ACE Research Area:
Automated Markets

Last Updated: 29 April 2012

Site Maintained By: Leigh Tesfatsion Department of Economics Iowa State University Ames, Iowa 50011-1070 http://www.econ.iastate.edu/tesfatsi/ tesfatsi AT iastate.edu Online ACE Course (Self-Study eBook) ACE Website

Table of Contents: Basic Issues Readings Software, Toolkits, and Demos Resource Sites, Groups, and Some Early Individual Researchers

Basic Issues

• If you were going to design a bot for e-commerce purposes, how would you go about it?
• What types of protocols should be set for automated markets, such as Internet auctions?
• How can rights to privacy be protected in automated markets? How much information should computational agents be allowed to collect? To disseminate?
• Has the growing reliance on the Internet, automated markets, and autonomous agents led to fundamentally new ways of creating economic value?

Readings

• M. R. Andersson and T. W. Sandholm (2001), "Leveled Commmitment Contracts with Myopic and Strategic Agents," Journal of Economic Dynamics and Control 25(3-4), March, pages 615-640.

• Phillip G. Bradford, Herbert E. Brown and Paula M. Saunders (2001), "Pricing, Agents, Perceived Value and the Internet" FirstMonday, Volume 6, Number 6, June.

  Abstract: "The Internet has changed the way people buy things. A pointed difference is the use of Internet auctions and bots. But, are these differences actually changing the role and function of price in the firm's marketing program? Are they possibly changing options for pricing, and perhaps even, the very notion of perceived value? Or in fact, does the new set of Internet pricing mechanisms merely require marketers to do what good marketers have always done, and that is to build customer-perceived value and use price to recapture it? The only difference may be that now, we can do it even better because we have better tools."

• Andy Clark (1997), Being There: Putting Brain, Body, and World Together Again, MIT Press, Cambridge, MA. Chapter 1: "Autonomous Agents: Walking on the Moon" (pp. 11-33), plus Leigh Tesfatsion (2002), "Notes on Clark Chapter 1" (html).

• Andy Clark (1997), Being There: Putting Brain, Body, and World Together Again, MIT Press, Cambridge, MA. Chapter 3: "Mind and World: The Plastic Frontier" (pp. 53-69), plus Leigh Tesfatsion (2002), "Notes on Clark Chapter 3" (html).

• Andy Clark (1997), Being There: Putting Brain, Body, and World Together Again, MIT Press, Cambridge, MA. Chapter 4: "Collective Wisdom, Slime-Mold Style" (pp. 71-84), plus Leigh Tesfatsion (2002), "Notes on Clark Chapter 4" (html).

• Andy Clark (1997), Being There: Putting Brain, Body, and World Together Again, MIT Press, Cambridge, MA. Chapter 9: "Minds and Markets" (pp. 179-192), plus L. Tesfatsion (2002), "Notes on Clark Chapter 9" (html).

• Dave Cliff and Janet Bruten, "Shop 'Til You Drop II: Collective Adaptive Behavior of Simple Autonomous Trading Agents in Simulated `Retail' Markets" (pdf,257KB). ON-LINE

  Can computational traders outperform human traders in certain types of markets? If so, what types, and how much "intelligence" does it take? This work builds on the seminal work of Gode and Sunder (JPE, 1993); see below.

• Brad DeLong (1998), "Speculative Microeconomics for Tomorrow's Economy (html), Working Paper, Department of Economics, University of California at Berkeley.

• 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 2004, available from Science Direct.

  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, pp. 119-137. Published article available at JSTOR.

  Gode and Sunder report on continuous double auction experiments with computational traders. They demonstrate 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 auction protocols restricting the order of executed trades. They 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?

• Amy R. Greenwald and Jeffrey Kephart (1999), "Shopbots and Pricebots", Sixteenth International Joint Conference on AI, Stockholm, Sweden, August, pp. 506-511. [This brief conference paper summarizes some of the authors' recent research on "shopbots," agents that automatically search the Internet for goods and services on behalf of consumers, and "pricebots," agents that set prices so as to maximize the profits of firms. Copies of this and related research articles are available from Amy Greenwald's publications site linked to her home page.]

• Jeffrey O. Kephart, James E. Hanson, and Amy R. Greenwald (2000), "Dynamic Pricing by Software Agents", Computer Networks, Special Issue on Trends and Research in e-Commerce, Vol. 32(6), pp. 731-752.

  This paper studies the potential impact on prices of the increasingly widespread reliance on "shopbots," agents that automatically search the Internet for goods and services on behalf of consumers, and "pricebots," agents that set prices so as to maximize the profits of firms. It also studies the price dynamics that might ensue from various mixtures of automated agents, the potential use of machine learning algorithms to improve profits, and more generally the interplay among learning, optimization, and dynamics in agent-based information economies.

• Jeffrey K. MacKie-Mason and Michael Wellman, "Automated Markets and Trading Agents", 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: Computer automation has the potential, just starting to be realized, of transforming the design and operation of markets, and the behaviors of agents trading in them. We discuss the possibilities for automating markets, presenting a broad conceptual framework covering resource allocation as well as enabling marketplace services such as search and transaction execution. One of the most intriguing opportunities is provided by markets implementing computationally sophisticated negotiation mechanisms, for example combinatorial auctions. An important theme that emerges from the literature is the centrality of design decisions about matching the domain of goods over which a mechanism operates to the domain over which agents have preferences. When the match is imperfect (as is almost inevitable), the market game induced by the mechanism is analytically intractable, and the literature provides an incomplete characterization of rational bidding policies. A review of the literature suggests that much of our existing knowledge comes from computational simulations, including controlled studies of abstract market designs (e.g., simultaneous ascending auctions), and research tournaments comparing agent strategies in a variety of market scenarios. An empirical game-theoretic methodology combines the advantages of simulation, agent-based modeling, and statistical and game-theoretic analysis.

• Beau Roy, "Using Agents to Make and Manage Markets Across a Supply Web," Complexity, Vol. 3/No. 4, March/April 1998, 31-36.

• Leigh Tesfatsion, "Learning Algorithms: Illustrative Examples (pdf,861KB).

  These slides review a variety of learning representations, such as derivative-follower reinforcement learning, Roth-Erov reactive reinforcement learning, anticipatory learning (e.g., Q-learning for dynamic programming value functions) evolutionary learning (e.g., genetic algorithms), and connectionist learning (e.g., artificial neural networks).

Software, Toolkits, and Demos

• Annotated list of pointers to ACE/CAS General Software and Toolkits

• Annotated list of pointers to ACE/CAS Computational Labs and Demonstration Software

Resource Sites, Groups, and Some Early Individual Researchers

• ACE/CAS Research Sites and Groups

• Individual Researchers using ACE/CAS Tools

• Filippo Menczer (School of Informatics and Department of Computer Science, Indiana University, Bloomington) organized a Fall 1999 seminar series on Complex Adaptive Systems and Their Business Applications at the University of Iowa, sponsored by the Santa Fe Institute. Interested readers can access a detailed report on this seminar series, including speakers, topic abstracts, powerpoint presentations, and downloadable papers and related resources, at the above seminar report site.

• Hal Varian (School of Information Management and Systems, UC Berkeley) maintains a Web site on The Information Economy focusing on the economics of the internet, information goods, intellectual property, and related issues.

• The UMBC Institute for Global Electronic Commerce maintains an extensive e-commerce directory (ecTechWeb) of news, information, and Internet resources for both product developers and academic researchers.

• Erik Aurell (Department of Computational Biology, KTH, Sweden): Kista, Sweden): Automated markets; Bioinformatics.

• Dave Cliff (Department of Computer Science, University of Bristol, UK): Adaptive software trading agents; Complex adaptive systems; Artificial life; Evolutionary computation (genetic algorithms).

• Peyman Faratin (Laboratory of Computer Science, MIT, Cambridge, MA): Application of artificial intelligence (AI) techniques in networks; Design of coordination mechanisms for intelligent software agents using techniques and models from AI, multi-agent systems, and economics.

• Amy R. Greenwald (Computer Science Department, Brown University, Providence, RI): Internet agent economics; Game theory and learning.

• Bernardo Huberman (Hewlett Packard Laboratories) heads a research effort in information dynamics and economics. His recent research has focused on the dynamics and growth of large-scale distributed systems such as electronic markets.

• Sverker Janson (Intelligent Systems Laboratory, Swedish Institute of Computer Science, Kista, Sweden): Software agents, electronic markets, and interactive environments.

• Nick Jennings (Electronics and Computer Science, University of Southampton, UK): Basic and applied research in agent-based computing; Process control; Business process management (agent-enabled workflow); E-commerce; Telecommunications network management; Virtual laboratories.

• Michael Luck (Professor of Computer Science and Head of the Department of Informatics at King's College London): Agents and intelligent systems

• Jeff MacKie-Mason (School of Information, University of Michigan, Ann Arbor): Computational market mechanisms and their applications to various distributed environments; Dynamic agent learning in information economies; Economically-intelligent artificial agents.

• Pattie Maes (MIT Media Laboratory, Cambridge, Massachusetts): Software agents; Electronic commerce; Artificial intelligence; Human-computer interaction; Computer-supported collaborative work; Information filtering.

• Filippo Menczer (School of Informatics and Computing, Indiana University, Bloomington): Scalable web and data mining applications; Integration of machine learning approaches with concepts from complex adaptive systems, artifical life and distributed agents; Applications to problems in information management, electronic commerce, computational economics, and biology.

• Augusto Rupérez Micola (Faculté de Droit, d'Economie et de Finance, University of Luxembourg): Market design; Market power; Energy markets; Experimental economics; Decision sciences.

• Valentin Robu (School of Electronics and Computer Science, University of Southampton, UK): Automated negotiation (especially in multi-issue negotiation models); Repeated and sequential auctions; Adaptive agent learning in electronic markets; Collaborative filtering and data mining; Applications to electronic commerce and transportation logistics.

• Tuomas Sandholm, (Agent-Mediated Electronic Marketplaces Lab, Computer Science Department, Carnegie Mellon University, Pittsburgh): Artificial intelligence; Electronic commerce; Game theory; Multiagent systems; Auctions and exchanges; Automated negotiations and contracting.

• Michael Wellman (Electrical Engineering and Computer Science, University of Michigan): Computational market mechanisms and their applications to various distributed environments (e.g., the Internet). Wellman was one of the developers of an on-line auction server (AuctionBot, now retired) that permitted participants to bid in existing auctions or to create WWW auctions of their own.

• Michael Wooldridge (Computer Science, University of Liverpool, UK): Formal methods for specifying and reasoning about multi-agent systems; Agent-oriented software engineering and negotiation, founder in 1997 of AgentLink, the European Network of Excellence in the area of agent-based computing.

Copyright © 2012 Leigh Tesfatsion. All Rights Reserved.