analytes introduced into the discharge. It is thus possible to obtain both molecular weight and structural fragment information from the same spectra. In order to extend the capabilities of this technique a laser ablation (LA) sampling system was coupled to a DC-pulsed GD and to a time-of-flight (TOF) mass spectrometer (MS) for characterizing organic samples such as oleic acid, reserpine, two different peptides, and a polymer. Both hard and soft ionization regimes were studied. These LAGD-TOFMS results were compared to matrix-assisted laser desorption ionization (MALDI) spectra using the same compounds (i.e., analytes, concentration, and matrix). It was found that LAGD offers tunable ionization and provides a reduced matrix dependence. However, the sensitivity achieved by the prototype LAGD-TOFMS was significantly lower when compared with commercially available MALDI-TOFMS instrumentation. Since LAGD-TOFMS is rather new, some technical details to increase its sensitivity are discussed. Figure Schematics of molecular and structural information of oleic acid molecule during the different temporal regimes of a pulsed G

Choquet OK?

There is a large theoretical literature in both economics and psychology on decision making under ambiguity (as distinct from risk) and many preference functionals proposed in this literature for describing behaviour in such contexts. However, the empirical literature is scarce and largely confined to testing between various proposed functionals. Using a new design, in which we create genuine ambiguity in the laboratory and can control the amount of ambiguity, we generate data which enables us to estimate several of the proposed preference functionals. In particular, we fit Subjective Expected Utility, Prospect Theory, Choquet Expected Utility, Maximin, Maximax, and Minimum Regret preference functionals, and examine how the fit changes when we vary the ambiguity. We find that the Choquet formulation performs best overall, though it is clear that different decision makers have different functionals. We also identify new decision rules which are not explicitly modelled in the literature.There is a large theoretical literature in both economics and psychology on decision making under ambiguity (as distinct from risk) and many preference functionals proposed in this literature for describing behaviour in such contexts. However, the empirical literature is scarce and largely confined to testing between various proposed functionals. Using a new design, in which we create genuine ambiguity in the laboratory and can control the amount of ambiguity, we generate data which enables us to estimate several of the proposed preference functionals. In particular, we fit Subjective Expected Utility, Prospect Theory, Choquet Expected Utility, Maximin, Maximax, and Minimum Regret preference functionals, and examine how the fit changes when we vary the ambiguity. We find that the Choquet formulation performs best overall, though it is clear that different decision makers have different functionals. We also identify new decision rules which are not explicitly modelled in the literature.Non-Refereed Working Papers / of national relevance onl

The Descriptive and Predictive Adequacy of Theories of Decision Making Under Uncertainty/Ambiguity

In this paper we examine the performance of theories of decision making under uncertainty/ambiguity from the perspective of their descriptive and predictive power, taking into account the relative parsimony of the various theories. To this end, we employ an innovative experimental design which enables us to reproduce ambiguity in the laboratory in a transparent and non-probabilistic way. We find that judging theories on the basis of their theoretical appeal, or on their ability to do well in testing contexts, is not the same as judging them on the basis of their explanatory and predictive power. We also find that the more elegant theoretical models do not perform as well as simple rules of thumb.In this paper we examine the performance of theories of decision making under uncertainty/ambiguity from the perspective of their descriptive and predictive power, taking into account the relative parsimony of the various theories. To this end, we employ an innovative experimental design which enables us to reproduce ambiguity in the laboratory in a transparent and non-probabilistic way. We find that judging theories on the basis of their theoretical appeal, or on their ability to do well in testing contexts, is not the same as judging them on the basis of their explanatory and predictive power. We also find that the more elegant theoretical models do not perform as well as simple rules of thumb.Non-Refereed Working Papers / of national relevance onl

Optimización de matrices origen-destino estimadas a partir de datos georeferenciados en redes sociales

A la hora de aplicar cualquier política urbana de transporte es muy importante conocer el patrón de movilidad de la población y por lo tanto las matrices Origen Destino que la describen. Los grandes volúmenes de datos arrojados por las nuevas tecnologías permiten obtener información dinámica del comportamiento de sus usuarios. En contraposición con los métodos clásicos de obtención de las matrices O-D el uso de las redes sociales aporta una manera más económica de realizar el estudio y un mayor dinamismo. En este trabajo se desarrolla un método para la actualización de matrices Origen-Destino correspondientes a tráfico vehicular, a partir de la información obtenida de datos disponibles en la red social Twitter. Para actualizar una matriz OD anterior con nueva información se consideró un problema de maximización de entropía restringiendo la distancia a la matriz original. El enfoque presentado se aplicó a analizar la movilidad diaria de las personas de CABA obteniendo las matrices O-D que la caracterizan y los resultados obtenidos se compararon con estudios previos que se realizaron utilizando otras metodologías comprobando la viabilidad del nuevo enfoque propuesto

Effect of Cereals and Legumes Processing on In Situ Rumen Protein Degradability: A Review

The determination of the ruminal degradability rate of feeds, mainly starch and crude protein, is one of the most common methods to evaluate the nutritional value of ruminant feed. The protein requirements for ruminants are met from microbial protein and undegraded dietary protein digested in the small intestine. In order to reach maximum productivity, high-quality proteins are needed, and the requirement for undegraded dietary protein increases with the performance of the animal. This protein can be supplied by reducing the ruminal degradation to increase the amount of protein digested post-rumen, but the form in which a feed is administered influences degradability, and grain processing, especially, is a common practice to improve feed efficiency. Despite these aspects, studies on the effects of feed processing methods on protein degradability are limited, even though more and more ruminants are fed with processed feeds. For these reasons, this review investigated the protein degradability of different processed cereals and legumes in ruminants based on the analysis of available literature in order to take stock of the state of the art on this topic. Results showed that: First, the majority of the papers are focused on the energy aspects mainly due to carbohydrate-rich feeds; second, the majority of the studies in the literature are quite old, probably because the changes occurred in the animal testing legislation that made in vivo studies more and more difficult in the last 20 years; third, as a consequence, the few data available in recent years concern in vitro experiments; fourth, we found a high variability of the experimental conditions thus affecting protein degradability and making it quite difficult to compare the different results

Optimización de matrices origen-destino estimadas a partir de datos georeferenciados en redes sociales

A la hora de aplicar cualquier política urbana de transporte es muy importante conocer el patrón de movilidad de la población y por lo tanto las matrices Origen Destino que la describen. Los grandes volúmenes de datos arrojados por las nuevas tecnologías permiten obtener información dinámica del comportamiento de sus usuarios. En contraposición con los métodos clásicos de obtención de las matrices O-D el uso de las redes sociales aporta una manera más económica de realizar el estudio y un mayor dinamismo. En este trabajo se desarrolla un método para la actualización de matrices Origen-Destino correspondientes a tráfico vehicular, a partir de la información obtenida de datos disponibles en la red social Twitter. Para actualizar una matriz OD anterior con nueva información se consideró un problema de maximización de entropía restringiendo la distancia a la matriz original. El enfoque presentado se aplicó a analizar la movilidad diaria de las personas de CABA obteniendo las matrices O-D que la caracterizan y los resultados obtenidos se compararon con estudios previos que se realizaron utilizando otras metodologías comprobando la viabilidad del nuevo enfoque propuesto.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Optimización de matrices origen-destino estimadas a partir de datos georeferenciados en redes sociales

A la hora de aplicar cualquier política urbana de transporte es muy importante conocer el patrón de movilidad de la población y por lo tanto las matrices Origen Destino que la describen. Los grandes volúmenes de datos arrojados por las nuevas tecnologías permiten obtener información dinámica del comportamiento de sus usuarios. En contraposición con los métodos clásicos de obtención de las matrices O-D el uso de las redes sociales aporta una manera más económica de realizar el estudio y un mayor dinamismo. En este trabajo se desarrolla un método para la actualización de matrices Origen-Destino correspondientes a tráfico vehicular, a partir de la información obtenida de datos disponibles en la red social Twitter. Para actualizar una matriz OD anterior con nueva información se consideró un problema de maximización de entropía restringiendo la distancia a la matriz original. El enfoque presentado se aplicó a analizar la movilidad diaria de las personas de CABA obteniendo las matrices O-D que la caracterizan y los resultados obtenidos se compararon con estudios previos que se realizaron utilizando otras metodologías comprobando la viabilidad del nuevo enfoque propuesto.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Sleeping Beauty: Anesthesia May Promote Relapse in Dogs With Diffuse Large B-Cell Lymphoma in Complete Remission After Chemo-Immunotherapy

Surgery-induced stress and anesthesia-related immunosuppression are believed to play a critical role in human oncology patients. Studies have hypothesized that anesthesia influences patients' outcome, promoting tumor recurrence and metastasis. Aim of the study was to investigate whether anesthesia promoted relapse in dogs with diffuse large B-cell lymphoma (DLBCL). Medical records were searched for dogs with DLBCL, that were in complete remission (CR) after the same chemo-immunotherapy protocol. Dogs receiving anesthesia were included if the procedure was performed while in CR. Time to relapse (TTR) was obtained via Kaplan–Meier method. Association between anesthesia and relapse was assessed using a nested case-control design and estimated using conditional logistic regression. Sixty-one dogs with DLBCL were included. Overall median TTR was 329 days (95% CI, 281–377). Forty-eight (79%) dogs relapsed during the study period, while 13 (21%) were still in CR at data analysis closure. Eighteen (30%) dogs received anesthesia with opioids, propofol, and isoflurane or sevoflurane. The relative risk of lymphoma relapse for dogs undergoing anesthesia was significantly higher compared with dogs not undergoing anesthesia, with an odds ratio of 3.09 (P = 0.019) on multivariable analysis. Anesthesia may promote relapse in dogs with DLBCL treated with chemo-immunotherapy, although a role of perioperative stress cannot be ultimately excluded. Considering the high frequency of anesthetic procedures required for diagnostic and therapeutic protocols among oncology patients, it is of utmost interest to characterize the effects of single anesthetic agents on the immune system. Further prospective studies are needed to better define the impact of anesthesia on patients' outcome