Ron Howard is recognizedas a pioneer in the fields of Markov decision processes and the general field of decision analysis (DA), a term that he introduced. In a series of papers published in the 1960s, Ron laid out the principles of applied decision theory and brought the techniques of practical DA to various audiences, especially operations research (OR) and engineering. He furthered the application of DA by combining his academic teaching objectives with the founding of the first DA management consulting organization.

Many companies set performance targets for their divisions to decentralize the decision-making process and communicate with outside investors. This paper analyzes the effects of performance targets on the decision-making behavior of the divisions. We introduce the notion of an 'effective utility function'-a function that a division should use in its selection of projects if it wishes to maximize the probability of achieving its targets. We show that many target-based incentives induce S-shaped utility functions and discuss the organizational problems they may pose. We then show how an organization can set targets that induce expected utility maximization. Copyright © 2008 John Wiley & Sons, Ltd.

Many companies set multiple performance targets for their managers and reward them on meeting a threshold value for each target or goal. Examples of such incentive structures abide in the managerial literature and in organizational settings. We show that this incentive structure, while popular, has two main problems: (i) it can induce managers who try to maximize the probability of meeting their performance targets to make decisions that are not compatible with expected utility maximizing decisions, and (ii) it may lead to trade-offs among the performance objectives that are inconsistent with the corporate value function. In this paper, we propose a method to remedy these two problems, while retaining a target-based incentive scheme. We define a multiattribute target as a deterministic region in the space of multiattribute outcomes that has two properties: (1) the probability that the outcome of a multiattribute lottery lies within the target region is equal to the expected utility of the lottery, and (2) all outcomes within the target region are preferred to all outcomes outside it. These two properties lead to a new quantity; which we call the ‘value aspiration equivalent’ that leads managers who maximize the probability of meeting their targets to simultaneously maximize the expected utility, and it also induces trade-offs that are consistent with the decision maker’s value function. Copyright r 2009 John Wiley & Sons, Ltd.

Decision analysis has been successfully cast as an intervention to pull together the right team of people to formulate and solve the decision maker's problem by building economic models of the situation, assessing the crucial uncertainties, and determining the risk and return of each alternative. For large one-of-kind problems this method works well. Many organizations claim that they have made millions using it. Yet, in spite of its accomplishments, this model has had limited sustained success. Decision analysts often find themselves relegated to disempowered staff, delighted when the executive occasionally calls on them. With a few important exceptions, internal Decision Analysis groups have generally failed to withstand the test of time. We document and explore the ongoing shift from interventional decision analysis to the decision organization, an embedded ecology of actors and patterns of behavior that needs to be guided to produce a sustainable pattern of good decisions. Enabled by the stunning increase in computing power and connectivity, organizational decision analysts need to use the old tools in new and more productive ways and to guide the organizational decision ecology toward sustainable profitability and growth. What will it mean to be a professional decision analyst in the twenty-first century?

We examine multiattribute decision problems where a value function is specified over the attributes of a decision problem, as is typically done in the deterministic phase of a decision analysis. When uncertainty is present, a utility function is assigned over the value function to represent the decision maker's risk attitude towards value, which we refer to as a value-based approach. A fundamental result of using the value-based approach is a closed form expression that relates the risk aversion functions of the individual attributes to the trade-off functions between them. We call this relation utility transversality. The utility transversality relation asserts that once the value function is specified there is only one dimension of risk attitude in multiattribute decision problems. The construction of multiattribute utility functions using the value-based approach provides the flexibility to model more general functional forms that do not require assumptions of utility independence. For example, we derive a new family of multiattribute utility functions that describes richer preference structures than the usual multilinear family. We also show that many classical results of utility theory, such as risk sharing and the notion of a corporate risk tolerance, can be derived simply from the utility transversality relations by appropriate choice of the value function. Copyright (C) 2007 John Wiley & Sons, Ltd.

Free AccessAboutSectionsView PDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked InEmail Go to SectionFree Access HomeINFORMS TutORials in Operations ResearchEmerging Theory, Methods, and Applications Decision Analysis = Decision EngineeringJames E. MathesonJames E. MathesonPublished Online:14 Oct 2014https://doi.org/10.1287/educ.1053.0015Abstract Decision analysis is an engineering approach to helping decision makers reach rational decisions. It incorporates systems engineering and decision theory, enhanced by the ability of modern computation to build value-based models and carry out computations needed to deal with complexity in the number of factors, uncertainties, and dynamics. It also includes the processes for reaching good decisions with real people (and real organizations) and gaining their commitment to carry them out. Decision analysis is not a single approach, but a discipline with underlying principles and procedures that are adapted to diverse situations. Decision analysis uses tools like decision theory, influence diagrams, system dynamics, game theory, etc. to “engineer” good decisions in new product development, business strategy, space system safety, etc. The paper ends with the “10 commandments of decision analysis.” You can guess the first. This publication has no references to display. Your Access Options Login Options INFORMS Member Login Nonmember Login Purchase Options Save for later Item saved, go to cart Tutorials in OR, TutorialsNew $20.00 Add to cart Tutorials in OR, TutorialsNew Checkout Other Options Token Access Insert token number Claim access using a token Restore guest access Applies for purchases made as a guest Previous Back to Top Next FiguresReferencesRelatedInformation Emerging Theory, Methods, and ApplicationsSeptember 2005 Article Information Metrics Information Published Online:October 14, 2014 Copyright © 2005, INFORMSCite asJames E. Matheson (2014) Decision Analysis = Decision Engineering. INFORMS TutORials in Operations Research null(null):195-212. https://doi.org/10.1287/educ.1053.0015 Keywordsdecision analysisdecision engineeringtutorialPDF download

AboutSectionsView PDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked InEmail Go to Section HomeDecision AnalysisVol. 2, No. 3 Influence DiagramsRonald A. Howard, James E. MathesonRonald A. Howard, James E. MathesonPublished Online:1 Sep 2005https://doi.org/10.1287/deca.1050.0020 This article appears in INFORMS Analytics Collections Vol. 15: 25 Years of INFORMS. Visit this collection for free access to more articles showcasing the evolution of INFORMS over the past 25 years. Previous Back to Top Next FiguresReferencesRelatedInformationCited byIntermittent sampling for statistical process control with the number of defectivesComputers & Operations Research, Vol. 161A new way of measuring effects of financial crisis on contagion in currency marketsInternational Review of Financial Analysis, Vol. 90Who should I trust? 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Howard, James E. Matheson, (2005) Influence Diagrams. Decision Analysis 2(3):127-143. https://doi.org/10.1287/deca.1050.0020 Keywordsinfluence diagramexpansionexpansion orderdecision treedecision-tree orderBayesarrow reversaldecision networkdecision-tree networkvalue of clairvoyanceBayesian networkbelief networkknowledge mapPDF download

The value of information and value of control calculations have long been two separate parts of a decision analyst’s efforts to extract as much insight as possible from a decision model. This paper unifies these concepts as interventions that modify the structure of the original problem, which have two key properties, purity and quality. Purity is an idealization that leads to Howard canonical form, clarifies the definition of control intervention, and allows us to extend and correct the calculation of the value of control. Quality is a characteristic that leads to generic models of imperfect intervention, which, because of their equivalence to any pure intervention, prevent misguided recommendations when the value of a perfect intervention is high but the value of a somewhat imperfect intervention is low. Quality is a number between 0 and 1 that normalizes and allows comparison of imperfect interventions between applications having very different value scales.

Since the invention of Influence diagrams in the mid-1970s, they have become a ubiquitous tool for representing uncertain situations. This single diagram replaced awkward manipulations of decision trees and nature's trees with a single representation that displays both the sequential and informational structure of decisions. The diagram permits high-level graphic communication, clear assessments and computation in a single graphical system. This retrospective discusses the evolution and application of influence diagrams.

This paper relates normative expected-utility decision making to target-based decision making, and introduces a new quantity, the aspiration equivalent. We show that using the aspiration equivalent as a target provides a new method for choosing between lotteries that is consistent with expected-utility maximization. Furthermore, we show that the aspiration-equivalent target provides a win-win situation for executive-manager delegation. This result furnishes a new link between normative decision analysis and target-based decision making. Copyright (C) 2005 John Wiley & Sons, Ltd.

OVERVIEW: Identifying linkages between the use of best practices and overall measures of corporate performance is difficult. Studies of several hundred companies, however, show that underlying cultural and organizational patterns lead to effective implementation of many best practices, which the authors call the “principles” of a smart organization. These patterns, which are measured with an organizational IQ test, correlated positively with overall corporate performance, leading to the conclusion that smart organizations perform better.

The roots of good strategic decision making are in cultural and organizational norms and patterns. One principle, which we call the outside-in strategic perspective, is essential for excellent strategy. This perspective led a company to recognize that its current strategy was counterproductive and that it must make almost a 180-degree turn. While many companies start with themselves and project market shares, earnings, and so forth out into the environment, the outside-in strategic perspective reverses this to start with the environment and work inwards to the company. In this case, we used a framework that starts with consumer spending in various entertainment areas and divided revenues along a simple value chain of retailers, wholesalers, and producers. An influence-diagram-based model led to counter-intuitive insights about the most valuable segments.