Cooperative peer-to-peer applications are designed to share the resources of each computer in an overlay network for the common good of everyone. However, users do not necessarily have an incentive to donate resources to the system if they can get the system's resources for free. This paper presents architectures for fair sharing of storage resources that are robust against collusions among nodes. We show how requiring nodes to publish auditable records of their usage can give nodes economic incentives to report their usage truthfully, and we present simulation results that show the communication overhead of auditing is small and scales well to large networks.

We examine the characteristics and relative strength of third-party sanctions in a series of experiments. We hypothesize that egalitarian distribution norms and cooperation norms apply in our experiments, and that third parties, whose economic payoff is unaffected by the norm violation, may be willing to enforce these norms although the enforcement is costly for them. Almost two-thirds of the third parties indeed punished the violation of the distribution norm and their punishment increased the more the norm was violated. Likewise, up to roughly 60 % of the third parties punished violations of the cooperation norm. Thus, our results show that the notion of strong reciprocity extends to the sanctioning behavior of ‘‘unaffected’’ third parties. In addition, these experiments suggest that thirdparty punishment games are powerful tools for studying the characteristics and the content of social norms. Further experiments indicate that second parties, whose economic payoff is reduced by the norm violation, punish the violation much more strongly than do third parties.

Recent experimental research has revealed forms of human behavior involving interaction among unrelated individuals that have proven difficult to explain in terms of kin or reciprocal altruism. One such trait, strong reciprocity is a predisposition to cooperate with others and to punish those who violate the norms of cooperation, at personal cost, even when it is implausible to expect that these costs will be repaid. We present evidence supporting strong reciprocity as a schema for predicting and understanding altruism in humans. We show that under conditions plausibly characteristic of the early stages of human evolution, a small number of strong reciprocators could invade a population of selfregarding types, and strong reciprocity is an evolutionary stable strategy. Although most of the evidence we report is based on behavioral experiments, the same behaviors are regularly described in everyday life, for example, in wage setting by firms, tax compliance, and cooperation in the protection

Abstract. Auctions are an area of great academic and commercial interest, from tiny auctions for toys on eBay to multi-billion-dollar auctions held by governments for resources or contracts. Although there has been significant research on auction theory, especially from the perspective of auction mechanisms, studies of autonomous bidding agents and their interactions are relatively few and recent. This paper examines several autonomous agent bidding strategies in the context of FAucS, a faithful simulation of a complex FCC spectrum auction. We introduce punishing randomized strategic demand reduction (PRSDR), a novel bidding strategy by which bidders can partition available goods in a mutually beneficial way without explicit inter-agent communication. When all use PRSDR, bidders obtain significantly better results than when using a reasonable baseline approach. The strategy automatically detects and punishes non-cooperating bidders to achieve robustness in the face of agent defection, and performs well under alternative conditions. The PRSDR strategy is fully implemented and we present detailed empirical results. 1

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Summary A collective action (CA), i.e., a group of individuals jointly producing a resource to be shared equally among themselves, is a common interaction in organizational contexts. Ancestral humans who were predisposed to cooperate in CAs would have risked being disadvantaged compared to free riders, but could have overcome this disadvantage through ‘greenbeard ’ reciprocity, that is, by assessing the extent to which co-interactants were also predisposed towards cooperation, and then cooperating to the extent that they expected co-interactants to reciprocate. Assessment of others ’ cooperativeness could have been based on the direct monitoring of, and on reputational information about, others ’ cooperativeness. This theory predicts that (1) CA participants should monitor accurately, and (2) perceived higher-cooperators should have better reputations. These predictions were supported in a study of real-life CAs carried out by a group of Shuar hunter-horticulturalists: (1) members accurately distinguished ‘intentional’ non-cooperators (who could have cooperated but chose not to) from ‘accidental ’ noncooperators (who were unable to cooperate), and their perceptions of co-member cooperativeness accurately reflected more objective measures of this cooperativeness; and (2) perceived intentional cooperators had better reputations than perceived intentional non-cooperators.

In this report we provide an overview of digital reputation management systems for virtual communities. We begin with a discussion on the how trust is created in the real world. We then highlight how the process of trust creation differs in the virtual world. Next, we talk about the different ways in which digital reputations are useful to virtual communities such as incentivizing cooperation and punishing ma-licious users, providing recommendations or as a form of distributed authentication. We then discuss how evolutionary biology and game theory have informed research in reputation systems. This is followed by a discussion of the various components of reputation such as context, the forms of evidence, first and second order repu-tation and architectural considerations including the role of distributed hash tables in decentralized reputation management systems. We conclude with a discussion of some reputation management systems such as complaints-based trust, EigenTrust, PeerTrust and ROCQ.

This chapter advances a new theory of altruism, competitive altruism, which might account for the uniquely moral altruistic tendency of humans. The need to form coalitions with non-kin for dealing with internal and external group threats created selective advantages for people with altruistic reputations. We present evidence from the anthropological, social psychological and nonhuman literatures, which by and large support competitive altruism theory. Finally, we discuss some implications of this theory for the establishment of reputation-based cooperation in modern human society.

A framework for studying the evolution of cooperative behaviour in a random environment, using evolution of finite state strategies, is presented. The interaction between agents is modelled by a repeated game with random observable payo#s. The agents are thus faced with a more complex situation, compared to the Prisoner's Dilemma that has been widely used for investigating the conditions for cooperation in evolving populations (Matsuo 1985; Axelrod 1987; Miller 1989; Lindgren 1992; Ikegami 1994; Lindgren & Nordahl 1994; Lindgren 1997). Still, there are robust cooperating strategies that usually evolve in a population of agents.