Regions of rationality: Maps for bounded agents

An important problem in descriptive and prescriptive research in decision making is to identify regions of rationality, i.e., the areas for which heuristics are and are not effective. To map the contours of such regions, we derive probabilities that heuristics identify the best of m alternatives (m > 2) characterized by k attributes or cues (k > 1). The heuristics include a single variable (lexicographic), variations of elimination-by-aspects, equal weighting, hybrids of the preceding, and models exploiting dominance. We use twenty simulated and four empirical datasets for illustration. We further provide an overview by regressing heuristic performance on factors characterizing environments. Overall, sensible heuristics generally yield similar choices in many environments. However, selection of the appropriate heuristic can be important in some regions (e.g., if there is low inter-correlation among attributes/cues). Since our work assumes a hit or miss decision criterion, we conclude by outlining extensions for exploring the effects of different loss functions.

A simple permutation test for clusteredness

Hierarchical clustering is a popular method for finding structure in multivariate data,resulting in a binary tree constructed on the particular objects of the study, usually samplingunits. The user faces the decision where to cut the binary tree in order to determine the numberof clusters to interpret and there are various ad hoc rules for arriving at a decision. A simplepermutation test is presented that diagnoses whether non-random levels of clustering are presentin the set of objects and, if so, indicates the specific level at which the tree can be cut. The test isvalidated against random matrices to verify the type I error probability and a power study isperformed on data sets with known clusteredness to study the type II error.

Evaluation cardiologique préopératoire avant chirurgie non cardiaque: du choix cornélien a l'arbre décisionnel [Pre-operative cardiac assessment in non-cardiac surgery: a frequent dilemma simplified by a decision tree].

In patients undergoing non-cardiac surgery, cardiac events are the most common cause of perioperative morbidity and mortality. It is often difficult to choose adequate cardiologic examinations before surgery. This paper, inspired by the guidelines of the European and American societies of cardiology (ESC, AHA, ACC), discusses the place of standard ECG, echocardiography, treadmill or bicycle ergometer and pharmacological stress testing in preoperative evaluations. The role of coronary angiography and prophylactic revascularization will also be discussed. Finally, we provide a decision tree which will be helpful to both general practitioners and specialists.

Rules or consequences? The role of ethical mindsets in moral dynamics

Recent research on the dynamics of moral behavior has documented two contrastingphenomena - moral consistency and moral balancing. Moral balancing refers to thephenomenon whereby behaving (un)ethically decreases the likelihood of doing so againat a later time. Moral consistency describes the opposite pattern - engaging in(un)ethical behavior increases the likelihood of doing so later on. Three studies supportthe hypothesis that individuals' ethical mindset (i.e., outcome-based versus rule-based)moderates the impact of an initial (un)ethical act on the likelihood of behaving ethicallyin a subsequent occasion. More specifically, an outcome-based mindset facilitates moralbalancing and a rule-based mindset facilitates moral consistency.

Equivalence of piecewise-linear approximation and Lagrangian relaxation for network revenue management

The network revenue management (RM) problem arises in airline, hotel, media,and other industries where the sale products use multiple resources. It can be formulatedas a stochastic dynamic program but the dynamic program is computationallyintractable because of an exponentially large state space, and a number of heuristicshave been proposed to approximate it. Notable amongst these -both for their revenueperformance, as well as their theoretically sound basis- are approximate dynamic programmingmethods that approximate the value function by basis functions (both affinefunctions as well as piecewise-linear functions have been proposed for network RM)and decomposition methods that relax the constraints of the dynamic program to solvesimpler dynamic programs (such as the Lagrangian relaxation methods). In this paperwe show that these two seemingly distinct approaches coincide for the network RMdynamic program, i.e., the piecewise-linear approximation method and the Lagrangianrelaxation method are one and the same.

A new compact linear programming formulation for choice network revenue management

The choice network revenue management model incorporates customer purchase behavioras a function of the offered products, and is the appropriate model for airline and hotel networkrevenue management, dynamic sales of bundles, and dynamic assortment optimization.The optimization problem is a stochastic dynamic program and is intractable. A certainty-equivalencerelaxation of the dynamic program, called the choice deterministic linear program(CDLP) is usually used to generate dyamic controls. Recently, a compact linear programmingformulation of this linear program was given for the multi-segment multinomial-logit (MNL)model of customer choice with non-overlapping consideration sets. Our objective is to obtaina tighter bound than this formulation while retaining the appealing properties of a compactlinear programming representation. To this end, it is natural to consider the affine relaxationof the dynamic program. We first show that the affine relaxation is NP-complete even for asingle-segment MNL model. Nevertheless, by analyzing the affine relaxation we derive a newcompact linear program that approximates the dynamic programming value function betterthan CDLP, provably between the CDLP value and the affine relaxation, and often comingclose to the latter in our numerical experiments. When the segment consideration sets overlap,we show that some strong equalities called product cuts developed for the CDLP remain validfor our new formulation. Finally we perform extensive numerical comparisons on the variousbounds to evaluate their performance.

Asymptotic robustness in multi-sample analysis of multivariate linear relations

Standard methods for the analysis of linear latent variable models oftenrely on the assumption that the vector of observed variables is normallydistributed. This normality assumption (NA) plays a crucial role inassessingoptimality of estimates, in computing standard errors, and in designinganasymptotic chi-square goodness-of-fit test. The asymptotic validity of NAinferences when the data deviates from normality has been calledasymptoticrobustness. In the present paper we extend previous work on asymptoticrobustnessto a general context of multi-sample analysis of linear latent variablemodels,with a latent component of the model allowed to be fixed across(hypothetical)sample replications, and with the asymptotic covariance matrix of thesamplemoments not necessarily finite. We will show that, under certainconditions,the matrix $\Gamma$ of asymptotic variances of the analyzed samplemomentscan be substituted by a matrix $\Omega$ that is a function only of thecross-product moments of the observed variables. The main advantage of thisis thatinferences based on $\Omega$ are readily available in standard softwareforcovariance structure analysis, and do not require to compute samplefourth-order moments. An illustration with simulated data in the context ofregressionwith errors in variables will be presented.

Finite sample nonparametric tests for linear regressions

We introduce several exact nonparametric tests for finite sample multivariatelinear regressions, and compare their powers. This fills an important gap inthe literature where the only known nonparametric tests are either asymptotic,or assume one covariate only.

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.

Solving non-linear stochastic models by parameterizing expectations: An application to asset pricing with production

A new algorithm called the parameterized expectations approach(PEA) for solving dynamic stochastic models under rational expectationsis developed and its advantages and disadvantages are discussed. Thisalgorithm can, in principle, approximate the true equilibrium arbitrarilywell. Also, this algorithm works from the Euler equations, so that theequilibrium does not have to be cast in the form of a planner's problem.Monte--Carlo integration and the absence of grids on the state variables,cause the computation costs not to go up exponentially when the numberof state variables or the exogenous shocks in the economy increase. \\As an application we analyze an asset pricing model with endogenousproduction. We analyze its implications for time dependence of volatilityof stock returns and the term structure of interest rates. We argue thatthis model can generate hump--shaped term structures.

Fate of linear and supercoiled multinucleosomic templates during transcription.

Electron microscopy was used to monitor the fate of reconstituted nucleosome cores during in vitro transcription of long linear and supercoiled multinucleosomic templates by the prokaryotic T7 RNA polymerase and the eukaryotic RNA polymerase II. Transcription by T7 RNA polymerase disrupted the nucleosomal configuration in the transcribed region, while nucleosomes were preserved upstream of the transcription initiation site and in front of the polymerase. Nucleosome disruption was independent of the topology of the template, linear or supercoiled, and of the presence or absence of nucleosome positioning sequences in the transcribed region. In contrast, the nucleosomal configuration was preserved during transcription from the vitellogenin B1 promoter with RNA polymerase II in a rat liver total nuclear extract. However, the persistence of nucleosomes on the template was not RNA polymerase II-specific, but was dependent on another activity present in the nuclear extract. This was demonstrated by addition of the extract to the T7 RNA polymerase transcription reaction, which resulted in retention of the nucleosomal configuration. This nuclear activity, also found in HeLa cell nuclei, is heat sensitive and could not be substituted by nucleoplasmin, chromatin assembly factor (CAF-I) or a combination thereof. Altogether, these results identify a novel nuclear activity, called herein transcription-dependent chromatin stabilizing activity I or TCSA-I, which may be involved in a nucleosome transfer mechanism during transcription.