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

The BitTorrent peer-to-peer file-sharing system attempts to build robustness to free-riding by implementing a tit-for-tat-like strategy within its protocol. It is often believed that this strategy alone is responsible for the the high-levels of cooperation found within the BitTorrent system. However, we highlight some of the weaknesses of the approach and indicate where it would be easy to cheat and free-ride. Given that cheating of this kind currently appears rare, this motivates the question: why is the system not dominated by free-riders? We advance a hypothesis which argues that BitTorrent may resist free-riders in a way that has not been previously fully comprehended. Ironically, this process relies on what is commonly believed to be a weakness of BitTorrent - the lack of meta-data search. One consequence of this is to partition the BitTorrent network into numerous isolated swarms - often with several independent swarms for an identical file - which is one of the necessary conditions for a kind of evolutionary group selective process, a process that has been recently identified in similar simulated systems.

this paper, we present a framework for a distributed Competitive Attentionspace System, CASy, to allocate the scarce resource that is consumer attention via the techniques of dynamic market-based control [Clearwater 1995; Cheng and Wellman 1998; Gibney et al. 1999] and adaptive software agents [Weiss 1999; Guttman et al. 1998; Kephart et al. 2000]. In the example of an electronic shopping mall, CASy recommends shops to a consumer: the task of matching a consumer to a set of suitable shops is delegated to the individual shops, each of which evaluates the information that is available about the consumer and his or her interests (the consumer's interests and other information which the consumer is willing to provide; e.g. keywords, product queries, and available parts of a profile). Based on this information and on their domain knowledge, shops can make a monetary bid in an auction where a limited amount of consumer attention space, or banners, for the particular consumer is sold. To facilitate CASy, the system is designed as a multi-agent system where each shop is represented by a software agent that executes the task of bidding for the attention of each individual consumer. The use of learning software agents allows shops to rapidly adapt their bidding strategy such that they only bid for consumers that are likely to be interested in their offerings. Furthermore, efficient bidding for each customer is only feasible when automated: hence the use of software agents. These agents allow a shop to process a large number of small transactions, and enable them to make a deliberated bid for every customer entering the shopping mall

Abstract: Agent-based computational economics (ACE) is the computational study of economies modeled as evolving systems of autonomous interacting agents. Starting from initial conditions, specified by the modeler, the computational economy evolves over time as its constituent agents repeatedly interact with each other and learn from these interactions. ACE is therefore a bottom-up culture-dish approach to the study of economic systems. This chapter discusses the key characteristics and goals of the ACE methodology. Eight currently active research areas are highlighted for concrete illustration. Potential advantages and disadvantages of the ACE methodology are considered, along with open questions and possible directions for future research. Keywords: Agent-based computational economics; autonomous agents; interaction networks; learning; evolution; mechanism design; computational experiments; object-oriented programming. 1

Agent-based computational economics (ACE) is the computational study of economies modelled as evolving systems of autonomous interacting agents. Thus, ACE is a specialization to economics of the basic complex adaptive systems paradigm. This paper outlines the main objectives and defining characteristics of the ACE methodology, and discusses several active research areas. Key words: Agent-based computational economics, complex adaptive systems 1

Arthur [1, 2] provided a model to explain the circumstances that lead to technological lock-in into a specific trajectory. We contribute substantially to this area of research by investigating the circumstances under which technological development may break-out of a trajectory. We argue that for this to happen, a third selection mechanism— beyond those of the market and of technology—needs to upset the lock-in. We model the interaction, or mutual shaping among three selection mechanisms, and thus this paper also allows for a better understanding of when a technology will lock-in into a trajectory, when a technology may break-out of a lock-in, and when competing technologies may co-exist in a balance. As a system is conceptualized to gain a (third) degree of freedom, the possibility of bifurcation is introduced into the model. The equations, in which interactions between competition and selection mechanisms can be modeled, allow one to specify conditions for lock-in, competitive balance, and break-out.

Stock markets are very important in modern societies and their behaviour have serious implications in a wide spectrum of the world’s population. Investors, governing bodies and the society as a whole could benefit from better understanding of the behaviour of stock markets. The traditional approach to analyze such systems is the use of analytical models. However, the complexity of financial markets represents a big challenge to the analytical approach. Most analytical models make simplifying assumptions, such as perfect rationality and homogeneous investors, which threaten the validity of analytical results. This motivates the use of alternative methods. For those reasons, the study of such markets is a fertile field to use the agent-based methodology. In this work, we developed an artificial financial market and used it to study the behaviour of stock markets. In this market, we model technical, fundamental and noise traders. The technical traders are non-simple genetic programming based agents that co-evolve (by means of their fitness function) by predicting investment opportunities in the market using technical analysis as the main tool. Such traders are equipped with

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* Much of the material in this paper arises from the collaborative work done with Nicolas Jonard, and owes much to him. I also acknowledge the very helpful comments of Muge Ozman and encouraging words of Franco Malerba. 1

In a model of dynamic duopoly, optimal price policies are characterized assuming consumers learn adaptively about the relative quality of the two products. A contrast is made between belief-based and reinforcement learning. Under reinforcement learning, consumers can become locked into the habit of purchasing inferior goods. Such lock-in permits the existence of multiple history-dependent asymmetric steady states in which one firm dominates. In contrast, belief-based learning rules must lead asymptotically to correct beliefs about the relative quality of the two brands and so in this case there is a unique steady state.