Simple Pricing Scheme Research Articles

Simple pricing schemes for consumers with evolving values

We consider a pricing problem where a buyer is interested in purchasing/using a good, such as an app or music or software, repeatedly over time. The consumer discovers his value for the good only as he uses it, and the value evolves with each use.In this paper, we explore the performance of a class of pricing mechanisms that are extremely simple for both the buyer and the seller to use: the seller offers the product for free for a certain number of uses, and then charges an appropriate fixed price per usage. The buyer reacts to prices myopically without worrying about how his value evolves in the future. We show that under certain assumptions, a pricing mechanism of this form is approximately optimal regardless of the buyer's risk profile or fine details of the value evolution process.

Simple Pricing Schemes for the Cloud

The problem of pricing the cloud has attracted much recent attention due to the widespread use of cloud computing and cloud services. From a theoretical perspective, several mechanisms that provide strong efficiency or fairness guarantees and desirable incentive properties have been designed. However, these mechanisms often rely on a rigid model, with several parameters needing to be precisely known for the guarantees to hold. In this article, we consider a stochastic model and show that it is possible to obtain good welfare and revenue guarantees with simple mechanisms that do not make use of the information on some of these parameters. In particular, we prove that a mechanism that sets the same price per timestep for jobs of any length achieves at least 50% of the welfare and revenue obtained by a mechanism that can set different prices for jobs of different lengths, and the ratio can be improved if we have more specific knowledge of some parameters. Similarly, a mechanism that sets the same price for all servers even though the servers may receive different kinds of jobs can provide a reasonable welfare and revenue approximation compared to a mechanism that is allowed to set different prices for different servers.

Enabling Distributed Energy Storage by Incentivizing Small Load Shifts

Reducing peak demands and achieving a high penetration of renewable energy sources are important goals in achieving a smarter grid. To reduce peak demand, utilities are introducing variable rate electricity prices to incentivize consumers to manually shift their demand to low-price periods. Consumers may also use energy storage to automatically shift their demand by storing energy during low-price periods for use during high-price periods. Unfortunately, variable rate pricing provides only a weak incentive for distributed energy storage and does not promote its adoption at large scales. In this article, we present the storage adoption dilemma to capture the problems with incentivizing energy storage using variable rate prices. To address the problem, we propose a simple pricing scheme, called flat-power pricing , which incentivizes consumers to shift small amounts of load to flatten their demand rather than shift as much of their power usage as possible to low-price, off-peak periods. We show that compared to variable rate pricing, flat-power pricing (i) reduces consumers’ upfront capital costs, as it requires significantly less storage capacity per consumer; (ii) increases energy storage’s return on investment, as it mitigates free riding and maintains the incentive to use energy storage at large scales; and (iii) uses aggregate storage capacity within 31% of an optimal centralized approach. In addition, unlike variable rate pricing, we also show that flat-power pricing incentivizes the scheduling of elastic background loads, such as air conditioners and heaters, to reduce peak demand. We evaluate our approach using real smart meter data from 14,000 homes in a small town.

Reaping the Benefits of Bundling under High Production Costs

It has long been known in the economics literature that selling different goods in a single bundle can significantly increase revenue, even when the valuations for the goods are independent. However, bundling is no longer profitable if the goods have high production costs. To overcome this issue, we introduce Pure Bundling with Disposal for Cost (PBDC), where after buying the bundle, we allow the customer to return any subset of items for their production cost. We demonstrate using classical examples that PBDC captures the concentration effects of bundling while allowing for the flexibility of individual sales, extracting all of the consumer welfare in situations where previous simple mechanisms could not.Furthermore, we prove a theoretical guarantee on the performance of PBDC that holds for arbitrary independent distributions, using techniques from the mechanism design literature. We transform the problem with costs to a problem with negative valuations, extend the mechanism design techniques to negative valuations, and use the Core-Tail decomposition of Babaioff et al. from [BILW14] to show that either PBDC or individual sales will obtain at least 1/5.2 of the optimal profit. This also improves the bound of 1/6 from [BILW14]. We advance the upper bound as well, constructing two IID items with zero cost where mixed bundling earns 1.19 more revenue than either pure bundling or individual sales.Our numerical experiments show that PBDC outperforms all other simple pricing schemes, including the Bundle-Size Pricing (BSP) introduced by Chu et al. in [CLS11], under the same families of distributions used in [CLS11]. We also provide the first theoretical explanation for some of the great experimental successes in [CLS11]. All in all, our work shows establishes PBDC as a robust, computationally-minimal heuristic that is easy to market to the customer.

Load-shift incentives for household demand response: Evaluation of hourly dynamic pricing and rebate schemes in a wind-based electricity system

Applying a partial equilibrium model of the electricity market we analyse effects of exposing household electricity customers to retail products with variable pricing. Both short-term and long-term effects of exposing customers to hourly spot market prices and a simpler rebate scheme are analysed under scenarios with large shares of wind power in a Danish case study. Our results indicate strategies that could be favourable in ensuring high adoption of products and efficient response by households. We find that simple pricing schemes, though economically less efficient, could become important in an early phase to initialise the development of household demand response. At a later point, when long-term dynamics take effect, a larger effort should be made to shift consumers onto real-time rates, and an increased focus on overall adoption of variable pricing will be required. Another finding is that demand response under variable pricing makes wind power more valuable. These gains in value reduce the need for support, and could be redistributed in further support of demand response.

Economic Viability of Paris Metro Pricing for Digital Services

Nowadays digital services, such as cloud computing and network access services, allow dynamic resource allocation and virtual resource isolation. This trend can create a new paradigm of flexible pricing schemes. A simple pricing scheme is to allocate multiple isolated service classes with differentiated prices, namely Paris Metro Pricing (PMP). The benefits of PMP are its simplicity and applicability to a wide variety of general digital services, without considering specific performance guarantees for different service classes. The central issue of our study is whether PMP is economically viable, namely whether it will produce more profit for the service provider and whether it will achieve more social welfare. Prior studies had only considered specific models and arrived at conflicting conclusions. In this article, we identify unifying principles in a general setting and derive general sufficient conditions that can guarantee the viability of PMP. We further apply the results to analyze various examples of digital services.

Optimal Scheduling of Critical Peak Pricing Considering Wind Commitment

Demand response has been widely implemented as one of the “virtual” control mechanisms to make peak load management more efficient and economic. One of the popular demand response programs is called critical peak pricing (CPP). Relatively simple pricing schemes and convenient implementation within current energy system metering infrastructure make it well accepted by many utilities and load-serving entities (LSEs). In this paper, we investigate the optimal scheduling of CPP events from the perspective of an LSE which has wind energy to sell into the day-ahead market. The goal is to minimize the total operational cost for the whole planning horizon, taking into account the energy purchasing cost, revenue from the CPP, and wind energy sales, as well as imbalance penalties due to wind energy over- and under-commitments. We propose a multi-stage stochastic mixed integer nonlinear programming model. In addition, we perform various analyses of both the special case of a single-stage problem and the general multi-stage problem analytically and experimentally. Our analysis leads to useful operational insights and policy implications on how to manage a renewable-integrated system more efficiently.

A pricing scheme for QoS in overlay networks based on first-price auctions and reimbursement

Providing Assured-Quality Services over data networks has been a key objective for the past few decades. Research and commercial activities have been focused on several aspects related to this main objective, such as implementing services over heterogeneous networks, providing scalable solutions and verifying network performance. However, less attention has been devoted to the interaction of these technical aspects with the business plane. Although several quality-based pricing schemes have been proposed, reimbursement proposals, while quite common in other scenarios as health, hotel reservation or airlines, are still rare in the field of Internet Economics. In this work, we propose a simple pricing scheme and study it in detail, in order to use Quality of Service monitoring information as feedback to the business plane, with the ultimate objective of improving the seller's revenue. In our framework, Assured-Quality Services are sold through first-price auctions, and in case of failure, a percentage of the price paid for the service is given back to the buyers. We derive the expression for the willingness to pay and we model the reimbursement problem through a zero-sum Stackelberg game. We show that the Nash equilibrium of such game implies reimbursing 100 % in case of failures.

Simple Pricing Schemes for Pollution Control under Asymmetric Information

Abstract Most policies for pricing pollution under asymmetric information proposed in the literature to date are rarely – if ever – used in practice. This is likely due to their complexity. We investigate the scope for using somewhat simpler policies that are more closely related to pricing schemes already used by regulators in many jurisdictions. These schemes have a discrete block pricing (DBP) structure whereby a given unit price for pollution is applied up to a specified level of pollution for any given polluter, and a higher unit price is applied to any pollution from that polluter above the specified level. If the same price schedule is applied uniformly to all firms, we call it UDBP. We derive the optimal UDBP schedule for any given number of price blocks. We also derive the optimal limiting case of the UDBP schedule (with an infinite number of price blocks) as a uniform linear increasing marginal price schedule (ULIMP). The optimal ULIMP scheme strikes a balance between the information-related benefits of increasing marginal prices on one hand, and an increase in aggregate abatement cost, due to the non-equalization of marginal abatement costs across firms, on the other. In particular, the optimal schedule is steeper with larger aggregate uncertainty about marginal abatement costs, and flatter with more observable heterogeneity across firms. We then compare our price schemes with those proposed by Weitzman (1978) and Roberts and Spence (1976).

Disease Forecasts and Livestock Health Disclosure: A Shepherd's Dilemma

AbstractWe analyze how to induce sellers to disclose food safety. With repeated interactions and safety correlated over time, cash transfers alone do not ensure disclosure. Perfect, but costly, testing ensures disclosure with a complex lottery that may be difficult to implement in practice. In contrast, even a noisy quality forecast allows the buyer to induce perfect disclosure with a simple pricing scheme. Forecast introduction may benefit or harm sellers. After introduction, sellers may suffer from increases in forecast precision. As an illustration, we cast our model in the context of Rift Valley fever in an East African livestock market.

Supply Chain Coordination by Contracts with Inventory Holding Cost Share

In this document it is analyzed the importance of contracts for coordination between two companies in a supply chain. In the studied situation, one company, or supplier, supplies one product to the other company, who is a retailer. The companies are going to coordinate by two types of decisions: economic (concerning prices fixed on a contract), and physical exchange (concerning the inventory to be held). Two types of contracts will be presented: one contract with a simple pricing scheme and two contracts with inventory holding cost shared among the companies of the supply chain. The objective is to show that contracts with inventory holding cost share allow the two companies to efficiently coordinate the chain they form.

Monte Carlo based probabilistic partitioning of price zone with scale-space hierarchical clustering

It is important for establishing a simple and efficient zonal pricing scheme to accurately partition price zone in the presence of congestion. In the existed partition methods, price zones are usually established based on the operators’ experience, considering the determinate locations of congested lines. However, in actual power system operation, the congestion conditions of the transmission network vary with many uncertain market factors. In order to consider the market uncertainties in price zone partition caused by the generation bids, loads and bilateral transactions, this paper proposes a Monte Carlo based probabilistic partitioning method of price zone with scale-space hierarchical clustering. In this method, the lines that are most likely to get congested are determined firstly through a Monte Carlo based probability analysis while taking into account the market uncertainties in the generation bids, loads and bilateral transactions. Secondly the sensitivities of nodal power injections to power flows on all the congested lines are extracted as features of nodal price, forming the cluster samples. Then, by means of the scale-space theory simulating human visual system, a scale-space based hierarchical clustering method for price zone partition is proposed. Through this method, an accurate and rational price zone partition is realised without any prior zone partitioning information. Finally, taking IEEE 118-node system for example, the effectiveness and feasibility of this proposed method are verified.

Coordinating Supply Chains with Simple Pricing Schemes: The Role of Vendor-Managed Inventories

We characterize supply chain settings in which perfect coordination can be achieved with simple wholesale pricing schemes: either retailer-specific constant unit wholesale prices or retailer-specific volume discount schemes. We confine ourselves to two-echelon supply chains with a single supplier servicing a network of retailers who compete with each other by selecting sales quantities. We identify a key sufficient condition, in terms of interdependencies between chain members' operational decisions, under which perfect coordination via simple schemes is feasible, under general cost and demand functions. This condition, which we refer to as echelon operational autonomy (EOA), states that the costs incurred by the supplier for a given vector of sales volumes depends only on operational decisions she controls herself. At the same time, the costs incurred by the retailers may depend on operational decisions controlled by the supplier, in which case, the supplier's operational decisions are made to minimize chainwide costs. We show how vendor-managed inventory (VMI) partnerships create EOA and compare the resulting coordinating pricing schemes with those required in a traditional decentralized setting (without EOA). We also discuss compliance issues with the coordinating schemes in view of the Robinson-Patman act and provide remedies to overcome these issues.

Editorial

Given the current status of research and implementation, the various contributions show that research, and in particular the analysis of the impacts of reforms, must proceed in parallel with implementation, in order to create a virtuous cycle in which research and policy making mutually feed each other. Besides a “window of opportunity” is open for what concerns interurban and urban transport. Several experiences in different European cities confirm that an increasing number of cities are already well into the process of rationalising and increasing the sustainability of their transport systems, notably through the design and implementation of pricing policies. And also for inter-urban transport things are moving, e.g. the proposed revised Eurovignette Directive can be seen as a step in the right direction, although still containing many constraints which would prevent fair and efficient pricing of road freight vehicles. In general it can be said that Social Marginal Cost Pricing is still developing. Most practical experiences only use simple pricing schemes, which are far from optimal prices. In the long term fine-tuning of objectives and schemes will be necessary. Some general recommendations based on several Imprint seminars and the papers presented in this issue.

Incentive pricing in multiclass systems

In modern communication networks which offer multiple classes of services, an appropriate assignment of service classes to users (or applications) will have a key influence on the performance profile. Differentiated pricing is an important tool for guiding the user's choice. We consider a basic model for a multiclass system that offers multiclass services to multiple types of traffic, and propose a pricing framework which is based on the concept of nominal traffic assignment. Users (or their associated traffic) are categorized into a finite number of traffic types, which are distinct in their performance utilities at the different service classes. The system administrator specifies a required traffic assignment, which associates with each traffic type a nominal service class. Users, on the other hand, choose service classes so as to optimize their own performance. Optimal prices should provide incentives for the users to assign each traffic type to its nominal service class. Our goal is to implement a simple pricing scheme that provides such incentives. We establish necessary and sufficient conditions for the existence of optimal prices and provide an algorithm for their computation. We indicate that optimal prices can tolerate fluctuations in the various parameters. Next, we propose a distributed algorithm, which can be used by the system to compute optimal prices even when it does not have sufficient knowledge of traffic characteristics. We then generalize our analysis to an extended model, which explicitly includes congestion effects.

Differential QoS and pricing in networks: Where flow control meets game theory

The paper looks at ways of providing Quality of Service to users based on a simple pricing scheme. It is primarily aimed at elastic traffic, and it is users rather than the network who define the flow control schemes. A framework for assessing schemes and algorithms via a distributed game is presented.

Effets de diffusion et incitations à la R&D sur des marchés homogènes

This article deals with the regulation of competition on homogeneous markets, featuring firms that select cost-reducing R&D investments and then prices. In the free competition case, the equilibrium market structure is a monopoly with its traditional inefficiences, which are nevertheless alleviated by the existence of spillovers and their impact on the effectiveness of potential competition. Rather than setting up a Research Joint Venture for internalization purposes, our solution goes through providing guidelines for the determination of prices. Indeed, our regulation involves committing firms to use simple pricing schemes aimed at providing incentives for R&D by rewarding efficiency. This mean of regulation proves relevant for low or high levels of spillovers whereas average levels trigger an intense potential competition that may induce in itself socially desirable behaviors.

A plan for prospective payment for inpatient psychiatric care.

Taken as a whole, this plan offers a relatively simple pricing scheme that creates some incentives for economizing behavior. With respect to the setting of the rate and the determination of covered costs, we expect that the same rules will be followed for psychiatrid care as for medical care: the prospective payment rate will be a complex combination of the hospital's cost base, the regional rate, and the national rate. In addition, we would recommend that the rate be the same for all diagnoses unless there is consistent evidence that the average per diem cost for the first four days of treatment differs significantly by diagnosis. Our proposal is driven by a concern over the possible undesirable effects of incentives created by a case-based prospective payment system using diagnosis-related groups. It is our view that heterogeneity within DRGs, coupled with the inability to identify patients above and below the group average, may lead to responses that harm the sickest patients. Recent studies offer empi...