Should a non-rival public good always be provided centrally?

This paper discusses the problem of optimal design of a jurisdiction structure from the view point of a utilitarian social planner when individuals with identical utility functions for a non-rival public good and private consumption have private information about their contributive capacities. It shows that the superiority of a centralized provision of a non-rival public good over a federal one does not always hold. Specifically, when differences in individuals’ contributive capacities are large, it is better to provide the public good in several distinct jurisdictions rather than to pool these jurisdictions into a single one. In the specific situation where individuals have logarithmic utilities, the paper provides a complete characterization of the optimal jurisdiction structure in the two-type case.

The Human in the Loop: User Participation in Self-Adaptive Software

Self-adaptive software provides a profound solution for adapting applications to changing contexts in dynamic and heterogeneous environments. Having emerged from Autonomic Computing, it incorporates fully autonomous decision making based on predefined structural and behavioural models. The most common approach for architectural runtime adaptation is the MAPE-K adaptation loop implementing an external adaptation manager without manual user control. However, it has turned out that adaptation behaviour lacks acceptance if it does not correspond to a user’s expectations – particularly for Ubiquitous Computing scenarios with user interaction. Adaptations can be irritating and distracting if they are not appropriate for a certain situation. In general, uncertainty during development and at run-time causes problems with users being outside the adaptation loop. In a literature study, we analyse publications about self-adaptive software research. The results show a discrepancy between the motivated application domains, the maturity of examples, and the quality of evaluations on the one hand and the provided solutions on the other hand. Only few publications analysed the impact of their work on the user, but many employ user-oriented examples for motivation and demonstration. To incorporate the user within the adaptation loop and to deal with uncertainty, our proposed solutions enable user participation for interactive selfadaptive software while at the same time maintaining the benefits of intelligent autonomous behaviour. We define three dimensions of user participation, namely temporal, behavioural, and structural user participation. This dissertation contributes solutions for user participation in the temporal and behavioural dimension. The temporal dimension addresses the moment of adaptation which is classically determined by the self-adaptive system. We provide mechanisms allowing users to influence or to define the moment of adaptation. With our solution, users can have full control over the moment of adaptation or the self-adaptive software considers the user’s situation more appropriately. The behavioural dimension addresses the actual adaptation logic and the resulting run-time behaviour. Application behaviour is established during development and does not necessarily match the run-time expectations. Our contributions are three distinct solutions which allow users to make changes to the application’s runtime behaviour: dynamic utility functions, fuzzy-based reasoning, and learning-based reasoning. The foundation of our work is a notification and feedback solution that improves intelligibility and controllability of self-adaptive applications by implementing a bi-directional communication between self-adaptive software and the user. The different mechanisms from the temporal and behavioural participation dimension require the notification and feedback solution to inform users on adaptation actions and to provide a mechanism to influence adaptations. Case studies show the feasibility of the developed solutions. Moreover, an extensive user study with 62 participants was conducted to evaluate the impact of notifications before and after adaptations. Although the study revealed that there is no preference for a particular notification design, participants clearly appreciated intelligibility and controllability over autonomous adaptations.

Risk Aversion in Inventory Management

Traditional inventory models focus on risk-neutral decision makers, i.e., characterizing replenishment strategies that maximize expected total profit, or equivalently, minimize expected total cost over a planning horizon. In this paper, we propose a framework for incorporating risk aversion in multi-period inventory models as well as multi-period models that coordinate inventory and pricing strategies. In each case, we characterize the optimal policy for various measures of risk that have been commonly used in the finance literature. In particular, we show that the structure of the optimal policy for a decision maker with exponential utility functions is almost identical to the structure of the optimal risk-neutral inventory (and pricing) policies. Computational results demonstrate the importance of this approach not only to risk-averse decision makers, but also to risk-neutral decision makers with limited information on the demand distribution.

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Aldose reductase and the importance of experimental design

Kinetic studies on the AR (aldose reductase) protein have shown that it does not behave as a classical enzyme in relation to ring aldose sugars. As with non-enzymatic glycation reactions, there is probably a free radical element involved derived from monosaccharide autoxidation. in the case of AR, there is free radical oxidation of NADPH by autoxidizing monosaccharides, which is enhanced in the presence of the NADPH-binding protein. Thus any assay for AR based on the oxidation of NADPH in the presence of autoxidizing monosaccharides is invalid, and tissue AR measurements based on this method are also invalid, and should be reassessed. AR exhibits broad specificity for both hydrophilic and hydrophobic aldehydes that suggests that the protein may be involved in detoxification. The last thing we would want to do is to inhibit it. ARIs (AR inhibitors) have a number of actions in the cell which are not specific, and which do not involve them binding to AR. These include peroxy-radical scavenging and effects of metal ion chelation. The AR/ARI story emphasizes the importance of correct experimental design in all biocatalytic experiments. Developing the use of Bayesian utility functions, we have used a systematic method to identify the optimum experimental designs for a number of kinetic model data sets. This has led to the identification of trends between kinetic model types, sets of design rules and the key conclusion that such designs should be based on some prior knowledge of K-m and/or the kinetic model. We suggest an optimal and iterative method for selecting features of the design such as the substrate range, number of measurements and choice of intermediate points. The final design collects data suitable for accurate modelling and analysis and minimizes the error in the parameters estimated, and is suitable for simple or complex steady-state models.

A novel Bayesian approach to drug design with simple and complex enzymes: Use with lens aldehyde dehydrogenase and the glyoxalase pathway

Purpose: Acquiring details of kinetic parameters of enzymes is crucial to biochemical understanding, drug development, and clinical diagnosis in ocular diseases. The correct design of an experiment is critical to collecting data suitable for analysis, modelling and deriving the correct information. As classical design methods are not targeted to the more complex kinetics being frequently studied, attention is needed to estimate parameters of such models with low variance. Methods: We have developed Bayesian utility functions to minimise kinetic parameter variance involving differentiation of model expressions and matrix inversion. These have been applied to the simple kinetics of the enzymes in the glyoxalase pathway (of importance in posttranslational modification of proteins in cataract), and the complex kinetics of lens aldehyde dehydrogenase (also of relevance to cataract). Results: Our successful application of Bayesian statistics has allowed us to identify a set of rules for designing optimum kinetic experiments iteratively. Most importantly, the distribution of points in the range is critical; it is not simply a matter of even or multiple increases. At least 60 % must be below the KM (or plural if more than one dissociation constant) and 40% above. This choice halves the variance found using a simple even spread across the range.With both the glyoxalase system and lens aldehyde dehydrogenase we have significantly improved the variance of kinetic parameter estimation while reducing the number and costs of experiments. Conclusions: We have developed an optimal and iterative method for selecting features of design such as substrate range, number of measurements and choice of intermediate points. Our novel approach minimises parameter error and costs, and maximises experimental efficiency. It is applicable to many areas of ocular drug design, including receptor-ligand binding and immunoglobulin binding, and should be an important tool in ocular drug discovery.

Efficient and accurate experimental design for enzyme kinetics: Bayesian studies reveal a systematic approach

In areas such as drug development, clinical diagnosis and biotechnology research, acquiring details about the kinetic parameters of enzymes is crucial. The correct design of an experiment is critical to collecting data suitable for analysis, modelling and deriving the correct information. As classical design methods are not targeted to the more complex kinetics being frequently studied, attention is needed to estimate parameters of such models with low variance. We demonstrate that a Bayesian approach (the use of prior knowledge) can produce major gains quantifiable in terms of information, productivity and accuracy of each experiment. Developing the use of Bayesian Utility functions, we have used a systematic method to identify the optimum experimental designs for a number of kinetic model data sets. This has enabled the identification of trends between kinetic model types, sets of design rules and the key conclusion that such designs should be based on some prior knowledge of K-M and/or the kinetic model. We suggest an optimal and iterative method for selecting features of the design such as the substrate range, number of measurements and choice of intermediate points. The final design collects data suitable for accurate modelling and analysis and minimises the error in the parameters estimated. (C) 2003 Elsevier Science B.V. All rights reserved.

Optimal hedging with higher moments

This study proposes a utility-based framework for the determination of optimal hedge ratios (OHRs) that can allow for the impact of higher moments on hedging decisions. We examine the entire hyperbolic absolute risk aversion family of utilities which include quadratic, logarithmic, power, and exponential utility functions. We find that for both moderate and large spot (commodity) exposures, the performance of out-of-sample hedges constructed allowing for nonzero higher moments is better than the performance of the simpler OLS hedge ratio. The picture is, however, not uniform throughout our seven spot commodities as there is one instance (cotton) for which the modeling of higher moments decreases welfare out-of-sample relative to the simpler OLS. We support our empirical findings by a theoretical analysis of optimal hedging decisions and we uncover a novel link between OHRs and the minimax hedge ratio, that is the ratio which minimizes the largest loss of the hedged position. © 2011 Wiley Periodicals, Inc. Jrl Fut Mark

Visual attention and attribute attendance in multi-attribute choice experiments

Decision strategies in multi-attribute Choice Experiments are investigated using eye-tracking. The visual attention towards, and attendance of, attributes is examined. Stated attendance is found to diverge substantively from visual attendance of attributes. However, stated and visual attendance are shown to be informative, non-overlapping sources of information about respondent utility functions when incorporated into model estimation. Eye-tracking also reveals systematic nonattendance of attributes only by a minority of respondents. Most respondents visually attend most attributes most of the time. We find no compelling evidence that the level of attention is related to respondent certainty, or that higher or lower value attributes receive more or less attention

Using attribute importance rankings within discrete choice experiments: an application to valuing bread attributes

We present a new Bayesian econometric specification for a hypothetical Discrete Choice Experiment (DCE) incorporating respondent ranking information about attribute importance. Our results indicate that a DCE debriefing question that asks respondents to rank the importance of attributes helps to explain the resulting choices. We also examine how mode of survey delivery (online and mail) impacts model performance, finding that results are not substantively a§ected by the mode of survey delivery. We conclude that the ranking data is a complementary source of information about respondent utility functions within hypothetical DCEs

A estrutura de decisão do operador de transporte de cargas pelo usuário varejista de pequeno porte

Este trabalho tem por objetivo investigar e identificar a influência dos atributos que estruturam a escolha do transportador de carga geral fracionada pelos usuários, em uma determinada rota nacional, baseado na modelagem da demanda. xii A modelagem da demanda é efetuada com base em Modelos Comportamentais Desagregados, utilizando-se as técnicas de Preferência Declarada (Stated Preference), na obtenção dos dados. A determinação das preferências dos decisores são analisadas, buscandose assim quantificar o valor das variáveis que compõem o nível de serviço desejado pelos varejistas usuários. O estudo enfoca o comportamento do varejista usuário de serviços de transporte de cargas com relação a tomada de decisão sobre a transportadora que executará o serviço de transporte de carga. Esta tomada de decisão do varejista usuário leva em consideração que cada operador valoriza os atributos em diferentes graus e que estes fazem parte do nível de serviço de cada transportadora. As técnicas de Preferência Declarada forneceram dados para estimar as funções de Utilidade levando em consideração os diferentes níveis de atributos de cada transportadora. A partir da função de Utilidade de cada transportadora, é estimada a probabilidade de escolha de cada transportadora. A modelagem permite a realização de simulações, a partir de alterações no grau dos atributos das variáveis do modelo, na qual se determinará a parcela de mercado de cada transportadora e a sua respectiva participação no mercado em estudo. Dentre os principais resultados, pode se observar que a modelagem da demanda em transporte de cargas, apesar de pouco utilizada, é coerente com a realidade analisada, validando a metodologia utilizada neste estudo.

Um modelo de competição baseada em tempo nos mercados financeiros

O objetivo deste trabalho é modelar o comportamento estratégico dos indivíduos diante de um choque estocástico que desloca o preço de determinado ativo financeiro do seu equilíbrio inicial. Investiga-se o caminho do preço de mercado em direção ao novo equilíbrio, conduzido pelas sucessivas negociações dos agentes em busca de oportunidades de obter lucros imediatos. Os operadores, que por suposição possuem funções de utilidade avessas ao risco, devem escolher a quantidade ótima transacionada e quanto devem aguardar para executar as suas ordens, tendo em vista a diminuição da volatilidade do preço do ativo à medida que as transações se sucedem após o choque. Procura-se demonstrar que os operadores que aceitam incorrer em riscos mais elevados negociam com maior frequência e em volumes e velocidades maiores, usufruindo lucros esperados mais altos que os demais.

Preferences, common knowledge and speculative trade

We study the proposition that if it is common knowledge that en allocation of assets is ex-ante pareto efficient, there is no further trade generated by new information. The key to this result is that the information partitions and other characteristics of the agents must be common knowledge and that contracts, or asset markets, must be complete. It does not depend on learning, on 'lemons' problems, nor on agreement regarding beliefs and the interpretation of information. The only requirement on preferences is state-additivity; in particular, traders need not be risk-averse. We also prove the converse result that "no-trade results" imply that traders' preferences can be represented by state-additive utility functions. We analyze why examples of other widely studied preferences (e.g., Schmeidler (1989)) allow "speculative" trade.

Beyond indifferent players: on the existence of prisoners dilemmas in games with amicable and adversarial preferences

Why don’t agents cooperate when they both stand to gain? This question ranks among the most fundamental in the social sciences. Explanations abound. Among the most compelling are various configurations of the prisoner’s dilemma (PD), or public goods problem. Payoffs in PD’s are specified in one of two ways: as primitive cardinal payoffs or as ordinal final utility. However, as final utility is objectively unobservable, only the primitive payoff games are ever observed. This paper explores mappings from primitive payoff to utility payoff games and demonstrates that though an observable game is a PD there are broad classes of utility functions for which there exists no associated utility PD. In particular we show that even small amounts of either altruism or enmity may disrupt the mapping from primitive payoff to utility PD. We then examine some implications of these results.