Design and Effects of Information Feedback in Continuous Combinatorial Auctions

Advancements in information technologies offer opportunities for designing and deploying innovative market mechanisms. For example, combinatorial auctions, in which bidders can bid on combinations of goods, can increase the economic efficiency of a trade when goods have complementarities. However, lack of real-time bidder support tools has been a major obstacle preventing this mechanism from reaching its full potential. This study uses novel feedback mechanisms to aid bidders in formulating bids in real-time to facilitate participation in continuous combinatorial auctions. Laboratory experiments examine the effectiveness of our feedback mechanisms; the study is the first to examine how bidders behave in such information-rich environments. Our results indicate that feedback results in higher efficiency and higher seller’s revenue compared to the baseline case where bidders are not provided feedback. Furthermore, contrary to conventional wisdom, even in complex economic environments, individuals effectively integrate rich information in their decision making

Online Auctions

The economic literature on online auctions is rapidly growing because of the enormous amount of freely available field data. Moreover, numerous innovations in auction-design features on platforms such as eBay have created excellent research opportunities. In this article, we survey the theoretical, empirical, and experimental research on bidder strategies (including the timing of bids and winner's-curse effects) and seller strategies (including reserve-price policies and the use of buy-now options) in online auctions, as well as some of the literature dealing with online-auction design (including stopping rules and multi-object pricing rules).

Toward Understanding the Dynamics of Bidder Behavior in Continuous Combinatorial Auctions: Agent-Based Simulation Approach

Combinatorial auctions represent sophisticated market mechanisms that are becoming increasingly important in various business applications due to their ability to improve economic efficiency and auction revenue, especially in settings where participants tend to exhibit more complex user preferences and valuations. While recent studies on such auctions have found heterogeneity in bidder behavior and its varying effect on auction outcomes, the area of bidder behavior and its impact on economic outcomes in combinatorial auctions is still largely underexplored. One of the main reasons is that it is nearly impossible to control for the type of bidder behavior in real world or experimental auction setups. We propose an agent-based modeling approach to replicate human bidder behavior in continuous combinatorial auctions and leverage our agents to simulate a wide variety of competition types, including experimentally unobserved ones that could not otherwise be studied. The capabilities of the proposed approach enable more comprehensive studies (via richer controlled experiments) of bidding behavior in the complex and highly dynamic decision environment of continuous combinatorial auctions

Environmental analysis for application layer networks

Die zunehmende Vernetzung von Rechnern über das Internet lies die Vision von Application Layer Netzwerken aufkommen. Sie umfassen Overlay Netzwerke wie beispielsweise Peer-to-Peer Netzwerke und Grid Infrastrukturen unter Verwendung des TCP/IP Protokolls. Ihre gemeinsame Eigenschaft ist die redundante, verteilte Bereitstellung und der Zugang zu Daten-, Rechen- und Anwendungsdiensten, während sie die Heterogenität der Infrastruktur vor dem Nutzer verbergen. In dieser Arbeit werden die Anforderungen, die diese Netzwerke an ökonomische Allokationsmechanismen stellen, untersucht. Die Analyse erfolgt anhand eines Marktanalyseprozesses für einen zentralen Auktionsmechanismus und einen katallaktischen Markt. --Grid Computing

Using Feedback to Mitigate Coordination and Threshold Problems in Iterative Combinatorial Auctions

Package bids, i.e., bids on sets of items, are an essential aspect of combinatorial auctions. They can allow bidders to accurately express their preferences. However, bidders on packages consisting of few items are often unable to outbid provisionally winning bids on large packages. To resolve this, both coordination as well as cooperation are needed. Coordination, since smaller bidders need to bid on packages that are disjoint; cooperation, since typically bid increases from more than one bidder are required to overcome the threshold to outbid a larger package bid. The authors design an information system that supports the implementation of an iterative combinatorial auction; this system is specifically aimed at helping bidders overcome coordination and threshold problems. They study the effect of information feedback on the behavior of bidders in different auction settings. The authors test this in an experimental setting using human bidders, varying feedback from very basic information about provisionally winning bids/prices, to providing more advanced concepts such as winning and deadness levels, and coalitional feedback. The experiment indicates that coalitional feedback has a positive impact on economic efficiency in cases where difficult threshold problems arise; however, it appears to have an adverse effect when threshold problems are easy

Rate of Price Discovery in Iterative Combinatorial Auctions

We study a class of iterative combinatorial auctions which can be viewed as subgradient descent methods for the problem of pricing bundles to balance supply and demand. We provide concrete convergence rates for auctions in this class, bounding the number of auction rounds needed to reach clearing prices. Our analysis allows for a variety of pricing schemes, including item, bundle, and polynomial pricing, and the respective convergence rates confirm that more expressive pricing schemes come at the cost of slower convergence. We consider two models of bidder behavior. In the first model, bidders behave stochastically according to a random utility model, which includes standard best-response bidding as a special case. In the second model, bidders behave arbitrarily (even adversarially), and meaningful convergence relies on properly designed activity rules

Providing Information Feedback to Bidders in Online Multi-unit Combinatorial Auctions

Bidders in online multi-unit combinatorial auctions face the acute problem of estimating the valuations of an immense number of packages. Can the seller guide the bidders to avoid placing bids that are too high or too low? In the single unit case, fast methods are now available for incrementally computing, for each package at each time instant, the recommended lower bound (Deadness Level) and upper bound (Winning Level) on the next bid. But when there are multiple units of items, it becomes difficult to compute the Deadness Level of a package accurately. An upper bound on this quantity can be derived however, and a bid that stays within this bound and the Winning Level is “safe”, in the sense that it is not wasted and has the potential to become a winning bid. What is now needed is an incremental procedure for speeding up the computation of this bound

Combinatorial auctions : theory, experiments, and practice

This doctoral dissertation contributes to theory, experiments, and practice in combinatorial auctions. Combinatorial auctions are multi-object auctions, that enable bidders to bid on packages of items. In Chapter 2, we theoretically investigate the classical winner determination problem in geometrical settings. Specifically, we consider combinatorial auctions of items that can be arranged in rows, and the objective is, given bids on subsets of items, to find a subset of bids that maximizes auction revenue. Possible practical applications include allocating pieces of land for real estate development, or seats in a theater or stadium. We investigate several geometrical structures and bid shapes, and provide either a polynomial dynamic programming algorithm or an NP-hardness proof, filling in several gaps in academic literature. In Chapter 3, we combine theory with experiments, investigating coordination and threshold problems in combinatorial auctions. Bidders on small packages of items are unable to outbid provisionally winning bids on large packages alone; despite free-rider incentives, both coordination and cooperation are required. Coordination because smaller bidders need to bid on disjoint packages, and cooperation because more than one bidder is required to outbid a larger package bid. We propose indices quantifying both the coordination and the threshold problem, that can be used in providing feedback or generating valuations for laboratory experiments. Additionally, we develop coalitional feedback that is specifically aimed at helping bidders to overcome coordination and threshold problems. We test this in an experimental setting using human bidders, varying feedback from provisionally winning bids and prices, to winning and deadness levels, and coalitional feedback. We find that in situations where threshold problems are severe, coalitional feedback increases economic efficiency, but in easy or insurmountable threshold problems that is not always the case. Finally, in Chapter 4, we combine theory with practice. Scheduling a conference, based on preferences expressed by conference participants, can be seen as a combinatorial auction with public goods. In a situation with public goods, the utility of the final selected goods (presentations scheduled in parallel) are "consumed" by all bidders (conference participants). Constructing a good conference schedule is important: they are an essential part of academic research and require significant investments (e.g.\ time and money) from their participants. We provide computational complexity results, along with a combined approach of assigning talks to rooms and time slots, grouping talks into sessions, and deciding on an optimal itinerary for each participant. Our goal is to maximize attendance, considering the common practice of session hopping. On a secondary level, we accommodate presenters’ availabilities. This personalized conference scheduling approach has been applied to construct the schedule of the MathSport (2013), MAPSP (2015 and 2017) and ORBEL (2017) conferences