Effect of Level of Disaggregation on Conjoint Cross Validations: Some Comparative Findings*

Early formulations of conjoint models focused on part-worth estimation at the individual level. As the methodology's popularity grew so did industry demands for increasingly larger numbers of attributes and levels. In response to these demands, new approaches, based on partial or full data aggregation (such as clusterwise/latent class conjoint and choice-based conjoint), have appeared. This paper suggests that pooled-data models will often be successful in predicting market shares when researchers employ monotonic attributes. In these cases more of a good attribute (or less of a bad attribute) is always more preferred. In the more realistic case, in which some of the attributes may be nonmonotonic, we find that data aggregation does not predict holdout sample preferences as well as individual part-worth models.     

Inference for a binomial proportion in the presence of ties

We suppose a case is to be compared with controls on the basis of a test that gives a single discrete score. The score of the case may tie with the scores of one or more controls. However, scores relate to an underlying quantity of interest that is continuous and so an observed score can be treated as the rounded value of an underlying continuous score. This makes it reasonable to break ties. This paper addresses the problem of forming a confidence interval for the proportion of controls that have a lower underlying score than the case. In the absence of ties, this is the standard task of making inferences about a binomial proportion and many methods for forming confidence intervals have been proposed. We give a general procedure to extend these methods to handle ties, under the assumption that ties may be broken at random. Properties of the procedure are given and an example examines its performance when it is used to extend several methods. A real example shows that an estimated confidence interval can be much too small if the uncertainty associated with ties is not taken into account. Software implementing the procedure is freely available.     

Assessing large sample bias in misspecified model scenarios with reference to exposure model misspecification in errors-in-variable regression: A new computational approach

In this paper, we develop a numerical method for evaluating the large sample bias in estimated regression coefficients arising due to exposure model misspecification while adjusting for measurement errors in errors-in-variable regression. The application of the proposed method has been demonstrated in the case of a logistic errors-in-variable regression model. The method is based on the combination of Monte-Carlo, numerical and, in some special cases, analytic integration techniques. The proposed method facilitates the investigation of the limiting bias in the estimated regression parameters based on a single data set rather than on repeated data sets as required by the conventional repeated sample method. Simulation studies demonstrate that the proposed method provides very similar estimates of bias in the estimated regression parameters under exposure model misspecification in logistic errors-in-variable regression with a higher degree of precision as compared to the conventional repeated sample method.     

Branch-and-Bound Strategies for the Log Bucking Problem

Tree bucking is the initial production process in converting felled trees into useable wood products. This process has been previously modelled as a dynamic programming problem. Unlike other production problems that have been modelled as dynamic programming problems, there have been no serious attempts to formulate this problem as a branch-and-bound model and then examine the model's performance. This research develops the tree bucking problem as a branch-and-bound model to be tested by varying several parameters. The testing is performed in two phases: (1) a sensitivity analysis is performed to test two key parameters used by the model, and (2) branching strategies are tested on various problem scenarios. The size of the solution sets searched by the technique vary from as low as 40 to as many as 41,000 possible combinations.     

Modelling Resource Allocation in a Decentralized Organization with an AI-Based,Goal-Directive Model*

Modelling the resource allocation decision process in a decentralized organization using mathematical programming decomposition approaches has proven intractable for all but the smallest of problems. In this paper three artificial intelligence (AI) techniques are integrated to model the resource allocation decision process, and the solution for a three-level decentralized organization is illustrated. The techniques are (1) the filtered beam search, which selects a list of potential projects at the subordinate level, (2) the world model, which describes the ordinate and superordinate's worlds and their decision-making processes, and (3) the blackboard model, which allows for global, but selective, storage of information. Benefits of the new approach include: several different mechanisms of managerial control may be modelled, the true information flow in an organization is more closely mirrored, and problems of a more realistic size are now viable. There are three primary ways in which the new model could be useful to management: The desirability and effects of different organizational structures can be modeled, the efficacy of various coordination mechanisms along the organizational structure can be examined, and a subset of projects from a wider list of possibilities can be selected. Also, an example is given showing how the model may be extended to scenarios with asymetric information and divergence of preferences, whereby subordinates have different objectives than their managers and where there is no way for managers to verify the truthfulness of their subordinates' responses.     

Family Therapy: Inner,Outer and the Issue of Pathology

This paper sets out to examine the relationship between ‘the inner’, ‘the outer’, and ‘the issue of pathology’ in the family therapy field. It begins with the observations that ‘pathology’ has become a rarely mentioned issue in family therapy, and ‘what is wrong’ is increasingly located in ‘the outer’: the family ‘game’, ‘linguistic activity’ or ‘the cultural discourse’. At the same time, family therapy often hosts forums in which presenters are ‘attacked’ for not seeming to hold the ‘correct view’. The paper considers these phenomena in tandem, looks at the matter of ‘method’, and applies James Hillman's critique of psychoanalysis to family therapy. The suggestion is that family therapy has been blinded by its own metaphor of ‘seeing’, symbolised and literalised in the one way screen. Alternative metaphors privileging intuition, feeling and aesthetics are put forward, before discussion points are raised, and before this paper on therapy concludes poetically, or this paper concludes that therapy may be poetry.     

County child poverty rates in the US: a spatial regression approach

We apply methods of exploratory spatial data analysis (ESDA) and spatial regression analysis to examine intercounty variation in child poverty rates in the US. Such spatial analyses are important because regression models that exclude explicit specification of spatial effects, when they exist, can lead to inaccurate inferences about predictor variables. Using county-level data for 1990, we re-examine earlier published results [Friedman and Lichter (Popul Res Policy Rev 17:91–109, 1998)]. We find that formal tests for spatial autocorrelation among county child poverty rates confirm and quantify what is obvious from simple maps of such rates: the risk of a child living in poverty is not (spatially) a randomly distributed risk at the county level. Explicit acknowledgment of spatial effects in an explanatory regression model improves considerably the earlier published regression results, which did not take account of spatial autocorrelation. These improvements include: (1) the shifting of “wrong sign” parameters in the direction originally hypothesized by the authors, (2) a reduction of residual squared error, and (3) the elimination of any substantive residual spatial autocorrelation. While not without its own problems and some remaining ambiguities, this reanalysis is a convincing demonstration of the need for demographers and other social scientists to examine spatial autocorrelation in their data and to explicitly correct for spatial externalities, if indicated, when performing multiple regression analyses on variables that are spatially referenced. Substantively, the analysis improves the estimates of the joint effects of place-influences and family-influences on child poverty.

Time Insensitivity for Protective Investments

Investments in protective measures involve an initial immediate cost in exchange for a stream of potential benefits accruing over time in the form of reduced expected losses. This paper describes two studies in which individuals were asked both to make choices and indicate the maximum amount they were willing to pay (WTP) for such protective measures. By varying the number of years that the measures provided protection, we observed four different decision strategies that individuals use to determine their maximum WTP. The findings from these experiments strongly suggest that most individuals do not take into account the added benefits of having a protective measure in place over a longer period of time when determining the likelihood of purchasing protection or the maximum price they are willing to pay. The behavior of a relatively small proportion of the subjects was consistent with a discounted utility model. Most subjects were either myopic in their behavior or did not change their maximum WTP as the time horizon changed. Despite the fact that most subjects were insensitive to time changes, they were generally willing to purchase the protective measures. Implications of these results for investment in risk mitigation measures are briefly explored.