京都大学 / 新制・課程博士 / 博士(情報学) / 甲第24265号 / 情博第809号 / 新制||情||136(附属図書館) / 京都大学大学院情報学研究科数理工学専攻 / (主査)教授 梅野 健, 教授 山下 信雄, 准教授 加嶋 健司 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

This thesis makes a contribution to the algorithmic pricing literature by proposing and analyzing techniques for automatically pricing digital and information goods in order to maximize profit in different settings. We also consider the effect on social welfare when agents use these pricing algorithms. The digital goods considered in this thesis are electronic commodities that have zero marginal cost and unlimited supply e.g., iTunes apps. On the other hand, an information good is an entity that bridges the knowledge gap about a product between the consumer and the seller when the consumer cannot assess the utility of owning that product accurately e.g., Carfax provides vehicle history and can be used by a potential buyer of a vehicle to get information about the vehicle. With the emergence of e-commerce, the customers are increasingly price sensitive and search for the best opportunies anywhere. It is almost impossible to manually adjust the prices with rapidly changing demand and competition. Moreover, online shopping platforms also enable sellers to change prices easily and quickly as opposed to updating price labels in brick and mortar stores so they can also experiment with different prices to maximize their revenue. Therefore, e-marketplaces have created a need for designing sophisticated practical algorithms for pricing. This need has evoked interest in algorithmic pricing in the computer science, economics, and operations research communities. In this thesis, we seek solutions to the following two algorithmic pricing problems: (1) In the first problem, a seller launches a new digital good (this good has unlimited supply and zero marginal cost) but is unaware of its demand in a posted-price setting (i.e., the seller quotes a price to a buyer, and the buyer makes a decision depending on her willingness to pay); we look at the question --- how should the seller set the prices in order to maximize her infinite horizon discounted revenue? This is a classic problem of learning while earning. We propose a few algorithms for this problem and demonstrate their effectiveness using rigorous empirical tests on both synthetic datasets and real-world datasets from auctions at eBay and Yahoo!, and ratings on jokes from Jester, an online joke recommender system. We also show that under certain conditions the myopic Bayesian strategy is also Bayes-optimal. Moreover, this strategy has finite regret (independent of time) which means that it also learns very fast. (2) The second problem is based on search markets: a consumer is searching for a product sequentially (i.e., she examines possible options one by one and on observing them decides whether to buy or not). However, merely observing a good, although partially informative, does not typically provide the potential purchaser with the complete information set necessary to execute her buying decision. This lack of perfect information about the good creates a market for intermediaries (we refer to them as experts) who can conduct research on behalf of the buyer and sell her this information about the good. The consumer can pay these intermediaries to learn more about the good which can help her in making a better decision about whether to buy the good or not. In this case, we study various pricing schemes for these information intermediaries in a search-based environment (e.g., selling a package of $k$ reports instead of selling a single report or offering a subscription based service). We show how subsidies can be an effective tool for a market designer to increase the social welfare. We also model quality level in the experts and study competition dynamics by computing equilibrium strategies for the searcher and two experts with different qualities. Surprisingly, sometimes an improvement in the quality of the higher-quality expert (holding everything constant) can be pareto-improving: not only that expert's profit increase, so does the other expert's profit and the searcher's utility. / Ph. D.

Software firms can use open source development model combined with proprietary development model to increase their profitability. Open source development models can help software firms create products with better technical features at a lower price. Since open source development is a community based development method the popularity of the software among customers will also increase. Using open source development method with proprietary method will also require firms to sell the product at a lower price. This creates a challenge for the firms to find the optimal price and level of openness to maximize their profit. Using the systems dynamics methodology, development, employment and customer choice for a typical software firm was captured in a simulation model to understand the dynamics of the software firm in a competitive market and to find the optimal level of openness and price. The model was built based on previous research literature, various software models and from the author's understanding of the software industry. Our analysis suggests that in a fast evolving market where customers spend less time researching and shopping for a software product (Antivirus market VS Operating Systems market), companies should maintain lower level of openness and higher proprietary type development to increase the Net Present Value of the organization. The software firm could benefit from a higher level of openness in a market where the customers base their purchasing decision on the popularity and compatibility of the software and strong network effects are present (e.g. Business intelligence software). / Master of Science

In this dissertation, we consider analytic and numeric approaches to the solution of probabilistic steady-state problems with specific applications in simulation and queueing theory. Our first objective on steady-state simulations is to develop new estimators for the variance parameter of a selected output process that have better performance than certain existing variance estimators in the literature. To complete our analysis of these new variance estimators, called linear combinations of overlapping variance estimators, we do the following: establish theoretical asymptotic properties of the new estimators; test the theoretical results on a battery of examples to see how the new estimators perform in practice; and use the estimators for confidence interval estimation for both the mean and the variance parameter. Our theoretical and empirical results indicate the new estimators' potential for improvements in accuracy and computational efficiency. Our second objective on steady-state simulations is to derive the expected values of various competing estimators for the variance parameter. In this research, we do the following: formulate the machinery to calculate the exact expected value of a given estimator for the variance parameter; calculate the exact expected values of various variance estimators in the literature; compute these expected values for certain stochastic processes with complicated covariance functions; and derive expressions for the mean squared error of the estimators studied herein. We find that certain standardized time series estimators outperform their competitors as the sample size becomes large. Our research on queueing theory focuses on pricing of the service provided to individual customers in a queueing system. We find sensitivity results that enable efficient computational procedures for dynamic pricing decisions for maximizing the long-run average reward in a queueing facility with the following properties: there are a fixed number of servers, each with the same constant service rate; the system has a fixed finite capacity; the price charged to a customer entering the system depends on the number of customers in the system; and the customer arrival rate depends on the current price of the service. We show that the sensitivity results considered significantly reduce the computational requirements for finding the optimal pricing policies.

The ability for domestic consumers to provide demand response to dynamic electricity pricing will become increasingly valuable for integrating the high penetrations of renewables that are expected to be connected to electricity networks in the future. The aim of this thesis is to investigate whether domestic consumers will be willing and able to provide demand response in such low-carbon futures. A broad approach is presented in this thesis, with research contributions on subjects including data privacy, behavioural economics, and battery modelling. The principle argument of the thesis is that studying the behaviour of consumers with grid-connected photovoltaic ('PV') systems can provide insight into how consumers might respond to dynamic pricing in future low-carbon power systems, as both experience irregular electricity prices that are correlated with intermittent renewable generation. Through a combination of statistical and qualitative methods, this thesis investigates the demand response behaviour of consumers with PV systems in the UK. The results demonstrate that these consumers exhibit demand response behaviour by increasing demand during the day and decreasing demand during the evening. Furthermore, this effect is more pronounced on days with higher irradiance. The results are novel in three ways. First, they provide quantified evidence that suggests that domestic consumers with PV systems engage in demand response behaviour. Second, they provide evidence of domestic consumers responding to irregular electricity prices that are correlated with intermittent renewable generation, thereby addressing the aim of this thesis, and supporting the assumption that consumers can be expected to respond to dynamic pricing in future markets with high penetrations of renewables. Third, they provide evidence of domestic consumers responding to dynamic pricing that is similar to real-time pricing, while prior evidence of this is rare and confined to the USA.

abstract: Today's competitive markets force companies to constantly engage in the complex task of managing their demand. In make-to-order manufacturing or service systems, the demand of a product is shaped by price and lead times, where high price and lead time quotes ensure profitability for supplier, but discourage the customers from placing orders. Low price and lead times, on the other hand, generally result in high demand, but do not necessarily ensure profitability. The price and lead time quotation problem considers the trade-off between offering high and low prices and lead times. The recent practices in make-to- order manufacturing companies reveal the importance of dynamic quotation strategies, under which the prices and lead time quotes flexibly change depending on the status of the system. In this dissertation, the objective is to model a make-to-order manufacturing system and explore various aspects of dynamic quotation strategies such as the behavior of optimal price and lead time decisions, the impact of customer preferences on optimal decisions, the benefits of employing dynamic quotation in comparison to simpler quotation strategies, and the benefits of coordinating price and lead time decisions. I first consider a manufacturer that receives demand from spot purchasers (who are quoted dynamic price and lead times), as well as from contract customers who have agree- ments with the manufacturer with fixed price and lead time terms. I analyze how customer preferences affect the optimal price and lead time decisions, the benefits of dynamic quo- tation, and the optimal mix of spot purchaser and contract customers. These analyses necessitate the computation of expected tardiness of customer orders at the moment cus- tomer enters the system. Hence, in the second part of the dissertation, I develop method- ologies to compute the expected tardiness in multi-class priority queues. For the trivial single class case, a closed formulation is obtained. For the more complex multi-class case, numerical inverse Laplace transformation algorithms are developed. In the last part of the dissertation, I model a decentralized system with two components. Marketing department determines the price quotes with the objective of maximizing revenues, and manufacturing department determines the lead time quotes to minimize lateness costs. I discuss the ben- efits of coordinating price and lead time decisions, and develop an incentivization scheme to reduce the negative impacts of lack of coordination. / Dissertation/Thesis / Ph.D. Industrial Engineering 2012

The application of revenue management (RM) is changing more rapidly than ever before, driven as an important factor of the daily operation to keep prices competitive and to create real-time optimal pricing. In the age of the Internet and social media, negotiated fixed rates have become outmoded. Consumers now have access to online rate comparisons and real time reviews. They think more strategically when making purchasing decisions. Thus, they become more demanding. This research provides an empirical study of revenue management and pricing with an emphasis given to the hospitality industry. The aim of this research is to examine the gap between the theoretical approach and the empirical analysis, the rationality between the implementation of dynamic pricing approaches and the impact on the customer. Furthermore, the research examines the perception of consumers’ willingness to pay when using the Name-Your-Own-Price (NYOP) mechanism, which allows customers to have a greater influence on the amount they are prepared to pay. Instead of posting a price, the seller waits for a potential buyer’s offer, which he or she can either accept or reject. Finally, this study examines, whether the use of social media plays a decisive role in the online purchase environment used by the hospitality sector and the effect it has on a consumer’s willingness to pay. Accordingly, hotel revenue managers will be able to use the findings of this study to effectively plan their short-term, and long-term pricing strategies to generate a stronger revenue management performance for their property, namely to increase the RevPAR (revenue per available room). The research can be useful to businesses, as empirical data and tests were employed to determine what kind of impact the different pricing policies have on the long-term profit optimization. These practical and theoretical elements of the field reinforce each other‚ as well as to a large extent, the constructive interplay of theory and practice. The research is twofold, the holistic approach, which discusses the development of the theoretical dimension, is complemented by the practical analysis of the collected data of the surveys. This approach ensures the relevant observation of ‘real-time’ data and the evaluation of the set of hypotheses. The study conducted two large scale interrelated structured surveys. The first structural survey (NYOP) provides a better understanding of the final consumer, by using the name-your-own-price mechanism and by observing the extended role of social media in the booking procedure. Hypotheses were tested and in the second survey in-depth data from revenue managers and executives working across the tourism industry was collected, in an attempt to measure the use of pricing strategies within the industry. The research contributes to the theory by empirical testing how the extended RM objectives influence RM and pricing. It provides a clear picture of the necessary elements for a successful implementation of pricing strategies. Finally, the study has implications for the consumer. Thus, the researcher investigates consumer’s perception to the NYOP model and the expanding role of social media to the consumer-booking pattern.

The development of e-commerce has seen a considerable rise in the last decade, with many companies starting online stores. While there is research regarding e-commerce, the concept of dynamic pricing within the e-commerce ecosystem still has a gap. This study seeks to explore and present how a dynamic pricing system can be delivered within an e-commerce setting. With insights from DynamicX, an intelligent dynamic pricing system organization, and through thematic analysis, the result is presented through four themes regarding e-commerce and dynamic pricing. The findings presented in the discussion related to the past, the present, and the future of dynamic pricing systems in e-commerce with a focus on context, technologies, and practices.

Parking is an old concept, which fundamentally involves leaving a vehicle at a place. Parking has been considered as a subsidiary activity to owning a car. However, these days owning a car has become the norm, which leads to a greater demand for parking. Unregulated parking demand often leads to increased traffic congestion, when there are not enough parking spaces to keep up with the demand. Congestion itself has a negative impact on the environment and causes safety issues. A common solution to reduce congestion have been by influencing the demand for parking spaces through parking prices. During recent years, the existing pricing strategies have not been able to keep up with the daily changes in demand. Therefore, stakeholders in the parking industry have started to shift towards working for dynamic pricing. Dynamic pricing utilizes a pricing strategy that sets the price according to the current demand and occupancy. However, the parking industry is missing a key feature to fully enable dynamic pricing. There is no communication standard in the parking industry. Thus, there is no efficient communication mean for the stakeholders to share their parking-related information (such as location, occupancy, and tariff data). This thesis has developed and proposes a protocol for sharing such parking-related information. The aim is that the protocol will be used as a communication standard in the parking industry. Due to limited time, the most focus was put on completing the protocol for tariff data. However, the developed protocol can be considered as a partial solution towards dynamic pricing. Because the protocol can still be used to properly share tariff data. Based on the evaluation, the protocol could express a variety of tariffs. The tariffs that are expressible have use cases such as early bird, residential, or on-street parking. To make integration easier, for the parking industry, the protocol includes tools to aid integrations of the protocol. A future work will be to complete the support of location and occupancy related data. Additionally, it has been discussed that the protocol will onwards be developed as open-source. / Parkering har sedan länge varit ett stort område, vilket enkelt innebär att ett fordon lämnas på en plats. Parkering har för det mesta haft ett sekundärt syfte från att äga en bil. Men eftersom antalet bilägare ökar, ökar även parkeringsbehovet. Om det inte finns tillräckligt med parkeringar för att kunna tillfredsställa behovet, leder det till en ökad trafikträngsel. Trafikträngsel skapar både miljöproblem och säkerhetsproblem. Den huvudsakliga metoden för att påverka parkeringsbehovet har varit genom att skapa lägre en efterfrågan. Efterfrågan har sänkts genom att justeringar av parkeringsavgifter. Då efterfrågan på senaste tiden har ökat markant, räcker de traditionella parkeringsavgifterna inte längre till. För att lösa problemen, har många bolag och organisationer börjat jobba mot en dynamisk prissättning. Dynamisk prissättning använder sig av en prisstrategi som sätter parkeringsavgifterna i realtid baserat på den nuvarande efterfrågan och tillgång. Däremot har parkeringsindustrin i nuläget inte de nödvändiga kommunikationskanalerna som krävs för att anta en dynamisk prissättning. Examensarbetets huvudsyfte har varit att utveckla ett protokoll som gör det möjligt att dela parkeringsrelaterade data så som: plats-, ockuperings- och tariffdata. Huvudmålet med protokollet är att det senare ska kunna bli en standard i parkeringsindustrin. På grund av tidsbegränsningar, har den största fokusen av utvecklingen lagt på stöd för tariffdata. Därmed kan inte protokollet antas som den fullständiga lösningen för dynamisk prissättning. Dock, kan protokollet ses som en delvis lösning, då det med protokollet är möjligt att korrekt dela med sig av tariffdata. Evalueringen visade att det gick, med hjälp av det utvecklade protokollet, att beskriva flera sorters tariffer utan att förlora någon viktig information. Tariffer som gick att beskriva används för bland annat: gatu-, infarts- och boendeparkeringar. Ett framtida projekt blir att utveckla och färdigställa protokollet för fullt stöd av plats- och ockuperingsdata. Ytterligare har det diskuterats om att den fortsatta utvecklingen av protokollet, ske som öppen källkod (open-source).

In the last decade, the landscape of storage hardware and software has changed considerably. Storage hardware has diversified from hard disk drives and solid state drives to include persistent memory (PMEM) devices such as phase change memory (PCM) and Flash-backed DRAM. On the software side, the increasing adoption of cloud services for building and deploying consumer and enterprise applications is driving the use of cloud storage services. Cloud providers have responded by providing a plethora of choices of storage services, each of which have unique performance characteristics and pricing. We argue this variety represents an opportunity for modern storage systems, and it can be leveraged to improve operational costs of the systems. We propose that storage tiering is an effective technique for balancing operational or de- ployment costs and performance in such modern storage systems. We demonstrate this via three key techniques. First, THMCache, which leverages tiering to conserve the lifetime of PMEM devices, hence saving hardware upgrade costs. Second, CAST, which leverages tiering between multiple types of cloud storage to deliver higher utility (i.e. performance per unit of cost) for cloud tenants. Third, we propose a dynamic pricing scheme for cloud storage services, which leverages tiering to increase the cloud provider's profit or offset their management costs. / Master of Science