Analysis in support of strategic policy making

Policy making involves choice between alternatives in situations that are usually very complex. The process of choice in such situations is not such that a straightforward analytical procedure can be prescribed yielding a uniquely rational solution. However, decision problems arising in policy making situations can be investigated using methods based on recent developments in game and metagame theory. This methodology can form the basis of a rational decision structure of the type envisaged by Diesing that can be incorporated in the day-to-day work of most organizations.     

PORTFOLIO MODELING IN MULTIPLE-CRITERIA SITUATIONS UNDER UNCERTAINTY

There have been many models for portfolio selection, but most do not explicitly include uncertainty and multiple objectives. This paper presents an approach that includes these aspects using a form of stochastic integer programming with recourse. The method involves the use of a time-based decision tree structure called a “project tree.” Using this basic format, an illustrative six-project example is presented and analyzed. Various forms of objectives are discussed, ranging from the maximization of expected portfolio value to the maximization of the minimum weighted portfolio deviation from two goals. In each case, formulated numerical problems are given, and the solutions derived are presented. The approach is shown to be very flexible and capable of handling a variety of situations and objectives.     

LEARNING WITH DETERMINISTIC DECISION RULES

While many problems of uncertainty are commonly analyzed by means of stochastic models, under certain circumstances this may not be an appropriate approach. The latter situation arises when the decision maker knows that the uncertain variables are not generated by a stochastic process, or when he is unwilling, or unable, to compute subjective probabilities. One of the nonstochastic approaches to uncertainty is the expectational approach in which the decision maker forms deterministic expectations about the uncertain aspects of his environment. This paper is concerned with some criteria for selecting among available expectations, or anticipations functions, and the possibility of ordering them according to these criteria. This study focuses especially on the learning criterion. The discussion brings out conceptual problems in connection with the definition of learning, as well as some technical difficulties that one encounters when attempting to compare different anticipations functions from the point of view of the learning criterion. As an illustration of the issues discussed, the paper reports on the results of some simulated decision rules. These show that decision rules in which no learning takes place, and in which some information is ignored, may perform better than more sophisticated rules.     

Reasoned Decision Making Without Math? Adaptability and Robustness in Response to Surprise

Many real‐world planning and decision problems are far too uncertain, too variable, and too complicated to support realistic mathematical models. Nonetheless, we explain the usefulness, in these situations, of qualitative insights from mathematical decision theory. We demonstrate the integration of info‐gap robustness in decision problems in which surprise and ignorance are predominant and where personal and collective psychological factors are critical. We present practical guidelines for employing adaptable‐choice strategies as a proxy for robustness against uncertainty. These guidelines include being prepared for more surprises than we intuitively expect, retaining sufficiently many options to avoid premature closure and conflicts among preferences, and prioritizing outcomes that are steerable, whose consequences are observable, and that do not entail sunk costs, resource depletion, or high transition costs. We illustrate these concepts and guidelines with the example of the medical management of the 2003 SARS outbreak in Vietnam.     

A DYNAMIC PROGRAMMING MODEL FOR OPTIMAL ALLOCATION OF DECISION TIME

Recurrent decision making by a lower-level manager can be viewed as a sequential decision process in which time and uncertainty are limiting factors. Under these conditions, the manager must determine how to best utilize his decision making time consistent with his own particular set of decision values. A dynamic programming model was devised to determine the optimal (consistent) allocation of decision time among five different types of problems for a sequence of simulated recurrent decision situations. Fifty-one lower-level managers were interviewed about their use of decision time and decision procedures. The model was validated by comparing model assumptions and results with the findings from the interviews. The model was used to determine the effects of variations in the levels of time available and uncertainty upon the optimal allocation of decision time.     

Modeling intelligent adversaries for terrorism risk assessment: some necessary conditions for adversary models

Intelligent adversary modeling has become increasingly important for risk analysis, and a number of different approaches have been proposed for incorporating intelligent adversaries in risk analysis models. However, these approaches are based on a range of often-implicit assumptions about the desirable properties of intelligent adversary models. This "Perspective" paper aims to further risk analysis for situations involving intelligent adversaries by fostering a discussion of the desirable properties for these models. A set of four basic necessary conditions for intelligent adversary models is proposed and discussed. These are: (1) behavioral accuracy to the degree possible, (2) computational tractability to support decision making, (3) explicit consideration of uncertainty, and (4) ability to gain confidence in the model. It is hoped that these suggested necessary conditions foster discussion about the goals and assumptions underlying intelligent adversary modeling in risk analysis.     

How to integrate strategic planning into your management process

The primary purpose of strategic planning is to enable management to make decisions today which will affect the firm over the long run where the long run is characterized by risk and uncertainty. If strategic planning is to be useful to management it must be an integral part of the decision making process and not merely an appendage to the management process. In this paper we shall define some of the problems involved in integrating strategic planning and strategic planning tools into the decision process. We shall also suggest some possible solutions to these problems.     

AN APPROACH TO COST—VOLUME—PROFIT ANALYSIS UNDER UNCERTAINTY*

Cost-Volume-Profit analysis is a tool of cost accountants aiding in the choice of an optimal course of action in the solution of a managerial problem. C-V-P is a limited analysis since all the needs of management in determining the optimal strategy are not fulfilled. Factors pertinent to the choice of the optimal strategy under uncertainty are variables, although traditional C-V-P treats these factors as constants. Hence, an approach to C-V-P where pertinent factors necessary for decision making are considered as variables is more desirable than traditional C-V-P. By introducing uncertainty, it is also beneficial to broaden C-V-P to fulfill the following desire of management. Should management choose a course of action based only on its own estimates of the decision parameters or is it advisable to postpone a terminal decision until additional research into the decision parameters is accomplished? Broadened C-V-P solves this problem with the aid of Bayesian Decision Theory for the choice of the optimal course of action in the face of uncertainty.     

How Much Uncertainty is Too Much and How Do We Know? A Case Example of the Assessment of Ozone Monitor Network Options

Limited time and resources usually characterize environmental decision making at policy organizations such as the U.S. Environmental Protection Agency. In these climates, addressing uncertainty, usually considered a flaw in scientific analyses, is often avoided. However, ignoring uncertainties can result in unpleasant policy surprises. Furthermore, it is important for decisionmakers to know how defensible a chosen policy option is over other options when the uncertainties of the data are considered. The purpose of this article is to suggest an approach that is unique from other approaches in that it considers uncertainty in two specific ways-the uncertainty of stakeholder values within a particular decision context and data uncertainty in the light of the decision-contextual data-values relationship. It is the premise of this article that the interaction between data and stakeholder values is critical to how the decision options are viewed and determines the effect of data uncertainty on the relative acceptability of the decision options, making the understanding of this interaction important to decisionmakers and other stakeholders. This approach utilizes the recently developed decision analysis framework and process, multi-criteria integrated resource assessment (MIRA). This article will specifically address how MIRA can be used to help decisionmakers better understand the importance of uncertainty on the specific (i.e., decision contextual) environmental policy options that they are deliberating.     

Flash Flood Risks and Warning Decisions: A Mental Models Study of Forecasters,Public Officials,and Media Broadcasters in Boulder,Colorado

Timely warning communication and decision making are critical for reducing harm from flash flooding. To help understand and improve extreme weather risk communication and management, this study uses a mental models research approach to investigate the flash flood warning system and its risk decision context. Data were collected in the Boulder, Colorado area from mental models interviews with forecasters, public officials, and media broadcasters, who each make important interacting decisions in the warning system, and from a group modeling session with forecasters. Analysis of the data informed development of a decision‐focused model of the flash flood warning system that integrates the professionals’ perspectives. Comparative analysis of individual and group data with this model characterizes how these professionals conceptualize flash flood risks and associated uncertainty; create and disseminate flash flood warning information; and perceive how warning information is (and should be) used in their own and others’ decisions. The analysis indicates that warning system functioning would benefit from professionals developing a clearer, shared understanding of flash flood risks and the warning system, across their areas of expertise and job roles. Given the challenges in risk communication and decision making for complex, rapidly evolving hazards such as flash floods, another priority is development of improved warning content to help members of the public protect themselves when needed. Also important is professional communication with members of the public about allocation of responsibilities for managing flash flood risks, as well as improved system‐wide management of uncertainty in decisions.     

Risk Decision Making in Operational Safety Management– Experience from the Nordic Benchmark Study

Technical Research Centre of Finland (VTT) and Studsvik AB, Sweden, have simulated decision making of the Swedish Nuclear Power Inspectorate and a power company by applying decision models in a benchmark study. Based on the experience from the benchmark study, a decision analysis framework to be used in safety related problems is outlined. By this framework both the power companies and the safety authorities could be provided with a more rigorous, systematic approach in their decision making. A decision analytic approach provides a structure for identifying the information requirements of the problem solving. Thus it could serve as a discussion forum between the authorities and the utilities. In this context, probabilistic safety assessment (PSA) has a crucial role of expressing the plant safety status in terms of reactor core damage accident probability and of risk contributions from various accident precursors. However, a decision under uncertainty should not be based solely on probabilities, particularly when the event in question is a rare one and its probability of occurrence is estimated by means of different kinds of approximations.     

Analysis of a complex decision situation — The Simpsons/Simpsons-Sears merger proposal

Many of the decision situations that arise in modern organizations are very complex. Conventional methods of decision analysis have only limited applicability in these situations. This paper describes a method that can be used as an aid to decision making in such situations where interactions between the parties involved are an important factor. The method is illustrated by reference to a recent situation in which there was competition between a number of participants with regard to the future of the firm of Simpsons Ltd of Toronto. The analysis described illustrates the application of a step-by-step procedure that can be used by participants in the analysis of such situations.     

Robust Climate Policies Under Uncertainty: A Comparison of Robust Decision Making and Info‐Gap Methods

This study compares two widely used approaches for robustness analysis of decision problems: the info‐gap method originally developed by Ben‐Haim and the robust decision making (RDM) approach originally developed by Lempert, Popper, and Bankes. The study uses each approach to evaluate alternative paths for climate‐altering greenhouse gas emissions given the potential for nonlinear threshold responses in the climate system, significant uncertainty about such a threshold response and a variety of other key parameters, as well as the ability to learn about any threshold responses over time. Info‐gap and RDM share many similarities. Both represent uncertainty as sets of multiple plausible futures, and both seek to identify robust strategies whose performance is insensitive to uncertainties. Yet they also exhibit important differences, as they arrange their analyses in different orders, treat losses and gains in different ways, and take different approaches to imprecise probabilistic information. The study finds that the two approaches reach similar but not identical policy recommendations and that their differing attributes raise important questions about their appropriate roles in decision support applications. The comparison not only improves understanding of these specific methods, it also suggests some broader insights into robustness approaches and a framework for comparing them.     

The Role of Quantitative Risk Assessments for Characterizing Risk and Uncertainty and Delineating Appropriate Risk Management Options, with Special Emphasis on Terrorism Risk

The purpose of this article is to discuss the role of quantitative risk assessments for characterizing risk and uncertainty and delineating appropriate risk management options. Our main concern is situations (risk problems) with large potential consequences, large uncertainties, and/or ambiguities (related to the relevance, meaning, and implications of the decision basis; or related to the values to be protected and the priorities to be made), in particular terrorism risk. We look into the scientific basis of the quantitative risk assessments and the boundaries of the assessments in such a context. Based on a risk perspective that defines risk as uncertainty about and severity of the consequences (or outcomes) of an activity with respect to something that humans value we advocate a broad risk assessment approach characterizing uncertainties beyond probabilities and expected values. Key features of this approach are qualitative uncertainty assessment and scenario building instruments.     

Expressing Economic Risk—Review and Presentation of a Unifying Approach

Risk related to economic values is treated by many disciplines, including safety and production engineering, business, and project management. Within each of these and across these disciplines different nomenclature and principles are adopted for describing and communicating risk. The situation is rather confusing. In this article, we review various approaches and concepts that are used to express risk. We present and discuss a unifying approach for dealing with economic risk, with uncertainty being the key risk concept. The approach represents a rethinking on how to implement the Bayesian paradigm in practice to support decision making.     

A framework for optimizing managerial decision

There have been numerous attempts to analyze and formulate management problems within the general framework of Management Science and Operations Research, so as to help managers in their decision making problems. This paper focuses on the fundamental aspects of management decision making and introduces a conceptual framework in which problems of management and its different levels of functioning are systematically identified. Management problems are considered within two major categories: Enterprise and Operations problems. In spite of the difficulties in defining a clear-cut boundary between Enterprise and Operations problems, such a broad classification leads to three basic levels of management functioning, namely: Policy making, coordinating and implementation. The universal relationship among these three basic levels of functioning provides a basic functional element called the Management Triad. In contrast to classical higherarchial structure, triad structure provides a methodology to analyze relationships among different levels of management functions in terms of a given decision problem. It is demonstrated that for different decision making problems individuals can shift from one end of the triad to another and perform a different management function.A quantitative approach has been introduced for operations management decision making by adapting Shewhart's control chart philosophy which in turn provides for quasi-automated decisions for corrective action and also for filtering significant signals of change to appropriate management functioning levels.     

MODEL UNCERTAINTY

We study decision problems in which consequences of the various alternative actions depend on states determined by a generative mechanism representing some natural or social phenomenon. Model uncertainty arises because decision makers may not know this mechanism. Two types of uncertainty result, a state uncertainty within models and a model uncertainty across them. We discuss some two‐stage static decision criteria proposed in the literature that address state uncertainty in the first stage and model uncertainty in the second (by considering subjective probabilities over models). We consider two approaches to the Ellsberg‐type phenomena characteristic of such decision problems: a Bayesian approach based on the distinction between subjective attitudes toward the two kinds of uncertainty; and a non‐Bayesian approach that permits multiple subjective probabilities. Several applications are used to illustrate concepts as they are introduced.     

Scenario Discovery with Multiple Criteria: An Evaluation of the Robust Decision‐Making Framework for Climate Change Adaptation

There is increasing concern over deep uncertainty in the risk analysis field as probabilistic models of uncertainty cannot always be confidently determined or agreed upon for many of our most pressing contemporary risk challenges. This is particularly true in the climate change adaptation field, and has prompted the development of a number of frameworks aiming to characterize system vulnerabilities and identify robust alternatives. One such methodology is robust decision making (RDM), which uses simulation models to assess how strategies perform over many plausible conditions and then identifies and characterizes those where the strategy fails in a process termed scenario discovery. While many of the problems to which RDM has been applied are characterized by multiple objectives, research to date has provided little insight into how treatment of multiple criteria impacts the failure scenarios identified. In this research, we compare different methods for incorporating multiple objectives into the scenario discovery process to evaluate how they impact the resulting failure scenarios. We use the Lake Tana basin in Ethiopia as a case study, where climatic and environmental uncertainties could impact multiple planned water infrastructure projects, and find that failure scenarios may vary depending on the method used to aggregate multiple criteria. Common methods used to convert multiple attributes into a single utility score can obscure connections between failure scenarios and system performance, limiting the information provided to support decision making. Applying scenario discovery over each performance metric separately provides more nuanced information regarding the relative sensitivity of the objectives to different uncertain parameters, leading to clearer insights on measures that could be taken to improve system robustness and areas where additional research might prove useful.     

Inventory control under different forms of uncertainty: Ambiguity and stochastic variability

Inventory decision makers routinely face ambiguity due to the psychological awareness that there is unknown information about salient events that is knowable in principle. Researchers on inventory control behavior in the face of uncertainty have primarily focused on uncertainty due to stochastic variability. However, most decision situations in the naturally occurring world involve both forms of uncertainty—ambiguity and stochastic variability. We report the results of two experiments that partial out the effects of ambiguity and stochastic variability by orthogonal manipulation of these two forms of uncertainty in a newsvendor task. Contrary to established mathematical models of decision making under uncertainty, increased ambiguity results in increased mean absolute percentage error, and a corresponding decrease in profit. We also find a systematic bias toward underordering associated with increased ambiguity, which is over and above the bias associated with increased stochastic variability. We do not see evidence for learning with repeated play, so that the effects of induced ambiguity appear to persist. Finally, based on our findings, we suggest measures that managers can use to ameliorate the effects of ambiguity.