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Register a new userMarket Effects of Loyalty and Cost Factors in a Price Discrimination Environment
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Product cost heterogeneity across firms and loyalty models of customers are two topics that have garnered limited attention in prior studies on competitive price discrimination. Costs are generally assumed negligible or equal for all firms, and loyalty is modeled as an additive bias in customer valuations. We extend these previous treatments by considering cost asymmetry and a richer class of loyalty models in a game-theoretic model involving two asymmetric firms. Here firms may incur different non-negligible product costs, and customers can have firm-specific loyalty levels. We characterize the effects of loyalty levels and product cost difference on market outcomes such as prices, market share and profits. Our analysis and numerical simulations shed new light on market equilibrium structures arising from the interplay between product cost difference and loyalty levels.
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Online platforms collect rich information about participants and then share some of this information back with them to improve market outcomes. In this paper we study the following information disclosure problem in two-sided markets: If a platform wants to maximize revenue, which sellers should the platform allow to participate, and how much of its available information about participating sellers quality should the platform share with buyers? We study this information disclosure problem in the context of two distinct two-sided market models: one in which the platform chooses prices and the sellers choose quantities (similar to ride-sharing), and one in which the sellers choose prices (similar to e-commerce). Our main results provide conditions under which simple information structures commonly observed in practice, such as banning certain sellers from the platform while not distinguishing between participating sellers, maximize the platforms revenue. An important innovation in our analysis is to transform the platforms information disclosure problem into a constrained price discrimination problem. We leverage this transformation to obtain our structural results.
The behavior of complex systems is one of the most intriguing phenomena investigated by recent science; natural and artificial systems offer a wide opportunity for this kind of analysis. The energy conversion is both a process based on important physical laws and one of the most important economic sectors; the interaction between these two aspects of energy production suggests the possibility to apply some of the approaches of the dynamic systems analysis. In particular, a phase plot, which is one of the methods to detect a correlation between quantities in a complex system, provides a good way to establish qualitative analogies between the ecological systems and the economic ones and may shed light on the processes governing the evolution of the system. The aim of this paper is to highlight the analogies between some peculiar characteristics of the oil production vs. price and show in which way such characteristics are similar to some behavioral mechanisms found in Nature.
The electricity sector has tended to be one of the first industries to face technology change motivated by sustainability concerns. Whilst efficient market designs for electricity have tended to focus upon market power concerns, environmental externalities pose extra challenges for efficient solutions. Thus, we show that ad hoc remedies for market power alongside administered carbon prices are inefficient unless they are integrated. Accordingly, we develop an incentive-based market clearing design that can include externalities as well as market power mitigation. A feature of the solution is that it copes with incomplete information of the system operator regarding generation costs. It is uses a network representation of the power system and the proposed incentive mechanism holds even with energy limited technologies having temporal constraints, e.g., storage. The shortcomings of price caps to mitigate market power, in the context of sustainability externalities, are overcome under the proposed incentive mechanism.
We analyze statistical discrimination in hiring markets using a multi-armed bandit model. Myopic firms face workers arriving with heterogeneous observable characteristics. The association between the workers skill and characteristics is unknown ex ante; thus, firms need to learn it. Laissez-faire causes perpetual underestimation: minority workers are rarely hired, and therefore, underestimation towards them tends to persist. Even a slight population-ratio imbalance frequently produces perpetual underestimation. We propose two policy solutions: a novel subsidy rule (the hybrid mechanism) and the Rooney Rule. Our results indicate that temporary affirmative actions effectively mitigate discrimination caused by insufficient data.
In an observed generalized semi-Markov regime, estimation of transition rate of regime switching leads towards calculation of locally risk minimizing option price. Despite the uniform convergence of estimated step function of transition rate, to meet the existence of classical solution of the modified price equation, the estimator is approximated in the class of smooth functions and furthermore, the convergence is established. Later, the existence of the solution of the modified price equation is verified and the point-wise convergence of such approximation of option price is proved to answer the tractability of its application in Finance. To demonstrate the consistency in result a numerical experiment has been reported.
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