Showing papers on "Fixed price published in 2019"

Solving the Buyer and Seller’s Dilemma: A Dual-Deposit Escrow Smart Contract for Provably Cheat-Proof Delivery and Payment for a Digital Good without a Trusted Mediator

Abstract: A fundamental problem for electronic commerce is the buying and selling of digital goods between individuals that may not know or trust each other. Traditionally, this problem has been addressed by the use of trusted third-parties such as credit-card companies, mediated escrows, legal adjudication, or reputation systems. Despite the rise of blockchain protocols as a way to send payments without trusted third parties, the important problem of exchanging a digital good for payment without trusted third parties has been paid much less attention. We refer to this problem as the Buyer and Seller’s Dilemma and present for it a dual-deposit escrow trade protocol which uses double-sided payment deposits in conjunction with simple cryptographic primitives, and that can be implemented using a blockchain-based smart contract. We analyze our protocol as an extensive-form game and prove that the Sub-game Perfect Nash Equilibrium for this game is for both the buyer and seller to cooperate and behave honestly. We address this problem under the assumption that the digital good being traded is known and verifiable, with a fixed price known to both parties.

Efficiency versus Fairness in the Management of Public Housing Assets in Palermo (Italy)

Abstract: Public housing policy has been proposing plans of public housing (PH) stock alienation or, as an alternative, property enhancement plans, since administrative and financial commitments have become too heavy for municipalities. This paper deals with one of the current public housing management policy initiatives, undertaken by the Municipality of Palermo (Italy), which aimed at transferring a significant part of the public housing asset to the current tenants, according to some terms and conditions, and applying a politically fixed price. This policy is described in general, focusing on the amount of the assets involved, reporting the terms and conditions for transferring them at an affordable price, and analysing their concentration/distribution in the urban areas. The main aim of the paper is to provide a valuation pattern for defining the trade-off between the efficiency and fairness of such a tool, recognising the conditions for the consistency between the transfer price established by municipality, the merit of the public housing asset, and the market value. A detailed study on two representative neighbourhoods was carried out in order to measure the value of solidarity of this policy and to propose some corrective rules.

Small Probabilistic Discounts Stimulate Spending: Pain of Paying in Price Promotions

Abstract: We find that small probabilistic price promotions effectively stimulate demand, even more so than comparable fixed price promotions (e.g., “1% chance it’s free” vs. “1% off,” respectively),...

Collaborative Optimization of Dynamic Pricing and Seat Allocation for High-Speed Railways: An Empirical Study From China

Abstract: In order to improve the high-speed trains’ service levels and increase their market shares, the Chinese high-speed railway (HSR) enterprise is reforming its ticket pricing strategy. A collaborative model that incorporates seat allocation decision into HSR dynamic pricing problem based on the revenue management theory is proposed, in which the objective is to maximize the total ticket revenue of enterprise under the constrains of price ceilings. A two-stage algorithm is developed to solve practical problems. The first stage solves the optimal price problem, and the second is to obtain the optimal seat allocation decisions. Finally, a case study based on the actual ticket data of Beijing-Shanghai HSR in China is implemented to show the effectiveness of the proposed approach, for which the results show that compared with the fixed price case, the revenue improvement ranges from 4.47% to 4.95% by using dynamic pricing strategy. Also, the case analysis shows that dynamic pricing strategy will lead to an increase in short-haul demands whereas a decrease in long-haul demands.

LORE: a large-scale offer recommendation engine with eligibility and capacity constraints

Abstract: Businesses, such as Amazon, department store chains, home furnishing store chains, Uber, and Lyft, frequently offer deals, product discounts and incentives to drive sales, increase new product acceptance and engage with users. In order to appeal to diverse user groups, these businesses typically design more than one promotion offer but market different ones to different users. For instance, Uber offers a percentage discount in the rides to some users and a low fixed price to others. In this paper, we propose solutions to optimally recommend promotions and items to maximize user conversion constrained by user eligibility and item or offer capacity (limited quantity of items or offers) simultaneously. We achieve this through an offer recommendation model based on Min-Cost Flow network optimization, which enables us to satisfy the constraints within the optimization itself and solve it in polynomial time. We present two approaches that can be used in various settings: single period solution and sequential time period offering. We evaluate these approaches against competing methods using counterfactual evaluation in offline mode. We also discuss three practical aspects that may affect the online performance of constrained optimization: capacity determination, traffic arrival pattern and clustering for large scale setting.

Physicians' altruism in incentives contracts: Medicare's quality race

Abstract: The paper analyzes the impact of physicians' altruism and motivation on the outcomes of pay-for-performance schemes in healthcare, where a fixed price contract on quantity is supplemented with a relative performance contract on quality. Our theoretical model forecasts crowding out of most altruistic types. In an empirical application to the Medicare's nationwide natural experiment with a relative performance contract on quality for acute inpatient care since 2013, we observe the proof of this prediction. Namely, the quality dimensions, which are linked to patient's benefit, demonstrate higher deterioration among top-performing hospitals than other incentivized dimensions.

When Fixed Price Meets Priority Auctions: Competing Firms with Different Pricing and Service Rules

Abstract: We consider a service system with two competing firms offering service via two different pricing and service rules. With the fixed price firm, customers obtain service at a fixed price and have hom...

Fixed-Price Approximations to Optimal Efficiency in Bilateral Trade

Abstract: This paper studies fixed-price mechanisms in bilateral trade with ex ante symmetric agents. We show that the optimal price is particularly simple: it is exactly equal to the mean of the agents' distribution. The optimal price guarantees a worst-case performance of at least 1/2 of the first-best gains from trade, regardless of the agents' distribution. We also show that the worst-case performance improves as the number of agents increases, and is robust to various extensions. Our results offer an explanation for the widespread use of fixed-price mechanisms for size discovery, such as in workup mechanisms and dark pools.

Small-Scale Markets for a Bilateral Energy Sharing Economy

Abstract: Motivated by the ever-increasing installation of photovoltaics in homes and small businesses, we consider a general small-scale market for agent-to-agent (energy) resource sharing, in which each agent could either be a producer (server) or a consumer (client) in each time period. We use a simple mechanism under which servers and clients are matched at random, and may each choose an offered price. If the client offers more than the server proposes, the transaction is successful. We model this system in the form of a mean field game, and prove the existence of a mean field equilibrium in which the server's bid is a fixed price that the client either accepts or rejects based on its current budgetary surplus. We also show in simulations that the system can attain near 100% trade ratios. Finally, using a case study over a synthetic electric grid, we show that the system can benefit both individuals and the system as a whole.

Demonstration of Nimbus: Model-based Pricing for Machine Learning in a Data Marketplace

Abstract: Various domains such as business intelligence and journalism have made many achievements with help of data analytics based on machine learning (ML). While a lot of work has studied how to reduce the cost of training, storing, and deploying ML models, there is little work on eliminating the data collection and purchase cost. Existing data markets provide only simplistic mechanism allowing the sale of fixed datasets with fixed price, which potentially hurts not only ML model availability to buyers with limited budget, but market expansion and thus sellers' revenue as well. In this work, we demonstrate Nimbus, a data market framework for ML model exchange. Instead of pricing data, Nimbus prices ML models directly, which we call model-based pricing (MBP). Through interactive interfaces, the audience can play the role of sellers to vend their own ML models with different price requirements, as well as the role of buyers to purchase ML model instances with different accuracy/budget constraints. We will further demonstrate how much gain of sellers' revenue and buyers' affordability Nimbus can achieve with low runtime cost via both real time and offline results.

Data Analysis of Shipment for Textiles and Apparel from Logistics Warehouse to Store Considering Disposal Risk

Abstract: Given the rapid diversification of products in the textile and apparel industry, manufacturers face significant new challenges in production. The life cycle of apparel products has contracted and is now, generally, a several-week season, during which time a majority of products are supposed to be sold. Products that do not sell well may be sold at a price lower than the fixed price, and products that do not sell at all within the sales period may eventually become forced disposal. This creates long-term management and environmental problems. In practice, shipping personnel determine when to ship products to stores after reviewing product sales information. However, they may not schedule or structure these shipments properly because they cannot effectively monitor sales for a large number of products. In this paper, shipment is considered to reduce the risk of product disposal on the premise of selling at a fixed price. Although shipment quantities are determined by various factors, we only consider the change in inventory at the logistics warehouse, since it is difficult to incorporate all factors into the analysis. From cluster analysis, it is found that shipping personnel should recognize a policy to sell products gradually over time. Furthermore, to reduce the risk of disposal, we forecast the inventory from conditional probability and are able to extract products out of a standard grouping using past data.

Pricing and Matching in the Sharing Economy

Abstract: Sharing economy platforms use crowdsourced suppliers to provide customers with services or goods. Their decision making often revolves around pricing and matching. Platforms like Uber charge the customers a price for using the services or goods and offer the crowdsourced suppliers a wage or pay for providing the services or goods. First, we study how the platform could optimally set the price and the wage for a single service or product in different market conditions, and investigate the performance of the fixed commission contract which uses a fixed commission percentage across all market conditions. Second, even with determined pricing decisions, the platform also faces the task of matching customers with suppliers. We consider a stochastic, dynamic model with multiple demand types to be matched with multiple supply types over a planning horizon. We characterize the optimal matching policy by determining the priorities of the demand-supply pairs, under a sufficient condition on the reward structure. Then, the results are applied to two cases with more specific reward structures; namely, the horizontal reward structure and the vertical reward structure, to better characterize the optimal policy. Finally, we study the joint pricing and matching decision by a platform for a single service or product and take into account suppliers’ and customers’ forward-looking behavior. We propose a simple heuristic policy: fixed price and wage plus waiting compensation, in conjunction with the greedy matching policy on a first-come-first-served basis. This heuristic policy induces forward-looking suppliers and customers to behave myopically and is shown to be asymptotically optimal.

Ride-Sharing Networks with Mixed Autonomy

Abstract: We consider ride-sharing networks served by human-driven vehicles and autonomous vehicles. First, we propose a novel model for ride-sharing in this mixed autonomy setting for a multi-location network in which the platform sets prices for riders, compensation for drivers, and operates autonomous vehicles for a fixed price. Then we study the possible benefits, in the form of increased profits, to the ride-sharing platform that are possible by introducing autonomous vehicles. We first establish a nonconvex optimization problem characterizing the optimal profits for a network operating at a steady-state equilibrium and then propose a convex problem with the same optimal profits that allows for efficient computation. Next, we study the relative mix of autonomous and human-driven vehicles that results at equilibrium for various costs of operation for autonomous vehicles. In particular, we show that there is a regime for which the platform will choose to mix autonomous and human-driven vehicles in order to optimize profits. Our results provide insights into how such ride-sharing platforms might choose to integrate autonomous vehicles into their fleet.

Optimal stocking strategies for inventory mechanism with a stochastic short-term price discount and partial backordering

Abstract: Price discount is an important research topic in the field of inventory management. The existing research on this topic mainly considers fixed price discount, but ignores the situation in which sto...

Auction Based Scheme for Resource Allotment in Cloud Computing

Abstract: In cloud computing resource allotment is one of the most demanding areas Resources are attempt through fixed price model by the cloud provider and users, which is not much efficient and justified scenario The optimal processing cost for each task by using resource bidding procedures which consider the impact of cost on long-term trade Most of the existing resource allocation techniques focus on static task based allocation The dynamic resource bidding price model based on auction is efficient and achieves optimal cost computation The proposed dynamic model namely Double Auction Procurement Game for Resource Allocation (DAPGRA) uses winner determination scheme for cost computation to achieve optimal resource allocation for tasks The technique takes into account requirements of both users and Cloud Service Providers (CSPs) and calculates the final cost, based on the trade information Results show that proposed schemes/mechanisms outperform other existing schemes/mechanism

How Should a Firm Go Public? A Dynamic Model of the Choice between Fixed-Price Offerings and Auctions in IPOs and Privatizations

Abstract: We develop a theoretical analysis of the choice of firms between fixed-price offerings and uniform-price auctions for selling shares in IPOs and privatizations. We consider a setting in which a firm goes public by selling a fraction of its equity in an IPO market where insiders have private information about intrinsic firm value. Outsiders can, however, produce information at a cost about the firm before bidding for shares. Firm insiders care about the extent of information production by outsiders, since this information will be reflected in the secondary market price, giving a higher secondary market price for higher intrinsic-value firms. We show that auctions and fixed-price offerings have different properties in terms of inducing information production. Thus, in many situations, firms prefer to go public using fixed-price offerings rather than IPO auctions in equilibrium. We relate the equilibrium choice between fixed-price offerings and IPO auctions to various characteristics of the firm going public. Unlike the existing literature, our model is able to explain not only the widely-documented empirical finding that underpricing is lower in IPO auctions than in fixed-price offerings (e.g., Derrien andWomack (2000)), but also the fact that, despite this, auctions are losing market share around the world. Our model thus suggests a resolution to the above “IPO auction puzzle,” and indicates how current IPO auction mechanisms can be reformed to become more competitive with fixed-price offerings. Our results also provide various other hypotheses for further empirical research. JEL Classification Code: G30, G32, C72, D44, D82 How Should A Firm Go Public? A Dynamic Model of the Choice Between Fixed-Price Offerings and Auctions in IPOs and Privatizations

Empirical studies of consumer search and market power

Abstract: This thesis explores the interplay of search frictions and market power. In the first essay, we study how prices are negotiated between consumers and firms. In the electricity market that we study, with competitive retailers, fixed and variable charges vary widely across consumers. We implement an audit study to identify the sources of price dispersion. We create a call centre staffed by actors that call real call centres to obtain rates for fictitious consumers with experimentally-assigned combinations of consumer characteristics. We find that of offline search leads to larger discounts than online search. Firms reduce their profit margins by 30% for call-in consumers who are informed about and who negotiate using low reference prices. We also document cross-sectional price discrimination between new consumers in a market and existing consumers. Holding price informedness and other consumer characteristics fixed, firms are less willing to negotiate lower prices with new arrivals than with existing clients of rival firms. My second essay investigates the impact of a mandatory information disclosure policy on market competition in the retail gasoline context. Information disclosure policies enhance search and are implemented with the aims of increasing demand elasticities and creating competition. However, if price transparency also makes it easier for firms to monitor their rivals’ behaviour, this raises concerns about tacit collusion. As such, the equilibrium impact on competition depends on which effect dominates. My study shows that the price disclosure policy leads to margin-enhancing effects in small regional markets. Digging deeper, I find that these margin-enhancing effects are directly associated with an equilibrium price transition, where a dominant firm uses price leadership to communicate their intention to transit from a price cycle equilibrium to a more profitable fixed price equilibrium. This transition, which occurred immediately after the information disclosure policy was introduced, suggests that firms were potentially using the platform to coordinate with each other. The final essay investigates how consumer search on price transparency platforms varies across socio-economic groups. In recent years, there has been a push for demand-side policies that aim to help consumers, especially disadvantaged households, make more informed decisions. However, it is not well-known who these initiatives benefit most. Therefore, this essay investigates how users on a price transparency platform who belong to different socio-economic backgrounds respond to changes in price dispersion. In the context of retail gasoline, my analysis reveals heterogeneous search responses to changes in price dispersion across socio-economic groups. In particular, I find that users who get the most value from search relative to income, such as the most vulnerable households, are least engaged in search.

Modular-Based Framework of Key Performance Indicators Regulating Maintenance Contracts

Abstract: Key performance indicators (KPI) are necessary for regulating maintenance performance, setting goals as well as for follow up and improvement. Several standards and models for measuring maintenance performance exist today, but these are mainly developed for in-house maintenance. For outsourced maintenance, which is regulated in a service contract, other kinds of KPIs are needed. The procurement of maintenance and contract forms is also changing; an alternative to the traditional maintenance contract based on fixed price and predetermined activities are the performance-based contracts. Cooperation contracts based on mutual trust and fairness for all parties are also available. New KPI models for regulating maintenance service contracts are therefore needed. In this paper, a KPI framework for maintenance contracts based on the concept of modular maintenance offerings is proposed. Modular maintenance offerings is a way to classify maintenance services offerings with increasing integration of the offering, and increasing focus on utility for the customer and the business ecosystem. The KPI framework proposes indicators for regulating contracts on three levels (resource, performance and utility level) and includes six categories of indicators: economic, technical, organisational, quality, safety and health, and relationship between actors.

Competitive Algorithms for Online Conversion Problem with Interrelated Prices

Abstract: The classical uni-directional conversion algorithms are based on the assumption that prices are arbitrarily chosen from the fixed price interval [m,M] where m and M represent the estimated lower and upper bounds of possible prices 0 < m <= M. The estimated interval is erroneous and no attempts are made by the algorithms to update the erroneous estimates. We consider a real world setting where prices are interrelated, i.e., each price depends on its preceding price. Under this assumption, we derive a lower bound on the competitive ratio of randomized non-preemptive algorithms. Motivated by the fixed and erroneous price bounds, we present an update model that progressively improves the bounds. Based on the update model, we propose a non-preemptive reservation price algorithm RP* and analyze it under competitive analysis. Finally, we report the findings of an experimental study that is conducted over the real world stock index data. We observe that RP* consistently outperforms the classical algorithm.

Design development post contract signing in New Zealand:Client's or contractor's cost?

Abstract: By offering fixed-price contracts for designs supplied by clients, contractors legally warrant that they can build what has been designed and do so within their fixed price. Yet detailed drawings a...

Peak Load Forecasting in Electrical Deregulated Market Environment - The Dynamic Tariffs

Abstract: Load peak forecasting (LPF) is an important decision tool in a deregulated market in order to help the operator to choose the best efficient mix production for the next day, satisfying the demand. The construction of the first built price of energy (e/MWh) is made until 11 p.m. of the day before. For example, in the case of MIBEL (Iberian Electricity Market), there is one starting session during the day before and seven-intra sessions during the current day. The concept of the Dynamic Tariffs (DT) is bringing to the low voltage consumer a certain “dynamic” in the energy prices, (which are indexed to the MIBEL prices), during several hours of the day instead of a traditional fixed price of energy. For peak load time the price increases and for the empty hours the price is cheaper. The result of these actions is to shift peak load consumption to the low-peak periods. In this paper, the results of the forecasts were evaluated, simulating their effects in the environment of DT and comparing them with actions that would be taken knowing in advance the real diagram of the consumption. This paper presents a methodology for 24 hours forecasting loads and it was incorporated in a dynamic tariff environment. The model was applied to a case study. The active power data was collected in EDP Distribution System in the city of Lisbon.

Revenue-Optimal Deterministic Auctions for Multiple Buyers with Ordinal Preferences over Fixed-price Items

Abstract: In this paper, we introduce a Bayesian revenue-maximizing mechanism design model where the items have fixed, exogenously-given prices. Buyers are unit-demand and have an ordinal ranking over purchasing either one of these items at its given price, or purchasing nothing. This model arises naturally from the assortment optimization problem, in that the single-buyer optimization problem over deterministic mechanisms reduces to deciding on an assortment of items to "show". We study its multi-buyer generalization in the simplest setting of single-winner auctions, or more broadly, any service-constrained environment. Our main result is that if the buyer rankings are drawn independently from Markov Chain ranking models, then the optimal mechanism is computationally tractable, and structurally a virtual welfare maximizer. We also show that for ranking distributions not induced by Markov Chains, the optimal mechanism may not be a virtual welfare maximizer.

Power of Dynamic Pricing in Revenue Management with Strategic (Forward-looking) Customers

Abstract: The present paper considers a canonical revenue management problem wherein a monopolist seller seeks to maximize revenue from selling a fixed inventory of a product to customers who arrive over time. We assume that customers are forward-looking and rationally strategize the timing of their purchases, an empirically confirmed aspect of modern customer behavior. We consider a broad class of customer utility models that allow customer disutility from waiting to be heterogeneous and correlated with product valuations. Chen et al. [1] show that the so-called fixed price policy is asymptotically optimal in the high-volume regime where both the seller's initial inventory and the length of the selling horizon are proportionally scaled. Specifically, the revenue loss of the fixed price policy is O( k1/2), where k is the system's scaling parameter. In the present paper, we present a novel real-time pricing policy. This policy repeatedly updates the fixed price policy in Chen et al. [1] by taking into account the volatility of the historic sales. We force the price process under this policy to be non-decreasing over time. Therefore, our policy incentivizes strategic customers to behave myopically. We show that if the seller updates the price for only a single time, then the revenue loss of our policy can be arbitrarily close to O(k1/3 ln k). If the seller updates the prices with a frequency O(lnk/ln ln k), then the revenue loss of our policy can be arbitrarily close to O((ln k)3). These results are novel and show the power of dynamic pricing in the presence of forward-looking customers, at least for the problem setting considered in this paper.

Bargaining conversation in indonesian traditionalmarket: an analysis of its rhetorical structure andlinguistic features

Abstract: This study provides the analysis of bargaining conversation in Indonesian Traditional Market as a particular type of discourse, the genre of bargaining. Since the study about the analysis of bargaining conversation in Indonesia is hardly ever done this study examines to analyze the bargaining conversation in Indonesian traditional market. The research questions about the rhetorical structure and linguistic features of bargaining conversation in Indonesian traditional market. Further, the research objectives of this study is to identify the rhetorical structure of bargaining conversation in Indonesia and to investigate the linguistic features of bargaining conversation in Indonesia. This sudy using mixed-method design both qualitative and quantitative and a data set of 11 bargaining conversations and vopice recording as the instrument. The result of the rhetorical structure consisted of greeting, product talk, confirming & describing, price talk, confirming the price, bargaining, rejecting, justifying, flirtation talk, offering fixed price, dealing, and the last is thanking. The result of linguistic features revealed the content and the function words. The content words were divided into several categories, such as: nominal price, product talk, and the price talk. According to the result of corpus analysis, the total number of occurrences from the content words was 916 and the function words was 2,119. Furthermore, this study found most participants used less polite Javanese language during the bargaining process.

Ordering policies under currency risk sharing agreements: a Markov chain approach

Abstract: If an exporter or a wholesaler sells goods at a fixed price to be paid in the currency of the seller’s country, then the purchase price of the importer depends upon the prevailing exchange rate of their respective currencies. Ideally, in a floating exchange rate system, the purchase price has to change according to shifts in the exchange rate. In such a scenario the entire exchange rate risk is borne by the importer/buyer. However, in international trade, it is customary for the parties to enter into a risk-sharing agreement, under which the buyer does not pay the seller on the basis of the prevailing exchange rate, but pays a mutually agreed upon price that falls within a range of fluctuating exchange rates. In this manner, the profit or loss due to fluctuations in the exchange rate would be shared by both the parties. These stochastic variations in purchase prices are modeled through a Markov chain. In this article, the resulting purchase and inventory problem is analyzed by identifying a regenerative cycle. An optimal selling price that maximizes the expected profit per unit time is also discussed. Further, optimal ordering policies under no stock-out conditions are derived with an optimal uniform demand corresponding to the optimal selling price. Through sensitivity analyses, differences in profit function with respect to carrying cost fraction, setup costs, and purchase prices are also shown. An investigation into the possible loss if this model solution is not implemented is also made through numerical illustrations. A discussion of a special case of two-purchase price scenario gives additional insight into the problem.

Fixed-price Diffusion Mechanism Design

Abstract: We consider a fixed-price mechanism design setting where a seller sells one item via a social network, but the seller can only directly communicate with her neighbours initially. Each other node in the network is a potential buyer with a valuation derived from a common distribution. With a standard fixed-price mechanism, the seller can only sell the item among her neighbours. To improve her revenue, she needs more buyers to join in the sale. To achieve this, we propose the very first fixed-price mechanism to incentivize the seller's neighbours to inform their neighbours about the sale and to eventually inform all buyers in the network to improve seller's revenue. Compared with the existing mechanisms for the same purpose, our mechanism does not require the buyers to reveal their valuations and it is computationally easy. More importantly, it guarantees that the improved revenue is at least 1/2 of the optimal.

Information Asymmetry, Rents, and Cost Risk Allocation: An Investigation of the BMW Supply Chain

Abstract: This paper studies a dynamic procurement process used by many European automakers and BMW in particular. An automaker (she) must procure components from an upstream supplier (him) to assemble cars in a given production period. Demand for vehicles is stochastic, and retail prices are endogenous. The automaker may or may not be privy to the supplier's raw material product cost, which is also stochastic. In the former case, we say that information is symmetric; in the latter case, information is asymmetric. (Existing work in this area is limited to static models with deterministic demand and symmetric information.) The contracts BMW uses are the material-plus-surcharge (MPS) and the fixed price contract (FP). In that environment, we explain how to choose between the contracts that BMW uses currently, and we identify a new, optimal contract for the asymmetric case that would net the automaker an additional profit boost. We conclude by assessing the empirical support for our predictions using a unique data set of contractual arrangements provided to us by BMW. Our analysis sharpens our understanding of contract choice in dynamic procurement settings and identifies a list of key factors in making such choices.