Portrayal of body weight on children's television sitcoms: a content analysis

Television programs portray characters with idealized body types that for most viewers are unattainable. These body types have become a common source of comparison for many young viewers who evaluate their own self-worth and bodies based on the models they see on television. This study examines body weight, both in terms of frequency and portrayals, focusing on how preadolescent and adolescent characters’ bodies are presented on the sitcoms from three children's television networks. The results indicate that while children's situation comedies have relatively few overweight characters, this small percentage closely parallels national figures. Male and female characters do not differ in their distribution across weight categories. However, when character ethnicity is factored, significant shifts occur. Surprisingly, no differences were observed in the portrayal of physical and mental attributes among weight categories except in social popularity where overweight characters were not as embedded in social networks. Compared to other relevant studies, this research provides some evidence that the treatment and portrayal of overweight characters in children-targeted sitcoms is more positive, equitable, and less stereotypical than in other programming venues and that children are experiencing body type diversity in these fictional portrayals. Nonetheless, above average weight characters still suffer some social marginality and ethnic misrepresentation.     

Output‐sensitive complexity of multiobjective combinatorial optimization

We study output‐sensitive algorithms and complexity for multiobjective combinatorial optimization problems. In this computational complexity framework, an algorithm for a general enumeration problem is regarded efficient if it is output‐sensitive, that is, its running time is bounded by a polynomial in the input and the output size. We provide both practical examples of multiobjective combinatorial optimization problems for which such an efficient algorithm exists as well as problems for which no efficient algorithm exists under mild complexity theoretic assumptions.     

The watershed years of 1958-1962 in the Harvard Pigeon Lab

During the years 1958-1962, the final years of support by the National Science Foundation for B. F. Skinner's Pigeon Lab in Memorial Hall at Harvard University, 20 or so pigeon experiments (plus some with other organisms) ran concurrently 7 days a week. The research style emphasized experimental analyses, exploratory procedures, and the parametric exploration of variables. This reminiscence describes some features of the laboratory, the context within which it operated, and the activities of some of those who participated in it.     

An evaluation of exact methods for the multiple subset maximum cardinality selection problem

The maximum cardinality subset selection problem requires finding the largest possible subset from a set of objects, such that one or more conditions are satisfied. An important extension of this problem is to extract multiple subsets, where the addition of one more object to a larger subset would always be preferred to increases in the size of one or more smaller subsets. We refer to this as the multiple subset maximum cardinality selection problem (MSMCSP). A recently published branch‐and‐bound algorithm solves the MSMCSP as a partitioning problem. Unfortunately, the computational requirement associated with the algorithm is often enormous, thus rendering the method infeasible from a practical standpoint. In this paper, we present an alternative approach that successively solves a series of binary integer linear programs to obtain a globally optimal solution to the MSMCSP. Computational comparisons of the methods using published similarity data for 45 food items reveal that the proposed sequential method is computationally far more efficient than the branch‐and‐bound approach.     

Several programming models with unascertained parameters and their applications

Several programming models are introduced with the consideration of available unascertained information. In this case, the so‐called unascertained information is some numerical values whose ranges are known but their exact values are not. These models resolve several vital weaknesses of the traditional programming methods to a certain degree. Our study includes considerations of linear and non‐linear programming models with grey parameters, grey 0–1 programming, and satisfactory and quasi‐optimal solutions of grey linear programmings. Finally, some practical applications are given in order to test the applicability of our theory. Copyright © 1999 John Wiley & Sons, Ltd.     

The decision tree polytope and its application to sequential decision problems

This paper describes a new mathematical programming approach to sequential decision problems that have an underlying decision tree structure. The approach, based upon a characterization of strategies as extreme points of a 0–1 polytope called the ‘decision tree polytope’, is particularly suited to the direct examination of risk-return and other tradeoffs amongst strategies. However, it can also be used for conventional utility maximization if a utility function is available. Further, the approach requires no algorithmic development—it can be implemented using commercially available algebraic modeling software and can solve large problems. A related, and already known, approach can be used for some more general Markov decision problems. © 1998 John Wiley & Sons, Ltd.     

Optimal trade-offs of multiple factors in transfer pricing problems

Transfer pricing problems have been extensively researched by a number of scholars. It is well recognized that transfer pricing problems have a multiple-criteria (objectives) feature and can be formulated as a model of multiple-criteria linear programming. However, few methods have the capability of dealing with all possible optimal trade-offs of multiple criteria in optimal solutions of the models. In this paper a linear multiple-factor model is developed to provide managers with a more systematic and comprehensive scenario of all possible optimal transfer prices depending on both multiple criteria and multiple constraint levels. The trade-offs of all possible optimal transfer prices can be used as a basis for managers of a corporation to make a high-quality decision in selecting their transfer pricing systems for business competition. © 1998 John Wiley & Sons, Ltd.     

A note on the use of goal programming for the multiplicative AHP

The main aim of this paper is to identify the opportunities of utilizing goal programming (GP) in the multiplicative analytic hierarchy process (MAHP). It starts with the issue of weight derivation from judgemental matrices. The use of GP for the weight derivation problem is not new, but GP is viewed in this paper from the perspective of augmenting the capabilities of the widely used row geometric mean method (RGMM) of the logarithmic least squares technique (LLST). Different possible approaches using GP are discussed. It is shown that the formulation of the GP problem can be easily modified to provide the same weights as those of the LLST. While this proposed GP technique is not superior to the RGMM in terms of computational ease or speed, it is quite useful in solving certain other problems of the MAHP, such as interval judgements and missing judgements, which cannot be readily solved by the RGMM. The proposed technique provides extensive scope for utilizing the vast literature on non-linear programming, say, for conducting sensitivity analysis. It also has the potential to be useful to more complicated issues of the MAHP, such as group decision making and interlevel dependence, hitherto little explored areas of the MAHP. © 1997 John Wiley & Sons, Ltd.     

A Multiobjective Diet Planning Support System Using the Satisficing Trade-off Method

This paper shows that the satisficing trade-off method (STOM), one of the interactive multiobjective programming techniques, can be applied effectively to the formulation of livestock rations. Nutrient requirements are considered as soft constraints whose right-hand sides are flexible to some extent. This is easily done by regarding the values of the right-hand sides of the constraints as aspiration levels of the decision maker for objective functions. In this way a well-balanced solution can be obtained by STOM with automatic trade-off analysis. In STOM, moreover, because the objective functions and the constraint functions are interchangeable, decision makers are not required to consider the role of objective and constraint as fixed from the beginning. The authors’ experience is that the method makes ration formulation very easy, rapid and flexible. In addition, this paper shows how effectively STOM can be applied not only to diet planning but also to nutritional diagnosis. © 1997 by John Wiley & Sons, Ltd. J. Multi-Crit. Decis. Anal. 6 : 131–139 (1997) No. of Figures: 1. No. of Tables: 3. No. of References: 28.