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A General Model of Subjective Value and Stimulus-Intensity-Sensitive Hedonic Editing Strategy

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Abstract

Subjective value is foundational to decision-making processes and people's sense of happiness. To better represent decision-makers' psychological characteristics when subjective value is formed, we establish an attention-and-reference-dependent subjective value model by simultaneously considering the absolute subjective value, relative subjective value, and attention distribution. Our model provides researchers with a theoretical tool for explaining, predicting, and adjusting decision behaviors. Additionally, to maximize the total experienced subjective value brought by multiple events, we compare hedonic editing strategies and demonstrate that the optimal hedonic editing strategy is sensitive to the stimulus intensity. This stimulus-intensity-sensitive strategy is contrary to the hedonic editing strategy inferred from the prospect theory value function when stimulus intensity is relatively small. The results of this study enrich transaction utility theory and expectation-disconfirmation theory and can guide decision-makers in enhancing happiness by rationally dealing with gains and losses.

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Notes

  1. In our formulation, golfers do not consistently deliver maximum effort for each putt. Golfers may devote different amounts of effort to their putts throughout the Tournament. This conceptualization is consistent with previous works, which found that rather than playing consistently across every hole, golfers' performance varies according to the incentives and pressures they face (Jennifer, 2011; Ozbeklik & Smith, 2017; Elmore & Urbaczewski, 2021).

  2. For simplicity, Pope & Schweitzer start by assuming that if a golfer misses his first putt, he makes his following putt and earns a lower score x-1 His utility function is: \(U = \left[ { f \left( e \right) + \varepsilon } \right]V \left( x \right) + \left[ {1 - f \left( e \right) - \varepsilon } \right]V \left( {x {-}1} \right) - \cos t\left( e \right)\). Here e is the effort level, f(e) is the shot accuracy, \(\varepsilon\) is an error term, and V(\(x\)) is the value function under the score x. We can see there exists a unique optimum (but not maximum) effort level e that the golfer could generate a maximum utility.

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Funding

The study is supported by the Natural Science Foundation of Zhejiang Province [Grant No. LQ20E060001], and the National Natural Science Foundation of China [Grant No. 71901096, 71971099].

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Authors and Affiliations

  1. School of Civil Engineering and Architecture, Zhejiang Sci-Tech University, Hangzhou, 310018, China

    Haijiao Cui

  2. School of Management, Jinan University, Guangzhou, 510632, China

    Bin Cao & Aimei Li

  3. School of Business, Shandong University, Weihai, 264209, China

    Zhaohui Li

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  1. Haijiao Cui
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  2. Bin Cao
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  3. Aimei Li
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  4. Zhaohui Li
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Contributions

All authors contributed to the study conception and design. Material preparation and analysis were performed by Haijiao Cui, Bin Cao, Aimei Li and Zhaohui Li. The first draft of the manuscript was written by Haijiao Cui and all authors commented on previous/revised versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Bin Cao or Zhaohui Li.

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Cui, H., Cao, B., Li, A. et al. A General Model of Subjective Value and Stimulus-Intensity-Sensitive Hedonic Editing Strategy. J Happiness Stud 24, 1191–1217 (2023). https://doi.org/10.1007/s10902-023-00635-5

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