Social Network Decision Making with Linguistic Trustworthiness–Based Induced OWA Operators

Classic aggregation operators in group decision making such as the ordered weighted averaging (OWA), induced ordered weighted averaging (IOWA), C‐IOWA, P‐IOWA, and I‐IOWA have shown to be successful tools to provide flexibility in the aggregation of preferences. However, these operators do not take advantage of information related to the interaction between experts. Experts involved in a group decision‐making problem may have developed opinions about the reliability of other experts' judgments, either because they have previous history of interaction with each other or because they have knowledge that informs them on the reliability of other colleagues in the group in solving decision‐making problems in the past. In this paper, and within the framework of social network decision making, we present three new social network analysis based IOWA operators that take advantage of the linguistic trustworthiness information gathered from the experts' social network to aggregate the social group preferences. Their use is analysed with simple but illustrative examples.

[1]  L. A. ZADEH,et al.  The concept of a linguistic variable and its application to approximate reasoning - I , 1975, Inf. Sci..

[2]  R. Hanneman.  Introduction to Social Network Methods , 2001 .

[3]  Piero P. Bonissone,et al.  Selecting Uncertainty Calculi and Granularity: An Experiment in Trading-off Precision and Complexity , 1985, UAI.

[4]  Francisco Herrera,et al.  Computing with words in decision making: foundations, trends and prospects , 2009, Fuzzy Optim. Decis. Mak..

[5]  Michele Fedrizzi,et al.  A Fuzzy Approach to Social Network Analysis , 2009, 2009 International Conference on Advances in Social Network Analysis and Mining.

[6]  Francisco Chiclana,et al.  A social network analysis trust-consensus based approach to group decision-making problems with interval-valued fuzzy reciprocal preference relations , 2014, Knowl. Based Syst..

[7]  P. Fishburn.  The Theory Of Social Choice , 1973 .

[8]  Francisco Herrera,et al.  Integrating three representation models in fuzzy multipurpose decision making based on fuzzy preference relations , 1998, Fuzzy Sets Syst..

[9]  Leo Katz,et al.  A new status index derived from sociometric analysis , 1953 .

[10]  H. B. Mitchell,et al.  A Modified OWA Operator and its Use in Lossless DPCM Image Compression , 1997, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[11]  Ronald R. Yager,et al.  Quantifier guided aggregation using OWA operators , 1996, Int. J. Intell. Syst..

[12]  Francisco Herrera,et al.  Cardinal Consistency of Reciprocal Preference Relations: A Characterization of Multiplicative Transitivity , 2009, IEEE Transactions on Fuzzy Systems.

[13]  Xuesong Wang,et al.  Investigation of road network features and safety performance. , 2013, Accident; analysis and prevention.

[14]  Lotfi A. Zadeh,et al.  The Concepts of a Linguistic Variable and its Application to Approximate Reasoning , 1975 .

[15]  S. Orlovsky.  Decision-making with a fuzzy preference relation , 1978 .

[16]  Robert Ivor John,et al.  Alpha-Level Aggregation: A Practical Approach to Type-1 OWA Operation for Aggregating Uncertain Information with Applications to Breast Cancer Treatments , 2011, IEEE Transactions on Knowledge and Data Engineering.

[17]  Ronald R. Yager,et al.  On ordered weighted averaging aggregation operators in multicriteria decisionmaking , 1988, IEEE Trans. Syst. Man Cybern..

[18]  Francisco Chiclana,et al.  Linguistic majorities with difference in support , 2014, Appl. Soft Comput..

[19]  R. Yager,et al.  Operations for granular computing: mixing words and numbers , 1998, 1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36228).

[20]  Peter C. Fishburn,et al.  Preference Structures and Their Numerical Representations , 1999, Theor. Comput. Sci..

[21]  Francisco Chiclana,et al.  Type‐Reduction of General Type‐2 Fuzzy Sets: The Type‐1 OWA Approach , 2013, Int. J. Intell. Syst..

[22]  Stefan Richter,et al.  Centrality Indices , 2004, Network Analysis.

[23]  Francisco Chiclana,et al.  A social network representation for Collaborative Filtering Recommender Systems , 2011, 2011 11th International Conference on Intelligent Systems Design and Applications.

[24]  John Scott.  Social Network Analysis , 1988 .

[25]  R. Yager.  Fuzzy decision making including unequal objectives , 1978 .

[26]  Francisco Herrera,et al.  A 2-tuple fuzzy linguistic representation model for computing with words , 2000, IEEE Trans. Fuzzy Syst..

[27]  C. Liau,et al.  Positional Analysis in Fuzzy Social Networks , 2007, IEEE International Conference on Granular Computing.

[28]  N. Lin.  Foundations of social research , 1976 .

[29]  J. Kacprzyk.  Group decision making with a fuzzy linguistic majority , 1986 .

[30]  B. Wellman.  The Development of Social Network Analysis: A Study in the Sociology of Science , 2008 .

[31]  WuJian,et al.  A social network analysis trust-consensus based approach to group decision-making problems with interval-valued fuzzy reciprocal preference relations , 2014 .

[32]  Tsau Young Lin,et al.  Positional Analysis in Fuzzy Social Networks , 2007, 2007 IEEE International Conference on Granular Computing (GRC 2007).

[33]  Ronald R. Yager,et al.  On ordered weighted averaging aggregation operators in multicriteria decision-making , 1988 .

[34]  J. Seeley.  The net of reciprocal influence; a problem in treating sociometric data. , 1949 .

[35]  Francisco Herrera,et al.  Aggregation operators for linguistic weighted information , 1997, IEEE Trans. Syst. Man Cybern. Part A.

[36]  Francisco Chiclana,et al.  Type-1 OWA methodology to consensus reaching processes in multi-granular linguistic contexts , 2014, Knowl. Based Syst..

[37]  K. Eames,et al.  Integrating contact network structure into tuberculosis epidemiology in meerkats in South Africa: Implications for control. , 2011, Preventive veterinary medicine.

[38]  Luis Martínez-López,et al.  An analysis of symbolic linguistic computing models in decision making , 2013, Int. J. Gen. Syst..

[39]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994, Structural analysis in the social sciences.

[40]  Freimut Bodendorf,et al.  Warning system for online market research - Identifying critical situations in online opinion formation , 2011, Knowl. Based Syst..

[41]  Robert Ivor John,et al.  Type-1 OWA operators for aggregating uncertain information with uncertain weights induced by type-2 linguistic quantifiers , 2008, Fuzzy Sets Syst..

[42]  Francisco Herrera,et al.  A consistency‐based procedure to estimate missing pairwise preference values , 2008, Int. J. Intell. Syst..

[43]  S. Orlovsky.  Decision-making with a fuzzy preference relation , 1978 .

[44]  Oliver Hinz,et al.  The social embeddedness of decision making: opportunities and challenges , 2011, Electron. Mark..

[45]  Francisco Herrera,et al.  Some induced ordered weighted averaging operators and their use for solving group decision-making problems based on fuzzy preference relations , 2007, Eur. J. Oper. Res..