Behavioral and procedural consequences of structural variation in value trees |
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| Affiliation: | 1. State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, Wuhan 430074, China;2. School of Electrical and Electronics Engineering, Nanyang Technological University, Singapore 639798, Singapore;3. State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China;4. Department of Electrical and Electronics Engineering, University of Liverpool, Liverpool L69 3GJ, U.K.;5. School of Computer Science and Engineering, Nanyang Technological University, Singapore 639798, Singapore;6. Kunming Engineering Corporation Limited, Kunming, Yunnan 650051, China;7. Electric Power Research Institute of Yunnan Power Grid, Kunming, Yunnan 650217, China;8. Graduate School of Information Science and Engineering, Tokyo Institute of Technology, Tokyo 152-8552, Japan;1. Department of Mathematics, University of Bayreuth, Bayreuth 95440, Germany;2. Department of Economics, University of Bayreuth, Bayreuth 95440, Germany |
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| Abstract: | Our experiment shows that the division of attributes in value trees can either increase or decrease the weight of an attribute. The structural variation of value trees may also change the rank of attributes. We propose that our new findings related to the splitting bias, some other phenomena appearing with attribute weighting in value trees, and the number-of-attribute-levels effect in conjoint analysis may have the same origins. One origin for these phenomena is that decision makers' responses mainly reflect the rank of attributes and not to the full extent the strength of their preferences as the value theory assumes. We call this the unadjustment phenomenon. A procedural source of biases is the normalization of attribute weights. One consequence of these two factors is that attribute weights change if attributes are divided in a value tree. We also discuss how the biases in attribute weighting could be avoided in practice. |
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