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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

Economists have lately been called upon not only to analyze markets, but to design them. Market design involves a responsibility for detail, a need to deal with all of a market’s complications, not just its principle features. Designers therefore cannot work only with the simple conceptual models used for theoretical insights into the general working of markets. Instead, market design calls for an engineering approach. Drawing primarily on the design of the entry level labor market for American doctors (the National Resident Matching Program), and of the auctions of radio spectrum conducted by the Federal Communications Commission, this paper makes the case that experimental and computational economics are natural complements to game theory in the work of design. The paper also argues that some of the challenges facing both markets involve dealing with related kinds of complementarities, and that this suggests an agenda for future theoretical research.

This paper provides evidence that free riders are heavily punished even if punishment is costly and does not provide any material benefits for the punisher. The more free riders negatively deviate from the group standard the more they are punished. As a consequence, the existence of an opportunity for costly punishment causes a large increase in cooperation levels because potential free riders face a credible threat. We show, in particular, that in the presence of a costly punishment opportunity almost complete cooperation can be achieved and maintained although, under the standard assumptions of rationality and selfishness, there should be no cooperation at all. We also show that free riding causes strong negative emotions among cooperators. The intensity of these emotions is the stronger the more the free riders deviate from the group standard. Our results provide, therefore, support for the hypothesis that emotions are guarantors of credible threats.

In this paper we present a new improved design for multi-object auctions and report on the results of experimental tests of that design. We merge the better features of two extant but very di#erent auction processes, the Simultaneous Multiple Round (SMR) design used by the FCC to auction spectrum and the Adaptive User Selection Mechanism (AUSM) of Banks, Ledyard, and Porter (1989). Then, by adding one crucial new feature, we are able to create a new design, the Resource Allocation Design (RAD) auction process, which performs better than both. Our experiments demonstrate, in both simple and complex environments, that the RAD auction achieves higher e#ciencies, lower bidder losses, and faster times to completion without increasing the complexity of a bidder's problem.

Most economic models are based on the self-interest hypothesis that assumes that all people are exclusively motivated by their material self-interest. In recent years experimental economists have gathered overwhelming evidence that systematically refutes the self-interest hypothesis and suggests that many people are strongly motivated by concerns for fairness and reciprocity. Moreover, several theoretical papers have been written showing that the observed phenomena can be explained in a rigorous and tractable manner. These theories in turn induced a new wave of experimental research offering additional exciting insights into the nature of preferences and into the relative performance of competing theories of fairness. The purpose of this paper is to review these recent developments, to

We compare a partners condition where the same small group of subjects plays a repeated public good game to a strangers condition where subjects play this game in changing group formations. Subjects in the partners condition contribute from the first period on significantly more to the public good than subjects in the strangers condition. In the strangers condition, contributions show a continual decay, while in the partners condition, contributions fluctuate on a high level until they decrease in the final periods. We interpret subjects' behavior in terms of conditional cooperation which is characterized by both future-oriented and reactive behavior. Keywords: Experimental Economics, Public Goods, Voluntary Contributions, Cooperation JEL-Code: C92, H41, * The authors want to thank Simon Gchter, Roy Gardner, Alfred Mller, Reinhard Selten, Bodo Vogt, and two anonymous referees for their valuable comments on an earlier version of this paper. Thanks are also due to Otto Perdeck for pro...

Member-maintained communities ask their users to perform tasks the community needs. From Slashdot, to IMDb, to Wikipedia, groups with diverse interests create communitymaintained artifacts of lasting value (CALV) that support the group’s main purpose and provide value to others. Said communities don’t help members find work to do, or do so without regard to individual preferences, such as Slashdot assigning meta-moderation randomly. Yet social science theory suggests that reducing the cost and increasing the personal value of contribution would motivate members to participate more. We present SuggestBot, software that performs intelligent task routing (matching people with tasks) in Wikipedia. SuggestBot uses broadly applicable strategies of text analysis, collaborative filtering, and hyperlink following to recommend tasks. SuggestBot’s intelligent task routing increases the number of edits by roughly four times compared to suggesting random articles. Our contributions are: 1) demonstrating the value of intelligent task routing in a real deployment; 2) showing how to do intelligent task routing; and 3) sharing our experience of deploying a tool in Wikipedia, which offered both challenges and opportunities for research.

We consider the dynamic private provision of funds to projects that generate public benefits. Participants have complete information about the environment, but imperfect information about individual actions: each period they observe only the aggregate contribution. Each player may contribute any amount in any period before the contributing horizon is reached. All Nash equilibrium outcomes are characterized. In many cases they are all also perfect Bayesian equilibrium outcomes. If the horizon is long, if the players ’ preferences are similar, and if they are patient or the period length is short, perfect Bayesian equilibria exist that essentially complete the project. In some of them the completion time shrinks to zero with the period length—efficiency is achieved in the limit. 1.