Application Of Decision Theory Research Articles

Working with Policy Makers on Their Choices: A Decision Analyst Reminisces

As a decision consultant and researcher, I discuss 40 years of working with policy makers on energy, environmental and foreign policy, defense, and other national issues, making heavy use of applied decision theory. I focus attention on decisions with significant human interest—where clients have sought not only to improve their own decisions, but also to justify them and to influence the decisions of others. I reflect on political, administrative, legal, and personal considerations involved.

Application of decision theory to conservation management: recovery of Hector's dolphin

Decision theory provides an organised approach to decision making in natural resource conservation. The theory requires clearly stated objectives, decision alternatives and decision-outcome utilities, and thus allows for the separation of values (conservation and other societal objectives) from beliefs. Models express belief in the likely response of the system to conservation actions, and can range from simple, graphical representations to complex computer models. Models can be used to make predictions about likely decision-outcomes, and hence guide decision making. Decision making must account for uncertainty, which can be reduced but never eliminated. Uncertainty can be described via probabilities, which in turn can be used to compute the expected value of alternative decisions, averaging over all relevant sources of uncertainty. Reduction of uncertainty, where possible, improves decision making. Adaptive management involves the reduction of uncertainty via prediction under two or more alternative, structural models, comparison of model predictions to monitoring, and feedback via Bayes’ Theorem into revising model weights, which in turn influences decision making. As part of a 3-day workshop on structured decision making (SDM) and adaptive resource management (ARM), we constructed a prototypical decision model for the recovery for Hector’s dolphin (Cephalorynchus hectori), an endangered dolphin endemic to New Zealand coastal waters. Our model captures several steps in the process of building an SDM/ARM framework, and should be useful for managers wishing to apply these principles to dolphin conservation or other resources problems.

Applying Decision Theory to Utility Master Planning

Applying Decision Theory to Utility Master Planning

Qualitative decision impact model: A tri‐partite broadcast social network approach

AbstractDecision theory is the mathematics of making decisions under certain conditions of uncertainty. A major shortcoming in the application of decision theory to human problem‐solving is that it is difficult to sufficiently account for the influence of specific instances of human social‐psychological conditions in making value judgments. Equally challenging to decision theory are the complexity and diversity of human problems. The P2h.D. decision impact model describes the configuration of a structured and intentionally sustained network of podcasts, blogs, and wikis to provide informal resources and examples of human experience to supplement formal resources developed out of decision theory modeling.

Managing fish stocks under climate uncertainty

Abstract The quantitative evaluation of the management of fish stocks under uncertainty requires a formal framework. Decision theory provides that framework. Application of decision theory to fishery management requires information about both the fish stock and the state of the environment. Using Georges Bank haddock as a case study, it is possible to determine the probability of good or poor recruitment using past data and a constant environment. Understanding the state of the environment is more difficult, however, because fixed levels of recruitment, in particular, are associated with different population characteristics, which drastically reduce the sample size for any particular recruitment–environment scenario. Decision theory challenges us to improve our capability of predicting the state of nature, and it appears that this can be accomplished best by reducing the length of the causal chain, a goal now made feasible by the availability of high-resolution, high-frequency ocean models.

Optimal decisions under complex uncertainty – basic notions and a general algorithm for data‐based decision making with partial prior knowledge described by interval probability

AbstractA powerful application of decision theory to engineering problems often has failed: The uncertainty underlying is too complex to be modelled adequately by a (precise) probability distribution. The present paper shows how recent generalizations of the usual calculus of probability can be utilized to deal powerfully with complex uncertainty in decision problems. Basic notions of the resulting theory of generalized expected loss and generalized risk are developed and discussed. In addition to this, also a general applicable algorithm is proposed to calculate optimal decision functions by linear programming.

A Bayesian Tutoring System for Newtonian Mechanics: Can it Adapt to Different Learners?

Newtonian mechanics is a core module in technology courses, but is difficult for many students to learn. Computerized tutoring can assist the teachers to provide individualized instruction. This article presents the application of decision theory to develop a tutoring system, iTutor, to select optimal tutoring actions under uncertainty of students' mastery states. The novelties of this research are: 1) the automation of student diagnosis that is made possible when tutoring alternatives and the utilities for different outcomes are incorporated to the Bayesian network; and 2) the ability of the tutoring system to select test items with difficulties that are appropriate for the students. The results from formative evaluation on iTutor indicate that it is adaptive, working in a well-structured knowledge space, and able to use the information gathered from the student's responses to dynamically modify the presentation in clearly defined ways.

Understanding Deutsch's probability in a deterministic multiverse

Difficulties over probability have often been considered fatal to the Everett interpretation of quantum mechanics. Here I argue that the Everettian can have everything she needs from ‘probability’ without recourse to indeterminism, ignorance, primitive identity over time or subjective uncertainty: all she needs is a particular rationality principle.The decision-theoretic approach recently developed by Deutsch and Wallace claims to provide just such a principle. But, according to Wallace, decision theory is itself applicable only if the correct attitude to a future Everettian measurement outcome is subjective uncertainty. I argue that subjective uncertainty is not available to the Everettian, but I offer an alternative: we can justify the Everettian application of decision theory on the basis that an Everettian should care about all her future branches. The probabilities appearing in the decision-theoretic representation theorem can then be interpreted as the degrees to which the rational agent cares about each future branch. This reinterpretation, however, reduces the intuitive plausibility of one of the Deutsch–Wallace axioms (measurement neutrality).

Three quantitative approaches to the diagnosis of abdominal pain in children: Practical applications of decision theory

Background/Purpose: The authors compared 3 quantitative methods for assisting clinicians in the differential diagnosis of abdominal pain in children, where the most common important endpoint is whether the patient has appendicitis. Pretest probability in different age and sex groups were determined to perform Bayesian analysis, binary logistic regression was used to determine which variables were statistically significantly likely to contribute to a diagnosis, and recursive partitioning was used to build decision trees with quantitative endpoints. Methods: The records of all children (1,208) seen at a large urban emergency department (ED) with a chief complaint of abdominal pain were immediately reviewed retrospectively (24 to 72 hours after the encounter). Attempts were made to contact all the patients' families to determine an accurate final diagnosis. A total of 1,008 (83%) families were contacted. Data were analyzed by calculation of the posttest probability, recursive partitioning, and binary logistic regression. Results: In all groups the most common diagnosis was abdominal pain (ICD-9 Code 789). After this, however, the order of the most common final diagnoses for abdominal pain varied significantly. The entire group had a pretest probability of appendicitis of 0.06. This varied with age and sex from 0.02 in boys 2 to 5 years old to 0.16 in boys older than 12 years. In boys age 5 to 12, recursive partitioning and binary logistic regression agreed on guarding and anorexia as important variables. Guarding and tenderness were important in girls age 5 to 12. In boys age greater than 12, both agreed on guarding and anorexia. Using sensitivities and specificities from the literature, computed tomography improved the posttest probability for the group from.06 to.33; ultrasound improved it from.06 to.48; and barium enema improved it from.06 to.58. Conclusions: Knowing the pretest probabilities in a specific population allows the physician to evaluate the likely diagnoses first. Other quantitative methods can help judge how much importance a certain criterion should have in the decision making and how much a particular test is likely to influence the probability of a correct diagnosis. It now should be possible to make these sophisticated quantitative methods readily available to clinicians via the computer. J Pediatr Surg 36:1375-1380. Copyright © 2001 by W.B. Saunders Company.

Formulating quantitative methods to evaluate fishery-management systems: what fishery processes should be modelled and what trade-offs should be made?

A wide variety of modelling approaches have been developed over the past few decades to provide quantitative advice to fishery managers about the potential consequences of proposed alternative management actions and to improve the design of fishery- management systems. This article surveys these approaches to identify the main differences among them in their application of decision theory, particularly in the methods used to account for uncertainty, and to identify model structures that need to be considered. Six interlinked model structures are identified: population dynamics, data collection, data analysis and stock assessment, setting harvest controls, the harvest decision process, and imperfect implementation. Their inclusion and design will depend on the institutional arrangements for fishery management. For example, some evaluations have included the last two structures to account for the flexibility of fishery managers in their making of annual harvest decisions and limitations on their ability to implement management policy precisely. Other evaluations have excluded them because they have evaluated “management procedures” whereby all participants would be bound to strictly follow a preset protocol for data collection, data analysis, and a harvest decision rule. In addition, this article identifies some trade-offs that are often made when modelling methods are formulated that can strongly affect model results and the advice given. Partly as a result of these trade-offs, analysts have recently been combining some of the originally divergent aspects of different modelling approaches. Examples of this include combining the use of Bayesian statistical methods with the use of alternative operating models (rather than only one) to account for both parameter and model uncertainty.

Ecology, statistics, and the art of misdiagnosis: The need for a paradigm shift

This paper approaches ecological data analysis from a different vantage point and has implications for ecological risk assessment. Despite all the advances in theoretical ecology over the past four decades and the huge amounts of data that have been collected in various marine monitoring programs, we still do not know enough about how marine ecosystems function to be able to make valid predictions of impacts before they occur, accurately assess ecosystem ``health,'' or perform valid risk assessments. Comparisons are made among the fields of psychology, social science, and ecology in terms of the applications of decision theory or approach to problem diagnosis. In all of these disciplines, researchers are dealing with phenomena whose mechanisms are poorly understood. One of the biggest impediments to the interpretation of ecological data and the advancement of our understanding about ecosystem function is the desire of marine scientists and policy regulators to cling to the ritual of null hypothesis significance testing (NHST) with mechanical dichotomous decisions around a sacred 0.05 criterion. The paper is divided into three main sections: first, a brief overview of common misunderstandings about NHST; second, why diagnosis of ecosystem health is and will be such a difficult task; and finally, some suggestions about alternative approaches for ecologists to improve our "diagnostic accuracy'' by taking heed of lessons learned in the fields of clinical psychology and medical epidemiology. Key words: statistical significance, Bayesian statistics, risk assessment

Statistical analysis of watermarking schemes for copyright protection of images

In this paper, we address the problem of the performance analysis of image watermarking systems that do not require the availability of the original image during ownership verification. We focus on a statistical approach to obtain models that can serve as a basis for the application of decision theory to the design of efficient detector structures. Special attention is paid to the possible nonexistence of a statistical description of the original image. Different modeling approaches are proposed for the cases when such a statistical characterization is known and when it is not. Watermarks may encode a message, and the performance of the watermarking system is evaluated using as a measure the probability of false alarm, the probability of detection when the presence of the watermark is tested, and the probability of error when the information that it carries is extracted. Finally, the modeling techniques studied are applied to the analysis of two watermarking schemes, one of them defined in the spatial domain, and the other in the direct cosine transform (DCT) domain. The theoretical results are contrasted with empirical data obtained through experimentation covering several cases of interest. We show how choosing an appropriate statistical model for the original image can lead to considerable improvements in performance.

Some applications of decision theory to electrochemical processes

The application of minimax risk and Bayesian concepts in decision theory to electrochemical processes is illustrated by means of an electrolytic pollution control problem, and a quality control problem involving electrolytically deposited nickel–iron alloy coatings.

Apie plonąjį praktinį racionalumą ir jo pastorinimus

Straipsnyje supažindinama su sprendimų teorijos eksplikuojama formaliojo racionalumo samprata, aptariamos jos modifikacijos, kurias ši samprata patiria, kai sprendimų teorija savo reikmėms naudojasi socialiniai mokslininkai ir filosofai. Šių modifikacijų rezultatas vadinamas „storąja“, „standžiąja“, „tankiąja“ arba „plačiąja“ racionalumo samprata. Sprendimų teorija yra veiksmų racionalaus pasirinkimo teorija. Racionaliu čia vadinamas pasirinkimas veiksmo, kuris maksimizuoja veikėjo laukiamą naudą. Straipsnyje taip pat parodoma, kuo skiriasi tos formaliosios racionalumo teorijos „storinimo“ strategijos, kuriomis naudojasi socialiniai mokslininkai, nuo tų, kurias taiko filosofai. Apžvelgiamos konceptualios sąsajos tarp formaliosios praktinio racionalumo sampratos ir tos, kuri tradiciškai yra praktinės filosofijos (etikos) dėmesio centre (ji straipsnyje vadinama absoliučiojo praktinio racionalumo vardu). Straipsnyje pristatoma „santykinio praktinio racionalumo“ vardu pavadinta praktinio racionalumo samprata, kurios kontūrai paskutiniaisiais dešimtmečiais ryškėja analitinėje veiklos bei socialinėje filosofijoje.

Decision-theoretic limits on earthquake prediction

SUMMARY I apply Decision Theory to the question of how accurate earthquake predictions must be to serve as a reliable basis for action. Even with optimistic estimates of the parameters involved, the lower bound on the required accuracy is extraordinarily demanding, being over 10 times higher than that of current meteorological forecasts. Given the abruptly self-organizing nature of earthquakes, it is extremely unlikely that precursors can attain such levels of accuracy. I therefore conclude that prediction of major earthquakes is, in any practical sense, impossible.

Credibility using a loss function from Spline theory

Current formulas in credibility theory often calculate net premium as a weighted sum of the average experience of the policyholder and the average experience of the entire collection of policyholders. Because these formulas are linear, they are easy to use. Another advantage of linear formulas is that the estimate changes a fixed amount per change in claim experience; if an insurer uses such a formula, then the policyholder can predict the change in premium. On the other hand, Venter (1990) points out that in some cases, the loss of accuracy makes a linear formula undesirable. We apply decision theory to develop a credibility formula that minimizes a loss function that is a linear combination of a squared-error term and a second-derivative term. The squared-error term measures the accuracy of the estimator, while the second-derivative term constrains the estimator to be close to linear. An actuary may balance the sometimes conflicting goals of accuracy and linearity by changing a single parameter in the loss function. Our loss function is similar to one used in spline theory, although in a different context.

Integrating case-based reasoning and decision theory

Case-based reasoning (CBR) and decision-theoretic techniques can be complementary. Decision theory helps CBR deal with uncertainties in the problem domain, while CBR helps decision theory handle complicated problems with many variables. The goal of integrating CBR and decision theory is to improve the ability of CBR systems to solve problems in domains of incomplete information. Our methodology views the retrieval of old cases in CBR as a decision problem, where each case from the case base provides an alternative solution and a prediction of the possible outcomes for the problem. When case-based problem solving encounters uncertainty, our methodology applies decision theory to evaluate each case in terms of the attributes that are significant for the problem, so that the most desirable case can be selected. We implemented our methodology in a case-based design assistant that helps chemists design pharmaceuticals.

Application of decision theory to DUI assessment.

The application of decision theory to screening of driving under the influence (DUI) offenders is illustrated in an evaluation study that investigated the validity of a structured interview and a survey instrument. These findings are examined graphically using the relative operating characteristic (ROC). This graph relates the proportion of true-positive cases to the proportion of false-positive cases for various placements of the decision cut-off score. The ROC's two principal parameters, test sensitivity and examiner bias, are used to provide a complete quantitative description of the performance of two screening instruments. A major goal of this study is to show how a new measure of examiner bias, the "cost ratio," improves the evaluation of DUI screening programs.

Overview and critique of judgement and decision making in health care: social and procedural dimensions.

This paper presents an outline of the scope for the application of decision theory to health care. Firstly, the main approaches to and assumptions of decision theory are discussed. Secondly, health care decision making is reviewed. It is noted that decision theory can be applied to either the health care professional or to the lay person. Applications of decision theory to clinical practice, to the management of care and to resourcing are considered. Thirdly, some areas which would repay further research are identified. These include social processes in individual and group decision making, the temporal distribution of outcomes and the development of techniques capable of dealing with the complex and dynamic features of decisions. On the basis of the foregoing, some conclusions are drawn.

The application of decision theory to sustainable management of an upland Scottish estate

Financial, social and ecological cost and benefit criteria were used to examine the sustainability of land-use options on an upland Scottish estate. The costs and benefits of alternative land uses were examined using a psychometric modelling technique based on decision-conferencing, using an expert group of people. Three decision models based respectively on financial, social and ecological criteria were developed, compared and then integrated. The management options that emerged from the integrated model as best compromises appeared to be reasonable and feasible within the framework of facts and projections available to the group. The effects of different future scenarios, such as changes in fiscal policy, were also examined using the models. A three-dimensional model constructed from a principal components analysis of the input data produced axes that seemed to reflect certain socio-political archetypes.