A software-supported process for assembling evidence and handling uncertainty in decision-making

Complex socio-technical decisions, such as infrastructure investment decisions, are based on large quantities of evidence assembled and manipulated by multi-disciplinary teams. Information about decision options and future states of nature will often be ambiguous, incomplete or conflicting. In this article, a software-supported approach to assembling, structuring and representing evidence in a decision, based on hierarchical modelling of the processes leading up to a decision, is presented. Uncertainty in the available evidence is represented and propagated through the evidence hierarchy using Interval Probability Theory (IPT), providing a commentary on sources and implications of uncertainty in the decision. Case studies in the oil and civil engineering industries demonstrate how the approach has helped to develop shared understanding of the implications of uncertainty. It has enabled experts to externalise their knowledge and has facilitated discussion and negotiation.

[1]  D. Schum.  The Evidential Foundations of Probabilistic Reasoning , 1994 .

[2]  R. M. Oliver,et al.  Influence diagrams, belief nets and decision analysis , 1992 .

[3]  A. Dempster.  UPPER AND LOWER PROBABILITY INFERENCES FOR FAMILIES OF HYPOTHESES WITH MONOTONE DENSITY RATIOS , 1969 .

[4]  Malcolm C. Harrison,et al.  An analysis of four uncertainty calculi , 1988, IEEE Trans. Syst. Man Cybern..

[5]  George J. Klir,et al.  Fuzzy sets, uncertainty and information , 1988 .

[6]  D. I. Blockley,et al.  Uncertain Inference in Knowledge‐Based Systems , 1987 .

[7]  D. E. Bell,et al.  Decision making: Descriptive, normative, and prescriptive interactions. , 1990 .

[8]  L. Zadeh.  Fuzzy sets as a basis for a theory of possibility , 1999 .

[9]  James F. Baldwin,et al.  Evidential support logic programming , 1987 .

[10]  Henry Mintzberg,et al.  The Structure of "Unstructured" Decision Processes , 1976 .

[11]  Brian Warboys,et al.  The Software Process: Modelling and Technology , 1999, Software Process: Principles, Methodology, Technology.

[12]  David I Blockley.  Engineering from reflective practice , 1992 .

[13]  David I Blockley,et al.  Reflective Practice in Engineering Design , 1995 .

[14]  August-Wilhelm Scheer,et al.  ARIS - Business Process Modeling , 1998 .

[15]  D. Schoen,et al.  The Reflective Practitioner: How Professionals Think in Action , 1985 .

[16]  R. Keeney.  Decision analysis: an overview. , 1982, Operations research.

[17]  William R. Ferrell,et al.  Discrete subjective probabilities and decision analysis: Elicitation, calibration and combination. , 1994 .

[18]  Tawfik Jelassi,et al.  DSS research and practice in perspective , 1994, Decis. Support Syst..

[19]  Ralph L. Keeney,et al.  Feature Article - Decision Analysis: An Overview , 1982, Oper. Res..

[20]  Richard Boland,et al.  Mapping the Process of Problem Reformulation: Implications for Understanding Strategic Thought , 1990 .

[21]  John M. Wilson,et al.  Business Processes: Modelling and Analysis for Re-engineering and Improvement , 1995 .

[22]  J. Mumpower.  Decision sciences: An integrative perspective, Paul R. Kleindorfer, Howard C. Kunreuther, and Paul J. H. Schoemaker, Cambridge: Cambridge University Press, 1993, 470 pp., ISBN 0‐521‐32867‐5 (hc), ISBN 0521‐33812‐3 (pb) , 1995 .

[23]  Dominic A. Clark,et al.  Representing uncertain knowledge - an artificial intelligence approach , 1993 .

[24]  Robert A. Snowdon,et al.  An Intoduction to the IPSE 2.5 Project , 1990, SEE.

[25]  Dennis A. Gioia,et al.  Mapping Strategic Thought. , 1992 .

[26]  David I Blockley.  Uncertainty analysis in expert systems , 1987 .

[27]  Didier Dubois,et al.  A Discussion of Uncertainty Handling in Support Logic Programming , 1990 .

[28]  R. Jeffrey.  The Logic of Decision , 1984 .

[29]  Mike Fitzpatrick.  Choice , 2004, The Lancet.

[30]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[31]  Glenn Shafer,et al.  A Mathematical Theory of Evidence , 2020, A Mathematical Theory of Evidence.

[32]  W. Cui,et al.  Interval probability theory for evidential support , 1990, Int. J. Intell. Syst..

[33]  Ray W. Baines,et al.  A state of the art review of IDEFO , 1993 .

[34]  August-Wilhelm Scheer,et al.  ARIS - Business Process Frameworks , 1998 .

[35]  R. A. Snowdon,et al.  An introduction to process-centred environments , 1994 .

[36]  J. P. Davis,et al.  Managing Assets under Uncertainty , 2000 .

[37]  James W. Hall,et al.  Uncertain inference using interval probability theory , 1998, Int. J. Approx. Reason..

[38]  Donald A. Schön,et al.  The Reflective Practitioner: How Professionals Think in Action. , 1987 .

[39]  Brian J. Loasby,et al.  Choice, Complexity and Ignorance: An Enquiry into Economic Theory and the Practice of Decision-Making , 1976 .

[40]  H. Robbins.  On the Measure of a Random Set , 1944 .

[41]  Didier Dubois,et al.  Random sets and fuzzy interval analysis , 1991 .

[42]  A. Tversky,et al.  The framing of decisions and the psychology of choice. , 1981, Science.

[43]  Nikola Kasabov.  Fril—fuzzy and evidential reasoning in artificial intelligence , 1996 .

[44]  A. Strauss,et al.  The discovery of grounded theory: strategies for qualitative research aldine de gruyter , 1968 .

[45]  Shawn P. Curley,et al.  Using Belief Functions to Represent Degrees of Belief , 1994 .

[46]  M. W. Merkhofer,et al.  Quantifying judgmental uncertainty: Methodology, experiences, and insights , 1987, IEEE Transactions on Systems, Man, and Cybernetics.

[47]  H. Simon,et al.  The shape of automation for men and management , 1965 .

[48]  Glenn Shafer,et al.  Readings in Uncertain Reasoning , 1990 .

[49]  David I Blockley.  PROCESS MODELLING FROM REFLECTIVE PRACTICE FOR ENGINEERING QUALITY , 1999 .

[50]  R. Cooke.  Experts in Uncertainty: Opinion and Subjective Probability in Science , 1991 .

[51]  Jim W. Hall,et al.  Uncertainty analysis of coastal projects , 1999 .