37 research outputs found

    A Grey-Box Approach to Automated Mechanism Design

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    Auctions play an important role in electronic commerce, and have been used to solve problems in distributed computing. Automated approaches to designing effective auction mechanisms are helpful in reducing the burden of traditional game theoretic, analytic approaches and in searching through the large space of possible auction mechanisms. This paper presents an approach to automated mechanism design (AMD) in the domain of double auctions. We describe a novel parametrized space of double auctions, and then introduce an evolutionary search method that searches this space of parameters. The approach evaluates auction mechanisms using the framework of the TAC Market Design Game and relates the performance of the markets in that game to their constituent parts using reinforcement learning. Experiments show that the strongest mechanisms we found using this approach not only win the Market Design Game against known, strong opponents, but also exhibit desirable economic properties when they run in isolation.Comment: 18 pages, 2 figures, 2 tables, and 1 algorithm. Extended abstract to appear in the proceedings of AAMAS'201

    Planning and Doing Things

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    The University of Edinburgh and research sponsors are authorised to reproduce and distribute reprints and on-line copies for their purposes notwithstanding any copyright annotation hereon. The views and conclusions contained herein are the author’s and shouldn’t be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of other parties.I was interested in computers by the age of 15 and gave talks on them at school. I attended evening classes a couple of years later while still at school travelling on the bus for an hour in the evening to a college in Leeds to learn programming (in COBOL!). Computers at that time filled a room, you submitted your exercises on punched card and got the results the following day. I built my first AI planner over 35 years ago. I’d already been on an early AI course at Lancaster University where the language of choice for teaching a range of topics was POP-2 and wanted to do a Summer project to create a problem solver. With support from Donald Michie and his team at Edinburgh I tried to create a Graph Traverser along the lines they were working on. Boy, am I glad I got involved with Computers, AI and planning technology

    Bidding optimally in concurrent second-price auctions of perfectly substitutable goods

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    We derive optimal bidding strategies for a global bidding agent that participates in multiple, simultaneous second-price auctions with perfect substitutes. We first consider a model where all other bidders are local and participate in a single auction. For this case, we prove that, assuming free disposal, the global bidder should always place non-zero bids in all available auctions, irrespective of the local bidders' valuation distribution. Furthermore, for non-decreasing valuation distributions, we prove that the problem of finding the optimal bids reduces to two dimensions. These results hold both in the case where the number of local bidders is known and when this number is determined by a Poisson distribution. This analysis extends to online markets where, typically, auctions occur both concurrently and sequentially. In addition, by combining analytical and simulation results, we demonstrate that similar results hold in the case of several global bidders, provided that the market consists of both global and local bidders. Finally, we address the efficiency of the overall market, and show that information about the number of local bidders is an important determinant for the way in which a global bidder affects efficiency

    Automated Auction Mechanism Design with Competing Markets

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    Resource allocation is a major issue in multiple areas of computer science. Despite the wide range of resource types across these areas, for example real commodities in e-commerce and computing resources in distributed computing, auctions are commonly used in solving the optimization problems involved in these areas, since well designed auctions achieve desirable economic outcomes. Auctions are markets with strict regulations governing the information available to traders in the market and the possible actions they can take. Auction mechanism design aims to manipulate the rules of an auction in order to achieve specific goals. Economists traditionally use mathematical methods, mainly game theory, to analyze auctions and design new auction forms. However, due to the high complexity of auctions, the mathematical models are typically simplified to obtain results, and this makes it difficult to apply results derived from such models to market environments in the real world. As a result, researchers are turning to empirical approaches. Following this line of work, we present what we call a grey-box approach to automated auction mechanism design using reinforcement learning and evolutionary computation methods. We first describe a new strategic game, called \cat, which were designed to run multiple markets that compete to attract traders and make profit. The CAT game enables us to address the imbalance between prior work in this field that studied auctions in an isolated environment and the actual competitive situation that markets face. We then define a novel, parameterized framework for auction mechanisms, and present a classification of auction rules with each as a building block fitting into the framework. Finally we evaluate the viability of building blocks, and acquire auction mechanisms by combining viable blocks through iterations of CAT games. We carried out experiments to examine the effectiveness of the grey-box approach. The best mechanisms we learnt were able to outperform the standard mechanisms against which learning took place and carefully hand-coded mechanisms which won tournaments based on the CAT game. These best mechanisms were also able to outperform mechanisms from the literature even when the evaluation did not take place in the context of CAT games. These results suggest that the grey-box approach can generate robust double auction mechanisms and, as a consequence, is an effective approach to automated mechanism design. The contributions of this work are two-fold. First, the grey-box approach helps to design better auction mechanisms which can play a central role in solutions to resource allocation problems in various application domains of computer science. Second, the parameterized view and the reinforcement learning-based search method can be used in other strategic, competitive situations where decision making processes are complex and difficult to design and evaluate manually

    Design and Operations on the Supply Side of Online Marketplaces

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    Online platforms like eBay, Upwork, Airbnb, and Uber have transformed their markets, and many more are about to emerge. The rise of platforms has become one of the predominant economic and social developments of our time. Moreover, it has created many opportunities and challenges for both practitioners and researchers. My dissertation focuses on the design and operations on the supply side of online marketplaces. In particular, I study supply-side levers (e.g., listing policy and information provision policy) in different marketplace context (e.g., auction marketplace and service platform), with the consideration of strategic behavior of market participants and various friction involved in transactions (e.g., participation cost, information asymmetry, and supply adjustment friction). The first essay investigates how a one-sided liquidation auction marketplace maximizes its revenue by managing the supply-side market thickness under an exogenous supply inflow. The second essay examines the operational impacts of service platforms’ information disclosure regarding service providers’ qualities and revealing their mechanisms. The last essay studies whether two-sided marketplaces benefit or suffer from sellers’ quantity competition under unanticipated demand shocks. We further show that marketplaces can maneuver the competition in favorable directions by manipulating the supply adjustment friction. Overall, the findings from the three essays show that marketplaces’ operational levers on the supply side have significant effects on the strategies of all participants, which impacts the marketplaces’ operational performance. The dissertation offers both theoretical insights on the mechanisms of the studied supply-side levers and practical implications on how these levers should be designed and implemented

    Revenue management in airline operations : booking systems and aircraft maintenance services

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    Although the principles of Revenue Management (RM) have vaguely been used in business for a long time, an increasing number of organizations are implementing well structured RM systems in the last few decades due to the developments in science and technology, especially in economics, statistics, operations research and computer science. The improvements in information and telecommunication technologies, wide use of Internet, rise of e-commerce and successful supply chain management strategies have enabled organizations to model and solve complex RM problems. This dissertation research concentrates on airlines, the earliest and leading user of RM. Today, airlines face serious financial problems due to the increasing costs and competition. They continuously explore new opportunities especially in terms of RM to make profit and survive. In this study, two problems are analyzed within this scope; airline booking process with adapted options approach and aircraft maintenance order control through RM. First; a new approach, financial options approach, is proposed to sell tickets in airline reservation systems. The options are used to overcome the uncertainty in air travel demand and competitors' actions. The seat inventory control problem is formulated with overbooking and embedded options respectively. Then a simulation study is conducted the potential of using options in airlines booking process. Accordingly, empirical results show that they present an opportunity both to utilize capacity more efficiently and to value seats more precisely compared to overbooking approach. Secondly; a peak load pricing concept is applied for aircraft maintenance order control problem. Aircraft maintenance centers face with peak loads in some seasons and the capacity is underutilized in other seasons. A peak load pricing model is proposed to shift some of the price elastic demand from peak seasons to off-peak seasons to balance demand and supply around the year. A dynamic programming algorithm is developed to solve the model and a code is written in C++. Results show that the model improves both annual capacity loading factors and revenues without causing a discomfort from the perspective of the customers. The details of both studies are presented in this dissertation research. [PUBLICATION ABSTRACT

    Can Upward Brand Extensions be an Opportunity for Marketing Managers During the Covid-19 Pandemic and Beyond?

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    Early COVID-19 research has guided current managerial practice by introducing more products across different product categories as consumers tried to avoid perceived health risks from food shortages, i.e. horizontal brand extensions. For example, Leon, a fast-food restaurant in the UK, introduced a new range of ready meal products. However, when the food supply stabilised, availability may no longer be a concern for consumers. Instead, job losses could be a driver of higher perceived financial risks. Meanwhile, it remains unknown whether the perceived health or financial risks play a more significant role on consumers’ consumptions. Our preliminary survey shows perceived health risks outperform perceived financial risks to positively influence purchase intention during COVID-19. We suggest such a result indicates an opportunity for marketers to consider introducing premium priced products, i.e. upward brand extensions. The risk-as�feelings and signalling theories were used to explain consumer choice under risk may adopt affective heuristic processing, using minimal cognitive efforts to evaluate products. Based on this, consumers are likely to be affected by the salient high-quality and reliable product cue of upward extension signalled by its premium price level, which may attract consumers to purchase when they have high perceived health risks associated with COVID-19. Addressing this, a series of experimental studies confirm that upward brand extensions (versus normal new product introductions) can positively moderate the positive effect between perceived health risks associated with COVID-19 and purchase intention. Such an effect can be mediated by affective heuristic information processing. The results contribute to emergent COVID-19 literature and managerial practice during the pandemic but could also inform post-pandemic thinking around vertical brand extensions
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