Two-Sided Fairness for Repeated Matchings in Two-Sided Markets: A Case Study of a Ride-Hailing Platform
pp. 3082-3092, 2019.
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Abstract:
Ride hailing platforms, such as Uber, Lyft, Ola or DiDi, have traditionally focused on the satisfaction of the passengers, or on boosting successful business transactions. However, recent studies provide a multitude of reasons to worry about the drivers in the ride hailing ecosystem. The concerns range from bad working conditions and work...More
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Introduction
- Two-sided sharing economy platforms, such as Uber, Lyft, or AirBnB, have brought in disruptions in multiple business landscapes [25, 31].
- There are three stakeholders in the two-sided economy: (i) providers of goods and services, (ii) customers who pay for them, and (iii) the platform which performs the matching between the providers and customers.
- With increasingly many people depending on the sharing economy to earn a living, it becomes crucial to ask the question of what a fair distribution of income on such platforms is and what power and means the platform has in shaping these distributions.
- Allon et al [1] studied how drivers make labor decisions in such on-demand services e.g., when to work and for how long, and attempted to design better financial incentives for drivers to increase their number of work hours
Highlights
- Two-sided sharing economy platforms, such as Uber, Lyft, or AirBnB, have brought in disruptions in multiple business landscapes [25, 31]
- We propose a novel framework to think about fairness in the matching mechanisms of ride hailing platforms
- According to a 2016 report by the United States Department of Commerce, the total spending in the sharing economy was 5% of the total spending, and this ratio is predicted to grow to 50% by 2025 [29]
- With increasingly many people depending on the sharing economy to earn a living, it becomes crucial to ask the question of what a fair distribution of income on such platforms is and what power and means the platform has in shaping these distributions
- We focus on studying whether it is possible to optimize the matchings for equitable income distributions
- This paper explored the problem of two-sided fairness for producers and consumers in a sharing economy platform
Methods
- TO MATCH DRIVERS AND CUSTOMERS
the authors consider different strategies to match drivers and customers in ride-hailing platforms.
5.1 Nearest Driver First (NDF)
Under this strategy, each customer, making a request in a given round, chooses a driver available in the round that is closest to the customer. - Ties are broken randomly
- The pseudocode for this algorithm is provided in the Reproducibility Supplement A.
- It is worth making the following observation : Any divergence from the Nearest Driver strategy will necessarily decrease the average utility of the drivers in a given round under the assumed utility functions if ties are broken perfectly.
- Assuming that variables Mi,j encode a proper matching where all requesting customers get matched to a driver, the value of the expression Mi,j · d (C j
Results
- The authors analyze the performance of the ILP with Objectives 2 and 3, using λ = {0, 0.5, 1.0}.
- The authors describe the dataset and the preprocessing methods in the Reproducibility Appendix A.1.
- The filtered dataset consists of 28k matching rounds with a total of 1462 drivers and 231k job requests.
- The average duration of a job is 14.39 rounds, which corresponds to 22 minutes.
- The average distance from pickup location to the destination of the customer is 8.89 km.
- The number of active rounds
Conclusion
- This paper explored the problem of two-sided fairness for producers and consumers in a sharing economy platform.
- The authors have explored the space of various mechanisms for achieving these fairness goals.
- The authors experimented with an optimization problem balancing equality of both consumers and producers, which led to an increase in equality while preserving the utility volumes.
- The authors showed that implementing an optimization objective which is a direct translation of equality goals in practice still requires a very careful thought about initialization of utility values
Summary
Introduction:
Two-sided sharing economy platforms, such as Uber, Lyft, or AirBnB, have brought in disruptions in multiple business landscapes [25, 31].- There are three stakeholders in the two-sided economy: (i) providers of goods and services, (ii) customers who pay for them, and (iii) the platform which performs the matching between the providers and customers.
- With increasingly many people depending on the sharing economy to earn a living, it becomes crucial to ask the question of what a fair distribution of income on such platforms is and what power and means the platform has in shaping these distributions.
- Allon et al [1] studied how drivers make labor decisions in such on-demand services e.g., when to work and for how long, and attempted to design better financial incentives for drivers to increase their number of work hours
Methods:
TO MATCH DRIVERS AND CUSTOMERS
the authors consider different strategies to match drivers and customers in ride-hailing platforms.
5.1 Nearest Driver First (NDF)
Under this strategy, each customer, making a request in a given round, chooses a driver available in the round that is closest to the customer.- Ties are broken randomly
- The pseudocode for this algorithm is provided in the Reproducibility Supplement A.
- It is worth making the following observation : Any divergence from the Nearest Driver strategy will necessarily decrease the average utility of the drivers in a given round under the assumed utility functions if ties are broken perfectly.
- Assuming that variables Mi,j encode a proper matching where all requesting customers get matched to a driver, the value of the expression Mi,j · d (C j
Results:
The authors analyze the performance of the ILP with Objectives 2 and 3, using λ = {0, 0.5, 1.0}.- The authors describe the dataset and the preprocessing methods in the Reproducibility Appendix A.1.
- The filtered dataset consists of 28k matching rounds with a total of 1462 drivers and 231k job requests.
- The average duration of a job is 14.39 rounds, which corresponds to 22 minutes.
- The average distance from pickup location to the destination of the customer is 8.89 km.
- The number of active rounds
Conclusion:
This paper explored the problem of two-sided fairness for producers and consumers in a sharing economy platform.- The authors have explored the space of various mechanisms for achieving these fairness goals.
- The authors experimented with an optimization problem balancing equality of both consumers and producers, which led to an increase in equality while preserving the utility volumes.
- The authors showed that implementing an optimization objective which is a direct translation of equality goals in practice still requires a very careful thought about initialization of utility values
Tables
- Table1: Performance of all Matching-Mechanisms after 30 Days; Green values indicate a positive change vs. NDF, Red values indicate a negative change vs. NDF
Funding
- This research was supported in part by a European Research Council (ERC) Advanced Grant for the project “Foundations for Fair Social Computing", funded under the European Union’s Horizon 2020 Framework Programme (grant agreement no. 789373)
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