Impact assessment (IA) in policy making processes has received increasing attention in recent years. One of the major challenges in IA is how to rationally handle and make maximum use of information in uncertain and qualitative data so that the best course of action can be reliably identified. It is discussed in this chapter how the Evidential Reasoning (ER) approach for multiple criteria decision analysis (MCDA) can be used to take the challenge. The ER approach and its software implementation, called the Intelligent Decision System (IDS), are developed with a focus on rationally handling a large amount of information of both a qualitative and quantitative nature and possibly with different degrees of uncertainties in assessment problems. It applies belief decision matrices for problem modelling so that different formats of available data and uncertain knowledge can be incorporated into assessment processes. It uses an evidential reasoning process on the data to generate assessment outcomes that are informative, rational and reliable. Several examples are examined to demonstrate how IDS can be used to support activities in different stages of an IA process, namely (a) problem structuring, (b) assessment model building, including value elicitation, (c) data collection, management, and aggregation, and (d) data presentation and sensitivity analysis. This investigation shows that IDS is not only a versatile assessment supporting tool, but also a knowledge management tool which helps to organise assessment knowledge and data systematically for better traceability, consistency and efficiency in assessment.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
- Impact Assessment
- Decision Matrix
- Evidential Reasoning
- Multiple Criterion Decision Analysis
- Belief Degree
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Balestra, G. and Tsoukias A. (1990), Multicriteria analysis represented by artificial intelligence techniques, The journal of the Operational Research Society, Vol. 41, No. 5, 419–430.
Belton, V. and Stewart, T.J. (2002), Multiple Criteria Decision Analysis — An Integrated Approach, Kluwer Publishers, London.
Beynon, M., Cosker, D. and Marshall, D. (2001), An expert system for multi-criteria decision making using Dempster Shafer theory, Expert Systems with Applications, Vol. 20, 357–367.
BRE (Better Regulation Executive) (2006), The Tools to Deliver Better Regulation. Available from http://www.cabinetoffice.gov.uk/regulation/documents/ria/pdf/consultation.pdf, accessed Feb. 2007.
Buchanan, B.G. and Shortliffe, E.H. (1984), Rule-Based Expert Systems, Addison-Wesley, Reading, MA.
Chen, L.H. (1997), An extended rule-based inference for general decision-making problems, Information Sciences, Vol. 102, 111–131.
Chin, K.S., Yang, J.B., Lam, J. and Guo, M. (in press), An evidential reasoning-interval based method for new product design assessment, IEEE transactions on Engineering Management (in press).
EFQM (1999), The EFQM Excellence Model, European Foundation for Quality Management, Brussels, ISBN 90-5236-082-0.
French, S. (1989), Readings in Decision Analysis, Chapman and Hall, London.
George, C. and Kirkpatrick, C. (eds.) (2007), Impact Assessment and Sustainable Development European – Practice and Experience, Edward Elgar Publisher, Northampton, MA.
Huynh, V.N., Nakamori, Y., Ho, T.B. and Murai, T. (2006), Multiple-attribute decision making under uncertainty: The Evidential Reasoning approach revisited, IEEE Transactions on Systems, Man, and Cybernetics—Part A: Systems and Humans, Vol. 36, No. 4, 804–822.
Hwang, C.L. and Yoon, K. (1981), Multiple Attribute Decision Making Methods and Applications, Springer, Berlin Heidelberg New York.
Keeney, R.L. (1992), Value Focused Thinking, Harvard University Press, Cambridge, MA.
Keeney, R.L. and Raiffa, H. (1976), Decisions with Multiple Objectives, Cambridge University Press, Cambridge, MA.
Liu, J., Yang, J.B., Wang, J. and Sii, S.K. (2002), Review of uncertainty reasoning approaches as guidance for maritime and offshore safety-based assessment, Journal of UK Safety and Reliability Society, Vol. 23, 63–80.
Manoliadis, O.G. and Vatalis, K.I. (2003), An Environmental Impact Assessment Decision Analysis System for Irrigation Systems, Selected from papers presented at the 8th International Conference on Environmental Science and Technology, 8–10 September 2003, Lemnos, Greece. Available from http://www.gnest.org/journal/Vol5_No2/09_Manoliadis.pdf (accessed in Feb 2007).
OECD (1997), Regulatory impact analysis: Best practices in OECD countries, Paris, OECD.
Phillips, L.D. (1984), A Theory o Requisite Models, London School of Economics and Political Science, Decision Analysis Unit.
Porter, L. and Tanner, J. (1998), Assessing Business Excellence, Butterworth-Heinmann, London.
Pöyhönen, M. and Hämäläinen, R.P. (2001), On the convergence of multiattribute weighting methods, European Journal of Operational Research, Vol. 129, No. 3, 106–122.
Roy, B. and Vanderpooten, D. (1997), The European school of MCDA: Emergence, basic features, and current works, European Journal of Operational Research, Vol. 99, No. 1, 26–27.
Saaty, T.L. (1988), The Analytic Hierarchy Process, University of Pittsburgh.
Saltelli, A., Tarantola, S. and Chan, K. (1999), A role for sensitivity analysis in presenting the results from MCDA studies to decision makers, Journal of Multi-Criteria Decision Analysis, Vol. 8, No. 3, 139–145.
Sen, P. and Yang, J.B. (1994), Design decision making based upon multiple attribute evaluation and minimal preference information, Mathematical and Computer Modelling, Vol. 20, No. 3, 107–124.
Seppälä, J. (2001), Decision Analysis Frameworks for Life-Cycle Impact Assessment, Journal of Industrial Ecology, Vol. 5, No. 4, 45–68.
Shafer, G.A. (1976), Mathematical Theory of Evidence,Princeton University Press, Princeton, NJ.
Siow, C.H.R., Yang, J.B. and Dale, B.G. (2001), A new modelling framework for organisational self-assessment: development and application, Quality Management Journal, Vol. 8, 34–47.
Sonmez, M., Yang, J.B., Graham, G. and Holt, G.D. (2002), An evidential reasoning based decision making process for pre-qualifying construction contractors, Journal of Decision Systems, Special issue on Decision Making on Urban & Civil Engineering, Vol. 11, 355–381.
Stewart, T.J. (1992), A critical survey on the status of multiple criteria decision making theory and practice, OMEGA International Journal of Management Science, Vol. 20, No. 5–6, 569–586.
von Winterfeldt, D. and Edwards, W. (1986), Decision Analysis and Behavioural Research, Cambridge University Press, Cambridge, MA.
White, C.C. (1990), A survey on the integration of decision analysis and expert systems for decision support, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 20, No. 2, 358–364.
Wright, G. and Goodwin, P. (1999), Rethinking value elicitation for personal consequential decisions, Journal of Multi-Criteria Decision Analysis, Vol. 8, 3–10.
Xu, D.L. (2005), Web-based assessment via the evidential reasoning approach, Workshop on Human-Computer Interface Issues in e-Democracy, Manchester UK.
Xu, D.L. and Yang, J.B. (2002), MCDM analysis using IDS on whether UK should join the Euro, MCDM Winter Conference in Semmering, Austria.
Xu, D.L. and Yang, J.B. (2003), Intelligent decision system for self-assessment, Journal of Multi-criteria Decision Analysis Vol. 12, 43–60.
Xu, D.L. and Yang, J.B. (2004), Making decisions under uncertainties using the evidential reasoning approach, Proceedings of the 15th Mini-EURO Conference on Managing Uncertainty in Decision Support Models, Portugal.
Xu, D.L. and Yang, J.B. (2005), An intelligent decision system based on the evidential reasoning approach and its applications, Journal of Telecommunications and Information Technology, No. 3, 73–80.
Yager, R.R. (1987), On the Dempster–Shafer framework and new combination rules, Information Sciences, Vol. 41, No. 2, 93–137.
Yager, R.R. (1995), Decision-making under various types of uncertainties, Journal of Intelligent and Fuzzy Systems, Vol. 3, No. 4, 317–323.
Yang, J.B. (2001), Rule and utility based evidential reasoning approach for multiple attribute decision analysis under uncertainty, European Journal of Operational Research, Vol. 131, 31–61.
Yang, J.B. and Sen, P. (1997), Multiple attribute design evaluation of large engineering products using the evidential reasoning approach, Journal of Engineering Design, Vol. 8, 211–230.
Yang, J.B. and Singh, M.G. (1994), An evidential reasoning approach for multiple attribute decision making with uncertainty, IEEE Transactions on Systems, Man, and Cybernetics, Vol. 24, 1–18.
Yang, J.B. and Xu, D.L. (2002), On the evidential reasoning algorithm for multiple attribute decision analysis under uncertainty, IEEE Transactions on Systems, Man and Cybernetics Part A: Systems and Humans, Vol. 32, 289–304.
Yang, J.B. and Xu, D.L. (2002), Nonlinear information aggregation via evidential reasoning in multiple attribute decision analysis under uncertainty, IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans, Vol. 32, 376–393.
Yen, J. (1989), GERTIS: A Dempster–Shafer approach to diagnosing hierarchical hypotheses, Communications of the ACM, Vol. 32, No. 5, 573–585.
Zhang, Z.J., Yang, J.B. and Xu, D.L. (1989), A hierarchical analysis model for multiobjective decision making, In Analysis, Design and Evaluation of Man-Machine System (Selected Papers from the 4th IFAC/IFIP/IFORS/IEA Conference, Xian, P.R. China, September 1989), Pergamon, Oxford, UK, 13–18.
Zhao, M.Y., Cheng, C.T., Chau, K.W., and Li G. (2006), Multiple criteria data envelopment analysis for full ranking units associated to environment impact assessment, International Journal of Environment and Pollution, Vol. 28, No. 3/4, 448–464.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Xu, DL., Yang, JB., Liu, X. (2008). Handling Uncertain and Qualitative Information in Impact Assessment – Applications of IDS in Policy Making Support. In: Da Ruan, Hardeman, F., van der Meer, K. (eds) Intelligent Decision and Policy Making Support Systems. Studies in Computational Intelligence, vol 117. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78308-4_9
Download citation
DOI: https://doi.org/10.1007/978-3-540-78308-4_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78306-0
Online ISBN: 978-3-540-78308-4
eBook Packages: EngineeringEngineering (R0)