Estimating Bayesian decision problems with heterogeneous priors

In many areas of economics there is a growing interest in how expertise andpreferences drive individual and group decision making under uncertainty. Increasingly, we wish to estimate such models to quantify which of these drive decisionmaking. In this paper we propose a new channel through which we can empirically identify expertise and preference parameters by using variation in decisionsover heterogeneous priors. Relative to existing estimation approaches, our \Prior-Based Identification" extends the possible environments which can be estimated,and also substantially improves the accuracy and precision of estimates in thoseenvironments which can be estimated using existing methods.

Efficacy of brief motivational intervention at army conscription to address alcohol use and related problems: a randomized controlled trial

Objectives: The aim of this study was to evaluate the efficacy of brief motivational intervention (BMI) in reducing alcohol use and related problems among binge drinkers randomly selected from a census of 20 year-old French speaking Swiss men and to test the hypothesis that BMI contributes to maintain low-risk drinking among non-bingers. Methods: Randomized controlled trial comparing the impact of BMI on weekly alcohol use, frequency of binge drinking and occurrence of alcohol-related problems. Setting: Army recruitment center. Participants: A random sample of 622 men were asked to participate, 178 either refused, or missed appointment, or had to follow military assessment procedures instead, resulting in 418 men randomized into BMI or control conditions, 88.7% completing the 6-month follow-up assessment. Intervention: A single face-to-face BMI session exploring alcohol use and related problems in order to stimulate behaviour change perspective in a non-judgmental, empathic manner based on the principles of motivational interviewing (MI). Main outcome measures: Weekly alcohol use, binge drinking frequency and the occurrence of 12 alcohol-related consequences. Results: Among binge drinkers, we observed a 20% change in drinking induced by BMI, with a reduction in weekly drinking of 1.5 drink in the BMI group, compared to an increase of 0.8 drink per week in the control group (incidence rate ratio 0.8, 95% confidence interval 0,66 to 0,98, p = 0.03). BMI did not influence the frequency of binge drinking and the occurrence of 12 possible alcohol-related consequences. However, BMI induced a reduction in the alcohol use of participants who, after drinking over the past 12 months, experienced alcohol-related consequences, i.e., hangover (-20%), missed a class (-53%), got behind at school (-54%), argued with friends (-38%), engaged in unplanned sex (-45%) or did not use protection when having sex (-64%). BMI did not reduce weekly drinking in those who experienced the six other problems screened. Among non-bingers, BMI did not contribute to maintain low-risk drinking. Conclusions: At army conscription, BMI reduced alcohol use in binge drinkers, particularly in those who recently experienced alcohol-related adverse consequences. No preventive effect of BMI was observed among non-bingers. BMI is an interesting preventive option in young binge drinkers, particularly in countries with mandatory army recruitment.

Signal search analysis server.

Signal search analysis is a general method to discover and characterize sequence motifs that are positionally correlated with a functional site (e.g. a transcription or translation start site). The method has played an instrumental role in the analysis of eukaryotic promoter elements. The signal search analysis server provides access to four different computer programs as well as to a large number of precompiled functional site collections. The programs offered allow: (i) the identification of non-random sequence regions under evolutionary constraint; (ii) the detection of consensus sequence-based motifs that are over- or under-represented at a particular distance from a functional site; (iii) the analysis of the positional distribution of a consensus sequence- or weight matrix-based sequence motif around a functional site; and (iv) the optimization of a weight matrix description of a locally over-represented sequence motif. These programs can be accessed at: http://www.isrec.isb-sib.ch/ssa/.

Median problems in networks

The P-median problem is a classical location model par excellence . In this paper we, firstexamine the early origins of the problem, formulated independently by Louis Hakimi andCharles ReVelle, two of the fathers of the burgeoning multidisciplinary field of researchknown today as Facility Location Theory and Modelling. We then examine some of thetraditional heuristic and exact methods developed to solve the problem. In the third sectionwe analyze the impact of the model in the field. We end the paper by proposing new lines ofresearch related to such a classical problem.

Supply chain management: An opportunity for metaheuristics

In today s highly competitive and global marketplace the pressure onorganizations to find new ways to create and deliver value to customersgrows ever stronger. In the last two decades, logistics and supply chainhas moved to the center stage. There has been a growing recognition thatit is through an effective management of the logistics function and thesupply chain that the goal of cost reduction and service enhancement canbe achieved. The key to success in Supply Chain Management (SCM) requireheavy emphasis on integration of activities, cooperation, coordination andinformation sharing throughout the entire supply chain, from suppliers tocustomers. To be able to respond to the challenge of integration there isthe need of sophisticated decision support systems based on powerfulmathematical models and solution techniques, together with the advancesin information and communication technologies. The industry and the academiahave become increasingly interested in SCM to be able to respond to theproblems and issues posed by the changes in the logistics and supply chain.We present a brief discussion on the important issues in SCM. We then arguethat metaheuristics can play an important role in solving complex supplychain related problems derived by the importance of designing and managingthe entire supply chain as a single entity. We will focus specially on theIterated Local Search, Tabu Search and Scatter Search as the ones, but notlimited to, with great potential to be used on solving the SCM relatedproblems. We will present briefly some successful applications.

The effect of unemployment spells on subsequent wages in Spain

In our analysis we try and recover the wage loss from unemploymentin Spain and see how it is affected by previous unemploymentexperience, unemployment duration, eligibility for unemploymentbenefits, and previous wages. We also study its variations acrossgroups. Our main conclusion is that while there is some evidencethat labour market rigidities tend to lower it, the wage loss ofdisplaced workers is remarkably high: more than 30%, that is,twice the equivalent figure for the US and France. Wages in Spainsuffer from a serious mismeasurement problems that we do our best tocontrol, so that our results are less robust than the ones thatwould be obtained with better data sets. However, they indicate a large level of wage flexibility in Spain.

Strategies for an integrated distribution problem

The problems arising in the logistics of commercial distribution are complexand involve several players and decision levels. One important decision isrelated with the design of the routes to distribute the products, in anefficient and inexpensive way.This article explores three different distribution strategies: the firststrategy corresponds to the classical vehicle routing problem; the second isa master route strategy with daily adaptations and the third is a strategythat takes into account the cross-functional planning through amulti-objective model with two objectives. All strategies are analyzed ina multi-period scenario. A metaheuristic based on the Iteratetd Local Search,is used to solve the models related with each strategy. A computationalexperiment is performed to evaluate the three strategies with respect to thetwo objectives. The cross functional planning strategy leads to solutions thatput in practice the coordination between functional areas and better meetbusiness objectives.

Enforcement problems and secondary markets

There is a large and growing literature that studies the effects of weak enforcement institutions on economic performance. This literature has focused almost exclusively on primary markets, in which assets are issued and traded to improve the allocation of investment and consumption. The general conclusion is that weak enforcement institutions impair the workings of these markets, giving rise to various inefficiencies.But weak enforcement institutions also create incentives to develop secondary markets, in which the assets issued in primary markets are retraded. This paper shows that trading in secondary markets counteracts the effects of weak enforcement institutions and, in the absence of further frictions, restores efficiency.

On certain greedoid polyhedra, partially indexable scheduling problems and extended restless bandit allocation indices

We present a polyhedral framework for establishing general structural properties on optimal solutions of stochastic scheduling problems, where multiple job classes vie for service resources: the existence of an optimal priority policy in a given family, characterized by a greedoid(whose feasible class subsets may receive higher priority), where optimal priorities are determined by class-ranking indices, under restricted linear performance objectives (partial indexability). This framework extends that of Bertsimas and Niño-Mora (1996), which explained the optimality of priority-index policies under all linear objectives (general indexability). We show that, if performance measures satisfy partial conservation laws (with respect to the greedoid), which extend previous generalized conservation laws, then theproblem admits a strong LP relaxation over a so-called extended greedoid polytope, which has strong structural and algorithmic properties. We present an adaptive-greedy algorithm (which extends Klimov's) taking as input the linear objective coefficients, which (1) determines whether the optimal LP solution is achievable by a policy in the given family; and (2) if so, computes a set of class-ranking indices that characterize optimal priority policies in the family. In the special case of project scheduling, we show that, under additional conditions, the optimal indices can be computed separately for each project (index decomposition). We further apply the framework to the important restless bandit model (two-action Markov decision chains), obtaining new index policies, that extend Whittle's (1988), and simple sufficient conditions for their validity. These results highlight the power of polyhedral methods (the so-called achievable region approach) in dynamic and stochastic optimization.

Recent misconceptions about the "database search problem": a probabilistic analysis using Bayesian networks

This paper analyses and discusses arguments that emerge from a recent discussion about the proper assessment of the evidential value of correspondences observed between the characteristics of a crime stain and those of a sample from a suspect when (i) this latter individual is found as a result of a database search and (ii) remaining database members are excluded as potential sources (because of different analytical characteristics). Using a graphical probability approach (i.e., Bayesian networks), the paper here intends to clarify that there is no need to (i) introduce a correction factor equal to the size of the searched database (i.e., to reduce a likelihood ratio), nor to (ii) adopt a propositional level not directly related to the suspect matching the crime stain (i.e., a proposition of the kind 'some person in (outside) the database is the source of the crime stain' rather than 'the suspect (some other person) is the source of the crime stain'). The present research thus confirms existing literature on the topic that has repeatedly demonstrated that the latter two requirements (i) and (ii) should not be a cause of concern.

Solving higher-dimensional continuous time stochastic control problems by value function regression

The paper develops a method to solve higher-dimensional stochasticcontrol problems in continuous time. A finite difference typeapproximation scheme is used on a coarse grid of low discrepancypoints, while the value function at intermediate points is obtainedby regression. The stability properties of the method are discussed,and applications are given to test problems of up to 10 dimensions.Accurate solutions to these problems can be obtained on a personalcomputer.

Adaptive approach heuristics for the generalized assignment problem

The Generalized Assignment Problem consists in assigning a setof tasks to a set of agents with minimum cost. Each agent hasa limited amount of a single resource and each task must beassigned to one and only one agent, requiring a certain amountof the resource of the agent. We present new metaheuristics forthe generalized assignment problem based on hybrid approaches.One metaheuristic is a MAX-MIN Ant System (MMAS), an improvedversion of the Ant System, which was recently proposed byStutzle and Hoos to combinatorial optimization problems, and itcan be seen has an adaptive sampling algorithm that takes inconsideration the experience gathered in earlier iterations ofthe algorithm. Moreover, the latter heuristic is combined withlocal search and tabu search heuristics to improve the search.A greedy randomized adaptive search heuristic (GRASP) is alsoproposed. Several neighborhoods are studied, including one basedon ejection chains that produces good moves withoutincreasing the computational effort. We present computationalresults of the comparative performance, followed by concludingremarks and ideas on future research in generalized assignmentrelated problems.

Skin problems associated with pegylated liposomal doxorubicin-more than palmoplantar erythrodysesthesia syndrome.

Liposomal pegylated doxorubicin is an encapsulation form of doxorubicin, with an improved pharmacokinetic profile and the ability to selectively accumulate into tumor tissue. As a result, the tolerated dose of the drug can be increased, followed by a reduced incidence of neutropenia and cardiotoxicity in comparison to doxorubucin treatment. However, a common adverse dose-schedule limiting effect of the treatment is palmoplantar erythrodysesthesia syndrome. In this retrospective study we included six patients hospitalised in the University Hospital of Zurich during the last 2 years, in connection with side effects caused by pegylated liposomal doxorubicin. These patients received this chemotherapeutic agent for treatment of various malignancies such as breast cancer, ovarian cancer, mycosis fungoides and cutaneous B-cell lymphoma. Three of six patients in this study developed classical palmoplantar erythrodysesthesia, one developed palmoplantar erythrodysesthesia associated with extensive bullous disease, one developed eruption of lymphocyte recovery syndrome and one developed intertrigo like dermatitis with stomatitis. Pegylated liposomal doxorubicin induces various skin reactions including palmoplantar erythrodysesthesia syndrome. However, the exact clinical presentation might depend on pre-existing skin diseases.