Deep Learning Meets Mechanism Design: Key Results and Some Novel Applications

Mechanism design is essentially reverse engineering of games and involves inducing a game among strategic agents in a way that the induced game satisfies a set of desired properties in an equilibrium of the game. Desirable properties for a mechanism include incentive compatibility, individual rationality, welfare maximisation, revenue maximisation (or cost minimisation), fairness of allocation, etc. It is known from mechanism design theory that only certain strict subsets of these properties can be simultaneously satisfied exactly by any given mechanism. Often, the mechanisms required by real-world applications may need a subset of these properties that are theoretically impossible to be simultaneously satisfied. In such cases, a prominent recent approach is to use a deep learning based approach to learn a mechanism that approximately satisfies the required properties by minimizing a suitably defined loss function. In this paper, we present, from relevant literature, technical details of using a deep learning approach for mechanism design and provide an overview of key results in this topic. We demonstrate the power of this approach for three illustrative case studies: (a) efficient energy management in a vehicular network (b) resource allocation in a mobile network (c) designing a volume discount procurement auction for agricultural inputs. Section 6 concludes the paper

Characterizations of Network Auctions and Generalizations of VCG

With the growth of networks, promoting products through social networks has become an important problem. For auctions in social networks, items are needed to be sold to agents in a network, where each agent can bid and also diffuse the sale information to her neighbors. Thus, the agents' social relations are intervened with their bids in the auctions. In network auctions, the classical VCG mechanism fails to retain key properties. In order to better understand network auctions, in this paper, we characterize network auctions for the single-unit setting with respect to weak budget balance, individual rationality, incentive compatibility, efficiency, and other properties. For example, we present sufficient conditions for mechanisms to be efficient and (weakly) incentive compatible. With the help of these properties and new concepts such as rewards, participation rewards, and so on, we show how to design efficient mechanisms to satisfy incentive compatibility as much as possible, and incentive compatibility mechanisms to maximize the revenue. Our results provide insights into understanding auctions in social networks.Comment: To appear in ECAI 202

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/

Essays In Algorithmic Market Design Under Social Constraints

Rapid technological advances over the past few decades---in particular, the rise of the internet---has significantly reshaped and expanded the meaning of our everyday social activities, including our interactions with our social circle, the media, and our political and economic activities This dissertation aims to tackle some of the unique societal challenges underlying the design of automated online platforms that interact with people and organizations---namely, those imposed by legal, ethical, and strategic considerations. I narrow down attention to fairness considerations, learning with repeated trials, and competition for market share. In each case, I investigate the broad issue in a particular context (i.e. online market), and present the solution my research offers to the problem in that application. Addressing interdisciplinary problems, such as the ones in this dissertation, requires drawing ideas and techniques from various disciplines, including theoretical computer science, microeconomics, and applied statistics. The research presented here utilizes a combination of theoretical and data analysis tools to shed light on some of the key challenges in designing algorithms for today\u27s online markets, including crowdsourcing and labor markets, online advertising, and social networks among others

Multiattribute Call Markets.

Multiattribute auctions support automated negotiation in settings where buyers and sellers have valuations for alternate configurations of a good, as defined by configuration attributes. Bidders express offers to buy or sell alternate configurations by specifying configuration-dependent reserve prices, and the auction determines both the traded goods and transaction prices based on these offers. While multiattribute auctions have been deployed in single-buyer procurement settings, the development of double-sided multiattribute auctions-allowing the free participation of both buyers and sellers-has received little attention from academia or industry. In this work I develop a multiattribute call market, a specific type of double auction in which bids accumulate over an extended period of time, before the auction determines trades based on the aggregate collection of bids. Building on a polynomial-time clearing algorithm, I contribute an efficient algorithm for information feedback. Supporting the implementation of market-based algorithms, information feedback support extends the range of settings for which multiattribute call markets achieve efficiency. Multiattribute auctions are only one of many auction variants introduced in recent years. The rapidly growing space of alternative auctions and trading scenarios calls for both a standardized language with which to specify auctions, as well as a computational test environment in which to evaluate alternate designs. I present a novel auction description language and deployment environment that supports the specification of a broad class of auctions, improving on prior approaches through a scripting language that employs both static parameter settings and rule-based behavior invocation. The market game platform, AB3D, can execute these auction scripts to implement multi-auction and multi-agent trading scenarios. The efficiency of multiattribute call markets depends crucially on the underlying valuations of participants. I analyze the expected performance limitations of multiattribute call markets, using existing analytical results where applicable. Addressing a lack of theoretical guidance in many natural settings, I introduce a computational metric on bidder valuations, and show a correlation between this metric and the expected efficiency of multiattribute call markets. As further validation, I integrate multiattribute markets into an existing supply chain simulation, demonstrating efficiency gains over a more conventional negotiation procedure.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/60822/1/klochner_1.pd

Agent-based common value auctions

Scope and Method of Study: This research is composed of three essays about agent-based common value auctions. The objective of the first essay is to establish an agent-based first-price common-value auction to determine the impact of a reserve price with two buyers and with three buyers. In the second essay, the agent-based common-value auction model is used to provide theoretical insight into the likely change in beef packers' market power before and after the Livestock Mandatory Price Reporting Act. The objective in the third essay is to determine if a first-price common-value auction with a reserve price or a posted-price market provides a seller with the larger expected revenue using agent-based models. In these three essays several theoretical contributions are made to the auction literature, and developing an agent-based common-value auction extends the agent-based modeling literature.Findings and Conclusions: Results from these essays provide unique insight into auction theory, agent-based modeling, and federal agricultural policy. From the first essay, a reserve price increases the equilibrium winning bid price and decreases the probability that the item is sold in the two and three buyer auctions. Additionally, a reserve price increases the winning bid price more than an additional buyer and no reserve price. In the second essay, results provide a unique theoretical argument that the Livestock Mandatory Price Reporting Act benefits producers by reducing beef packers' market power. Results from the third essay show the seller is indifferent between a posted price and auctioning an item when the seller and the buyers have similar noisy signals. However, when the seller has perfect information or buyers have less uncertainty than the seller, the seller prefers the posted-price market

Land use change through market dynamics : a Microsimulation of land development, the bidding process, and location choices of households and firms

textRapid urbanization is a pressing issue for planners, policymakers, transportation engineers, air quality modelers and others. Due to significant environmental, traffic and other impacts, the process of land development highlights a need for land use models with behavioral foundations. Such models seek to anticipate future settlement and transport patterns, helping ensure effective public and private investment decisions and policymaking, to accommodate growth while mitigating environmental impacts and other concerns. A variety of land use models now exist, but a market-based model with sufficient spatial resolution and defensible behavioral foundations remains elusive. This dissertation addresses this goal by developing and applying such a model. Real estate markets involve numerous interactive agents and real estate with a great level of heterogeneity. In the absence of tractable theory for realistic real estate markets, this research takes a “bottom-up” approach and simulates the behavior of tens of thousands of individual agents based on actual data. Both the supply and demand sides of the market are modeled explicitly, with endogenously determined property prices and land use patterns (including distributions of households and firms). Notions of competition were used to simulate price adjustment, and market-clearing prices were obtained in an iterative fashion. When real estate markets reach equilibrium, each agent is aligned with a single, utility-maximizing location and each allocated location is occupied by the highest bidding agent(s). This approach helps ensure a form of local equilibrium (subject to imperfect information on the part of most agents) along with useroptimal land allocation patterns. The model system was applied to the City of Austin and its extraterritorial jurisdiction. Multiple scenarios reveal the strengths and limitations of the market simulation and available data sets. While equilibrium prices in forecast years are generally lower than observed or expected, the spatial distributions of property values, new development, and individual agents are reasonable. Longer-term forecasts were generated to test the performance the model system. The forecasted households and firm distributions in year 2020 are consistent with expectations, but property prices are forecasted to experience noticeable changes. The model dynamics may be much improved by more appropriate maximum bid prices for each property. More importantly, this work demonstrates that microsimulation of real estate markets and the spatial allocation of households and firms is a viable pursuit. Such approaches herald a new wave of land use forecasting opportunities, for more effective policymaking and planning.Civil, Architectural, and Environmental Engineerin