Computational intelligence in economic games and policy design [Research Frontier]

Developing CI techniques for economic games and policies is a very promising and fast-growing field. Several interesting multi-disciplinary subfields exist, which require researchers of various disciplines to collaborate with each other and contribute to the advances of knowledge in this emerging new field. Obviously, in both computer science and economics, there are still many open questions and challenges, ranging from robustness issues to co-learning aspects, and from economic modeling, validation, and interpretation to large- scale simulation of complex adaptive systems. It is essential that such multi- disciplinary research challenges are tackled in a true multi disciplinary approach by both computer scientists and economists. We hope this short article will encourage more researchers and practitioners to join the exciting research in CI in economics.

[1]  Herbert Dawid,et al.  Learning benevolent leadership in a heterogenous agents economy , 2010 .

[2]  Han La Poutré,et al.  A Fast Method for Learning Non-linear Preferences Online Using Anonymous Negotiation Data , 2006, TADA/AMEC.

[3]  Elizabeth Sklar,et al.  Co-Evolution of Auction Mechanisms and Trading Strategies: Towards a Novel Approach to Microeconomic , 2002 .

[4]  Robert Axelrod,et al.  The Evolution of Strategies in the Iterated Prisoner's Dilemma , 2001 .

[5]  Michele Marchesi,et al.  Using an artificial financial market for assessing the impact of Tobin-like transaction taxes , 2008 .

[6]  Michael A. Goodrich,et al.  Learning to compete, compromise, and cooperate in repeated general-sum games , 2005, ICML.

[7]  Leigh Tesfatsion,et al.  Handbook of Computational Economics, Volume 2: Agent-Based Computational Economics (Handbook of Computational Economics) , 2006 .

[8]  X. Yao,et al.  How important is your reputation in a multi-agent environment , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

[9]  Angela L. Duckworth,et al.  The Economics and Psychology of Personality Traits , 2008, The Journal of Human Resources.

[10]  Karl Tuyls,et al.  An Overview of Cooperative and Competitive Multiagent Learning , 2005, LAMAS.

[11]  G. Loewenstein Emotions in Economic Theory and Economic Behavior , 2000 .

[12]  X. Yao Evolutionary stability in the n-person iterated prisoner's dilemma. , 1996, Bio Systems.

[13]  Herbert Gintis,et al.  Handbook of Computational Economics: Agent-Based Computational Economics (Handbook of Computational Economics S.) by K. L. Judd, L. Tesfatsion, M. D. Intriligator and Kenneth J. Arrow (eds.) , 2007, J. Artif. Soc. Soc. Simul..

[14]  Paul Windrum,et al.  Empirical Validation in Agent-based Models: Introduction to the Special Issue , 2007 .

[15]  B Johansson,et al.  Substantial genetic influence on cognitive abilities in twins 80 or more years old. , 1997, Science.

[16]  Alfons Balmann,et al.  Does structure matter? The impact of switching the agricultural policy regime on farm structures , 2008 .

[17]  Qub Montrkal,et al.  Genetic algorithm learning and the cobweb model , 2002 .

[18]  Xin Yao,et al.  An Experimental Study of N-Person Iterated Prisoner's Dilemma Games , 1993, Informatica.

[19]  Blake LeBaron,et al.  Introduction to the Special Issue on Agent-Based Models for Economic Policy Advice , 2008 .

[20]  Craig Boutilier,et al.  Local Utility Elicitation in GAI Models , 2005, UAI.

[21]  Herbert Dawid,et al.  EURACE: A massively parallel agent-based model of the European economy , 2008, Appl. Math. Comput..

[22]  Sander M. Bohte,et al.  Learning from induced changes in opponent (re)actions in multi-agent games , 2006, AAMAS '06.

[23]  Michael P. Wellman,et al.  Online learning about other agents in a dynamic multiagent system , 1998, AGENTS '98.

[24]  Peter Tiño,et al.  Measuring Generalization Performance in Coevolutionary Learning , 2008, IEEE Transactions on Evolutionary Computation.

[25]  M. P. Wellman,et al.  Price Prediction in a Trading Agent Competition , 2004, J. Artif. Intell. Res..

[26]  Leigh Tesfatsion,et al.  Introduction to the CE Special Issue on Agent-Based Computational Economics , 2001 .

[27]  Herbert Dawid,et al.  Skills, Innovation, and Growth: An Agent-Based Policy Analysis , 2008 .

[28]  陳樹衡,et al.  Co-Evolving Trading Strategies to Analyze Bounded Rationality in Double Auction Markets , 2009 .

[29]  Xin Yao,et al.  Co-Evolution in Iterated Prisoner's Dilemma with Intermediate Levels of Cooperation: Application to Missile Defense , 2002, Int. J. Comput. Intell. Appl..

[30]  Hans M. Amman,et al.  ON SOCIAL LEARNING AND ROBUST EVOLUTIONARY ALGORITHM DESIGN IN THE COURNOT OLIGOPOLY GAME , 2007, Comput. Intell..

[31]  Xin Yao,et al.  Does extra genetic diversity maintain escalation in a co-evolutionary arms race , 2000 .

[32]  Thomas Brenner,et al.  Agent Learning Representation - Advice in Modelling Economic Learning , 2004 .

[33]  Shou-De Lin,et al.  Designing the Market Game for a Trading Agent Competition , 2001, IEEE Internet Comput..

[34]  Thomas C. Schelling,et al.  Dynamic models of segregation , 1971 .

[35]  Derek W. Bunn,et al.  Agent-based simulation-an application to the new electricity trading arrangements of England and Wales , 2001, IEEE Trans. Evol. Comput..

[36]  Derek W. Bunn and Fernando Oliveira An Application of Agent-based Simulation to the New Electricity Trading Arrangements of England and Wales , 2001 .

[37]  Xin Yao,et al.  On Evolving Robust Strategies for Iterated Prisoner's Dilemma , 1993, Evo Workshops.

[38]  Franco Malerba,et al.  Competition and industrial policies in a ‘history friendly’ model of the evolution of the computer industry , 2001 .

[39]  Leigh Tesfatsion,et al.  Market power and efficiency in a computational electricity market with discriminatory double-auction pricing , 2001, IEEE Trans. Evol. Comput..

[40]  Paul Windrum,et al.  Empirical Validation of Agent-Based Models: Alternatives and Prospects , 2007, J. Artif. Soc. Soc. Simul..

[41]  Han La Poutré,et al.  Repeated Auctions with Complementarities , 2005, AMEC@AAMAS/TADA@IJCAI.

[42]  Yoav Shoham,et al.  New Criteria and a New Algorithm for Learning in Multi-Agent Systems , 2004, NIPS.

[43]  Xin Yao,et al.  Multiple Choices and Reputation in Multiagent Interactions , 2007, IEEE Transactions on Evolutionary Computation.

[44]  Valentin Robu,et al.  Modeling complex multi-issue negotiations using utility graphs , 2005, AAMAS '05.

[45]  Amy Greenwald,et al.  Bidding under Uncertainty: Theory and Experiments , 2004, UAI.

[46]  Michael Neugart,et al.  Labor Market Policy Evaluation with Ace , 2006 .

[47]  I. B. Vermeulen,et al.  An efficient turnkey agent for repeated trading with overall budget and preferences , 2004, IEEE Conference on Cybernetics and Intelligent Systems, 2004..

[48]  G. Fagiolo,et al.  Agent-based models for economic policy design: Introduction to the special issue , 2008 .

[49]  Jasmina Arifovic,et al.  An initial implementation of the Turing tournament to learning in repeated two-person games , 2006, Games Econ. Behav..

[50]  Nicholas R. Jennings,et al.  Developing a bidding agent for multiple heterogeneous auctions , 2003, TOIT.

[51]  Bin-Tzong Chie,et al.  Lottery markets design, micro-structure, and macro-behavior: An ACE approach , 2005 .

[52]  Haber Gottfried,et al.  Monetary and Fiscal Policy Analysis With an Agent-Based Macroeconomic Model , 2008 .

[53]  Enrico Gerding,et al.  Multi-Issue Negotiation Processes by Evolutionary Simulation, Validation and Social Extensions , 2003 .

[54]  J. Elster Emotions and Economic Theory , 1998 .

[55]  Enrico Gerding,et al.  Bilateral bargaining with multiple opportunities: knowing your opponent's bargaining position , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[56]  Fagiolo Giorgio,et al.  Special issue on "Agent-Based Models for Economic Policy Design" , 2008 .

[57]  Xin Yao,et al.  The Iterated Prisoners' Dilemma - 20 Years On , 2007, Advances in Natural Computation.

[58]  Sander M. Bohte,et al.  Market-based recommendation: Agents that compete for consumer attention , 2004, ACM Trans. Internet Techn..

[59]  Xin Yao,et al.  Behavioral diversity, choices and noise in the iterated prisoner's dilemma , 2005, IEEE Transactions on Evolutionary Computation.