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The Benefits of Social Influence in Optimized Cultural Markets

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  • Andrés Abeliuk
  • Gerardo Berbeglia
  • Manuel Cebrian
  • Pascal Van Hentenryck

Abstract

Social influence has been shown to create significant unpredictability in cultural markets, providing one potential explanation why experts routinely fail at predicting commercial success of cultural products. As a result, social influence is often presented in a negative light. Here, we show the benefits of social influence for cultural markets. We present a policy that uses product quality, appeal, position bias and social influence to maximize expected profits in the market. Our computational experiments show that our profit-maximizing policy leverages social influence to produce significant performance benefits for the market, while our theoretical analysis proves that our policy outperforms in expectation any policy not displaying social signals. Our results contrast with earlier work which focused on showing the unpredictability and inequalities created by social influence. Not only do we show for the first time that, under our policy, dynamically showing consumers positive social signals increases the expected profit of the seller in cultural markets. We also show that, in reasonable settings, our profit-maximizing policy does not introduce significant unpredictability and identifies “blockbusters”. Overall, these results shed new light on the nature of social influence and how it can be leveraged for the benefits of the market.

Suggested Citation

  • Andrés Abeliuk & Gerardo Berbeglia & Manuel Cebrian & Pascal Van Hentenryck, 2015. "The Benefits of Social Influence in Optimized Cultural Markets," PLOS ONE, Public Library of Science, vol. 10(4), pages 1-20, April.
  • Handle: RePEc:plo:pone00:0121934
    DOI: 10.1371/journal.pone.0121934
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    Cited by:

    1. Mahsa Derakhshan & Negin Golrezaei & Vahideh Manshadi & Vahab Mirrokni, 2022. "Product Ranking on Online Platforms," Management Science, INFORMS, vol. 68(6), pages 4024-4041, June.
    2. Andrés Abeliuk & Gerardo Berbeglia & Manuel Cebrian & Pascal Van Hentenryck, 2016. "Assortment optimization under a multinomial logit model with position bias and social influence," 4OR, Springer, vol. 14(1), pages 57-75, March.
    3. Rui Chen & Hai Jiang, 2020. "Assortment optimization with position effects under the nested logit model," Naval Research Logistics (NRL), John Wiley & Sons, vol. 67(1), pages 21-33, February.
    4. Morgan R Frank & Manuel Cebrian & Galen Pickard & Iyad Rahwan, 2017. "Validating Bayesian truth serum in large-scale online human experiments," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-13, May.
    5. Ningyuan Chen & Anran Li & Kalyan Talluri, 2021. "Reviews and Self-Selection Bias with Operational Implications," Management Science, INFORMS, vol. 67(12), pages 7472-7492, December.
    6. Ningyuan Chen & Ying-Ju Chen, 2021. "Duopoly Competition with Network Effects in Discrete Choice Models," Operations Research, INFORMS, vol. 69(2), pages 545-559, March.
    7. Berbeglia, Franco & Berbeglia, Gerardo & Van Hentenryck, Pascal, 2021. "Market segmentation in online platforms," European Journal of Operational Research, Elsevier, vol. 295(3), pages 1025-1041.

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