To what extent do real-world auction markets satisfy the standard assumptions underlying auction market theory?
To what extent are auction market outcomes the result of market protocols vs. the result of the rationality and learning capabilities of the auction market participants?
Can even zero-intelligent agents do well in strongly structured auction markets (e.g., in double auctions)?
Efficiency, market power, security, and reliability concerns for auction markets
Andrew Byde (2002), Applying Evolutionary Game Theory to
Auction Mechanism Design(pdf,8pp.),
Hewlett-Packard Company, Report HPL-2002-321, November.
Abstract: "In this paper we describe an evolution-based method
for evaluating auction mechanisms, and apply it to a space of mechanisms that
including the standard first- and second-price sealed bid auctions. We
replicate results known already in the auction theory literature regarding
the suitability of different mechanisms for different bidder environments,
and extend the literature by establishing the superiority of novel mechanisms
over standard mechanisms, for commonly occurring scenarios. Thus this paper
simultaneously extends auction theory, and provides a systematic method for
further extension."
Andy Clark (1998), Being There: Putting Brain, Body, and World Together Again(EntireBook,pdf,2.4MB),
MIT Press, 308 pp., 1997, ISBN 0-262-53156-9. See Chapter 9: "Minds and Markets."
Dan Friedman and John Rust, eds. (1993), The Double
Auction Market: Institutions, Theories, and Evidence, Santa Fe
Institute Studies in the Sciences of Complexity, Proceedings Volume
XIV, New York: Addison-Wesley.
Steven Gjerstad and John Dickout (1998), "Price Formation in Double Auctions"(pdf preprint,283KB),
Games and Economic Behavior, Vol. 22, 1-29.
Abstract: The authors develop a model of information processing and
strategy choice for participants in a double auction market. Sellers form beliefs that an offer will be
accepted by some buyer, and buyers form beliefs that a bid will be accepted. These beliefs
are formed on the basis of observed market data, including frequencies of asks, bids, accepted
asks, and accepted bids. The traders choose an action that maximizes their own expected surplus.
The trading activity resulting from these beliefs and strategies is sufficient to achieve transaction
prices at competitive equilibrium and complete market efficiency after several periods of trading.
D. K. Gode and Shyam Sunder (2004), Double Auction Dynamics:
Structural Effects of Non-Binding Price Controls, Journal of Economic
Dynamics and Control, Vol. 28, No. 9, July, available from
Science Direct.
Abstract: The authors build a simple dynamic model of a double auction market
with "zero intelligence" (ZI) computer traders that accounts for many, though
not all, of the discrepancies between human-subject experimental data and
theoretical competitive equilibrium (Walrasian tatonnement) predictions.
They focus in particular on the effects of non-binding price controls (i.e.,
price floors below and ceilings above the competitive equilibrium).
D. K. Gode and Shyam Sunder (1993), "Allocative Efficiency of Markets
with Zero Intelligence Traders: Market as a partial substitute for individual
rationality"(pdf,1.4MB),
Journal of Political Economy, Vol. 101, No.
1, 1993, 119-137. ON-LINEJournal of Political Economy, Vol. 101, pp. 119-137.
Published article available at
JSTOR.
Abstract: Gode and Sunder report on continuous double auction market experiments with
computational traders. They find that high market efficiency is generally
obtained even when traders randomly select bids and offers from within their
budget sets as long as these "zero intelligence" traders abide by certain
protocols restricting the order of executed trades. The authors conclude
that the high market efficiency typically observed in continuous double
auction market experiments with human subjects is due to the structure of the
auction market and not to learning. Their seminal work has highlighted an important
issue now being actively pursued by many other researchers: what are the
relative roles of learning and institutional arrangements in the
determination of economic, social, and political outcomes?
Atakelty Hailu and Steven Schilizzi (2003), Learning in a
`Basket of Crabs': An Agent-Based Computational Model of Repeated Conservation Auctions(pdf,229KB),
Discussion Paper, School of Agriculture and Resource Economics, The University of Western Australia. The final published version
of the paper ("Are Auctions More Efficient than Fixed-Price Schemes When Bidders Learn?",
Australian Journal Of Management, Vol. 29, No. 2, 2004) is available at DOI: 10.1007/3-540-27296-8_3
Note: This study begins with careful description of the
empirical auction market at hand, and the difficulties encountered in trying
to use the existing theoretical auction market literature (constrained by analytical
tractability concerns) to investigate this problem. It also provides an
exceptionally thoughtful discussion of the potential role of human-subject
and computational-agent experiments in helping to advance the understanding
of real-world auction markets.
Charles A. Holt (1995), Industrial Organization: A Survey of
Laboratory Research, in: John H. Kagel and Alvin E. Roth,
Handbook of Experimental Economics, Princeton University Press,
Princeton, N.J., Chapter 5, pp. 349-443.
Abstract: This excellent survey by a well known experimental economist provides
a careful thoughtful look at human-subject laboratory experiments focusing on
market efficiency effects of alternative auction market mechanisms. The chapter is
organized around four themes: (1) monopoly regulation and potential entry;
(2) concentration and market power; (3) conditions that facilitate
cooperation; and (4) product differentiation.
Paul Klemperer (2000), Auction Theory: A Guide to the Literature(pdf,681KB),
Journal of Economic Surveys, 1999. Additional auction theory work by Klemperer can be obtained
here.
Paul Klemperer (2004), Auctions: Theory and Practice(on-line version),
Princeton University Press, Princeton, NJ.
Abstract: "(This book) provides a nontechnical
introduction to auction market theory, and emphasizes its practical
application. Although there are many extremely successful auction
markets, there have also been some notable fiascos, and Klemperer
provides many examples. He discusses the successes and failures of
the one-hundred-billion dollar `third generation' mobile phone
license auctions - he, jointly with Ken Bilmore, designed the first
of these. Klemperer also demonstrates the surprising power of
auction market theory to explain seemingly unconnected issues such as the
intensity of different forms of industrial competition, the costs of
litigation, and even stock trading `frenzies' and financial crashes."
Deddy P. Koesrindartoto (2004), "Treasury Auctions, Uniform or Discriminatory?: An Agent-Based Approach"(pdf,506KB),
Economics Working Paper No. 04013, Department of
Economics, Iowa State University, July.
Abstract: This study develops an agent-based
computational economics (ACE) framework to explore experimentally
how a Treasury should auction its securities. Buyers are modeled as
profit seekers capable of submitting strategic bids via
reinforcement learning. Two distinct auction pricing rules are
considered, uniform and discriminatory. The author shows that these
two rules result in systematically different auction outcomes under
different treatment conditions for relative capacity and for price
volatility in a secondary security market. In particular, which
auction pricing rule generates greater Treasury revenues varies
systematically with these treatment factor specifications. These
findings help to explain why previous Treasury auction studies
attempting to determine "the" best Treasury auction pricing rule
have reached contradictory conclusions.
Robert Marks (2006),
"Market Design Using Agent-Based Models",
in Leigh Tesfatsion and Kenneth L. Judd (editors),
Handbook of Computational Economics, Vol. 2: Agent-Based Computational
Economics, Handbooks in Economics Series, North-Holland/Elsevier, Amsterdam,
Spring 2006.
Abstract:
This chapter explores the state of the emerging practice of
designing markets by the use of agent-based modeling, with special
reference to electricity markets and computerized (on-line) markets,
perhaps including real-life electronic agents as well as human
traders.
Alan Mehlenbacher (2009), "Multiagent System Simulations of Treasury Auctions", Computational Economics, Vol 34, 67-117. Published paper available from
SpringerLink.
Abstract:
This study uses a multiagent system to determine which payment rule provides the most revenue in treasury auction markets. The agents learn how to bid using straightforward bid adjustment rules that are based on impulse balance learning. The market model encompasses the when-issued, auction, and secondary markets, as well as bidding constraints for primary dealers.
Alan Mehlenbacher (2009), "Multiagent System Simulations of Signal Averaging in English Auctions with Two-Dimensional Value Signals", Computational Economics, Vol 34, 119-143. Published paper available from
SpringerLink.
Abstract:
Computational experiments with a multiagent system show that bidders use signal averaging to avoid the winner’s curse in English auction markets. The results vary with the percent of common value in a two-dimensional value signal, information levels, uncertainty, and the number of bidders. The complexity introduced by the combinations of these factors affects the bidding strategies and auction market outcomes in interesting ways — usually nonlinearly and sometimes non-monotonically. Of main concern to a seller is the effect of these factors on revenue.
Paul Milgrom (2004), Putting Auction Theory to Work, Cambridge
University Press, Cambridge, UK.
Abstract: "This book provides a comprehensive
introduction to modern auction theory and its important new
applications. It is written by a leading economic theorist whose
suggestions guided the creation of the new spectrum auction designs.
Aimed at graduate students and professionals in economics, the book
gives the most up-to-date treatments of both traditional theories of
`optimal auctions' and newer theories of multi-unit auctions and
package auctions, and shows by example how these theories are used."
Alvin Roth (2002), "The Economist as Engineer: Game Theory,
Experimentation, and Computation as Tools for Design Economics"(pdf,412KB),
Econometrica, Vol. 70, No. 4 (July), 1341–1378
Abdolkarim Sadrieh (1998), The Alternating Double Auction Market: A
Game Theoretic and Experimental Investigation, Lecture Notes in Economics
and Mathematical Systems, Vol. 466, Springer, Berlin.
Leigh Tesfatsion (2020), A New Swing-Contract Design for Wholesale Power Markets(Slide-Set,1MB),
WebEx Presentation, sponsored by the Electric Power Research Institute(EPRI), September 18th.
Note: This talk includes a section explaining why centrally-managed wholesale power markets operating over high-voltage transmission grids do not satisfy a fundamental presumption of standard auction market theory: namely, that the transacted product is a commodity, i.e., an asset with a common unit of measurement permitting one unit to be substituted for another at any given time and location with no change in valuation.
Leigh Tesfatsion (2009), "Auction Basics for Wholesale Power Markets: Objectives and Pricing Rules"(Preprint,pdf,504KB),
(Talk-Slides,pdf,450KB),
Proceedings of the IEEE Power and Energy Society General Meeting, Calgary, Alberta, CA, July 26-30 (electronic).
Gerald Thompson and Sten Thore (1992), Computational Economics, The
Scientific Press, New York.
Note: Chapters of particular relevance for auction market design are Chapter 1: Transportation Problems (9-21) and Chapter 2: Discrete Auctions (23-32).
Robert Wilson (2002), "Architecture of Power Markets"(pdf preprint,75KB),
Econometrica, Volume 70, No. 4 (July), 1299-1340.
Abstract: Liberalization of infrastructure industries presents classic economic issues about how organization and procedure affect market performance. These issues are examined for wholesale power markets. The perspective from game theory complements standard economic theory to examine effects on efficiency and incentives.
tesfatsi AT iastate.edu
Online ACE Course (Self-Study eBook)
Basic Issues
