Linguistic Evaluation Scales

The complexity and uncertainty of objective thing and the fuzziness of human thought result in decision making with linguistic information in a wide variety of practical problems, such as personnel evaluation, military system performance evaluation, online auctions, supply chain management, venture capital, and medical diagnostics. In such problems, a realistic approach may be to use linguistic assessments instead of numerical values by means of linguistic variables, that is, variables whose values are not numbers but words or sentences in a natural or artificial language (Fan and Wang, 2003; 2004; Fan et al., 2002; Herrera and Herrera-Viedma, 2003; 2000a; 2000b; 1997; Herrera and Martínez, 2001a; 2001b; 2000a; 2000b; Herrera and Verdegay, 1993; Herrera et al., 2005; 2003; 2001a; 2001b; 2000; 1997; 1996a; 1996b; 1995; Herrera-Viedma, 2001; Herrera-Viedma and Peis, 2003; Herrera-Viedma et al., 2005; 2004; 2003; Wang and Chuu, 2004; Xu, 2010; 2009a; 2009b; 2009c; 2008; 2007a; 2007b; 2007c; 2007d; 2007e; 2006a; 2006b; 2006c; 2006d; 2006e; 2006f; 2006g; 2006h; 2006i; 2005a; 2005b; 2005c; 2005d; 2004a; 2004b; 2004c; 2004d; 2004e; 2004f; Xu and Da, 2003; 2002; Zadeh and Kacprzyk, 1999a; 1999b). This may arise for different reasons (Chen and Hwang, 1992): ① the information may be unquantifiable due to its nature; and ② the precise quantitative information may not be stated because either it is unavailable or the cost of its computation is too high and an “approximate value” may be tolerated. For example, when evaluating the “comfort” or “design” of a car, linguistic labels like “good”, “fair” and “poor” are usually used, and evaluating a car’s speed, linguistic labels like “very fast”, “fast” and “slow” can be used (Bordogna et al., 1997; Levrat et al., 1997).