Incorporating Value Judgments Research Articles

Representing storylines with causal networks to support decision making: Framework and example

Physical climate storylines, which are physically self-consistent unfoldings of events or pathways, have been powerful tools in understanding regional climate impacts. We show how embedding physical climate storylines into a causal network framework allows user value judgments to be incorporated into the storyline in the form of probabilistic Bayesian priors, and can support decision making through inspection of the causal network outputs.We exemplify this through a specific storyline, namely a storyline on the impacts of tropical cyclones on the European Union Solidarity Fund. We outline how the constructed causal network can incorporate value judgments, particularly the prospects on climate change and its impact on cyclone intensity increase, and on economic growth. We also explore how the causal network responds to policy options chosen by the user. The resulting output from the network leads to individualized policy recommendations, allowing the causal network to be used as a possible interface for policy exploration in stakeholder engagements.

Incorporating value judgments into port efficiency measurement models: insights from Turkish ports

Several inputs and outputs which have relative contributions to overall port performance are used in port efficiency measurement models. While some inputs are vital to produce higher output, some others are relatively less important. Also, for managers, some outputs are more desirable than others. Ignoring the issue of “relative contributions” and considering all inputs and outputs to be of equal importance may lead to unreliable results. This research aims to incorporate value judgments and strategic thinking into port efficiency measurement models. Data envelopment analysis is used to estimate port efficiency, and the assurance region method is applied to incorporate expert opinions into data envelopment models. Results show that expert opinions have a great impact on efficiency scores and may lead to dramatic decreases in average efficiency. The reasons of such changes are explained with the use of optimal weights and virtual inputs/outputs, and the necessity of value judgments in port efficiency measurement models is demonstrated.

Improving production possibility set with production trade-offs

Adding weight restrictions on multiplier DEA models to incorporate value judgments is equivalent to accepting trade-offs between inputs and outputs in envelopment forms. To obtain the corresponding DEA model with the trade-off axiom, the production possibility set (PPS) is changed which has led to the introduction of the three-stage model. Although the efficiency of DMUs and efficient targets can be determined by the three-stage model, the result might be unacceptable because of locating the PPS in the negative area; clarification for which has been provided by means of a numerical example. An improved PPS and its corresponding model will be presented in this paper. The structure of the modified PPS preserves its non-negativity and efficient targets can be achieved through solving merely one max-slack model.

Weight restrictions in Data Envelopment Analysis: A comprehensive Genetic Algorithm based approach for incorporating value judgments

The basic DEA model experiences the weights flexibility problem which is resolved by the method of weight restrictions. The current research incorporating Decision Makers’ (DMs) preferences into weight restrictions is subject to serious limitations such as lacking a framework for dual role factors and not incorporating organizational hierarchy in decision-making.The proposed Genetic Algorithm (GA) based approach for weight restrictions incorporates a dual role factor and organizational hierarchy in decision-making. The approach involves finding a set of weights which are at a minimum distance from all the DMs’ preferences. The approach is flexible and is able to generate a common set of weights and Decision Making Unit (DMU) specific weight restrictions simultaneously.Results from model validation in a well-known automobile spare parts manufacturer in India indicate that the majority of suppliers perceived as highly efficient were actually found to be inefficient in the GA based weight restrictions model.A major contribution of this study is a robust approach to deal with multiple DMs and DEA weights flexibility problem. Another key highlight of the research is translating DMs preferences into a distance function. Using that as a fitness measure within the proposed Evolutionary Algorithms has been done for the first time in the presence of multiple DMs.

Value-based data envelopment analysis: a piece-wise linear programming approach

Incorporating value judgments in data envelopment analysis (DEA) is a broad methodological framework that facilitates driving the efficiency assessments in line with individual preferences. Value-based DEA is a recent development that resorts to value assessment protocols from multiple criteria decision analysis (MCDA) to transform the original input/output data to a value scale. In this context, we introduce in this paper a piece-wise linear programming approach to value-based DEA, by employing a data transformation-variable alteration technique and assurance region constraints. We outline our approach by revisiting a previous efficiency assessment study drawn from the literature.

A model for evaluating efficiency - an application in information technology and systems investments

Despite being widely applied in real problems that tackle evaluating efficiency, Data Envel- opment Analysis (DEA) models are frequently criticized on account of the weights of evaluation criteria often being defined loosely. Thus, approaches to incorporating value judgments in DEA models have been used in order to obtain more consistent results with managerial reality. It is against this background that this paper proposes a DEA model for evaluating efficiency, where the value judgments of those responsible for evaluation, regarding the criteria, are defined based on the philosophy of the SMARTS method and incorporated into the model by the Assurance Region (AR) method. The model proposed is applied using information about the investments made in the area of Information Technology and Information Systems by Brazilian banks The aim is to exemplify the application of the model and raise points for discussion with regard to its merits.

Measuring the relative balance of DMUs

In basic Data Envelopment Analysis (DEA) models, the weights of performance criteria are endogenously determined, assigning to each decision-making unit (DMU) its best possible efficiency score. This property is widely considered to be a major strength of the method, but it is also a source of considerable problems concerning performance assessment and performance control. Such problems result from the possibility of zero-value weights that exclude criteria from the evaluation. Unlike approaches that deal with this issue by incorporating value judgments into the analysis, we suggest a complementary balance score that is derived from the given data set. This score evaluates the extent to which a DMU avoids concentration on only some of the crucial performance criteria. One of the possible decisions resulting from a balance analysis is to reduce the set of DMUs considered to serve as benchmarks. For this case, a modified CCR–O model is presented.

Psychiatry and Values

Psychiatry and Values Gregory E. Kaebnick This issue of the Hastings Center Report includes a special report that comes out of a three-year Hastings Center research project on controversies surrounding the diagnosis and treatment of mental disorders in children. Over the last couple of decades, the number of children diagnosed with mental disorders has risen significantly, and so, too, has the number of children prescribed medications. Some critics have accused psychiatry of overdiagnosis—of sometimes diagnosing children with psychiatric disorders when their behavior is actually within the range of normal. This controversy led Erik Parens and Josie Johnston, authors of the report and the Hastings Center investigators in this project, to ask what a psychiatric diagnosis is: what kind of statement is it? Is it just a statement of fact? Or does it necessarily reflect individual and cultural values? Similarly, how does a treatment decision work? These matters are exceedingly complex, Parens and Johnston argue. Both the diagnosis of a disorder and the decision about how to treat a disorder (when one is diagnosed) incorporate value judgments at several junctures. This dependence on value judgments helps explain disagreements in diagnosis and treatment. Of course, both diagnosis and treatment also incorporate facts that everyone can agree to, and ultimately, some factual claims about diagnosis are possible. One of the most significant of these noted in the report is that a careful examination of diagnostic patterns in pediatric psychiatry reveals not just overdiagnosis but also misdiagnosis and underdiagnosis. The special report is part of a growing body of work at the Center that looks at how "normal" is defined and deployed. Parens, for example, has also conducted projects on surgical normalization of children's bodies, on prenatal testing for disability, and on the use of enhancement technologies. The approach toward which Parens has been moving over the course of these projects is characterized by what he sometimes calls "binocularity." That word does not pop up in this report, but the idea certainly does. The values at stake in diagnosis and treatment decisions sometimes compete with each other. Parens and Johnston argue, for example, that a treatment decision can be influenced both by the value of efficiency—it's nice when treatments are quick and convenient—and the value of engagement—for there is value, too, in taking time to work with a child and with his environment. Efficiency and engagement compete with each other on occasion, and when they do, it's important to remember both of them. That's binocularity: keeping competing values in tension with each other, recognizing that both values pull on our allegiance and that while some individuals may lean toward one value, others may in similar circumstances lean toward the other, and neither may be wrong. Which is to say, neither is right. Both are defensible. As I understand it, then, binocularity assumes a distinction between public and private morality (between "cultural and individual values," in Parens and Johnston's language). That distinction is not widely accepted in bioethics, I believe—most discussions try to sort out which value should win out in which kinds of cases—but accepting it might well lead to a richer bioethics. [End Page 2] Copyright © 2011 The Hastings Center

A study of DEA models without explicit inputs

In performance evaluations, data without explicit inputs (such as index data, pure output data) are widely used. To directly use such data, this paper presents a study on building DEA models without explicit inputs, so-called DEA-WEI models, which are applicable to the evaluation applications where inputs are not directly considered. We provide an axiom foundation of these kinds of models, and further discuss how to incorporate value judgments of decision makers into these DEA-WEI models. Several such models are derived. Finally, applications of the DEA-WEI models are presented.

Conditional-normative accounting methodology: Incorporating value judgments and means-end relations of an applied science

The growing interest in ethical and other normative aspects of accounting promotes the search for an empirical-scientific methodology capable of handling normative issues. On the one side, traditional normative accounting theories have been rejected as unscientific, on the other side, “positive accounting theory” emphasizes the positive instead of the normative aspects of our discipline. Thus accounting possesses only a well-publicized positive methodology but no equally recognized normative counterpart (as has positive economics in normative economics, physics in engineering, biology in medicine, etc.). This expository paper attempts to fill the gap by outlining the methodological basis for a conditional-normative accounting theory which recognizes different information goals (norms) but enables the formulation of empirically confirmable relations between those goals and the means to achieve them. Such a theory is objective insofar as it 1. (i) discloses the underlying value judgments and 2. (ii) empirically tests whether the recommended means lead to the desired ends. The paper discusses past efforts and the present status of this methodology as well as future requirements. It not only draws attention to the relative neglect of policy analysis and purpose-oriented thinking, but shows the way to a synthesis of the normative and the positive approach to accounting.

Issues in Cost Effectiveness in Health Care

Cost-effectiveness analysis (CEA) is becoming increasingly popular as society moves toward rationalizing health costs. This review describes the applications and limitations of the technique. Conceptually simple though frequently complicated in application, CEA compares the cost of a procedure with its effectiveness, thus helping an administrator to judge whether the procedure is worth its cost. CEA also permits comparison of various interventions that result in a similar health outcome. A major benefit of CEA is that it forces decision makers to confront the tradeoffs implicit in all decisions regarding alternative approaches. Limitations of the CEA philosophy and technique also have to be understood if it is to be employed effectively; it is not an assessment of cost savings, nor is it a decision-making technique because it does not incorporate value judgments. A number of potential applications to dentistry are described.