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Sunday, Jan. 3, 2021
10:00 AM - 12:00 PM (EST)
We consider a platform which provides probabilistic forecasts to a customer using some algorithm. We introduce a concept of miscalibration, which measures the dis- crepancy between the forecast and the truth. We characterize the platform’s optimal equilibrium when it incurs some cost for miscalibration, and show how this equilibrium depends on the miscalibration cost: when the miscalibration cost is low, the platform uses more distant forecasts and the customer is less responsive to the platform’s fore- cast; when the miscalibration cost is high, the platform can achieve its commitment payoff in an equilibrium, and the only extensive-form rationalizable strategy of the plat- form is its strategy in the commitment solution. Our results show that miscalibration cost is a proxy for the degree of the platform’s commitment power, and thus provide a microfoundation for the commitment solution.
Dynamic Privacy Choices
I study a dynamic model of consumer privacy and platform data collection. In each period, consumers choose their level of platform activity. Greater activity generates more precise information about the consumer, thereby increasing platform profits. Although consumers value privacy, a platform is able to collect much information by gradually lowering the level of privacy protection. In the long-run, consumers become "addicted" to the platform, whereby they lose privacy and receive low payoffs, but continue to choose high activity levels. Competition is unhelpful because consumers have a higher incentive to use a platform to which they have lower privacy.
Consumer (and Driver) Decision-Making under Uncertainty on Digital Platforms
Inspired by the ride-sharing market in New Zealand, with Uber and Zoomy offering respectively a fixed price and an estimated price range per ride, we ask ourselves if competitors in the digital economy could deliberately offer distinct pricing schemes aimed at matching consumers and drivers with different levels of ambiguity tolerance to gain market shares. Our results suggest that in spite of the realistic asymmetric distribution of ambiguity-loving versus ambiguity-averse consumers and drivers - calibrated with the distribution of the attitudes toward ambiguity obtained in a suitable preliminary laboratory experiment - in equilibrium, both platforms offering respectively a fixed price and an estimated price range per ride can coexist in the market: Ambiguity-loving consumers and drivers are attracted toward the price range offers, whereas ambiguity-averse consumers shy away from them. In equilibrium, drivers from both platforms extract rents, so do consumers accepting price range offers; whereas all rents from consumers accepting fixed price offers are successfully extracted away from them.
This paper aims to improve the practical applicability of the classic theory of incentive contracts under moral hazard. We establish conditions such that the information provided by an A/B test of incentive contracts is a sufficient statistic for the question of how best to improve a status quo incentive contract, given a priori knowledge of the agent's monetary preferences. We assess the empirical relevance of this result using data from DellaVigna and Pope's (2017) study of a variety of incentive contracts. Finally, we discuss how our framework can be extended to incorporate additional considerations beyond those in the classic theory.
D4 - Market Structure, Pricing, and Design
D8 - Information, Knowledge, and Uncertainty