Reserve Price Research Articles

Individual consumer surplus in the Edgeworth Box

Subject. The consumer surplus conception is an important part of the modern microeconomic theory at the introductory and intermediate levels. Consumer surplus measures the change in the consumer’s real welfare. The article addresses the peculiarities of generation and the main property of the generalized individual consumer surplus, using the Edgeworth Box case. Objectives. The purpose is to find the main property of individual consumer surplus in the Edgeworth Box economy. Methods. The study draws on methods of logical and mathematical analysis. The generalized consumer surplus is correctly constructed, using the mathematical theory of curve integrals of the second type (the theory of line integrals in the Western mathematics). Results. The generalized individual consumer surplus is defined through the respective curve integral along some admissible trajectory in the simple exchange economy (Edgeworth Box). The paper also introduces the notion of the marginal individual consumer surplus, and demonstrates that consumer surplus is a correct individual welfare measure in the Edgeworth Box, and that consumer surplus is zero along any given indifference curve. I consider the numerical example of individual surplus calculation and presentation in the Edgeworth Box. Conclusions. The generalized individual consumer surplus is a correct measure of consumer’s utility change along monotone (weakly monotone) trajectories in the Edgeworth Box. Geometrically, the consumer surplus is presented as an area limited by the reservation price curve from the top and by the reallocation line (curve) from the bottom.

Basic Bidding Formats: Characteristics and Differences

Abstract In standard auction theory, the ‘revenue equivalence theorem’ asserts that the outcomes of the elementary allocation methods coincide. However, bidding processes differ fundamentally with regard to the decision situation of the participants: Is it at all imperative to take into consideration the number of competitors (‘stochastic’ strategy) or not (‘deterministic’ course of action)? Furthermore, established auction theory neglects the operating modes of procurement alternatives under uncertainty. Apart from the lacking knowledge how many rivals have to be beaten, tenderers regularly are ignorant of the buyer’s reserve price. Then it is even more tentative to calculate an offer based on probability theory. Consequently, the suppliers’ propensity to collude increases.

Capacity allocation and pricing for energy storage sharing in a smart community

The increasing energy storage resources at the end-user side require an efficient market mechanism to facilitate and improve the utilization of energy storage (ES). Here, a novel ES capacity trading framework is proposed for ES sharing of a smart community consisting of multiple ES owners (ESOs) and users. Specifically, an iterative clearing approach is designed to capture the interaction between the ESOs and users. Players submit trading parameters with consideration of their own preferences and profiles according to utility functions. After receiving the offering/bidding parameters, the market operator determines the capacity allocation and clearing price based on the principle of maximizing social welfare. This decentralized clearing process avoids complex calculation induced by the centralized decision method and preserves players’ privacy. Case studies are conducted to confirm the effectiveness of the proposed scheme. The results show that ES sharing scheme brings additional revenues to ESOs, decreases costs for users, and reduces the peak-to-valley difference of system loads. In addition, the clearing results are very close to global optimal solutions and mainly affected by the maximum capacity and reservation price of each ESO.

페루 청정개발제도(CDM) 사업 분석을 통한 온실가스 감축 비용 추정

There has been a widespread awareness of the need to achieve carbon neutrality by mid-20th century, especially upon the publication of IPCC 1.5℃ Special Report in 2018. In order to cost-effectively achieve greenhouse gas reduction at a global level, the importance of cooperation between countries and the use of the international carbon market are emphasized more than ever. The Clean Development Mechanism (CDM) under the Kyoto Protocol will be reorganized into a “Sustainable Development Mechanism (SDM)” under the Paris Agreement. As the SDM is expected to inherit much of the CDM system, it is necessary to closely examine the performance and economic aspects of CDM projects in order to maximize the availability of the new international carbon market, SDM under the Paris Agreement. Despite its importance, research on the economic assessment of the emission reduction projects under the CDM has so far been scant. With an aim to establish an analytical basis to assess the economic feasibility of each type of emission reduction project and to draw implications for the national strategy for implementing the reduction projects abroad, this study estimated mitigation cost ($/tCO₂) of 27 CDM projects in Peru. The reservation price of Certified Emission Reduction (CER) unit that ensures minimum economic feasibility of the project has been estimated and analyzed by project type. A comparison of reduction potential and cost of mitigation by CDM project type in Peru shows that methane recovery, landfill gas, wind power, and solar power generation are the most cost-effective types in Peru. For many countries including South Korea, engaging in more active reduction activities through overseas reduction projects may be an important means of achieving the national reduction goal and contributing to global efforts to abate greenhouse gas emissions. Thus, it is important for South Korea to put efforts to improve its understanding of the reduction potential, investment opportunities and cost effectiveness of mitigation projects in Peru as well as Chile, Vietnam, Myanmar and Sri Lanka designated as target bilateral cooperation countries.

An Equilibrium Analysis of a Secondary Mobile Data-Share Market

Internet service providers are offering shared data plans where multiple users may buy and sell their overage data in a secondary market managed by the ISP. We propose a game-theoretic approach to a software-defined network for modeling this wireless data exchange market: a fully connected, non-cooperative network. We identify and define the rules for the underlying progressive second price (PSP) auction for the respective network and market structure. We allow for a single degree of statistical freedom—the reserve price—and show that the secondary data exchange market allows for greater flexibility in the acquisition decision making of mechanism design. We have designed a framework to optimize the strategy space using the elasticity of supply and demand. Wireless users are modeled as a distribution of buyers and sellers with normal incentives. Our derivation of a buyer-response strategy for wireless users based on second price market dynamics leads us to prove the existence of a balanced pricing scheme. We examine shifts in the market price function and prove that our network upholds the desired properties for optimization with respect to software-defined networks and prove the existence of a Nash equilibrium in the overlying non-cooperative game.

In the basic auction model, the optimal reserve price may depend on the number of bidders

A widespread claim in the auction literature about the most well known auction setting with i.i.d. private values is that the optimal reserve price for a second price auction is independent of the number n of bidders. This is indeed the case if the virtual valuation function is increasing, but this result fails to hold if the virtual valuation is non-monotone. In such case the optimal reserve price is weakly increasing in n and, as n tends to infinity, it tends to the highest regular valuation with zero virtual value.

Measuring Customer Reservation Price for Maintenance, Repair and Operations of the Metro Public Transport System in Qatar

Getting to know the price that users assign to maintenance, repair and operations (MRO) has arisen as an essential consideration in gathering financial sustainability for metro public transport systems. The current research reveals customer reservation price for MRO in the main metro stations in Qatar. The purpose of the present work is to assess the willingness to pay for MRO services in eight metro stations in Doha in order to have a better understanding of user preferences. Qualitative research was carried out employing primary and secondary source of information. Primary data was collected by means of a mixture of data accumulation approaches: key informant meetings and focus-group conversations. Secondary data was collected from the account books, contracts, recordings of trans-actions, statements of work and activity reports given by the local rail committees. A stated preference investigation was applied through open text format questions to more than 1000 customers, and a Poisson regression model was used to evaluate the considerations affecting every higher value. Outputs reveal normal customer reservation prices per month and per train journey. The results also indicate a significant willingness to pay differential among the studied railway stations. The study of the decisive considerations elicits that the degree to which the MRO service can exclude paying consumers, the attending of rail conferences and the possibility of using another rail station are related with the customer reservation price. The outputs of this research are significant for railway public authorities willing to set up reasonable, adequate and realistic fares that support fund competent railway systems in Qatar.

Quasi-Stochastic Electricity Markets

With wind and solar becoming major contributors to electricity production in many systems, wholesale market operators have become increasingly aware of the need to address uncertainty when forming prices. Although implementing theoretically ideal stochastic market clearing to address uncertainty may be impossible, the use of operating reserve demand curves allows market designers to inject an element of stochasticity into deterministic market clearing formulations. The construction of these curves, which alter the procurement of reserves and therefore the pricing of both reserves and energy, relies on contentious administrative parameters that lack strong theoretical justification. This paper proposes instead to link their construction to outcomes that would be expected in efficient stochastic markets. The analysis considers the potential of these “quasi-stochastic” market clearing approaches to improve efficiency relative to the deterministic status quo as well as ways in which they are unable to fully replicate the stochastic ideal. Further, the paper argues that efficiently managing uncertainty entails a reexamination of the discriminatory uplift payments and enhanced pricing schemes currently employed to address nonconvexity.

The Nonlinear Impact of Institutional Quality on International Reserves: International Evidence

This paper investigates the nonlinear effect of institutional quality on the holding of international reserves by applying the two-step system generalized method of moments on data consisting of 67 countries from 1996 to 2016. The hypothesis is that the association between institutional quality and international reserves has an inverted U pattern. The coefficient sign of the institutional quality and its square term supports the hypothesis. Besides, the marginal effects suggest that the higher the institutional quality index, the lower the demand for reserves. The outputs indicate the importance of good institutional quality in reducing the overdependence on international reserves.

Projection of Private Values in Auctions

We explore how taste projection—the tendency to overestimate how similar others’ tastes are to one’s own—affects bidding in auctions. In first-price auctions with private values, taste projection leads bidders to exaggerate the intensity of competition and, consequently, to overbid—irrespective of whether values are independent, affiliated, or (a)symmetric. Moreover, the optimal reserve price is lower than the rational benchmark, and decreasing in the extent of projection and the number of bidders. With an uncertain common-value component, projecting bidders draw distorted inferences about others’ information. This misinference is stronger in second-price and English auctions, reducing their allocative efficiency compared to first-price auctions. (JEL D11, D44, D82, D83)

Childbearing and the distribution of the reservation price of fertility: The case of the Korean baby bonus program

We study the fertility effects of a Korean pro-natalist policy. We show that using the same assumptions and the same binary-choice models used in this literature, and we can estimate the entire unconditional distribution of the reservation price of fertility, which is the minimal compensation an agent must receive to induce her to have a (or an additional) child. Our estimates show that the program’s benefit level and budget would have to be orders of magnitude (about 15 times) larger for the program to bring South Korea back to desired levels of fertility. We also found that over 74% of the program’s expenditures are associated with infra-marginal births – these are births that would have occurred even in the absence of the financial incentive. We show that this is likely not a unique characteristic of the Korean pro-natalist policy but instead a problem of most programs of this nature.

Experimental Economics and the New Commodities Problem

Brynjolfsson et al. (2019a) have used experimental economics to measure the welfare benefits of free commodities and to define an extended measure of output, GDP‐B. In this paper, their methodological approach is generalized to measuring the benefits of new commodities which may or may not be free. Their approach leads to a new method for estimating Hicksian reservation prices. The new methodology in the present paper requires experimental estimates for household willingness to pay for new commodities or estimates for the compensation required for households to give up their use of a new commodity.

Automated Strategies for Determining Rewards for Human Work

We consider the problem of designing automated strategies for interactions with human subjects, where the humans must be rewarded for performing certain tasks of interest. We focus on settings where there is a single task that must be performed many times by different humans (e.g. answering a questionnaire), and the humans require a fee for performing the task. In such settings, our objective is to minimize the average cost for effectuating the completion of the task. We present two automated strategies for designing efficient agents for the problem, based on two different models of human behavior. The first, the Reservation Price Based Agent (RPBA), is based on the concept of a reservation price, and the second, the No Bargaining Agent (NBA), uses principles from behavioral science. The performance of the agents has been tested in extensive experiments with real human subjects, where NBA outperforms both RPBA and strategies developed by human experts.

Liquidity externality in a market of buying adjustable agents

The market dynamics produced by simple interactions among rational investors can generate complex phenomena such as bubbles, crashes, and business cycles. One powerful tool to understand those phenomena might be the use of Cellular Automata (CA) models. In this contribution we investigate spatial aggregations effects associated to the propagation of information contagion waves across a network of non-equivalent adjustable agents. The contagion dynamics is modeled by an inhomogeneous cellular automaton (InCA) as proposed by Weisbuch and Stauffer[Physica A, 1(323), 651-662 (2003)]. In this system, a tatonnement process emerges as the individual agents try to adjust their reservation price to follow the externality produced by the state of the neighborhood. Using rescale range analysis we show that the periodic oscillations of InCA average state (that resembles business cycles) have long memory effects (for a determined interval of time). The presence of long memory effects is a usual feature present in many real markets. Our results are obtained after the development of an efficient algorithm to count the cluster of agents with the same state. The application of this algorithm revealed that the number and the size of the agent's cluster increase as adjustment amplitude (the parameter that controls the velocity of the tatonnement process) increases. We then show that there is a value of this amplitude that maximizes the system's tendency to form large and synchronized clusters. By associating this adjustment parameter with the liquidity, we demonstrate that the dynamics of the inhomogeneous cellular automata can mimic liquidity externalities found in real markets.

Optimal offering of wind-photovoltaic-thermal generation company in energy and reserve markets in the presence of environmental and risk analysis

The integration of thermal units with renewable energy sources is increasing to reduce greenhouse gas emissions and fossil fuels consumption. Also, renewable energy sources such as wind farms (WF) and photovoltaic farms (PVF) can be used by Generation Company (GenCo) to maximize their expected profit. Providing the offering curves for each hour under the uncertainties as electricity and reserve prices, an imbalance price ratio for energy deviations and output powers of PVF and WF is the other important issue for the GenCo’s operator. Therefore, an ε-constraint approach is used in this paper to handle the environmental and economic constraints simultaneously in the proposed Wind-Photovoltaic-Thermal Generation Company (WPT-GenCo). On the other hand, a Downside Risk Constraints Technique (DRCT) is used to handle the risk associated with various considered uncertain parameters in the proposed WPT-GenCo. Besides, obtaining robust offering strategies considering a specific level of greenhouse gas emissions is the other target of this paper. Finally, the TOPSIS method is used to select the trade-off solution through the 3-dimension obtained results of expected profit, greenhouse gas emissions, and the risk associated with various uncertainties. Simulation results show that the proposed WPT-GenCo model is scheduled robustly, and the risk level is reduced dramatically, considering a specific level of greenhouse gas emissions.

What types of houses remain vacant? Evidence from a municipality in Tokyo, Japan

Preventing the occurrence of vacant houses and reutilizing existing vacant houses are two known ways of addressing housing vacancies. This study uses parcel-level data on single-family houses in the municipality of Toshima in Tokyo, Japan, to examine determinants of the occurrence and reutilization of vacant houses. The data reveal that, conditional on land prices, houses with restricted and costly redevelopment opportunities are more likely to become vacant. The results suggest that owners with such properties disregard the redevelopment potential and, as a result, tend to have a high reservation price relative to the market value. Consequently, owners face difficulty finding a transaction partner and even become reluctant to engage in a deal (i.e., they withdraw from the market). The results also suggest that revitalization of the neighborhood community may contribute to activating vacant houses. Local governments can use an empirical assessment such as this to implement efficient measures by targeting houses that have a high probability of becoming and remaining vacant.

Incentive Mechanism Design for Two-Layer Wireless Edge Caching Networks Using Contract Theory

Wireless caching technologies have been proposed to relieve the transmission pressures, especially, the transmission redundancy on back-haul channels. In this paper, we consider a two-layer caching network, consisting of traditional macro-cell base station (MBS) aided back-haul channels and small-cell base stations (SBSs) aided local links. The network service provider (NSP), who is in charge of the two layers, leases its resources of the secondary layer, i.e., coverage of the SBSs, to content providers (CPs) for making extra profits and releasing pressures on the back-haul channels. At the same time, CPs will evaluate whether they are provided with proper incentives to pre-cache their files in the SBSs. Considering different quality of services (QoS) provided by the two layers as well as the economical impact of the traditional layer on the secondary layer, the NSP designs the optimal incentive mechanisms within the framework of contract theory for maximizing its own profits. First, we formulate the utility of the NSP and CPs. Then, the minimum transmission requirement, reserve price and limited resources are considered as constraints in designing the optimal contract. Also, some important properties of these constraints are analyzed to facilitate the optimal contract determination process. At last, an optimal contract determination scheme is proposed, based on which the optimal coverage set is determined first, and then the corresponding optimal prices are derived with the aid of equal cost line. Numerical results are provided to demonstrate the effectiveness of the proposed optimal contract in increasing the NSP's profits and incentivizing CPs to transmit on the secondary layer.

Optimal Reserve Management of Electric Vehicle Aggregator: Discrete Bilevel Optimization Model and Exact Algorithm

This paper investigates the day-ahead optimal reserve management problem of electric vehicle (EV) aggregator. Geographically dispersed EVs are coordinated by the aggregator to participate in the day-ahead reserve market. A bilevel model is proposed to formulate the interaction between the aggregator and the EV owners. In the upper level, the EV aggregator aggregates the reserve capacity provided by the EV owners and then bids in the reserve market. In the lower level, the EV owners decide their optimal energy charging/discharging and reserve capacity based on the reserve price released by the aggregator. Compared with existing works, our proposed bilevel model is more practical. To be specific, we take into account the exclusive right constraint for accessing EV battery, i.e., the battery cannot be simultaneously accessed by the EV owner and the aggregator. This practical model leads to a bilevel mixed integer nonlinear program, which is difficult to solve because the lower level problem incorporates nonconvex integer variables. A novel exact algorithm is developed to solve it and the finite convergence is proved. Comprehensive case studies demonstrate the economic merits of our proposed model to both the aggregator and the EV owners. We also compare the solution optimality with the state-of-the-art approach and thus validate the effectiveness of our proposed exact algorithm.

Multi-stage stochastic programming based offering strategy for hydrogen fueling station in joint energy, reserve markets

Hydrogen fueling stations (HFSs) with onsite hydrogen production systems, which are usually composed of electrolyzers, hydrogen storage tanks and fuel cells, not only supply hydrogen for hydrogen-powered vehicles but also serve as a dispatchable technology that can bid in electricity markets. Except participating in energy market, joining in reserve market can compensate the cost in energy market and increase the total revenue of HFS. This paper proposes a multi-stage stochastic programming model to find the optimal offering strategy of the HFS in energy, reserve markets taking into account a series of uncertainties: day-ahead price, secondary reserve price, system imbalance price and hydrogen demand. Nonanticipativity constraints are employed to guarantee the decisions are made according to the realized uncertainty information up to the present stage. Compared with traditional stochastic programming model, the proposed model adequately considers the sequential bidding decisions with the gradual revealing of the uncertainty over time. Numerical experiments based on one case study indicate that the participation of reserve market greatly increase the revenue of HFS. In addition, the proposed multi-stage stochastic programming model is effective in characterizing the sequential decision.

BlockChain Based Combinatorial Grouping Auction with Reserve Price Mechanism in Cloud Computing

Background: The block-chain is a growing decentralized scheme applied in many applications, such as an auction, smart contract, Health, and banking sector. The cloud service providers can sell the resource to cloud consumers using an auction. The main challenge in resource allocation using auction is to provide reliability to the users. Methods: In this paper, a blockchain-based combinatorial grouping auction with a reserve price mechanism (BCGAWRP) was proposed. The proposed scheme maximizes the total revenue and resource utilization by assuring reliability. The proposed BCGAWRP performance was assessed by simulating the cloud environment. Results: The experimental result shows that the proposed BCGAWRP algorithm increases revenue more than the traditional combinatorial auction algorithm. Conclusion: Moreover, simulation studies show that reserve price is useful and provides a mechanism to achieve the trade-off between the seller's and the buyer’s virtual machines.