Rothschild, and three most helpful referees. Very special thanks for many years of helpful insights are due to Richard Day and Luigi Ermini. Hamlet: “What a piece of work is a man! how noble in reason! how infinite in faculties!”

Abstract: Agent-based computational economics (ACE) is the computational study of economies modeled as evolving systems of autonomous interacting agents. Thus, ACE is a specialization to economics of the basic complex adaptive systems paradigm. This study outlines the main objectives and defining characteristics of the ACE methodology, and discusses similarities and distinctions between ACE and artificial life research. Eight ACE research areas are identified, and a number of publications in each area are highlighted for concrete illustration. Open questions and directions for future ACE research are also considered. The study concludes with a discussion of the potential benefits associated with ACE modeling, as well some potential difficulties. Keywords: Agent-based computational economics; artificial life; learning; evolution of norms; markets; networks; parallel experiments with humans and computational agents; computational laboratories. 1

We develop a model of information processing and strategy choice for participants in a double auction. Sellers in this model form beliefs that an offer will be accepted by some buyer. Similarly, buyers form beliefs that a bid will be accepted. These beliefs

This report describes simple mechanisms that allow autonomous software agents to engage in bargaining behaviors in market-based environments. Groups of agents with such mechanisms could be used in applications including market-based control, internet commerce, and economic modelling. After an introductory discussion of the rationale for this work, and a brief overview of key concepts from economics, work in market-based control is reviewed to highlight the need for bargaining agents. Following this, the early experimental economics work of Smith (1962) and the recent results of Gode and Sunder (1993) are described.

Abstract-- This study reports experimental market power and efficiency outcomes for a computational wholesale electricity market operating in the short run under systematically varied concentration and capacity conditions. The pricing of electricity is determined by means of a clearinghouse double auction with discriminatory midpoint pricing. Buyers and sellers use a modified Roth-Erev individual reinforcement learning algorithm to determine their price and quantity offers in each auction round. It is shown that high market efficiency is generally attained, and that market microstructure is strongly predictive for the relative market power of buyers and sellers, independently of the values set for the reinforcement learning parameters. Results are briefly compared against results from an earlier study in which buyers and sellers instead engage in social mimicry learning via genetic algorithms. Index Terms – Wholesale electricity market, restructuring, repeated double auction, market power, efficiency, concentration, capacity, individual reinforcement learning, genetic algorithm social learning, agent-based computational economics. I.

. Collective intelligence is defined as the ability of a group to solve more problems than its individual members. It is argued that the obstacles created by individual cognitive limits and the difficulty of coordination can be overcome by using a collective mental map (CMM). A CMM is defined as an external memory with shared read/write access, that represents problem states, actions and preferences for actions. It can be formalized as a weighted, directed graph. The creation of a network of pheromone trails by ant colonies points us to some basic mechanisms of CMM development: averaging of individual preferences, amplification of weak links by positive feedback, and integration of specialised subnetworks through division of labor. Similar mechanisms can be used to transform the World-Wide Web into a CMM, by supplementing it with weighted links. Two types of algorithms are explored: 1) the co-occurrence of links in web pages or user selections can be used to compute a ma...

The use of the term institution has become widespread in the social sciences in recent years, reflecting the growth in institutional economics and the use of the institution concept in several other disciplines, including philosophy, sociology, politics, and geography. The term has a long history of usage in the social sciences, dating back at least to Giambattista Vico in his Scienza Nuova of 1725. However, even today, there is no unanimity in the definition of this concept. Furthermore, endless disputes over the definitions of key terms such as institution and organization have led some writers to give up matters of definition and to propose getting down somehow to practical matters instead. But it is not possible to carry out any empirical or theoretical analysis of how institutions or organizations work without having some adequate conception of what an institution or an organization is. This paper proposes that those that give up are acting in haste; potentially consensual definitions of these terms are possible, once we overcome a few obstacles and difficulties in the way. It is also important to avoid some biases in the study of institutions, where institutions and characteristics of a particular type are overgeneralized to the set

This paper describes the use of a genetic algorithm (GA) to find parameter-values for trading agents that operate in virtual "e-marketplaces", where the rules of the marketplaces are also under simultaneous control of the GA. The aim is to use the GA to automatically design new agent-based e-marketplaces that are more efficient than markets designed by (or populated by) humans. Das et al. (2001) recently demonstrated that ZIP software-agent traders consistently outperform human traders in Continuous Double Auction (CDA) marketplaces. Cliff (2001b) used a GA to explore a continuous space of auction mechanisms, with ZIP traders simultaneously evolving to operate efficiently in these evolved markets. The space of possible auction-types explored includes the CDA and also two purely one-sided mechanisms. Surprisingly, the GA did not settle on the CDA. Instead, in two experiments, optima were found at a one-sided auction mechanism; and in a third experiment a novel hybrid auction mechanism partway between the CDA and a onesided auction was evolved. This paper extends that research by studying the auction mechanisms that evolve when the market supply and demand schedules undergo a sudden "shock" change half-way through the evaluation process. It is shown that hybrid market mechanisms (again partway between the CDA and a one-sided mechanism) can evolve in place of the one-sided solutions that evolve when there are no market shocks. Furthermore it is demonstrated that the precise nature of the hybrid auction that evolves is dependent on the nature of the shock. These results indicate that the evolution of one-sided mechanisms reported by Cliff (2001b) is an artefact of using single fixed schedules, and that in general two-sided auctions will... evolve. These two-sided auctions m...

Introduction There has been much recent interest in studying the implications of economic behavior by simulating markets. For example, the group at the University of Arizona used the PLATO computer network to conduct laboratory experiments with double auctions using human participants 17 . More recent results from a double auction tournament with computer trading programs is reported by Rust et. al. 13 Smith 15 gives an overview of the literature on experimental economics. Work at Xerox PARC uses market-based ideas to allocate computer resources (see Huberman and Hogg 9 , Waldspurger et al. 16 , for example). The term "computational ecology" has been used to describe this emerging field. Finally, recent work at Cal Tech by Ledyard

Abstract not found