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Dealing with Imprecision in Consumer Theory: A New Approach to Fuzzy Utility Theory

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Human-Centric Decision-Making Models for Social Sciences

Part of the book series: Studies in Computational Intelligence ((SCI,volume 502))

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Abstract

This chapter presents a new approach to dealing with imprecision in the Classical Consumer Utility Theory based on the concept of Marginal Rate of Substitution (MRS) and using the concept of fuzzy sets and fuzzy numbers. The methodology developed applies imprecision to MRS, whereas previous studies placed the imprecision factor on final utility values and functions. The chapter considers fuzzy elements applied to MRS and uses the necessary formulations to obtain the results in Utility Theory. In this fuzzy environment, the final consumer decision problem is framed as a fuzzy nonlinear programming problem, maintaining the classical structure in which consumers maximize their fuzzy utility subject to budget constraints, and showing that the consumer optimum choice is a fuzzy set. The chapter will also address the problem of aggregation of utility functions in order to offer a multi-criteria approach.

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Notes

  1. 1.

    This is a general assumption in classic economic theory, but not restrictive in many cases, i.e. when producers are consumers of raw materials or goods like wood, steel, fuel, energy, water, oil, seeds ....

  2. 2.

    The first four axioms are generic in decision making.

  3. 3.

    This issue is discussed extensively in microeconometrics. See, i.e. [26], or [6, 20]

  4. 4.

    To illustrate this point, some examples of utility function are given:

    • Perfect substitute goods:

      $$\begin{aligned} U(x_1, x_2)=\beta _1x_1+\beta _2x_2,\quad \varvec{\beta }=(\beta _1, \beta _2) \end{aligned}$$
    • One good is bad:

      $$\begin{aligned} U(x_1, x_2)=-\beta _1x_1+\beta _2x_2,\quad \varvec{\beta }=(\beta _1, \beta _2) \end{aligned}$$
    • Perfect complement goods:

      $$\begin{aligned} U(x_1, x_2)=\min \{\beta _1x_1,\beta _2x_2\},\quad \varvec{\beta }=(\beta _1, \beta _2) \end{aligned}$$

      .

    Different consumers will have different parameters values.

  5. 5.

    It is also reasonable to associate a quasi-Gaussian membership function to these variables. However, the triangular fuzzy numbers are especially handy in operations where they are involved. The fuzzy trapezoidal numbers could also represent the behavior of consumers. Therefore, the approach chosen in this paper is adaptable to any type of membership function by changing the conditions of differentiability given later.

  6. 6.

    Actually, the term mutually utility independent is used in expected utility to show the independence. Outside of expected utility, the correct term is weak independent in difference, although the same term often used in expected utility can also be used here.

  7. 7.

    The addition of more than two non-interactive fuzzy numbers is required to use the associative property and to link operations of addition if expressed in terms of membership function. That is, \(\tilde{U}_1+\tilde{U}_2 + \tilde{U}_3= (\tilde{U}_1+\tilde{U}_2)+ \tilde{U}_3\), obtaining expressions of the sum of three fuzzy numbers. This procedure allows us to continue to link sums from the association adding \(k\) fuzzy numbers.

  8. 8.

    If there exists an initial predisposition for any good, the fuzzy CES MRS would adopt this form:

    $$\begin{aligned}\frac{\partial \tilde{U_1}/\partial x_1}{\partial \tilde{U_1}/\partial x_2}=\left( \dfrac{\tilde{\lambda _1}}{\tilde{\lambda _2}}\right) \left( \frac{x_1}{x_2}\right) ^{\tilde{\beta }-1}, \qquad \tilde{\beta }\subseteq (0,1],\quad \tilde{\lambda _1}, \tilde{\lambda _2}\subseteq [0,\infty ),\quad \forall (x_1, x_2)\in (S_1\times S_2),\end{aligned}$$

    for which the fuzzification of the CES utility function:

    \(\tilde{U}_1(x_1, x_2;\tilde{\varvec{\beta _1}})=(\tilde{\lambda }_1x_1^{\tilde{\beta }}+ \tilde{\lambda }_2x_2^{\tilde{\beta }})^{1/{\tilde{\beta }}},\qquad \tilde{\varvec{\beta _1}} =(\tilde{\beta }, \tilde{\lambda _1}, \tilde{\lambda _2})\quad \tilde{\beta }\subseteq (0,1],\quad \tilde{\lambda _1}, \tilde{\lambda _2}\subseteq [0,\infty ), \forall (x_1, x_2) \in (S_1\times S_2) \hbox {is not a solution}\) [18].

  9. 9.

    The characteristic function of a crisp set \(A\) that shows its membership:

    $$\begin{aligned} \chi _{A}(x)= \left\{ \begin{array}{cc} 1&{} \text { if } x\in A,\\ 0 &{} \text { otherwise } \end{array} \right. \end{aligned}$$

References

  1. Aliev, R., Pedrycz, W., et al.: Fuzzy logic-based generalized decision theory with imperfect information. Inf. Sci. 189, 18–42 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  2. Banerjee, A.: Fuzzy choice functions, revealed preference rationality. Fuzzy Sets Syst. 70, 13–43 (1995)

    Article  Google Scholar 

  3. Buckley, J.J., Feuring, T.: Introduction to fuzzy partial differential equations. Fuzzy Sets. Syst. 105(2), 241–248 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  4. Buckley, J.J., Feuring, T.: Fuzzy differential equations. Fuzzy Sets Syst. 110, 43–54 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  5. Chuoo, E.U., Shoner, B., Wedley, W.C.: Interpretation of criteria weights in multicriteria decision making. Comput. Ind. Eng. 37, 527–541 (1999)

    Article  Google Scholar 

  6. Colin, A., Trivedi, P.: Microeconometrics: Methods and Applications. Cambridge University Press, Cambridge (2005)

    Google Scholar 

  7. van Kooten, C., et al.: Preference uncertainty in non-market valuation: a fuzzy aproach. Am. J. Agric. Econ. 83(3), 487–500 (2001)

    Article  MATH  Google Scholar 

  8. Dean, P.-K., et al.: Product and cost estimation with fuzzy multi-attribute utility theory. Eng. Econ. 44(4), 303–331 (1999)

    Article  MathSciNet  Google Scholar 

  9. Debreu, G.: Topological methods in cardinal utility theory. Math. Methods Soc. Sci. 1959, 16–26 (1960) (Cowles Foundation paper 156).

    Google Scholar 

  10. De Wilde, P.: Fuzzy utility and equilibria. IEEE Trans. Syst. Man Cybern. 34(4), 1774–1785 (2004)

    Article  Google Scholar 

  11. Dubois, D., Prade, H.: Additions of interactive fuzzy numbers. IEEE Trans. Autom. Control 26(4), 926–936 (1981)

    Article  MathSciNet  Google Scholar 

  12. Duncan, R., Tukey, J.W.: Simultaneous conjoint measurement: a new type of fundamental measurement. J. Math. Psychol. 1, 1–27 (1964)

    Article  Google Scholar 

  13. Figueira, J., Greco, S., Ehrgott, M. (eds.): Multiple Criteria Decision Analysis. State of the Art Surveys. Springer, Berlin (2005)

    MATH  Google Scholar 

  14. Fishburn, P.: Independence in utility theory with whole product sets. Operat. Res. 13(3), 28–45 (1965)

    Article  MathSciNet  Google Scholar 

  15. Fodor, J., De Baets, B., Perny, P. (eds.): Preferences and Decisions Under Incomplete Knowledge. Springer, Berlin (2000)

    MATH  Google Scholar 

  16. Gálvez, D. and Pino, J.L.: The extension of Buckley-Feuring solutions for non-polynomial fuzzy partial differential equations. Application to Microeconomics Utility Theory. In: Proceedings of NAFIPS (The 28th North American Fuzzy Information Processing Society Annual Conference). IEEE, 2009.

    Google Scholar 

  17. Gálvez, D. and Pino, J.L.: The extension of Buckley-Feuring solutions for non-polynomial fuzzy partial differential equations. Application to Microeconomics Utility Theory and consumer decision. In: Proceedings of Fuzz-IEEE (2009 IEEE International Conference on Fuzzy Systems). IEEE, 2009.

    Google Scholar 

  18. Gálvez, D.: Tratamiento de la imprecisión en la teoría de la utilidad del consumidor. Universidad de Sevilla. 2009.

    Google Scholar 

  19. Goetschel, R., Voxman, W.: Elementary fuzzy calculus. Fuzzy Sets Syst. 18, 319–330 (1986)

    Google Scholar 

  20. Green, W.: Análisis Econométrico. Prentice hall, New Jersey (2002)

    Google Scholar 

  21. Hicks, JR: Value and Capital: An Inquiry into Some fundamental Principles of Economic Theory, pp. 18. Clarendon Press, Oxford (1939).

    Google Scholar 

  22. Kaufmann, A., Gupta, M.M.: Introduction to fuzzy arithmetic: theory and applications. Van Nostrand Reinhold, New York (1985)

    Google Scholar 

  23. Keeney, R.L., Raiffa, H.: Decisions with Multiple Objectives: Preferences and Value Trade-offs. Wiley, New York (1976)

    Google Scholar 

  24. Mathieu-Nicot, B.: Fuzzy expected utility. Fuzzy Sets Syst. 20(2), 163–173 (1986)

    Google Scholar 

  25. Mesiar, R.: Fuzzy set approach to the utility, preference relations, and agregation operators. Eur. J. Operat. Res. 176(1), 414–422 (2007)

    Google Scholar 

  26. Mora, J. J.: Introducción a la teoría del consumidor : de las preferencias a la estimación. Universidad ICESI (2002).

    Google Scholar 

  27. Nakamura, K.: Preference relations on a set of fuzzy utilities as a basis for decision making. Fuzzy Sets Syst. 20(2), 147–162 (1986)

    Google Scholar 

  28. Orlovsky, S.A.: Decision making with a fuzzy preference relation. Fuzzy Sets Syst. 1(3), 155–167 (1978)

    Google Scholar 

  29. Pareto, V.: Manual of Political Economy. Augustus M, Kelley, New York (1971)

    Google Scholar 

  30. Ponsard, C.: Fuzzy mathematical models in economics. Fuzzy Sets Syst. 28(3), 273–283 (1988)

    Google Scholar 

  31. Ramík, J., Vlach, M.: Generalized Concavity in Fuzzy Optimization and Decision Analysis. Kluwer Academic Publishers, The Netherlands (2002)

    Google Scholar 

  32. Robinson, J.: Economics is a Serious Subject. W. Heffer and Sons, Cambridge (1932)

    Google Scholar 

  33. Rothbard, M.N.: History economic thought, vol. 1. Economic thought to Adam Smith, Union Editorial (1999)

    Google Scholar 

  34. Rommelfanger, H. J.: Decision Making in fuzzy environment. Ways for getting practical decision models. Paper on line at http://www.uni-frankfurt.de. University of Frankfurt (1999)

  35. Salles, M.: Fuzzy utility. In: Barbera, S., et al. (eds.) Handbook of Utility Theory, vol. 1. Springer, Berlin (1999)

    Google Scholar 

  36. Stewart, T.J.: Simplified approaches for multi-criteria decision making under uncertainty. J. Multi-Criteria Decis. Anal. 4(4), 246–258 (1995)

    Google Scholar 

  37. Stewart, T.J.: Robustness of additive value function methods in MCDM. J. Multi-Criteria Decis. Anal. 5(4), 301–309 (1996)

    Google Scholar 

  38. Tenekedjiev, K., Nikolova, N.: Justification and numerical realization of the uniform method for finding point estimates of interval elicited scaling constants. Fuzzy Optim. Decis. Making 7(2), 119–145 (2008)

    Google Scholar 

  39. Zadeh, L.A.: Fuzzy Sets. Inf. Control 8, 338–353 (1965)

    Google Scholar 

  40. Zadeh, L.A.: The concept of Linguistic variables and its applications to approximate reasoning. Part I Inf. Sci. 8(3), 199–249 (1975)

    Google Scholar 

  41. Zadeh, L.A.: The concept of Linguistic variables and its applications to approximate reasoning. Part II Inf. Sci. 8(4), 301–357 (1975)

    Google Scholar 

  42. Zadeh, L.A.: The concept of Linguistic variables and its applications to approximate reasoning. Part III Inf. Sci. 9(1), 43–80 (1975)

    Google Scholar 

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Appendix A. Additive Independence

Appendix A. Additive Independence

1.1 Appendix A.1 Topological Approach by Debreu

If we assume a complete preference preordering \(\succeq \) on \(K=\prod _{i=1}^{k}C_i\) such that \(\{C_i\in K\mid C_i\succeq C_h\}\) and \(\{C_i\in K\mid C_i\preceq C_h\}\) are closed for all \(C_h\in K\) (this will always occur if there is a finite number of criteria), it will be necessary to hold the following conditions for additive form on utility function:

  1. 1.

    The \(n\) factors of \(K\) are mutually utility independent.

  2. 2.

    More than two of them are essential.

If \(K=\{C_1,...,C_k\}\) is the set of criteria, their factors \(K_1,...K_n\) are separable spaces such that:

$$\begin{aligned} K=\prod _{f=1}^{n} K_f \end{aligned}$$

Though it would be possible for one factor to contain more than one criterion, in this case, the uni-criterion utility functions are built for each one, so that, each factor (component of total utility) corresponds with each criterion.

In general, a factor \(K_f\) is essential if there exists a criterion \(C_i\) included in another factor \(K_{f'}\) such that not all criteria of \(K_f\) are indifferent according to the preordering established by \(c_i\).

As we said, in the case of consumer decision-making framework analyzed here, each factor corresponds to each criterion, therefore, as already shown, it is necessary to have as many summands as uni-criterion utility functions, that is, \(n = k\), and \(K_f=C_i\).

It is then possible to redefine the essential character of criteria: if \(c_i\) is the set of possible values for criteria \(C_i\), and \(c_h\) represents the same for \(C_h\), a criteria \(C_i\) is essential if there exists:

$$\begin{aligned} C_{i}(x_1, x_2),C_{i}(x_1',x_2')\in c_i, C_{h}(x_1,x_2)\in c_h \text { such that} \end{aligned}$$
$$\begin{aligned} (C_{i}(x_1,x_2),C_{h}(x_1,x_2))\not \sim (C_{i}(x_1',x_2'), C_{h}(x_1,x_2)). \end{aligned}$$

The biggest limitation of this condition may be that it requires at least three essential factors, which means that it can only be applied in decision-making frameworks with three or more criteria. Nevertheless, it is easily satisfied in any consumer decision-making problem by all criteria involved.

For cases in which only two criteria are taken into account, it will be more accurate to use the Luce and Tukey axioms.

1.2 Appendix A.2. Algebraic Approach by Luce and Tukey

Adapting Luce and Tukey conditions to the consumer decision problem: If \(C_1\) and \(C_2\) are the two criteria involved in the decision-making process, with: \(c_1=\{C_{1}(x_1,x_2), C_{1}(x_1',x_2'), C_{1}(x_1'',x_2''),...\}\) and \(c_2{=}\{C_{2}(x_1,x_2),C_{2}(\!x_1',x_2'\!), C_2(x_1'',x_2''), ...\}\) the sets of values that can be reached by \(C_1\) and \(C_2\) respectively, \(c_1 \times c_2\) is formed by pairs \((C_{1}(x_1,x_2),C_{2}(x_1,x_2))\), \((C_{1}(x_1,x_2),C_{2}(x_1',x_2'))\), \((C_{1}(x_1',x_2'),C_{2}(x_1,x_2))\), etc. Considering the binary relation \(\succeq \), criteria will be additive if the following axioms are satisfied:

  1. I

    Ordering axiom: \(\succeq \) is a weak order meeting the following axioms:

    • Reflexivity:

      $$\begin{aligned} (C_{1}(x_1,x_2),C_{2}(x_1,x_2))&\succeq (C_{1}(x_1,x_2),C_{2}(x_1,x_2)),\\ \forall C_{1}(x_1,x_2)&\in c_1, C_{2}(x_1,x_2)\in c_2. \end{aligned}$$
    • Transitivity:

      $$\begin{aligned} \text {If }(C_{1}(x_1,x_2),C_{2}(x_1,x_2))\succeq (C_{1}(x_1',x_2'),C_{2}(x_1',x_2')), \end{aligned}$$
      $$\begin{aligned} \text { and } (C_{1}(x_1',x_2'),C_{2}(x_1',x_2'))\succeq (C_{1}(x_1'',x_2''),C_{2}(x_1'',x_2'')), \end{aligned}$$
      $$\begin{aligned} \text {then: } (C_{1}(x_1,x_2),C_{2}(x_1,x_2))\succeq (C_{1}(x_1'',x_2''),C_{2}(x_1'',x_2'')). \end{aligned}$$
    • \(\succeq \) is closed:

      $$\begin{aligned} (C_{1}(x_1,x_2),C_{2}(x_1,x_2))\succeq (C_{1}(x_1',x_2'),C_{2}(x_1',x_2')) \end{aligned}$$
      $$\begin{aligned} \text { or } (C_{1}(x_1,x_2),C_{2}(x_1,x_2))\preceq (C_{1}(x_1',x_2'),C_{2}(x_1',x_2')), \text { or both}. \end{aligned}$$
    • Definition: \(* \quad (C_{1}(x_1,x_2),C_{2}(x_1,x_2))\sim (C_{1}(x_1',x_2'),C_{2}(x_1',x_2'))\text { only if }:\)

      $$\begin{aligned} (C_{1}(x_1,x_2),C_{2}(x_1,x_2))\succeq (C_{1}(x_1',x_2'),C_{2}(x_1',x_2')) \text { and } \end{aligned}$$
      $$\begin{aligned} (C_{1}(x_1,x_2),C_{2}(x_1,x_2))\preceq (C_{1}(x_1',x_2'),C_{2}(x_1',x_2')). \end{aligned}$$

      \(*\quad (C_{1}(x_1,x_2),C_{2}(x_1,x_2))\succ (C_{1}(x_1',x_2'),C_{2}(x_1',x_2'))\) only if:

      $$\begin{aligned} \lnot [(C_{1}(x_1',x_2'),C_{2}(x_1',x_2'))\succeq (C_{1}(x_1,x_2),C_{2}(x_1,x_2))]. \end{aligned}$$
  2. II

    Solution to equations:

    • For each \(C_{1}(x_1,x_2)\in c_1\) and \(C_{2}(x_1,x_2), C_{2}(x_1',x_2')\in c_2\), the equation \((C_{1}(x_1^{*},x_2^{*}),C_{2}(x_1,x_2))=(C_{1}(x_1,x_2),C_{2}(x_1',x_2'))\) has a solution \(C_{1}(x_1^{*},x_2^{*})\in c_1\) and,

    • For each \(C_{1}(x_1,x_2),C_{1}(x_1',x_2')\in c_1\) and \(C_{2}(x_1,x_2)\in c_2\), the equation \((C_{1}(x_1,x_2),C_{2}(x_1,x_2))=(C_{1}(x_1',x_2'),C_{2}(x_1^{*},x_2^{*}))\) has a solution \(C_{2}(x_1^{*},x_2^{*})\in c_2\).

  3. III

    Cancelation:

    $$\begin{aligned} \text {For all }C_{1}(x_1,x_2),&C_{1}(x_1',x_2'), C_{1}(x_1'',x_2'') \in c_1\text { and }C_{2}(x_1,x_2), C_{2}(x_1',x_2'),\\&C_{2}(x_1'',x_2'') \in c_2, \end{aligned}$$
    $$\begin{aligned} \text {if }(C_{1}(x_1,x_2),C_{2}(x_1'',x_2''))\succeq (C_{1}(x_1'',x_2''),C_{2}(x_1',x_2')) \end{aligned}$$
    $$\begin{aligned} \text {and } (C_{1}(x_1'',x_2''),C_{2}(x_1,x_2))\succeq (C_{1}(x_1',x_2'),C_{2}(x_1'',x_2'')), \end{aligned}$$
    $$\begin{aligned} \text {then: }(C_{1}(x_1,x_2),C_{2}(x_1,x_2))\succeq (C_{1}(x_1',x_2'),C_{2}(x_1',x_2')) .\end{aligned}$$
  4. IV

    Archimedean axiom: if \(\{C_{1}(x_1,x_2)_i, C_{2}(x_1,x_2)_i\}, i=0, 1, 2,...\) is a non-trivial and increasing dual standard sequence, for each \( C_{1}(x_1',x_2')\in c_1, C_{2}(x_1',x_2')\in c_2\), then, there exist two integers (positive or negative) \(m\) and \(n\) such that:

    $$\begin{aligned}(C_{1}(x_1,x_2)_n,C_{2}(x_1,x_2)_n)&\succeq (C_{1}(x_1',x_2'),C_{2}(x_1',x_2'))\\&\succeq (C_{1}(x_1,x_2)_m,C_{2}(x_1,x_2)_m).\end{aligned}$$

    An infinite sequence \(\{C_{1}(x_1,x_2)_t, C_{2}(x_1,x_2)_t\}, t=0, 1, 2,...\), with \(C_{1}(x_1,x_2)_t\in c_1, C_{2}(x_1,x_2)_t\in c_2\) is a dual standard sequence (dss) when \((C_{1}(x_1,x_2)_m, C_{2}(x_1,x_2)_n)= (C_{1}(x_1,x_2)_p, C_{2}(x_1,x_2)_q)\) for \(m+n=p+q\) for any \(m, n, p, q\) integer, positive, negative or null. A dss will be trivial if \(C_{1}(x_1,x_2)_t = C_{1}(x_1,x_2)_0\), or \(C_{2}(x_1,x_2)_t = C_{2}(x_1,x_2)_0\).

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Gálvez Ruiz, D., Pino Mejías, J.L. (2014). Dealing with Imprecision in Consumer Theory: A New Approach to Fuzzy Utility Theory. In: Guo, P., Pedrycz, W. (eds) Human-Centric Decision-Making Models for Social Sciences. Studies in Computational Intelligence, vol 502. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39307-5_9

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