An Analysis of the Dismal Theorem

In a series of papers, Martin Weitzman has proposed a Dismal Theorem. The general idea is that, under limited conditions concerning the structure of uncertainty and preferences, society has an indefinitely large expected loss from high-consequence, low-probability events. Under such conditions, standard economic analysis cannot be applied. The present study is intended to put the Dismal Theorem in context and examine the range of its applicability, with an application to catastrophic climate change. I conclude that Weitzman makes an important point about selection of distributions in the analysis of decision-making under uncertainty. However, the conditions necessary for the Dismal Theorem to hold are limited and do not apply to a wide range of potential uncertain scenarios.Dismal theorem, Uncertainty, Climate change, Catastrophes

Multicriteria analysis under uncertainty with IANUS - method and empirical results

IANUS is a method for aiding public decision-making that supports efforts towards sustainable development and has a wide range of application. IANUS stands for Integrated Assessment of Decisions uNder Uncertainty for Sustainable Development. This paper introduces the main features of IANUS and illustrates the method using the results of a case study in the Torgau region (eastern Germany). IANUS structures the decision process into four steps: scenario derivation, criteria selection, modeling, evaluation. Its overall aim is to extract the information needed for a sound, responsible decision in a clear, transparent manner. The method is designed for use in conflict situations where environmental and socioeconomic effects need to be considered and so an interdisciplinary approach is required. Special emphasis is placed on a broad perception and consideration of uncertainty. Three types of uncertainty are explicitly taken into account by IANUS: development uncertainty (uncertainty about the social, economic and other developments that affect the consequences of decision), model uncertainty (uncertainty associated with the prediction of the effects of decisions), and weight uncertainty (uncertainty about the appropriate weighting of the criteria). The backbone of IANUS is a multicriteria method with the ability to process uncertain information. In the case study the multicriteria method PROMETHEE is used. Since PROMETHEE in its basic versions is not able to process uncertain information an extension of this method is developed here and described in detail. --

Decision-making under risk and uncertainty and its application in strategic management

We introduce a new decision-making model that unifies risk and uncertain environments in the same formulation. For doing so, we present the induced probabilistic ordered weighted averaging (IPOWA) operator. It is an aggregation operator that unifies the probability with the OWA operator in the same formulation and considering the degree of importance of each concept in the aggregation. Moreover, it also uses induced aggregation operators that provide a more general representation of the attitudinal character of the decision-maker. We study its applicability and we see that it is very broad because all the previous studies that use the probability or the OWA operator can be revised and extended with this new approach. We briefly analyze some basic applications in statistics such as the implementation of this approach with the variance, the covariance, the Pearson coefficient and in a simple linear regression model. We focus on a multi-person decision-making problem in strategic management. Thus, we are able to construct a new aggregation operator that we call the multi-person IPOWA operator. Its main advantage is that it can deal with the opinion of several persons in the analysis so we can represent the information in a more complete way

Dynamic decision networks for decision-making in self-adaptive systems: a case study

Bayesian decision theory is increasingly applied to support decision-making processes under environmental variability and uncertainty. Researchers from application areas like psychology and biomedicine have applied these techniques successfully. However, in the area of software engineering and specifically in the area of self-adaptive systems (SASs), little progress has been made in the application of Bayesian decision theory. We believe that techniques based on Bayesian Networks (BNs) are useful for systems that dynamically adapt themselves at runtime to a changing environment, which is usually uncertain. In this paper, we discuss the case for the use of BNs, specifically Dynamic Decision Networks (DDNs), to support the decision-making of self-adaptive systems. We present how such a probabilistic model can be used to support the decision-making in SASs and justify its applicability. We have applied our DDN-based approach to the case of an adaptive remote data mirroring system. We discuss results, implications and potential benefits of the DDN to enhance the development and operation of self-adaptive systems, by providing mechanisms to cope with uncertainty and automatically make the best decision

Simplified models for multi-criteria decision analysis under uncertainty

Includes abstract.Includes bibliographical references.When facilitating decisions in which some performance evaluations are uncertain, a decision must be taken about how this uncertainty is to be modelled. This involves, in part, choosing an uncertainty format {a way of representing the possible outcomes that may occur. It seems reasonable to suggest {and is an aim of the thesis to show {that the choice of how uncertain quantities are represented will exert some influence over the decision-making process and the final decision taken. Many models exist for multi-criteria decision analysis (MCDA) under conditions of uncertainty; perhaps the most well-known are those based on multi-attribute utility theory [MAUT, e.g. 147], which uses probability distributions to represent uncertainty. The great strength of MAUT is its axiomatic foundation, but even in its simplest form its practical implementation is formidable, and although there are several practical applications of MAUT reported in the literature [e.g. 39, 270] the number is small relative to its theoretical standing. Practical applications often use simpler decision models to aid decision making under uncertainty, based on uncertainty formats that `simplify' the full probability distributions (e.g. using expected values, variances, quantiles, etc). The aim of this thesis is to identify decision models associated with these `simplified' uncertainty formats and to evaluate the potential usefulness of these models as decision aids for problems involving uncertainty. It is hoped that doing so provides some guidance to practitioners about the types of models that may be used for uncertain decision making. The performance of simplified models is evaluated using three distinct methodological approaches {computer simulation, `laboratory' choice experiments, and real-world applications of decision analysis {in the hope of providing an integrated assessment. Chapter 3 generates a number of hypothetical decision problems by simulation, and within each problem simulates the hypothetical application of MAUT and various simplified decision models. The findings allow one to assess how the simplification of MAUT models might impact results, but do not provide any general conclusions because they are based on hypothetical decision problems and cannot evaluate practical issues like ease-of-use or the ability to generate insight that are critical to good decision aid. Chapter 4 addresses some of these limitations by reporting an experimental study consisting of choice tasks presented to numerate but unfacilitated participants. Tasks involved subjects selecting one from a set of five alternatives with uncertain attribute evaluations, with the format used to represent uncertainty and the number of objectives for the choice varied as part of the experimental design. The study is limited by the focus on descriptive rather than real prescriptive decision making, but has implications for prescriptive decision making practice in that natural tendencies are identified which may need to be overcome in the course of a prescriptive analysis

Toward a relational concept of uncertainty: about knowing too little, knowing too differently, and accepting not to know

Uncertainty of late has become an increasingly important and controversial topic in water resource management, and natural resources management in general. Diverse managing goals, changing environmental conditions, conflicting interests, and lack of predictability are some of the characteristics that decision makers have to face. This has resulted in the application and development of strategies such as adaptive management, which proposes flexibility and capability to adapt to unknown conditions as a way of dealing with uncertainties. However, this shift in ideas about managing has not always been accompanied by a general shift in the way uncertainties are understood and handled. To improve this situation, we believe it is necessary to recontextualize uncertainty in a broader way¿relative to its role, meaning, and relationship with participants in decision making¿because it is from this understanding that problems and solutions emerge. Under this view, solutions do not exclusively consist of eliminating or reducing uncertainty, but of reframing the problems as such so that they convey a different meaning. To this end, we propose a relational approach to uncertainty analysis. Here, we elaborate on this new conceptualization of uncertainty, and indicate some implications of this view for strategies for dealing with uncertainty in water management. We present an example as an illustration of these concepts. Key words: adaptive management; ambiguity; frames; framing; knowledge relationship; multiple knowledge frames; natural resource management; negotiation; participation; social learning; uncertainty; water managemen

Participation in multicriteria decision support - the case of conflicting water allocation in the Spree River basin

This discussion paper presents the Integrated Methodological Approach for participatory multi-criteria decision support under uncertainty (IMA), which emerged from the debates about participation, multi-criteria analysis (MCA) and benefit-cost analysis (BCA). It provides a framework for participatory and science-based evaluation processes with combined use of BCA and MCA to support large-scale public decisions. While IMA does not claim to realize an all-inclusive participation scheme, it offers the advantage to improve the quality of decision making through advances in competence and fairness. Its practical application with emphasis on its participatory elements is demonstrated by the case study on the water allocation conflict of the German Spree River, which involves the German capital of Berlin, an important wetland, and the needs to remediate a post-mining landscape. --Participation,Multi-criteria analysis,Cost-benefit analysis,River basin management,Integrated Assesment

An Information Systems Teaching Case: Bayesian Probability Applied to Spam eMail Filters

Information Systems professionals can participate in the strategic planning and policy development of the business organization by applying sound techniques for rational decision making. Decision Support Systems often utilize inferential techniques to provide analysis and knowledge creation for business and its information systems. One common method of reasoning under uncertainty is the application of the Bayesian probability model. This teaching case can be used in an Information Systems program to teach one method of inferential reasoning as applied to policy and business rules for spam email filters

An Analysis of the Dismal Theorem

In a series of papers, Martin Weitzman has proposed a Dismal Theorem. The general idea is that, under limited conditions concerning the structure of uncertainty and preferences, society has an indefinitely large expected loss from high-consequence, low-probability events. Under such conditions, standard economic analysis cannot be applied. The present study is intended to put the Dismal Theorem in context and examine the range of its applicability, with an application to catastrophic climate change. I conclude that Weitzman makes an important point about selection of distributions in the analysis of decision-making under uncertainty. However, the conditions necessary for the Dismal Theorem to hold are limited and do not apply to a wide range of potential uncertain scenarios