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
Andy Clark (1998), Being There: Putting Brain, Body, and World Together
Again, MIT Press, Cambridge, MA, 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. 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
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
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
Abstract: Gode and Sunder report on continuous double auction 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 experiments with human subjects is due to the structure of the
auction 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
Discussion Paper, School of Agriculture and Resource Economics, The
University of Western Australia. Accessed on 5/19/03. 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
Abstract: "Auctions are increasingly being considered as
a mechanism for allocating conservation contracts to private landowners.
This interest is based on the widely held belief that competitive bidding
helps minimize information rents. This study constructs an agent-based model
to evaluate the long term performance of conservation auctions under settings
where bidders are allowed to learn from previous outcomes. The results
clearly indicate that the efficiency benefits of one-shot auctions are
quickly eroded under dynamic settings. Furthermore, the auction mechanism is
not found to be superior to fixed payment schemes except when the latter
involve the use of high prices."
Additional Note: This study begins with careful description of the
empirical auction problem at hand, and the difficulties encountered in trying
to use the existing theoretical auction 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 auctions.
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 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).
Other auction writings by Klemperer [e.g., What Really Matters in Auction Design (1999)] are
are available for downloading at
Klemperer's Papers Site.
Paul Klemperer (2004), Auctions: Theory and Practice(on-line version),
Princeton University Press, Princeton, NJ.
Abstract: "(This book) provides a nontechnical
introduction to auction 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 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",
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,
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. The chapter first reviews the use of evolutionary and
agent-based techniques of analyzing market behaviors and market
mechanisms, and economic models of learning, comparing genetic
algorithms with reinforcement learning. Ideal design would be
direct optimization of an objective function, but in practice the
complexity of markets and traders' behavior prevents this, except in
special circumstances. Instead, iterative analysis, subject to
design criteria trade-offs, using autonomous self-interested agents,
mimics the bottom-up evolution of historical market mechanisms by
trial and error. The chapter highlights ten papers that exemplify
recent progress in agent-based evolutionary analysis and design of
markets in silico, using electricity markets and on-line double
auctions as illustrations. A monopoly sealed-bid auction is
examined in the tenth paper, and a new auction mechanism is evolved
and analyzed. The chapter concludes that, as modeling the learning
and behavior of traders improves, and as the software and hardware
available for modeling and analysis improves, the techniques will
provide ever greater insights into improving the designs of existing
markets, and facilitating the design of new markets.
R. Preston McAfee and John McMillan (1987), "Auctions and
Bidding", Journal of Economic Literature 25, June, 699-738.
Kevin McCabe, Steven Rassenti, and Vernon L. Smith (1990), Auction
Design for Composite Goods: The Natural Gas Industry, Journal
of Economic Behavior and Organization 14, 127-149.
Alan Mehlenbacher (2009), "Multiagent System Simulations of Treasury Auctions", Computational Economics, Vol 34, 67-117. Published paper available from
This study uses a multiagent system to determine which payment rule provides the most revenue in treasury auctions. 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. I find that when the number of primary bidders is less than 13 (Canada) the Discriminatory payment rule is revenue superior to the Uniform payment across most market price spreads. When the number of primary bidders is greater than 14 (United States), Uniform payment is revenue superior to Discriminatory payment for all market price spreads. In general, revenue increases with the minimum bid constraint and with the number of primary dealers for Uniform, Average, and Vickrey payment rules.
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
Computational experiments with a multiagent system show that bidders use signal averaging to avoid the winner’s curse in English auctions. 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 outcomes in interesting ways—usually nonlinearly and sometimes non-monotonically. Of main concern to a seller is the effect of these factors on revenue. I find that revenue increases with the percent of common value in the two-dimensional value signal, decreases with uncertainty, and increases with the number of bidders. There is very little impact of information level on revenue when values are pure private and pure common. However, for the intermediate cases of two-dimensional value signals, revenue decreases with increased information.
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.
Gerald Thompson and Sten Thore (1992), Computational Economics, The
Scientific Press, New York.
Note: Chapters of particular relevance for
auction design are Chapter 1: Transportation Problems (9-21) and
Chapter 2: Discrete Auctions (23-32).
Steve Widergren, Junjie Sun, and Leigh Tesfatsion (2006), "Market Design Test Environments"(pdf,136KB),
Proceedings, IEEE Power Engineering Society General Meeting, Montreal, June.
Abstract: "Power industry restructuring continues
to evolve at multiple levels of system operations. At the bulk electricity
level, several organizations charged with regional system operation are
implementing versions of a Wholesale Power Market Platform (WPMP) in response
to U.S. Federal Energy Regulatory Commission initiatives. Recently the Energy
Policy Act of 2005 and several regional initiatives have been pressing the
integration of demand response as a resource for system operations. These
policy and regulatory pressures are driving the exploration of new market
designs at the wholesale and retail levels. The complex interplay among
structural conditions, market protocols, and learning behaviors in relation
to short-term and longer-term market performance demand a flexible computational
environment where designs can be tested and sensitivities to power system and market
rule changes can be explored. This paper discusses the use of agent-based
computational methods for the study of electricity markets at the wholesale and
retail levels, and explores distinctions in problem formulation between these levels."
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 in wholesale power markets. The perspective from game theory complements standard economic theory to examine effects on efficiency and incentives.
The AMES Market Package,
developed entirely in Java by an interdisciplinary team of researchers at Iowa State University,
is an extensible and modular agent-based test bed for studying the
performance of wholesale power markets restructured in
accordance with a market design recommended in April 2003 by the U.S. Federal Energy Regulatory Commission
and now implemented in over 50% of the U.S.
The framework models strategic traders interacting over time in an ISO-managed
wholesale power market operating over a transmission grid subject to
congestion effects. The day-ahead wholesale power market is a double auction.
Grid congestion in the day-ahead market is managed by means of locational
marginal prices derived from optimal power flow solutions.
The AMES Market Package is a free open-source tool suitable for research, teaching, and training
applications. It is designed for the intensive experimental study of small to medium-sized systems. A graphical
user interface permits the creation, modification, analysis and storage of scenarios,
parameter initialization and editing, specification of behavioral rules (e.g.
learning methods) for market participants, and output reports through table and chart displays.
AMES is an acronym for Agent-based Modeling of Electricity Systems.
(Economic Science Institute, Chapman University, Orange, CA): Design and tests of market performance;
Microeconomic models of economic environments; Experimental specifications of exchange mechanisms; Human
decision makers and models of human decision processes.
(Nuffield College, Oxford): Industrial economics theory and policy;
Competition policy; Microeconomic theory, particularly auction theory.
Deddy P. Koesrindartoto
(School of Business and Management, Bandung Institute of Technology, Indonesia): Industrial
organization; Financial economics; Computational economics; Electricity
markets; Market design.
(Australian Graduate School of Management,
University of New South Wales, Sydney, Australia): Strategic behavior in
markets with small numbers of sellers; Application of economic theory to
various social issues (e.g., illicit use of drugs, environmental impacts of
energy use); Learning and adaptive behavior in oligopolies.
(Harvard University, Cambridge): Game theory and experimental
economics; Market matching mechanisms in theory and practice.
(Department of Economics, University of Maryland, College Park, MD): Econometric and
computational methods for understanding and predicting human decision making
over time and under uncertainty; Artificial intelligence and automata trading
in double auction markets.
(Department of Economics, Iowa State University, Ames, Iowa): Market power
and efficiency in a computational electricity market with double-auction
pricing; Role of learning v. market protocols in determining outcomes in
double auctions and other types of markets.
M. Utku Ünver
(Department of Economics, Boston College, Chestnut Hill, MA): Social learning
in market games using genetic algorithms; Experimental economics; Game
theory; Two-sided and one-sided matching; Auctions.
(Electrical Engineering and Computer Science, University of
Michigan): Computational market mechanisms and their applications
to various distributed environments.
(Emeritus, Graduate School of Business, Stanford University): Auction
design, both theory and practice.