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Inducing Exploration in Service Platforms

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Book cover Sharing Economy

Part of the book series: Springer Series in Supply Chain Management ((SSSCM,volume 6))

Abstract

Crowd-sourced content in the form of online product reviews or recommendations is an integral feature of most Internet-based service platforms and marketplaces, including Yelp, TripAdvisor, Netflix, and Amazon. Customers may find such information useful when deciding between potential alternatives; at the same time, the process of generating such content is mainly driven by the customers’ decisions themselves. In other words, the service platform or marketplace “explores” the set of available options through its customers’ decisions, while they “exploit” the information they obtain from the platform about past experiences to determine whether and what to purchase. Unlike the extensive work on the trade-off between exploration and exploitation in the context of multi-armed bandits, the canonical framework we discuss in this chapter involves a principal that explores a set of options through the actions of self-interested agents. In this framework, the incentives of the principal and the agents towards exploration are misaligned, but the former can potentially incentivize the actions of the latter by appropriately designing a payment scheme or an information provision policy.

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Notes

  1. 1.

    Kleinberg and Slivkins (2017) also presented recently a comprehensive tutorial related to these issues.

  2. 2.

    There is also recent empirical work exploring operational issues on online marketplaces, e.g., Moon et al. (2017), Li and Netessine (2017), and Bimpikis et al. (2017b).

  3. 3.

    Our analysis can be readily extended to the case of more than two providers.

  4. 4.

    The probability density function of a Beta(s, f) random variable is given by

    $$\displaystyle \begin{aligned}g(x;s,f)=\frac{x^{s-1}(1-x)^{f-1}}{B(s,f)},\text{ for }x\in[0,1].\end{aligned}$$
  5. 5.

    The platform and the customers hold the same prior belief, so that platform actions (e.g., the choice of an information-provision policy) do not convey any additional information on provider quality to the customers (e.g., Bergemann and Välimäki 1997; Bose et al. 2006; Papanastasiou and Savva 2017).

  6. 6.

    Commitment is a reasonable assumption in the context of online platforms, where information provision occurs on the basis of pre-decided algorithms and the large volume of products/services hosted renders ad-hoc adjustments of the automatically-generated content prohibitively costly.

  7. 7.

    The generic term “message” refers to a specific configuration of information that is observed by the customer; examples of messages include detailed outcome histories (i.e., distributions of customer reviews), relative rankings of providers, or recommendations for a specific product.

  8. 8.

    More generally, our analysis is relevant for cases where the platform has a different (e.g., longer-run) objective than its users.

  9. 9.

    Note that for the case of a Bernoulli reward process the current probability of success (i.e., the Bayesian probability of the next trial being a success given the current state of the system) is equal to the immediate expected reward, r(x t, i) (e.g., Gittins et al. 2011).

  10. 10.

    This expectation can be computed by the period-t customer, since the ex ante probability that the state in period t is x t (i.e., unconditional on the message g(x t)) is known to the customer through her knowledge of the designer’s policy in previous periods and the preceding customers’ best response to this policy.

  11. 11.

    The result of Proposition 1 extends readily to the case of |S| = n providers (in this case, an ICRP consists of n possible recommendations, and each recommendation must satisfy n − 1 IC constraints per period), as well as to alternative platform objective functions (by replacing r(k, i) with suitable reward functions).

  12. 12.

    Note that the solution to LP (10.4) can also be used to retrieve the period-t customer’s belief over the system state upon entry to the platform; specifically, this belief is given by \(P(x_t=z)={\sum _{i\in S}\rho (z,i)}/ ({\sum _{k\in X_t}\sum _{i\in S}\rho (k,i)})\).

  13. 13.

    This is a natural generalization of the computation in the example of Sect. 10.3.

  14. 14.

    Che and Horner (2017) also consider the problem of optimally designing recommendation policies in a setting where information about the quality of two potential alternatives arrives continuously over time—their setting uses the exponential bandit framework of Keller et al. (2005) as a building block.

  15. 15.

    However, the analysis may, in general, be quite challenging.

  16. 16.

    The NetFlix Prize offered a million dollars to anyone who succeeded in improving the company’s recommendation algorithm by a certain margin and was concluded in 2009. The Heritage Prize was a multi-year contest whose goal was to provide an algorithm that predicts patient readmissions to hospitals. A successful breakthrough was obtained in 2013.

  17. 17.

    In addition to the work that we discuss here, which mainly focuses on the dynamics of learning and competition in contests, there is also an extensive body of work that explore a number of questions in a static framework, e.g., Terwiesch and Xu (2008), Ales et al. (2017), and Körpeoğlu and Cho (2017).

  18. 18.

    The term “encouragement” originates from the literature on strategic experimentation (e.g., Bolton and Harris 1999; Keller et al. 2005).

  19. 19.

    Bimpikis and Drakopoulos (2016) and Halac et al. (2017) also consider a strategic experimentation framework to study the interplay between a principal and the agents’ incentives and how appropriately designing information disclosure mechanisms may increase welfare.

  20. 20.

    Girotra et al. (2010), Kornish and Ulrich (2011), Huang et al. (2014), and Jiang et al. (2016) are recent empirical studies that consider the role of learning and feedback in crowdsourcing contests and, more broadly, in the innovation process.

  21. 21.

    There are also a number of notable recent papers that consider different aspects of contest design and its applications, e.g., Seel and Strack (2016), Hu and Wang (2017), and Strack (2016).

  22. 22.

    There is currently significant interest in the role of information in mitigating the potential misalignment of interests between a principal and an agent/set of agents, e.g., Renault et al. (2017), Ely (2017), and Orlov et al. (2017).

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Authors and Affiliations

  1. Graduate School of Business, Stanford University, Stanford, CA, USA

    Kostas Bimpikis

  2. Haas School of Business, University of California, Berkeley, CA, USA

    Yiangos Papanastasiou

Authors
  1. Kostas Bimpikis
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  2. Yiangos Papanastasiou
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Correspondence to Kostas Bimpikis .

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Editors and Affiliations

  1. Rotman School of Management, University of Toronto, Toronto, ON, Canada

    Ming Hu

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Bimpikis, K., Papanastasiou, Y. (2019). Inducing Exploration in Service Platforms. In: Hu, M. (eds) Sharing Economy. Springer Series in Supply Chain Management, vol 6. Springer, Cham. https://doi.org/10.1007/978-3-030-01863-4_10

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