An Investigation Report on Auction Mechanism Design

Auctions are markets with strict regulations governing the information available to traders in the market and the possible actions they can take. Since well designed auctions achieve desirable economic outcomes, they have been widely used in solving real-world optimization problems, and in structuring stock or futures exchanges. Auctions also provide a very valuable testing-ground for economic theory, and they play an important role in computer-based control systems. 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. This report aims to survey the theoretical and empirical approaches to designing auction mechanisms and trading strategies with more weights on empirical ones, and build the foundation for further research in the field

PERFORMANCE AND ANALYSIS OF SPOT TRUCK-LOAD PROCUREMENT MARKETS USING SEQUENTIAL AUCTIONS

Competition in a transportation marketplace is studied under different supply/demand conditions, auction formats, and carriers' behavioral assumptions. Carriers compete in a spot truck-load procurement market (TLPM) using sequential auctions. Carrier participation in a TLPM requires the ongoing solution of two distinct problems: profit maximization problem (chose best bid) and fleet management problem (best fleet assignment to serve acquired shipments). Sequential auctions are used to model an ongoing transportation market, where carrier competition is used to study carriers' dynamic vehicle routing technologies and decision making processes. Given the complexity of the bidding/fleet management problem, carriers can tackle it with different levels of sophistication. Carriers' decision making processes and rationality/bounded rationality assumptions are analyzed. A framework to study carrier behavior in TL sequential auctions is presented. Carriers' behavior is analyzed as a function of fleet management technology, auction format, carrier bounded rationality, market settings, and decision making complexity. The effects of fleet management technology asymmetries on a competitive marketplace are studied. A methodology to compare dynamic fleet management technologies is developed. Under a particular set of bounded rationality assumptions, bidding learning mechanisms are studied; reinforcement learning and fictitious play implementations are discussed. The performance of different auction formats is studied. Simulated scenarios are presented and their results discussed

High Frequency Trading in Financial Markets

Financial markets have undergone tremendous changes in the last decades. Next to the automation of the trading process and the improvement in market quality, High Frequency Trading (HFT) plays a major role in financial markets. This thesis provides a background on the evolution of financial markets and the role of HFT in price discovery and the nature of its interaction with human traders

Towards a blockchain-based trustful mechanism for IoT-enabled data trading systems

Internet of Things (IoT) devices generate and collect massive amounts of IoT data. Monetizing the flood of data generated by the IoT devices has enabled the creation of IoT data trading systems where individuals and businesses may trade data. In the current IoT data trading systems, a third-party broker collects and manages IoT data for buyers who would like to promote their services and make more profit. However, there are three main challenges that may hinder the development of secure IoT data trading systems. First, there is a lack of data transparency and ownership. While the economic value of IoT data is increasing, it is not very well known how this data can be conceptualized, measured, and monetized in a trusted and transparent way. Second, the literature lacks studies about performance models to demonstrate IoT data trading system usability in real-world systems. Third, the reputation of the trading parties is an important attribute that affects their profitability and trading prosperity. However, current reputation systems are prone to malicious manipulation and single point of failure. This thesis identifies and addresses the three above challenges for IoT data trading systems. First, this thesis introduces a trustful IoT data trading system based on the blockchain as a means of providing anonymity, security, transparency, and mutual trust for participants. Using a game-theoretic approach, this study develops a strategic negotiation model that maximizes data buyers’ utility. To ensure that data owners’ IoT data are accessible by trustful buyers, a novel mechanism design is used to impede untruthful buyers from accessing the IoT data. Second, this thesis evaluates the performance of the blockchain-based IoT data trading system using the Hyperledger blockchain. Unlike existing research, this study measures and analyzes transaction throughput, latency, elapsed time, and resource consumption (memory consumption, CPU utilization, and disc read/write operations). Third, this thesis proposes a blockchain-based reputation system capable of avoiding failures by enhancing the Raft consensus mechanism. This thesis also proposes an adaptive learning mechanism that allows the data providers and consumers to enhance their reputation and review credibility scores. Lastly, this thesis carries out extensive theoretical analysis with respect to economic and security properties

Competition between demand-side intermediaries in ad exchanges

Online advertising constitutes one of the main sources of revenue for the majority of businesses on the web. Online advertising inventory was traditionally traded via bilateral contracts between publishers and advertisers, vastly through a number of intermediaries. However, what caused an explosion in the volume and, consequently, the revenue of online ads was the incorporation of auctions as the major mechanism for trading sponsored search ads in all major search engines. This reduced transaction costs and allowed for the advertisement of small websites which constitute the majority of Internet traffic. Auction-based markets were harder to establish in the display advertising industry due to the higher volume of inventory and the pre-existence of traditional intermediaries, often leading to inefficiencies and lack of transparency. Nevertheless, this has recently changed with the introduction of the ad exchanges, centralized marketplaces for the allocation of display advertising inventory that support auctions and real-time bidding. The appearance of ad exchanges has also altered the market structure of both demand-side and supply side intermediaries which increasingly adopt auctions to perform their business operations. Hence, each time a user enters a publisher's website, the contracted ad exchange runs an auction among a number of demand-side intermediaries, each of which represents their interested advertisers and typically submits a bid by running a local auction among these advertisers.Against this background, within this thesis, we look both at the auction design problem of the ad exchange and the demand-side intermediaries as well as at the strategies to be adopted by advertisers. Specifically, we study the revenue and efficiency effects of the introduction and competition of the demand-side intermediaries in a single-item auction setting with independent private valuations. The introduction of these intermediaries constitutes a major issue for ad exchanges since they hide some of the demand from the ad exchange and hence can make a profit by pocketing the difference between what they receive from their advertisers and what they pay at the exchange. Ad exchanges were created to offer transparency to both sides of the market, so it is important to study the share of the revenue that intermediaries receive to justify their services offered given the competition they face by other such intermediaries. The existence of mediators is a well-known problem in other settings. For this reason, our formulation is general enough to encompass other areas where two levels of auctions arise, such as procurement auctions with subcontracting and auctions with colluding bidders.In more detail, we study the effects of the demand-side intermediaries' choice of auction for three widely used mechanisms, two variations of the second-price sealed-bid (known as Vickrey) auction, termed PRE and POST, and first-price sealed-bid (FPSB) auctions. We first look at a scenario with a finite number of intermediaries, each implementing the same mechanism, where we compare the profits attained for all stakeholders. We find that there cannot be a complete profit ranking of the three auctions: FPSB auctions yield higher expected profit for a small number of competing intermediaries, otherwise PRE auctions are better for the intermediaries. We also find that the ad exchange benefits from intermediaries implementing POST auctions. We then let demand-side intermediaries set reserve (or floor) prices, that are known to increase an auctioneer's expected revenue. For issues of analytical tractability, we only consider scenarios with two intermediaries but we also compare the two Vickrey variations in heterogeneous settings where one intermediary implements the first whereas the other implements the second variation. We find that intermediaries, in general, follow mixed reserve-price-setting strategies whose distributions are difficult to derive analytically. For this reason, we use the fictitious play algorithm to calculate approximate equilibria and numerically compare the revenue and efficiency of the three mechanisms for specific instances. We find that PRE seems to perform best in terms of attained profit but is less efficient than POST. Hence, the latter might be a better option for intermediaries in the long term.Finally, we extend the previous setting by letting advertisers strategically select one of the two intermediaries when the latter implement each of the two Vickrey variations. We analytically derive the advertisers' intermediary selection strategies in equilibrium. Given that, in some cases, these strategies are rather complex, we use again the fictitious play algorithm to numerically calculate the intermediaries' and the ad exchange's best responses for the same instances as before. We find that, when both intermediaries implement POST auctions, advertisers always select the low-reserve intermediary, otherwise they generally follow randomized strategies. Last, we find that the ad exchange benefits from intermediaries implementing the pre-award Vickrey variation compared to a setting with two heterogeneous Vickrey intermediary auctioneers, whereas the opposite is true for the intermediaries.<br/

The Impact of Computerized Agents on Immediate Emotions, Overall Arousal and Bidding Behavior in Electronic Auctions

The presence of computerized agents has become pervasive in everyday live. In this paper, we examine the impact of agency on human bidders’ affective processes and bidding behavior in an electronic auction environment. In particular, we use skin conductance response and heart rate measurements as proxies for the immediate emotions and overall arousal of human bidders in a lab experiment with human and computerized counterparts. Our results show that computerized agents mitigated 1) the intensity of bidders’ immediate emotions in response to discrete auction events, such as submitting a bid and winning or losing an auction, and 2) the bidders’ overall arousal levels during the auction. Moreover, agency affected bidding behavior and its relation to overall arousal: whereas overall arousal and bids were negatively correlated when competing against human bidders, we did not observe this relationship for computerized agents. In other words, lower levels of agency yield less emotional behavior. The results of our study have implications for the design of electronic auction platforms and markets that include both human and computerized actors

Two-Sided Markets: A Progress Report

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