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Register a new userPricing Social Goods
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Added by
Tomer Ezra
Publication date
2017
fields
Informatics Engineering
and research's language is
English
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Social goods are goods that grant value not only to their owners but also to the owners surroundings, be it their families, friends or office mates. The benefit a non-owner derives from the good is affected by many factors, including the type of the good, its availability, and the social status of the non-owner. Depending on the magnitude of the benefit and on the price of the good, a potential buyer might stay away from purchasing the good, hoping to free ride on others purchases. A revenue-maximizing seller who sells social goods must take these considerations into account when setting prices for the good. The literature on optimal pricing has advanced considerably over the last decade, but little is known about optimal pricing schemes for selling social goods. In this paper, we conduct a systematic study of revenue-maximizing pricing schemes for social goods: we introduce a Bayesian model for this scenario, and devise nearly-optimal pricing schemes for various types of externalities, both for simultaneous sales and for sequential sales.
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We study the dynamic pricing problem faced by a monopolistic retailer who sells a storable product to forward-looking consumers. In this framework, the two major pricing policies (or mechanisms) studied in the literature are the preannounced (commitment) pricing policy and the contingent (threat or history dependent) pricing policy. We analyse and compare these pricing policies in the setting where the good can be purchased along a finite time horizon in indivisible atomic quantities. First, we show that, given linear storage costs, the retailer can compute an optimal preannounced pricing policy in polynomial time by solving a dynamic program. Moreover, under such a policy, we show that consumers do not need to store units in order to anticipate price rises. Second, under the contingent pricing policy rather than the preannounced pricing mechanism, (i) prices could be lower, (ii) retailer revenues could be higher, and (iii) consumer surplus could be higher. This result is surprising, in that these three facts are in complete contrast to the case of a retailer selling divisible storable goods Dudine et al. (2006). Third, we quantify exactly how much more profitable a contingent policy could be with respect to a preannounced policy. Specifically, for a market with $N$ consumers, a contingent policy can produce a multiplicative factor of $Omega(log N)$ more revenues than a preannounced policy, and this bound is tight.
We study the power and limitations of posted prices in multi-unit markets, where agents arrive sequentially in an arbitrary order. We prove upper and lower bounds on the largest fraction of the optimal social welfare that can be guaranteed with posted prices, under a range of assumptions about the designers information and agents valuations. Our results provide insights about the relative power of uniform and non-uniform prices, the relative difficulty of different valuation classes, and the implications of different informational assumptions. Among other results, we prove constant-factor guarantees for agents with (symmetric) subadditive valuations, even in an incomplete-information setting and with uniform prices.
A decision-maker is deciding between an active action (e.g., purchase a house, invest certain stock) and a passive action. The payoff of the active action depends on the buyers private type and also an unknown state of nature. An information seller can design experiments to reveal information about the realized state to the decision-maker and would like to maximize profit from selling such information. We fully characterize, in closed-form, the revenue-optimal information selling mechanism for the seller. After eliciting the buyers type, the optimal mechanism charges the buyer an upfront payment and then simply reveals whether the realized state passed a certain threshold or not. The optimal mechanism features both price discrimination and information discrimination. The special buyer type who is a priori indifferent between the active and passive action benefits the most from participating in the mechanism.
Algorithmic pricing is the computational problem that sellers (e.g., in supermarkets) face when trying to set prices for their items to maximize their profit in the presence of a known demand. Guruswami et al. (2005) propose this problem and give logarithmic approximations (in the number of consumers) when each consumers values for bundles are known precisely. Subsequently severa
Despite the promising potential of network risk management services (e.g., cyber-insurance) to improve information security, their deployment is relatively scarce, primarily due to such service companies being unable to guarantee profitability. As a novel approach to making cyber-insurance services more viable, we explore a symbiotic relationship between security vendors (e.g., Symantec) capable of price differentiating their clients, and cyber-insurance agencies having possession of information related to the security investments of their clients. The goal of this relationship is to (i) allow security vendors to price differentiate their clients based on security investment information from insurance agencies, (ii) allow the vendors to make more profit than in homogeneous pricing settings, and (iii) subsequently transfer some of the extra profit to cyber-insurance agencies to make insurance services more viable. oindent In this paper, we perform a theoretical study of a market for differentiated security product pricing, primarily with a view to ensuring that security vendors (SVs) make more profit in the differentiated pricing case as compared to the case of non-differentiated pricing. In order to practically realize such pricing markets, we propose novel and emph{computationally efficient} consumer differentiated pricing mechanisms for SVs based on (i) the market structure, (ii) the communication network structure of SV consumers captured via a consumers emph{Bonacich centrality} in the network, and (iii) security investment amounts made by SV consumers.
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