From vicious to virtuous circles: problem structuring for quantified decision making in operationalization of corporate social responsibility

There is no formalised approach for problem structuring and quantitative decision support to operationalise corporate social responsibility (CSR) implementation. In this paper, techniques for considering criteria relationships are outlined and a holistic, systematic framework combining a qualitative and quantitative method for practical CSR integration is provided. Cognitive mapping (CM) is applied to structure the problem picture, and the cause–effect relationships between decision elements. Soft CM methodology is employed to assess the cross-criteria interactions, at both an individual and a collective level. The interactions of criteria can have a significant impact upon CSR implementation. Such impacts can be direct or indirect through their close linkages to other criteria. The causal strategic map serves as an input to the analytic network process (ANP) to carry out the multi-criteria decision analysis. Then, CM and ANP are applied in a comparative analysis to verify whether the measures of criteria significance do correspond. The key criteria in networks are identified using centrality in CM and single limited priorities in ANP. This study demonstrates that using criteria without considering their interactions will result in shortcomings in the evaluation and assessment of CSR programmes. The holistic framework, combining CM and ANP proposed in this work, enhances the process of problem structuring and supports preference-based evaluation of decision alternatives. The results of our study yield that the mapping procedure has an influence on the criteria significance in networks. The correspondence between CM and ANP is stronger when cause relationships are rigidly interpreted. More unambiguous interpretations of causal relations can be achieved if methods are used jointly and common peaks of importance in both CM and ANP could potentially serve as indications of key decision elements.     

Structuring decision problems: A case study and reflections for practitioners

This article reviews several approaches to problem structuring and, in particular, the three-step structuring process for decision analysis proposed by von Winterfeldt and Edwards: (1) identifying the problem; (2) selecting an appropriate analytical approach; (3) developing the a detailed analytic structure. This three-step process is re-examined in the context of a decision analysis of alternative policies to reduce electromagnetic field exposure from electric power lines. This decision analysis was conducted for a public health organization funded by the California Public Utilities Commission and it was scrutinized throughout by interested stakeholders. As a result a significant effort went into structuring this problem appropriately, with some successes and some missteps. The article extracts lessons from this experience, updating existing guidance on structuring problems for decision analysis, and concluding with some general insights for problem structuring.     

A DST-based approach for construction project risk analysis

Despite its huge potential in risk analysis, the Dempster–Shafer Theory of Evidence (DST) has not received enough attention in construction management. This paper presents a DST-based approach for structuring personal experience and professional judgment when assessing construction project risk. DST was innovatively used to tackle the problem of lacking sufficient information through enabling analysts to provide incomplete assessments. Risk cost is used as a common scale for measuring risk impact on the various project objectives, and the Evidential Reasoning algorithm is suggested as a novel alternative for aggregating individual assessments. A spreadsheet-based decision support system (DSS) was devised to facilitate the proposed approach. Four case studies were conducted to examine the approach's viability. Senior managers in four British construction companies tried the DSS and gave very promising feedback. The paper concludes that the proposed methodology may contribute to bridging the gap between theory and practice of construction risk assessment.     

Qualitative operators for reasoning maps: Evaluating multi-criteria options with networks of reasons

Cognitive/causal maps have been widely used as a powerful way of capturing decision-makers’ views about a problem, representing it as a cause–effect discourse. Several ways of making causal inferences from this type of model have been proposed in the Operational Research and Artificial Intelligence literatures, but none, as far as we are aware, has attempted to use a causal map structure to perform a multi-criteria evaluation of decision alternatives. Recently, we have proposed a new multi-criteria method, denominated as a Reasoning Map, which permits the use of decision-makers’ reasoning, structured as a network of means-and-ends (a particular type of causal map) to perform such an evaluation. In this manner, the model resembles the way that people talk and think about decisions in practice. The method also pays explicit attention to the cognitive limitations of decision-makers in providing preference information. Thus it employs qualitative assessment of preferences, utilises aggregation operators for qualitative data and provides also qualitative outputs. In this paper we discuss and evaluate possible ways of aggregating qualitative performance information in Reasoning Maps.     

An MCDA approach for the selection of bike projects based on structuring and appraising activities

This paper presents an MCDA approach for the structuring and appraising activities of a large and complex decision problem. More specifically, the paper makes use of the three-step structuring process for decision analysis proposed by von Winterfeldt and Edwards: (1) identifying the problem; (2) selecting an appropriate analytic approach; and (3) developing a detailed analytic structure. For illustration of the approach a case study dealing with the assessment task of prioritising and selecting initiatives and projects from a public pool with limited funds is examined throughout the paper. The process is embedded in a Decision Support System (DSS) making use of the REMBRANDT technique for pair wise comparisons to determine project rankings. A procedure for limiting the number of pair wise comparisons to be made in the process is in this connection presented. Finally, strengths and weaknesses in the approach are discussed and conclusions are made.     

Facilitation practices in decision workshops

Decision workshops, sometimes called decision conferences, help a group of decision makers gain a shared understanding of a decision problem, analyse issues and commit to an action plan under the guidance of an experienced facilitator. This work seeks to identify best practice in the early stages of the facilitation of such workshops when the emphasis is placed on problem structuring and the main issues of a complex decision problem are identified and explored. Four decision workshops, based on the same hypothetical scenario but facilitated by a different person, were organized. Video material of the simulated workshops was analysed to compare and contrast the facilitated meetings including the problem structuring methods used. A framework for studying facilitation practices emerged. The effect of a facilitator on the structuring of the problem, the group decision process and the outcome of the workshop was studied. The results of the work indicate that a facilitator's style and approach to the workshop may have an impact on the action plan devised. Further research is required to generalize the findings of our work.     

Structuring resource allocation decisions: A framework for building multi-criteria portfolio models with area-grouped options

Multi-criteria portfolio modelling has been extensively employed as an effective means to allocate scarce resources for investment in projects when considering costs, benefits and risks. Some of these modelling approaches allow the grouping of projects into organisational areas, thus also supporting the decision of resource allocation among organisational units in a way that is collectively efficient for the organisation. However, structuring in practice a portfolio model using this latter type of approach is not a trivial task. How should areas be defined? Where should new projects be included? How should one define the criteria to evaluate performance? As far as we know, there is very little indication in the operational research and decision sciences literatures on how to structure this type of model. This paper suggests different ways to structuring portfolio models where projects are divided into areas and evaluated by multiple criteria, and illustrates their use in two action-research projects. Drawing on these experiences it then suggests a general framework for the structuring of such models in practice. Directions for future research are also identified.     

Domain structuring for knowledge-based multiattribute classification (a verbal decision analysis approach)

Classification problems in decision making are, at least, ill-structured or even unstructured ones, since, among other things, human judgments (i.e., Decision Maker preferences and/or expert knowledge) are the primary sources of information for their solving. Thus, not only the classification rules eliciting, but the application domain structuring as well, is a complex problem itself. The paper focuses on knowledge-based classification problem structuring in the context of complete (up to the expert knowledge) and consistent knowledge base construction for a Diagnostic Decision Support System. Two structuring techniques are proposed as expert aids, as well as an approach to large-size problem decomposition. It is asserted that application domain structuring and classification rules eliciting have to be arranged as interconnected procedures.     

An introduction and survey of the evidential reasoning approach for multiple criteria decision analysis

The Evidential Reasoning (ER) approach is a general approach for analyzing multiple criteria decision problems under various types of uncertainty using a unified framework—belief structure. In this paper, the ER approach is surveyed from two aspects: theoretical development and applications. After a brief outline of its development and extension over a twenty year period, the ER approach is outlined with a focus on the links among its various developments. Future research directions in the area are also explored in the survey.     

A dialectical framework for problem structuring and information technology

This paper proposes that problem structuring is characterized by dialectical processes, and that different problem situations call for differing emphasis on the organizing versus disorganizing potential of information technologies. The term ‘dialectical’ emphasizes the conflicting forces of structuring and unstructuring in processes of problem solving. A case study of an enterprise software conversion project examines four processes: structuring, unstructuring, groping, and adjusting. Applying the dialectic perspective to the classic Gorry and Scott-Morton framework yields propositions as to which of these four processes should be emphasized by information systems addressing different types of problems. Implications for the design of decision support systems and for future research are discussed.     

Multicriteria evidential reasoning decision modelling and analysis—prioritizing voices of customer

In this paper, a new methodology is investigated to support the prioritization of the voices of customers through various customer satisfaction surveys. This new methodology consists of two key components: an innovative evidence-driven decision modelling framework for representing and transforming large amounts of data sets and a generic reasoning-based decision support process for aggregating evidence to prioritize the voices of customer on the basis of the Evidential Reasoning (ER) approach. Methods and frameworks for data collection and representation via multiple customer satisfaction surveys were examined first and the distinctive features of quantitative and qualitative survey data are analysed. Several novel yet natural and pragmatic rule-based functions are then proposed to transform survey data systematically and consistently from different measurement scales to a common scale, with the original features and profiles of the data preserved in the transformation process. These new transformation functions are proposed to mimic expert judgement processes and designed to be sufficiently flexible and rigorous so that expert judgements and domain specific knowledge can be taken into account naturally, systematically and consistently in the transformation process. The ER approach is used for synthesizing quantitative and qualitative data under uncertainty that can be caused due to missing data and ambiguous survey questions. A new generic method is also proposed for ranking the voices of customer based on qualitative measurement scales without having to quantify assessment grades to fixed numerical values. A case study is examined using an Intelligent Decision System (IDS) to illustrate the application of the decision modelling framework and decision support process for prioritizing the voices of customer for a world-leading car manufacturer.     

WASAN: The development of a facilitated methodology for structuring a waste minimisation problem

This paper contributes a new methodology called Waste And Source-matter ANalyses (WASAN) which supports a group in building agreeable actions for safely minimising avoidable waste. WASAN integrates influences from the Operational Research (OR) methodologies/philosophies of Problem Structuring Methods, Systems Thinking, simulation modelling and sensitivity analysis as well as industry approaches of Waste Management Hierarchy, Hazard Operability (HAZOP) Studies and As Low As Reasonably Practicable (ALARP). The paper shows how these influences are compiled into facilitative structures that support managers in developing recommendations on how to reduce avoidable waste production. WASAN is being designed as Health and Safety Executive Guidance on what constitutes good decision making practice for the companies that manage nuclear sites. In this paper we report and reflect on its use in two soft OR/problem structuring workshops conducted on radioactive waste in the nuclear industry.     

A flexible framework for Group Decision Support: WINGDSS version 3.0

A flexible framework for Group Decision Support on PCs in Microsoft Windows environment is presented. WINGDSS does not replace human judgment but highly supports the entire process of decision making, from problem structuring to post-decision analysis. The latest version of WINGDSS is a modular, open system with a dynamical connection to relational databases, an interpreter for defining problem specific evaluation procedures, a lot of interactive features from setting up the tree of criteria until the sensitivity analysis on individual/group ranking. By providing tools for recursively redefining the decision problem, WINGDSS helps the decision makers in achieving a result satisfactory to the whole group.Research supported in part by the Hungarian Foundation for Scientific Research, Grant No. 2568 and by the Hungarian National Committee for Technical Research and Development, Grant No. 93-97-67-0553.     

Eliciting and mapping qualitative preferences to numeric rankings in group decision making

Group work is becoming the norm in organizations. From strategy planning committees to quality management teams, organizational members are collaborating on problem solving. One area of team support that is often desired is the scoring and ranking of decision alternatives on qualitative/subjective domains, and the aggregation of individual preferences into group preferences. In this paper we present a new conceptual approach to qualitative preference elicitation and aggregation. This approach is based on well established decision analysis techniques. It significantly advances the state of the art of group decision making by addressing four common limitations: (1) the inability to deal with vagueness of human decision makers in articulating preferences; (2) difficulties in mapping qualitative evaluation to numeric estimates; (3) problems in aggregating individual preferences into meaningful group preference; and (4) the lack of simple user friendly techniques for dealing with a large number of decision alternatives. Our approach is easy to implement in stand alone personal computers and groupware. We illustrate this with a real-world problem.     

Towards a constructive approach to act-conditional subjective expected utility models

In structuring a decision problem under uncertainty, the uncertain environment may be affected by the choice of an act. In decision analysis, the decision maker provides subjective probabilities and utilities through separate elicitation processes, and then both components are combined together to give an index of his preference over decision alternatives. Based upon this conceptualisation of decision analysis, a constructive approach to act-conditional subjective expected utility theory is proposed. Two utility models have been addressed: the linear utility model and the weighted utility model.     

Interactive construction of graphical decision models based on causal mechanisms

We propose a framework for building graphical decision models from individual causal mechanisms. Our approach is based on the work of Simon [Simon, H.A., 1953. Causal ordering and identifiability. In: Hood, W.C., Koopmans, T.C. (Eds.), Studies in Econometric Method. Cowles Commission for Research in Economics. Monograph No. 14. John Wiley and Sons Inc., New York, NY, pp. 49–74 (Ch. III)], who proposed a causal ordering algorithm for explicating causal asymmetries among variables in a self-contained set of structural equations. We extend the causal ordering algorithm to under-constrained sets of structural equations, common during the process of problem structuring. We demonstrate that the causal ordering explicated by our extension is an intermediate representation of a modeler’s understanding of a problem and that the process of model construction consists of assembling mechanisms into self-contained causal models. We describe ImaGeNIe, an interactive modeling tool that supports mechanism-based model construction and demonstrate empirically that it can effectively assist users in constructing graphical decision models.     

Using possibilistic logic for modeling qualitative decision: Answer Set Programming algorithms

A qualitative approach to decision making under uncertainty has been proposed in the setting of possibility theory, which is based on the assumption that levels of certainty and levels of priority (for expressing preferences) are commensurate. In this setting, pessimistic and optimistic decision criteria have been formally justified. This approach has been transposed into possibilistic logic in which the available knowledge is described by formulas which are more or less certainly true and the goals are described in a separate prioritized base. This paper adapts the possibilistic logic handling of qualitative decision making under uncertainty in the Answer Set Programming (ASP) setting. We show how weighted beliefs and prioritized preferences belonging to two separate knowledge bases can be handled in ASP by modeling qualitative decision making in terms of abductive logic programming where (uncertain) knowledge about the world and prioritized preferences are encoded as possibilistic definite logic programs and possibilistic literals respectively. We provide ASP-based and possibilistic ASP-based algorithms for calculating optimal decisions and utility values according to the possibilistic decision criteria. We describe a prototype implementing the algorithms proposed on top of different ASP solvers and we discuss the complexity of the different implementations.     

A hierarchical interactive method for ranking alternatives with multiple qualitative criteria

In this study we deal with the problem of finding the most preferred composite ranking of a set of alternatives evaluated using a large number of criteria having a hierarchical structure. The criteria may be qualitative or quantitative. The decision maker evaluates alternatives using each criterion at the lowest (basic) level. That information is then used to construct the generalized correlation matrix to describe interdependencies between the criteria. The correlation matrix and the criterion hierarchy are the basic information used in the approach. Our interactive approach is designed to help the decision maker find the most preferred aggregation of the kth level criteria, which produces the criteria at the (k + 1)st level. As the final result of the aggregation we obtain the strength of the preference matrix for the criterion at the highest level. By means of that matrix, we produce the final ranking of the alternatives using the Bowman and Colantoni (1973) model. The approach is easy to implement and convenient to use. We have implemented an experimental version of it on an Apple III microcomputer. The graphical colour display is used as an aid in finding the most preferred aggregation. An illustrative example is provided.     

A decision support system for urban waste management

This paper describes a decision support system, for urban waste management in a regional area, to be used for evaluating general policies for service organisation of the collection and for identifying areas suitable for locating waste treatment and disposal plants. The Decision Support System (DSS) is applied to a selected provinces in Sicily. The decision support system allows the generation and evaluation of suitable alternatives with respect to salient features of the problem, especially environmental consequences. The paper describes the identification and collection of relevant information, the structuring of a database, the design of combinatorial optimisation algorithms for solving the core location problem, the study of models for evaluating the different alternatives and their framing in a complete multicriteria decision model. Finally, the solution of the case study, by means of the DSS, is described.