Demand Analysis with Aggregation Systems

The demand is a fundamental variable in economic analysis that measures the needs of goods and services of the consumers. This paper presents a new approach for representing the demand by using aggregation systems that consider the attitudinal character of the consumers and their beliefs regarding the degree of importance of the different variables that may affect them. Several developments are introduced by using multiperson systems, different criterion, attributes, and states of nature. These developments are focused on the use of the weighted average, the ordered weighted average, and the ordered weighted averaging weighted average. Further aggregation systems are suggested by using the concept of the demand growth. The paper ends with an application of the new approach in a forecasting process that considers the attitude of the decision maker and its subjective beliefs.

[1]  C. Pedro Macroeconomics. Rudiger Dornbusch, Stanley Fischer y Richard Startz McGraw-Hill Higher Education, 2011. , 2014 .

[2]  José M. Merigó,et al.  Generalized Moving averages, Distance Measures and OWA Operators , 2013, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[3]  José M. Merigó,et al.  Induced 2-tuple linguistic generalized aggregation operators and their application in decision-making , 2013, Inf. Sci..

[4]  Anna Maria Gil Lafuente,et al.  A Method for Decision Making Based on Generalized Aggregation Operators , 2013, Int. J. Intell. Syst..

[5]  J. Merigó,et al.  The connection between distortion risk measures and ordered weighted averaging operators , 2013 .

[6]  José M. Merigó,et al.  Decision making in the European Union under risk and uncertainty , 2012 .

[7]  E. Cabrera,et al.  An Empirical Evaluation of Training Using Multi-attribute Utility Analysis , 2012 .

[8]  A. M. G. Lafuente,et al.  Decision making techniques in business and economics based on the OWA operator , 2012 .

[9]  J. Merigó,et al.  Decision-making techniques with similarity measures and OWA operators , 2012 .

[10]  José M. Merigó,et al.  A unified model between the weighted average and the induced OWA operator , 2011, Expert Syst. Appl..

[11]  J. Merigó,et al.  Probabilistic aggregation operators and their application in uncertain multi-person decision-making , 2011 .

[12]  Janusz Kacprzyk,et al.  Recent Developments in the Ordered Weighted Averaging Operators: Theory and Practice , 2011, Studies in Fuzziness and Soft Computing.

[13]  José M. Merigó,et al.  Decision-making with distance measures and induced aggregation operators , 2011, Comput. Ind. Eng..

[14]  José M. Merigó,et al.  The uncertain induced quasi‐arithmetic OWA operator , 2011, Int. J. Intell. Syst..

[15]  Radko Mesiar,et al.  Aggregation functions: Means , 2011, Inf. Sci..

[16]  Huayou Chen,et al.  Generalized ordered weighted logarithm aggregation operators and their applications to group decision making , 2010, Int. J. Intell. Syst..

[17]  Wolfgang Jank,et al.  Research Note - Prerelease Demand Forecasting for Motion Pictures Using Functional Shape Analysis of Virtual Stock Markets , 2010, Mark. Sci..

[18]  Zeshui Xu,et al.  Generalized aggregation operators for intuitionistic fuzzy sets , 2010, Int. J. Intell. Syst..

[19]  Wilmar B. Schaufeli,et al.  How changes in job demands and resources predict burnout, work engagement, and sickness absenteeism , 2009 .

[20]  José M. Merigó,et al.  The induced 2-tuple linguistic generalized OWA operator and its application in linguistic decision making , 2009 .

[21]  Ronald R. Yager,et al.  Time Series Smoothing and OWA Aggregation , 2008, IEEE Transactions on Fuzzy Systems.

[22]  Haiyan Song,et al.  Tourism demand modelling and forecasting—A review of recent research , 2008 .

[23]  J. Merigó,et al.  The Induced Generalized OWA Operator , 2009, EUSFLAT Conf..

[24]  Gleb Beliakov,et al.  Aggregation Functions: A Guide for Practitioners , 2007, Studies in Fuzziness and Soft Computing.

[25]  Vicenç Torra,et al.  Modeling decisions - information fusion and aggregation operators , 2007 .

[26]  Vicenç Torra,et al.  Modeling Decisions: Information Fusion and Aggregation Operators (Cognitive Technologies) , 2006 .

[27]  S. Johansen,et al.  MAXIMUM LIKELIHOOD ESTIMATION AND INFERENCE ON COINTEGRATION — WITH APPLICATIONS TO THE DEMAND FOR MONEY , 2009 .

[28]  Ronald R. Yager,et al.  Generalized OWA Aggregation Operators , 2004, Fuzzy Optim. Decis. Mak..

[29]  Z. S. Xu,et al.  An overview of operators for aggregating information , 2003, Int. J. Intell. Syst..

[30]  A. Bakker,et al.  The job demands-resources model of burnout. , 2001, The Journal of applied psychology.

[31]  Vicki G. Morwitz,et al.  Sales Forecasts for Existing Consumer Products and Services: Do Purchase Intentions Contribute to Accuracy? , 2000 .

[32]  Dimitar Filev,et al.  Induced ordered weighted averaging operators , 1999, IEEE Trans. Syst. Man Cybern. Part B.

[33]  Ronald R. Yager,et al.  Including importances in OWA aggregations using fuzzy systems modeling , 1998, IEEE Trans. Fuzzy Syst..

[34]  J. Kacprzyk,et al.  The Ordered Weighted Averaging Operators: Theory and Applications , 1997 .

[35]  Janusz Kacprzyk,et al.  The Ordered Weighted Averaging Operators , 1997 .

[36]  Vicenç Torra,et al.  The weighted OWA operator , 1997, Int. J. Intell. Syst..

[37]  Marshall L. Fisher,et al.  Reducing the Cost of Demand Uncertainty Through Accurate Response to Early Sales , 1996, Oper. Res..

[38]  János C. Fodor,et al.  Characterization of the ordered weighted averaging operators , 1995, IEEE Trans. Fuzzy Syst..

[39]  M. Braga,et al.  Exploratory Data Analysis , 2018, Encyclopedia of Social Network Analysis and Mining. 2nd Ed..

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

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

[42]  Robert Goodell Brown,et al.  Practical Business Forecasting , 1987 .

[43]  H. Varian Intermediate Microeconomics: A Modern Approach , 1987 .

[44]  H. Varian The Nonparametric Approach to Demand Analysis , 1982 .

[45]  J. Muellbauer,et al.  An Almost Ideal Demand System , 1980 .

[46]  Robert Karasek,et al.  Job decision latitude and mental strain: Implications for job redesign , 1979 .

[47]  Douglas J. Dalrymple Sales forecasting methods and accuracy , 1975 .

[48]  D. S. Tull The Relationship of Actual and Predicted Sales and Profits in New-Product Introductions , 1967 .

[49]  F. Thomas Juster,et al.  Consumer Buying Intentions and Purchase Probability: An Experiment in Survey Design , 1966 .

[50]  M. Friedman The Demand for Money: Some Theoretical and Empirical Results , 1959, Journal of Political Economy.