On the use of the Choquet integral with fuzzy numbers in multiple criteria decision support

International audienceThis paper presents a multiple criteria decision support approach in order to build a ranking and suggest a best choice1 on a set of alternatives. The partial evaluations of the alternatives on the points of view can be fuzzy numbers. The aggregation is performed through the use of a fuzzy extension of the Choquet integral. We detail how to assess the coefficients of the aggregation operator by using alternatives which are well-known to the decision maker, and which originate from his domain of expertise

Children’s episodic and generic reports of alleged abuse

With the present data, we explored the relations between the language of interviewer questions, children’s reports, and case and child characteristics in forensic interviews. Results clearly indicated that the type of questions posed by interviewers – either probing generic or episodic features of an event – was related to the specificity of information reported by children. Further, interviewers appeared to adjust their questioning strategies based on the frequency of the alleged abuse. Children alleging single instances of abuse were asked more episodic questions than those alleging multiple abuses. In contrast, children alleging multiple incidents of abuse were asked a greater proportion of generic questions. Given that investigators often seek forensically-relevant episodic information, it is recommended that training for investigators focus on recognition of prompt selection tendencies and developing strategies for posing non-suggestive, episodically focused questions

Veto values in Group Decision Making within MAUT: aggregating complete rankings derived from dominance intensity measures

We consider a groupdecision-making problem within multi-attribute utility theory, in which the relative importance of decisionmakers (DMs) is known and their preferences are represented by means of an additive function. We allow DMs to provide veto values for the attribute under consideration and build veto and adjust functions that are incorporated into the additive model. Veto functions check whether alternative performances are within the respective veto intervals, making the overall utility of the alternative equal to 0, where as adjust functions reduce the utilty of the alternative performance to match the preferences of other DMs. Dominance measuring methods are used to account for imprecise information in the decision-making scenario and to derive a ranking of alternatives for each DM. Specifically, ordinal information about the relative importance of criteria is provided by each DM. Finally, an extension of Kemeny's method is used to aggregate the alternative rankings from the DMs accounting for the irrelative importance

A Hybrid Approach For Information Systems Security Risk Assessment In Electronic Business

In electronic business environment, it is critical for an enterprise to assess information systems security (ISS) risks. In this paper, we propose a hybrid approach for ISS risk assessment in e-business. Given there is a great deal of uncertainty in the ISS risk assessment in e-business environment, in the hybrid approach, we combine the evidence theory with fuzzy sets to deal with the uncertain evidence found in the ISS risk assessment. The proposed approach provides a new way to define the basic belief assignment in fuzzy measure. Moreover, the approach also provides a method of testing the evidential consistency, which can reduce the uncertainty derived from the conflicts of evidence. Finally, the approach is further demonstrated and validated via a case study, in which sensitivity analysis is employed to validate the reliability of the proposed approach

A single currency for Asia? Evaluation and comparison using hierarchical and model-based cluster analysis

Today, there is increased speculation on the possibility of an Asian currency, as the region begins to show increased promise as a region of nascent economic activity. Any monetary integration scheme in East Asia would likely have to include both China and India though, so this paper attempts to assess the evolution of convergence among the East Asian countries, including China and India, according to the optimum currency area theory criteria, which is operationalized through the use of cluster analysis. In this paper we use both traditional "hierarchical" clustering as well as the more recently developed "model-based" clustering techniques and compare the outcome in each case. As the East Asian crisis of 1997-98 is likely to a¤ect the results, the exercise is done for pre-crisis, crisis, and post-crisis periods. The results reveal some structure among the countries, an increase in the degree of subregional homogeneity, and a robust relationship between Malaysia and Singapore

Selecting socially responsible portfolios: A fuzzy multicriteria approach

[EN] We propose a multi-objective approach for portfolio selection, which allows investors to consider not only return and downside risk criteria but also to include environmental, social and governance (ESG) scores in the investment decision-making process. Owing to the uncertain environment of portfolio selection, the return and ESG score of each asset are considered as independent L-R power fuzzy variables. To make the model more realistic, we take budget, floor ceiling and cardinality constraints into account. In order to select the optimal portfolio along the efficient frontier, we apply the Sortino ratio in a credibilistic environment. The subsequent empirical application uses a data set from Bloomberg's ESG Data in combination with US Dow Jones Industrial Average data. The experimental results show that the proposed model offers promising results for socially responsible investors seeking ethical and sustainability goals beyond the return-risk trade-off and its ability to beat the benchmarkGarcía García, F.; Gonzalez-Bueno, J.; Oliver-Muncharaz, J.; Riley, N. (2019). Selecting socially responsible portfolios: A fuzzy multicriteria approach. Sustainability. 11(9). https://doi.org/10.3390/su11092496S119Ballestero, E., Pérez-Gladish, B., & Garcia-Bernabeu, A. (2014). The Ethical Financial Question and the MCDM Framework. International Series in Operations Research & Management Science, 3-22. doi:10.1007/978-3-319-11836-9_1Zopounidis, C., & Doumpos, M. (2002). Multicriteria classification and sorting methods: A literature review. European Journal of Operational Research, 138(2), 229-246. doi:10.1016/s0377-2217(01)00243-0ARRIBAS, I., GARCÍA, F., GUIJARRO, F., OLIVER, J., & TAMOŠIŪNIENĖ, R. (2016). MASS APPRAISAL OF RESIDENTIAL REAL ESTATE USING MULTILEVEL MODELLING. International Journal of Strategic Property Management, 20(1), 77-87. doi:10.3846/1648715x.2015.1134702García, F., Guijarro, F., Oliver, J., & Tamošiūnienė, R. (2018). HYBRID FUZZY NEURAL NETWORK TO PREDICT PRICE DIRECTION IN THE GERMAN DAX-30 INDEX. Technological and Economic Development of Economy, 24(6), 2161-2178. doi:10.3846/tede.2018.6394Xidonas, P., Doukas, H., Mavrotas, G., & Pechak, O. (2015). Environmental corporate responsibility for investments evaluation: an alternative multi-objective programming model. Annals of Operations Research, 247(2), 395-413. doi:10.1007/s10479-015-1820-xMiralles-Quirós, M. del M., & Miralles-Quirós, J. L. (2015). Improving Diversification Opportunities for Socially Responsible Investors. Journal of Business Ethics, 140(2), 339-351. doi:10.1007/s10551-015-2691-4JERÓNIMO SILVESTRE, W., ANTUNES, P., & LEAL FILHO, W. (2016). THE CORPORATE SUSTAINABILITY TYPOLOGY: ANALYSING SUSTAINABILITY DRIVERS AND FOSTERING SUSTAINABILITY AT ENTERPRISES. Technological and Economic Development of Economy, 24(2), 513-533. doi:10.3846/20294913.2016.1213199Rahman, S., Lee, C.-F., & Xiao, Y. (2016). The investment performance, attributes, and investment behavior of ethical equity mutual funds in the US: an empirical investigation. Review of Quantitative Finance and Accounting, 49(1), 91-116. doi:10.1007/s11156-016-0581-1Bouslah, K., Kryzanowski, L., & M’Zali, B. (2013). The impact of the dimensions of social performance on firm risk. Journal of Banking & Finance, 37(4), 1258-1273. doi:10.1016/j.jbankfin.2012.12.004Petrillo, A., De Felice, F., García-Melón, M., & Pérez-Gladish, B. (2016). Investing in socially responsible mutual funds: Proposal of non-financial ranking in Italian market. Research in International Business and Finance, 37, 541-555. doi:10.1016/j.ribaf.2016.01.027Fowler, S. J., & Hope, C. (2007). A Critical Review of Sustainable Business Indices and their Impact. Journal of Business Ethics, 76(3), 243-252. doi:10.1007/s10551-007-9590-2Jankalová, M., & Jankal, R. (2017). The assessment of corporate social responsibility: approaches analysis. Entrepreneurship and Sustainability Issues, 4(4), 441-459. doi:10.9770/jesi.2017.4.4(4)Smaliukienė, R., & Monni, S. (2019). A step-by-step approach to social marketing in energy transition. Insights into Regional Development, 1(1), 19-32. doi:10.9770/ird.2019.1.1(2)Anagnostopoulos, T., Skouloudis, A., Khan, N., & Evangelinos, K. (2018). Incorporating Sustainability Considerations into Lending Decisions and the Management of Bad Loans: Evidence from Greece. Sustainability, 10(12), 4728. doi:10.3390/su10124728Charlo, M., Moya, I., & Muñoz, A. (2017). Financial Performance of Socially Responsible Firms: The Short- and Long-Term Impact. Sustainability, 9(9), 1622. doi:10.3390/su9091622De Colle, S., & York, J. G. (2008). Why Wine is not Glue? The Unresolved Problem of Negative Screening in Socially Responsible Investing. Journal of Business Ethics, 85(S1), 83-95. doi:10.1007/s10551-008-9949-zDerwall, J., & Koedijk, K. (2009). Socially Responsible Fixed-Income Funds. Journal of Business Finance & Accounting, 36(1-2), 210-229. doi:10.1111/j.1468-5957.2008.02119.xWu, J., Lodorfos, G., Dean, A., & Gioulmpaxiotis, G. (2015). The Market Performance of Socially Responsible Investment during Periods of the Economic Cycle - Illustrated Using the Case of FTSE. Managerial and Decision Economics, 38(2), 238-251. doi:10.1002/mde.2772Chang, C. E., & Doug Witte, H. (2010). Performance Evaluation of U.S. Socially Responsible Mutual Funds: Revisiting Doing Good and Doing Well. American Journal of Business, 25(1), 9-24. doi:10.1108/19355181201000001Cortez, M. C., Silva, F., & Areal, N. (2008). The Performance of European Socially Responsible Funds. Journal of Business Ethics, 87(4), 573-588. doi:10.1007/s10551-008-9959-xInvesting in Socially Responsible Mutual Fundshttps://repository.upenn.edu/cgi/viewcontent.cgi?article=1444&context=fnce_papersJones, S., van der Laan, S., Frost, G., & Loftus, J. (2007). The Investment Performance of Socially Responsible Investment Funds in Australia. Journal of Business Ethics, 80(2), 181-203. doi:10.1007/s10551-007-9412-6Renneboog, L., Ter Horst, J., & Zhang, C. (2008). Socially responsible investments: Institutional aspects, performance, and investor behavior. Journal of Banking & Finance, 32(9), 1723-1742. doi:10.1016/j.jbankfin.2007.12.039Bauer, R., Koedijk, K., & Otten, R. (2005). International evidence on ethical mutual fund performance and investment style. Journal of Banking & Finance, 29(7), 1751-1767. doi:10.1016/j.jbankfin.2004.06.035Brzeszczyński, J., & McIntosh, G. (2013). Performance of Portfolios Composed of British SRI Stocks. Journal of Business Ethics, 120(3), 335-362. doi:10.1007/s10551-012-1541-xGoldreyer, E. F., & Diltz, J. D. (1999). The performance of socially responsible mutual funds: incorporating sociopolitical information in portfolio selection. Managerial Finance, 25(1), 23-36. doi:10.1108/03074359910765830Hamilton, S., Jo, H., & Statman, M. (1993). Doing Well While Doing Good? The Investment Performance of Socially Responsible Mutual Funds. Financial Analysts Journal, 49(6), 62-66. doi:10.2469/faj.v49.n6.62Revelli, C., & Viviani, J.-L. (2014). Financial performance of socially responsible investing (SRI): what have we learned? A meta-analysis. Business Ethics: A European Review, 24(2), 158-185. doi:10.1111/beer.12076McWilliams, A., & Siegel, D. (2001). Corporate Social Responsibility: a Theory of the Firm Perspective. Academy of Management Review, 26(1), 117-127. doi:10.5465/amr.2001.4011987El Ghoul, S., Guedhami, O., Kwok, C. C. Y., & Mishra, D. R. (2011). Does corporate social responsibility affect the cost of capital? Journal of Banking & Finance, 35(9), 2388-2406. doi:10.1016/j.jbankfin.2011.02.007Attig, N., El Ghoul, S., Guedhami, O., & Suh, J. (2013). Corporate Social Responsibility and Credit Ratings. Journal of Business Ethics, 117(4), 679-694. doi:10.1007/s10551-013-1714-2Cheng, B., Ioannou, I., & Serafeim, G. (2013). Corporate social responsibility and access to finance. Strategic Management Journal, 35(1), 1-23. doi:10.1002/smj.2131Hallerbach, W. (2004). A framework for managing a portfolio of socially responsible investments. European Journal of Operational Research, 153(2), 517-529. doi:10.1016/s0377-2217(03)00172-3Steuer, R. E., Qi, Y., & Hirschberger, M. (2006). Suitable-portfolio investors, nondominated frontier sensitivity, and the effect of multiple objectives on standard portfolio selection. Annals of Operations Research, 152(1), 297-317. doi:10.1007/s10479-006-0137-1Bilbao-Terol, A., Arenas-Parra, M., & Cañal-Fernández, V. (2012). A fuzzy multi-objective approach for sustainable investments. Expert Systems with Applications, 39(12), 10904-10915. doi:10.1016/j.eswa.2012.03.034Calvo, C., Ivorra, C., & Liern, V. (2014). Fuzzy portfolio selection with non-financial goals: exploring the efficient frontier. Annals of Operations Research, 245(1-2), 31-46. doi:10.1007/s10479-014-1561-2Li, Z. F., Minor, D., Wang, J., & Yu, C. (2018). A Learning Curve of the Market: Chasing Alpha of Socially Responsible Firms. SSRN Electronic Journal. doi:10.2139/ssrn.3224796Bilbao-Terol, A., Arenas-Parra, M., Cañal-Fernández, V., & Obam-Eyang, P. N. (2018). Multi-criteria analysis of the GRI sustainability reports: an application to Socially Responsible Investment. Journal of the Operational Research Society, 69(10), 1576-1598. doi:10.1057/s41274-017-0229-0Gasser, S. M., Rammerstorfer, M., & Weinmayer, K. (2017). Markowitz revisited: Social portfolio engineering. European Journal of Operational Research, 258(3), 1181-1190. doi:10.1016/j.ejor.2016.10.043Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353. doi:10.1016/s0019-9958(65)90241-xGupta, P., Mehlawat, M. K., & Saxena, A. (2013). Hybrid optimization models of portfolio selection involving financial and ethical considerations. Knowledge-Based Systems, 37, 318-337. doi:10.1016/j.knosys.2012.08.014Baoding Liu, & Yian-Kui Liu. (2002). Expected value of fuzzy variable and fuzzy expected value models. IEEE Transactions on Fuzzy Systems, 10(4), 445-450. doi:10.1109/tfuzz.2002.800692Barak, S., Abessi, M., & Modarres, M. (2013). Fuzzy turnover rate chance constraints portfolio model. European Journal of Operational Research, 228(1), 141-147. doi:10.1016/j.ejor.2013.01.036Huang, X. (2006). Fuzzy chance-constrained portfolio selection. Applied Mathematics and Computation, 177(2), 500-507. doi:10.1016/j.amc.2005.11.027Vercher, E., & Bermúdez, J. D. (2015). Portfolio optimization using a credibility mean-absolute semi-deviation model. Expert Systems with Applications, 42(20), 7121-7131. doi:10.1016/j.eswa.2015.05.020Gupta, P., Mittal, G., & Mehlawat, M. K. (2013). Expected value multiobjective portfolio rebalancing model with fuzzy parameters. Insurance: Mathematics and Economics, 52(2), 190-203. doi:10.1016/j.insmatheco.2012.12.002Mohebbi, N., & Najafi, A. A. (2018). Credibilistic multi-period portfolio optimization based on scenario tree. Physica A: Statistical Mechanics and its Applications, 492, 1302-1316. doi:10.1016/j.physa.2017.11.058Jalota, H., Thakur, M., & Mittal, G. (2017). Modelling and constructing membership function for uncertain portfolio parameters: A credibilistic framework. Expert Systems with Applications, 71, 40-56. doi:10.1016/j.eswa.2016.11.014García, F., González-Bueno, J., Oliver, J., & Tamošiūnienė, R. (2019). A CREDIBILISTIC MEAN-SEMIVARIANCE-PER PORTFOLIO SELECTION MODEL FOR LATIN AMERICA. Journal of Business Economics and Management, 20(2), 225-243. doi:10.3846/jbem.2019.8317Markowitz, H., Todd, P., Xu, G., & Yamane, Y. (1993). Computation of mean-semivariance efficient sets by the Critical Line Algorithm. Annals of Operations Research, 45(1), 307-317. doi:10.1007/bf02282055Sortino, F. A., & van der Meer, R. (1991). Downside risk. The Journal of Portfolio Management, 17(4), 27-31. doi:10.3905/jpm.1991.409343Vercher, E., & Bermúdez, J. D. (2012). Fuzzy Portfolio Selection Models: A Numerical Study. Financial Decision Making Using Computational Intelligence, 253-280. doi:10.1007/978-1-4614-3773-4_10Vercher, E., & Bermudez, J. D. (2013). A Possibilistic Mean-Downside Risk-Skewness Model for Efficient Portfolio Selection. IEEE Transactions on Fuzzy Systems, 21(3), 585-595. doi:10.1109/tfuzz.2012.2227487Liagkouras, K., & Metaxiotis, K. (2015). Efficient Portfolio Construction with the Use of Multiobjective Evolutionary Algorithms: Best Practices and Performance Metrics. International Journal of Information Technology & Decision Making, 14(03), 535-564. doi:10.1142/s0219622015300013Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182-197. doi:10.1109/4235.996017Gupta, P., Mehlawat, M. K., Inuiguchi, M., & Chandra, S. (2014). Portfolio Optimization with Interval Coefficients. Studies in Fuzziness and Soft Computing, 33-59. doi:10.1007/978-3-642-54652-5_2Sharpe, W. F. (1966). Mutual Fund Performance. The Journal of Business, 39(S1), 119. doi:10.1086/29484

Increasing environmental sustainability by incorporating stakeholders' intensities of preferences into the policy formation

In this paper a tractable methodology is presented to improve environmental sustainability by incorporating stakeholders’ intensities of preferences into the decision making process. The environmental decision making will be controversial when there is a complex issue at hand. The difficulty comes up as stakeholders cannot see how their preferences are taken into account in the policy making process. To reduce this controversy, we propose a qualitative method to elicit stakeholders’ intensities of preferences towards a set of environmental services. Subsequently, the elicited intensities of preferences are aggregated by a mathematical approach on each single criterion. Finally, a multi-criteria approach is applied to use the aggregated values across all criteria to provide the analyst with a rank order of existing alternative plans. In this way, the stakeholders are able to verify that their opinion is taken into account, even if it is contrary to the majority voice. The natural resources manager will benefit from an increased insight into the prevalent opinion on each of the criteria through the supplied social intensities of preferences, enabling a more easily communicated justification of the final decision, and an augmented tractability of the decision making process.Sustainability, stakeholder's preferences, tractable decision making, social support, qualitative valuation, Environmental Economics and Policy,

Stochastic logistic fuzzy maps for the construction of integrated multirates scenarios in the financing of infrastructure projects

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.In general, the development of economic infrastructure systems requires a behavioural comprehensive analysis of different financial variables or rates to establish its long-term success with regards to the Equity Internal Rate of Return (EIRR) expectation. For this reason, several financial organizations have developed economic scenarios supported by computational techniques and models to identify the evolution of these financial rates. However, these models and techniques have shown a series of limitations with regard to the financial management process and its impact on EIRR over time. To address these limitations in an inclusive way, researchers have developed different approaches and methodologies focused on the development of financial models using stochastic simulation methods and computational intelligence techniques. This paper proposes a Stochastic Fuzzy Logistic Model (S-FLM) inspired by a Fuzzy Cognitive Map (FCM) structure to model financial scenarios. Where the input consists in financial rates that are characterized as linguistic rates through a series of adaptive logistic functions. The stochastic process that explains the behaviour of the financial rates over time and their partial effects on EIRR is based on a Monte Carlo sampling process carried out on the fuzzy sets that characterize each linguistic rate. The S-FLM was evaluated by applying three financing scenarios to an airport infrastructure system (pessimistic, moderate/base, optimistic), where it was possible to show the impact of different linguistic rates on the EIRR. The behaviour of the S-FLM was validated using three different models: (1) a financial management tool; (2) a general FCM without pre-loaded causalities among the variables; and (3) a Statistical S-FLM model (S-FLMS), where the causalities between the concepts or rates were obtained as a result of an independent effects analysis applying a cross modelling between variables and by using a statistical multi-linear model (statistical significance level) and a multi-linear neural model (MADALINE). The results achieved by the S-FLM show a higher EIRR than expected for each scenario. This was possible due to the incorporation of an adaptive multi-linear causality matrix and a fuzzy credibility matrix into its structure. This allowed to stabilize the effects of the financial variables or rates on the EIRR throughout a financing period. Thus, the S-FLM can be considered as a tool to model dynamic financial scenarios in different knowledge areas in a comprehensive manner. This way, overcoming the limitations imposed by the traditional computational models used to design these financial scenarios

Partial information use in uncertainty quantification

Uncertainty exists frequently in our knowledge of the real world. Two forms of uncertainty are considered. One is variability coming from stochasticity. The other is epistemic uncertainty, also called 2nd order uncertainty and other names as well. Often it comes from ignorance or imprecision. In principle, this kind of uncertainty can be reduced by additional empirical data;Stochasticity is well studied in the field of probability theory. A variety of methods have been developed to address epistemic uncertainty. Some of these approaches are confidence limits, discrete convolutions, probabilistic arithmetic, Monte Carlo simulation, copulas, stochastic dominance, clouds, and distribution envelope determination. Belief and plausibility curves, upper and lower previsions, left and right envelopes and probability boxes designate an important type of representation for bounded uncertainty about distribution;Some methods combine probability theory and interval Mathematics; Intervals have the potential for bounding the result of an operation. Discretization error coming from discretizing distributions may be bounded by intervals. Distribution envelope determination (DEnv) uses interval based analysis. If the dependency is not specified, result bounds will include the entire range of possible dependencies. These bounds will be wider than if a particular dependency is specified. I have worked on new algorithms to process the dependency relationships. Pearson correlation can be used to improve the results, for example. Also partial dependence information might be available in the form of unimodality or of probability over a specified area of a joint distribution. If this information is used in the calculation, more accurate results can be obtained than that without using this information. Another situation is uncertainty about the parameters of a distribution. All these topics are researched in this work. They are implemented in the software we call Statool;Based on the developed methods, uncertainty can be flexibly considered and added into models. This can make the model closer to real situations. One problem posed by Sandia National Laboratory is studied in this work. Other applications include Pert networks, decision models and others