O regule decyzyjnej wspierającej wielokryterialne poszukiwanie optymalnej strategii czystej w warunkach niepewności

• Authors: Helena Gaspars-Wieloch

Title variants

EN

On a decision rule for searching an optimal pure strategy in uncertain multicriteria decision making

• Languages of publication: PL

Abstracts

PL

W pracy opisano propozycję nowego podejścia, które można wykorzystać w wielokryterialnym podejmowaniu decyzji w przypadku poszukiwania optymalnej strategii czystej w warunkach niepewności (decydent nie zna bądź nie zamierza skorzystać z informacji o prawdopodobieństwie wystąpienia poszczególnych stanów natury). Prezentowana reguła decyzyjna poprzedzona jest etapem prognostycznym, w ramach którego brane jest pod uwagę nastawienie decydenta do ryzyka (rozumianego jako możliwość uzyskania niekorzystnej wypłaty) mierzone współczynnikiem optymizmu. Etap ten służy do wyłonienia najbardziej „prawdopodobnego” (tj. odzwierciedlającego naturę decydenta) scenariusza bądź zbioru najbardziej „prawdopodobnych” scenariuszy i ma na celu zawężenie pierwotnej macierzy wypłat, na podstawie której wybierana jest najlepsza decyzja. Procedura odwołuje się do planowania scenariuszowego i do metody SF+AS (ang. Scenario Forecasting + Alternative Selection Method) przedstawionej w innym artykule i znajdującej zastosowanie w jednokryterialnych problemach decyzyjnych.

EN

The author describes a new approach which may be used in uncertain multicriteria decision making with scenario planning to searching an optimal pure strategy. The decision maker does not know the likelihood of particular scenarios. The decision rule is supported by a forecasting stage within which scenarios reflecting the decision maker’s attitude towards risk (understood as a possibility that some bad circumstances might happen) are selected. The nature of the decision maker is measured by the coefficient of optimism. Hence, the final strategy is chosen on the basis of a reduced aggregated payoff matrix. The procedure refers to SAW (Simple Additive Weighting Method) and to SF+AS method (Scenario Forecasting + Alternative Selection Method), presented in an other paper and devoted to one-criterion decision problems.

Keywords

PL

Niepewność; Planowanie scenariuszowe; Strategia czysta; Wielokryterialne podejmowanie decyzji; Współczynnik optymizmu

EN

Coefficient of optimism; Multicriteria decision making; Pure strategy; Scenario planning; Uncertainty

• Publisher: Uniwersytet Ekonomiczny w Katowicach
• Journal: Studia Ekonomiczne
• Year: 2015
• Volume: 248
• Pages: 42-61

Contributors

author

Helena Gaspars-Wieloch

References

• Aghdaie M.H., Zolfani S.H., Zavadskas E.K. (2013), Market segment evaluation and selection based on application of fuzzy AHP and COPRAS-G methods, „Journal of Business Economics and Management”, 14(1), s. 213-233.
• Bana e Costa C.A., Chakas M.P. (2004), A carter choice problem: an example of how to use MACBETH to build a quantitative value model based on qualitative value judgements, „European Journal of Operational Research”, s. 153.
• Basili M., Chateauneuf A., Fontini F. (2008), Precautionary principle as a rule of choice with optimism on windfall gains and pessimism on catastrophic losses, „Ecological Economics”, 67, s. 485-491.
• Basili M., Chateauneuf A. (2011), Extreme events and entropy: A multiple quantile utility model, „International Journal of Approximate Reasoning”, 52, s. 1095-1102.
• Ben Amor S., Jabeur K., Martel J. (2007), Multiple criteria aggregation procedure for mixed evaluations, „European Journal of Operational Research”, 181(3), s. 1506- -1515.
• Birge J.R., Louveaux F. (2011), Uncertainty and modeling issues, Introduction to Stochastic Programming, Springer Series in Operations Research and Financial Engineering, s. 55-100.
• Brans J.P., Mareschal B., Vincke Ph. (1984), PROMETHEE: A new family of outranking methods in multicriteria analysis [w:] J.P. Brans (red.), Operational research’84, North-Holland, Amsterdam.
• Chateauneuf A., Cohen M. (2000), Choquet expected utility model: A new approach to individual behavior under uncertainty and to social welfare [w:] M. Grabisch, T. Murofushi, M. Sugeno (red.) Fuzzy measures and integrals: theory and applications, Physica Verlag, s. 289-313.
• Churchman C.W., Ackoff R.L. (1954), An approximate measure of value, „Journal of Operations Research of America”, 2(1), s. 172-187.
• Courtney H., Kirkland J., Viquerie P. (1997), Strategy under uncertainty, „Harvard Business Review”, 75(6), s. 66-79.
• De Marco G., Morgan J. (2009), On multicriteria games with uncountable sets of equilibria, Centre for Studies in Economics and Finance (CSEF), University of Naples, Italy, CSEF Working Papers 01/2009.
• Dominiak C. (2006), Multicriteria decision aid under uncertainty, „Multiple Criteria Decision Making’ 05”, s. 63-81.
• Dominiak C. (2009), Multi-criteria decision aiding procedure under risk and uncertainty. „Multiple Criteria Decision Making’ 08”, s. 61-88.
• Domurat A., Zieliński T. (2013), Niepewność i niejasność jako uwarunkowania decyzji ekonomicznych, „Decyzje”, s. 20.
• Durbach I.N. (2014), Outranking under uncertainty using scenarios, „European Journal of Operational Research”, 232(1), s. 98-108.
• Durbach I.N., Stewart T.J. (2012), Modeling uncertainty in multi-criteria decision analysis, „European Journal of Operational Research”, 223(1), s. 1-14.
• Edwards W., Barron F.H., (1994), SMARTS and SMARTER: improved simple methods for multiattribute measurement, „Organizational Behaviour and Human Decision Process”, 60.
• Eiselt H.A., Marianov V. (2014), Multicriteria decision making under uncertainty: A visual approach, „International Transactions in Operational Research”, 21(4), s. 525-540.
• Ellsberg D. (2001), Risk, ambiguity and decision. Garland Publishing, New York.
• Etner J., Jeleva M., Tallon J.-M. (2012), Decision theory under ambiguity, „Journal of Economic Surveys”, 26(2), s. 234-270.
• Fishburn P.C. (1984), Foundations of risk measurement. I. Risk or probable Loss. „Management Science”, 30, s. 396-406.
• Gaspars H. (2007), Alokacja zasobu w warunkach niepewności: modele decyzyjne i procedury obliczeniowe, „Badania operacyjne i decyzje”, 2007/1, s. 5-27.
• Gaspars-Wieloch H. (2012), Ograniczona skuteczność metod optymalizacyjnych w rozwiązywaniu ekonomicznych problemów decyzyjnych, „Ekonomista”, 2012/3, s. 303-324.
• Gaspars-Wieloch H. (2013), On a decision rule supported by a forecasting stage based on the decision maker’s risk aversion [w:] L. Zadnik Stirn, J. Zerovnik, J. Povh, S. Drobne, A. Lisec (red.), SOR’13 Proceedings, The 12th International Symposium on Operational Research in Slovenia, 25-27 September 2013, Dolenjske Toplice, Slovenia, Slovenian Society INFORMATIKA, Section for Operational Research, s. 53-59.
• Gaspars-Wieloch H. (2014a), Propozycja hybrydy reguł Hurwicza i Bayesa w podejmowaniu decyzji w warunkach niepewności [w:] T. Trzaskalik (red.), Modelowanie Preferencji a Ryzyko 2014. „Studia Ekonomiczne. Zeszyty Naukowe Uniwersytetu Ekonomicznego w Katowicach” 178, Wydawnictwo Uniwersytetu Ekonomicznego w Katowicach, Katowice.
• Gaspars-Wieloch H. (2014b), On a decision rule for mixed strategy searching under uncertainty on the basis of the coefficient of optimism, „Procedia – social and behavioral sciences”, 110, s. 923-931.
• Gaspars-Wieloch H. (2014c), Modifications of the Hurwicz’s decision rules, „Central European Journal of Operations Research”, 22(4), s. 779-774.
• Gaspars-Wieloch H. (2014d), Modifications of the maximin joy criterion for decision making under uncertainty, „Quantitative methods in economics”, XV, s. 84-93.
• Gaspars-Wieloch H. (2014e), The use of a modification of the Hurwicz’s decision rule in multicriteria decision making under complete uncertainty, „Business, management and education”, 12(2), s. 283-302.
• Gaspars-Wieloch H. (2015a): Modifications of the Omega ratio for decision making under uncertainty, “Croatian operational research review”, 6(1), s. 181-194.
• Gaspars-Wieloch H. (2015b), On a decision rule supported by a forecasting stage based on the decision maker’s coefficient of optimism, „Central European Journal of Operations Research”, 23(3), s. 579-594.
• Gaspars-Wieloch H. (2015c), Innovative products and newsvendor problem under uncertainty without probabilities, [w:] L. Zadnik Stirn, J. Zerovnik, J. Povh, S. Drobne, A. Lisec (red.), SOR’15 Proceedings, The 13th International Symposium on Operational Research in Slovenia, 23-25 September 2015, Bled, Slovenia, Slovenian Society INFORMATIKA, Section for Operational Research. (w druku)
• Gaspars-Wieloch H. (2015d), A decision rule for uncertain multicriteria mixed decision making based on the coefficient of optimism, „Multiple Criteria Decision Making’ 15” (w druku).
• Ghirardato P., Maccheroni F., Marinacci M. (2004), Differentiating ambiguity and ambiguity attitude, „Journal of Economic Theory”, 118, s. 133-173.
• Gilboa I. (2009), Theory of decision under uncertainty, Cambridge University Press. Cambridge, New York.
• Gilboa I., Schmeidler D. (1989), Maxmin expected utility with non-unique prior, „Journal of mathematical economics”, 18, s. 141-153.
• Ginevičius R., Zubrecovas V. (2009), Selection of the optimal real estate investment project basing on multiple criteria evaluation using stochastic dimensions, „Journal of business economics and management”, 10(3), s. 261-270.
• Goodwin P., Wright G. (2001), Enhancing strategy evaluation in scenario planning: A role for decision analysis, „Journal of management studies”, 38(1), s. 1-16.
• Grigorieva X. (2014), Multicriteria coalitional model of decision-making over the set of projects with constant payoff matrix in the noncooperative game, „Applied mathematical sciences”, 8(170), s. 8473-8479.
• Groenewald M.E., Pretorius P.D. (2011), Comparison of decision making under uncertainty investment strategies with the money market, „Journal of financial studies and research”.
• Guo P. (2011), One-shot decision theory, „IEEE Transactions on systems, man, and cybernetics, Part A”, 41(5), s. 917-926.
• Hayashi T. (2008), Regret aversion and opportunity dependence, „Journal of economic theory”, 139(1), s. 242-268.
• Hopfe C.J., Augenbroe G.L.M., Hensen J.L.M. (2013), Multicriteria decision making under uncertainty in building performance assessment, „Building and environment”, 69, s. 8190.
• Hurwicz L. (1952), A criterion for decision making under uncertainty, Technical Report, s. 355, Cowles Commission.
• Hwang C.L., Yoon K. (1981), Multiple attribute decision making methods and applications: A state of the art survey, Springer-Verlag, New York.
• Ioan C., Ioan G. (2011), A method of choice of the best alternative in the multiple solutions case in the games theory, „The Journal of accounting and management”, 1(1), s. 5-8.
• Janjic A., Andjelkovic A., Docic M. (2013), Multiple criteria decision making under uncertainty based on stochastic dominance, „Proceedings of the 2013 International Conference on Applied Mathematics and Computational Methods in Engineering” 16-19 July 2013, Rhodes Island, Greece, s. 86-91.
• Kaliszewski I., Miroforidis J. (2010), Multiple criteria decision making: from exact to heuristic optimization, „Multiple Criteria Decision Making’ 09”, s. 113-120.
• Kaliszewski I., Miroforidis J., Podkopaev D. (2012), Interactive multiple criteria decision making based on preference driven evolutionary multiobjective optimization with controllable accuracy, „European Journal of Operational Research”, 216, s. 188-199.
• Karni E. (1985), Decision making under uncertainty. The case of state-dependent preferences, Harvard University Press, Cambridge.
• Knight F.H. (1921), Risk, uncertainty, profit, Hart. Boston MA, Schaffner & Marx, Houghton Mifflin Co.
• Konarzewska-Gubała E. (1989), Bipolar: multiple criteria decision aid using Bipolar reference system, LAMSADE, „Cahiers et Documents”, s. 56.
• Kopańska-Bródka D. (1998), Wprowadzenie do badań operacyjnych, Wydawnictwo Akademii Ekonomicznej w Katowicach, Katowice.
• Korhonen A. (2001), Strategic financial management in a multinational financial conglomerate: A multiple goal stochastic programming approach, „European Journal of Operational Research”, 128, s. 418-434.
• Kuchta D. (2010), Generalization of the critical chain method supporting the management of projects with a high degree of uncertainty and imperfect information, „Operations research and decisions”, 2010/2.
• Kukuła K. (2012). Propozycja budowy rankingu obiektów z wykorzystaniem cech ilościowych oraz jakościowych, „Metody ilościowe w badaniach ekonomicznych”, XIII/1, s. 5-16.
• Larichev O.I., Moshkovich H.M. (1995), ZAPROS-LM – A method and system for ordering multiattribute alternatives, „European Journal of Operational Research”, s. 82.
• Lee Y.-H. (2012), A fuzzy analytic network process approach to determining prospective competitive strategy in China: A case study for multinational biotech pharmaceutical enterprises, „Journal of business economics and management”, 13(1), s. 5-28.
• Liu M., Zhao L. (2009), Optimization of the emergency materials distribution network with time windows in anti-bioterrorism system, „International journal of innovative computing, information and control”, 5 (11A), s. 3615-3624.
• Liu Y., Fan Z., Hang Y. (2011), A method for stochastic multiple criteria decision making based on dominance degrees, „Information sciences”, 181(19), s. 4139-4153.
• Lootsma F.A. (1993), Scale sensitivity in the multiplicative AHP and SMART, „Journal of multi-criteria decision analysis”, 2(2), s. 87-110.
• Lozan V., Ungureanu V. (2013), Computing the Pareto-Nash equilibrium set in finite multi-objective mixed-strategy games, „Computer science journal of Moldova”, 21, 2(62), s. 173-203.
• Marinacci M. (2002), Probabilistic sophistication and multiple priors, „Econometrica”, 70, s. 755-764.
• Michnik J. (2012), What kinds of hybrid models are used in multiple criteria decision analysis and why?, „Multiple criteria decision making ’12”, s. 161-168.
• Michnik J. (2013), Wielokryterialne metody wspomagania decyzji w procesie innowacji, Wydawnictwo Uniwersytetu Ekonomicznego w Katowicach, Katowice.
• Mikhaidov L., Tsvetinov P. (2004), Evaluation of services using a fuzzy analytic hierarchy process, „Applied soft computing journal”, 5(1), s. 23-33.
• Milnor J. (1954), Games against nature in decision processes, Wiley, New York, s. 49-60.
• Montibeller G., Gummer H., Tumidei D. (2006), Combining scenario planning and multicriteria decision analysis in practice, „Journal of multi-criteria decision analysis”, 14, s. 5-20.
• Nakamura K. (1986), Preference Relations on a Set of Fuzzy Utilities as a Basis for Decision Making, „Fuzzy Sets and Systems”, 20, s. 147-162.
• Nowak M. (2004), Preference and veto thresholds in multicriteria analysis based on stochastic dominance, „European Journal of Operational Research”, 158(2), s. 339-350.
• Officer R.R., Anderson J.R. (1968), Risk, uncertainty and farm management decisions, „Review of marketing and agricultural economics”, 36(01).
• Ogryczak W. (2006), Problemy i modele decyzyjne, Wydawnictwa UW, Warszawa.
• Opricovic S. (1998), Multicriteria optimization of civil engineering systems, Technical Report. Faculty of Civil Engineering, Belgrade.
• Piasecki K. (1990), Decyzje i wiarygodne prognozy, Akademia Ekonomiczna w Poznaniu, Poznań.
• Pomerol J.C. (2001), Scenario development and practical decision making under uncertainty, „Decision support systems”, 31(2), s. 197-204.
• Puppe C., Schlag K. (2009), Choice under complete uncertainty when outcome spaces are state dependent, „Theory and Decision”, 66, s. 1-16.
• Ram C., Montibeller G., Morton A. (2010), Extending the use of scenario planning and MCDA for the evaluation of strategic options, „Journal of operational research society”, 62(5), s. 817-829.
• Ramík J., Hanclova J., Trzaskalik T., Sitarz S. (2008), Fuzzy multiobjective methods in multistage decision oroblems, „Multiple criteria decision making ‘07”, s. 186-201.
• Ravindran A.R. (2008), Operations research and management science handbook, Boca Raton, London, New York, CRS Press.
• Render B., Stair R.M., Hanna M.E. (2006), Quantitative analysis for management, Upper Saddle River, New Jersey, Pearson Prentice Hall.
• Roy B., Bouyssou D. (1993), Aide multicritere a la decision: methodes et cas, „Economica”, Paris.
• Savage L. (1961), The foundations of statistics reconsidered, „Studies in Subjective Probability”, Wiley, New York, s. 173-188.
• Saaty T.L. (1980), The analytic hierarchy process, McGraw Hill, New York.
• Saaty T.L. (1996), Decision making with dependence and feedback: analytic network process, RWS Publications, Pittsburgh.
• Schmeidler D. (1986), Integral representation without additivity, „Proceedings of the American Mathematical Society”, 97, s. 255-261.
• Sikora W. (red.) (2008), Badania Operacyjne, Polskie Wydawnictwo Ekonomiczne, Warszawa.
• Słowiński R., Kadziński M., Greco S. (2014), Robust ordinal regression for dominancebased approach under uncertainty, Joint Rough Set Symposium, Granada and Madrid, Spain, July 9-13 2014.
• Stewart T.J. (2005), Dealing with uncertainties in MCDA: state of the art surveys, „International series in operations research & management science”, 78, s. 445-466.
• Suo M.Q., Li Y.P., Huang G.H. (2012), Multicriteria decision making under uncertainty: an advanced ordered weighted averaging operator for planning electric power systems, „Engineering applications of artificial intelligence”, 25(1), s. 72-81.
• Triantaphyllou E., Lin C. (1996), Development and evaluation of five fuzzy multiattribute decision-making methods, „International journal of approximate reasoning”, 14(4), s. 281-310.
• Trzaskalik T. (2008), Wprowadzenie do badań operacyjnych z komputerem, Wyd. 2, Polskie Wydawnictwo Ekonomiczne, Warszawa.
• Trzaskalik T. (red.) (2014), Wielokryterialne wspomaganie decyzji, Polskie Wydawnictwo Ekonomiczne, Warszawa.
• Trzpiot G. (2006), Pomiar ryzyka finansowego w warunkach niepewności, „Badania operacyjne i decyzje”, 2006/2.
• Trzpiot G., Zawisza M. (2000), Dominacje stochastyczne i probabilistyczne w wielokryterialnej analizie decyzji w zakresie pomocy społecznej [w:] T. Trzaskalik (red.), Modelowanie Preferencji a Ryzyko '00, Wydawnictwo Akademii Ekonomicznej im. Karola Adamieckiego w Katowicach, s. 267-279.
• Tsaur S., Chang T., Yen C. (2002), The evaluation of airline service quality by fuzzy MCDM, „Tourism Management”, 23(2), s. 107-115.
• Tyszka T. (2010), Decyzje. Perspektywa psychologiczna i ekonomiczna, Warszawa.
• Tversky A., Kahneman D. (1992), Advances in prospect theory: cumulative representation of uncertainty, „Journal of risk and uncertainty”, 5, s. 297-323.
• Urli B., Nadeau R. (2004): PROMISE/scenarios: an interactive method for multiobjective stochastic linear programming under partial uncertainty, „European journal of operational research”, 155(2), s. 361-372.
• Van der Heijden K. (1996), Scenarios: the art of strategic conversation, John Wiley and Sons, Chichester.
• Von Neumann J., Morgenstern O. (1944), Theory of games and economic behavior, Princeton University Press, Princeton, New York.
• Voorneveld M., Vermeulen D., Borm P. (1999), Axiomatizations of Pareto equilibria in multicriteria games, „Games and economic behavior”, 28, s. 146-154.
• Voorneveld M., Grahn S., Dufwenberg M. (2000), Ideal equilibria in noncooperative multicriteria games, „Mathematical methods of operations research”, 52, s. 65-77.
• Wald A. (1950), Statistical decision functions, Wiley, New York.
• Walliser B. (2008), Cognitive economics, Springer, Berlin-Heidelberg.
• Wang Y., Elhag T. (2006), Fuzzy TOPSIS method based on alpha level sets with an application to bridge risk assessment, „Expert systems with applications”, 31(2), s. 309-319.
• Williams C., Smith M., Young P. (1997), Risk management and insurance, McGraw-Hill.
• Wojewnik P., Szapiro T. (2010), Bireference procedure FBI for interactive multicriteria optimization with fuzzy coefficients, „Central European journal of economic modelling and econometrics”, 2, s. 169-193.
• Xu R. (2000), Fuzzy least-squares priority method in the analytic hierarchy process, „Fuzzy sets and systems”, 112(3), s. 395-404.
• Yu C. (2002), A GP-AHP method for solving group decision-making fuzzy AHP Problems, „Computers and operations research”, 29(14), s. 1969-2001.

Identifiers

ISSN

2083-8611