Competition in Pricing Algorithms

Brown, Zach Y.; MacKay, Alexander

doi:10.3386/w28860

Cited by 26 publications

(27 citation statements)

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“…Based on the authors' conversations with managers and providers from major ride-hailing platforms, it is the case that platforms do not frequently adjust their pricing algorithms. This observation is consistent with the literature as well as Uber's own description of its pricing (Brown & MacKay, 2021;Uber, 2021d), suggesting that there is a rather fixed base price for any given region. As the phenomenon of driver collusion has been detected only fairly recently, at this point, platforms are unlikely to have fully considered strategic driver behavior in their pricing algorithms.…”

Section: Platform Pricing Schemesupporting

confidence: 91%

“…In practice, ride‐hailing platforms implement their pricing policies through algorithms, which set the desired prices and wages in reaction to observed market conditions. As policies and hence algorithms are determined ahead of time and changed rather infrequently (Brown & MacKay, 2021; Solman, 2017), we assume the pricing policy to be exogenous and known to the drivers, and we focus on the impact of strategic driver behavior, considering the steady‐state performance under a given pricing policy. In our model, customers decide whether to request the service from the platform, and drivers choose whether to offer their services through the platform.…”

Section: Introductionmentioning

confidence: 99%

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Driver collusion in ride‐hailing platforms

2022

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Many on‐demand service platforms employ state‐dependent pricing strategies to balance supply capacity and customer demand. In the context of ride‐hailing platforms, it has been observed that drivers strategically exploit the structure of such pricing policies by coordinating with each other to deactivate some drivers in order to create an artificial shortage of supply capacity and trigger so‐called surge pricing. We develop a simple and high‐level analytical framework to structurally characterize the drivers of such collusive behavior and the consequences for drivers and the platform. We find that collusive driver behavior is more likely in settings where customers exhibit moderate sensitivity to waiting time. For some of these cases, if customers continue to request service under driver collusion, the platform may benefit from the higher surge prices. For settings where driver collusion is harmful to the platform, we consider two possible mitigation strategies: a bonus payment structure to eliminate the drivers' incentives to collude, which comes at a direct cost to the platform, and a freeze period after deactivating the app during which drivers cannot reactivate. We show that with the appropriate duration, such a freeze period can effectively eliminate driver collusion without any direct costs to the platform.

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Section: Platform Pricing Schemesupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Driver collusion in ride‐hailing platforms

2022

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“…We have algorithms to determine what that market is." 34 From the workers' perspective, however, Uber is shifting the price regularly, whether it is doing so by tying compensation to an external indicator or a set of indicators.…”

Section: Algorithmically Personalized Wagesmentioning

confidence: 99%

Algorithmic Personalized Wages

Teachout

2023

Politics & Society

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This article explores algorithmically created personalized wages: what they are, what they mean, and what we can do about them. First, it establishes a taxonomy of five different forms of algorithmic wage differentiation: productivity-based wage adjustments, wages shifted through incentive bonuses and demerits, behavioral wages, dynamic wages, and wages shifted to conduct an experiment. It argues that these techniques are likely to spread from gig work to the formal employment context. Second, it argues that the spread of these techniques has democratic implications. They will increase economic and racial inequality. They will harm labor solidarity. Perhaps most importantly, they put workers in a profoundly humiliating position in relationship to their boss, one where speech and autonomy are discouraged because they can lead to lowered pay. Finally, it argues that we should understand these developments as innovations in power and domination and use old antimonopoly strategies as ways to limit the democratic downsides of these tools. We should explore bans or limits on first-degree labor pricing discrimination and enhanced antitrust enforcement.

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“…Klein (2021), Hansen et al (2021), Banchio and Skrzypacz (2022)) or empirical work (see e.g. Brown and MacKay (2021), Musolff (2021), Assad et al (2021)). The work of Calvano et al (2020) draws attention to strategies as a proxy for collusion: they argue that simply looking at outcomes of learning might be insufficient, as collusion might arise as a "mistake" by poorly designed algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

Adaptive Algorithms and Collusion via Coupling

Banchio¹,

Mantegazza²

2022

Preprint

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This paper studies the strategic interaction of algorithms in economic games. We analyze games where learning algorithms play against each other while searching for the best strategy. We first establish a fluid approximation technique that enables us to characterize the learning outcomes in continuous time. This tool allows to identify the equilibria of games played by Artificial Intelligence algorithms and perform comparative statics analysis. Thus, our results bridge a gap between traditional learning theory and applied models, allowing quantitative analysis of traditionally experimental systems. We describe the outcomes of a social dilemma, and we provide analytical guidance for the design of pricing algorithms in a Bertrand game. We uncover a new phenomenon, the coordination bias, which explains how algorithms may fail to learn dominant strategies.

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Competition in Pricing Algorithms

Cited by 26 publications

References 0 publications

Driver collusion in ride‐hailing platforms

Driver collusion in ride‐hailing platforms

Algorithmic Personalized Wages

Adaptive Algorithms and Collusion via Coupling

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