We consider the problem of detecting norm violations in open multi-agent systems (MAS). We show how, using ideas from scrip systems, we can design mechanisms where the agents comprising the MAS are incentivised to monitor the actions of other agents for norm violations. The cost of providing the incentives is not borne by the MAS and does not come from fines charged for norm violations (fines may be impossible to levy in a system where agents are free to leave and rejoin again under a different identity). Instead, monitoring incentives come from (scrip) fees for accessing the services provided by the MAS. In some cases, perfect monitoring (and hence enforcement) can be achieved: no norms will be violated in equilibrium. In other cases, we show that, while it is impossible to achieve perfect enforcement, we can get arbitrarily close; we can make the probability of a norm violation in equilibrium arbitrarily small. We show using simulations that our theoretical results hold for multi-agent systems with as few as 1000 agents---the system rapidly converges to the steady-state distribution of scrip tokens necessary to ensure monitoring and then remains close to the steady state.
Scrip systems provide a nonmonetary trade economy for exchange of resources. We model a scrip system as a stochastic game and study system design issues on selection rules to match potential trade partners over time. We show the optimality of one particular rule in terms of maximizing social welfare for a given scrip system that guarantees players' incentives to participate. We also investigate the optimal number of scrips to issue under this rule. In particular, if the time discount factor is close enough to one, or trade benefits one partner much more than it costs the other, the maximum social welfare is always achieved no matter how many scrips are in the system. When the benefit of trade and time discount are not sufficiently large, on the other hand, injecting more scrips in the system hurts most participants; as a result, there is an upper bound on the number of scrips allowed in the system, above which some players may default. We show that this upper bound increases with the discount factor as we... Scrip is a generic term for any substitute for real currency; it can be converted into goods or services sold by the issuer. In the classic scrip system model, one agent is helped by another in return for one unit of scrip. In this paper, we present an upgraded model, the one-to-n scrip system, where users need to find n agents to accomplish a single task. We provide a detailed analytical evaluation of this system based on a game-theoretic approach. We establish that a nontrivial Nash equilibrium exists in such systems under certain conditions. We study the effect of n on the equilibrium, on the distribution of scrip in the system and on its performance. Among other results, we show that the system designer should increase the average amount of scrip in the system when n increases in order to optimize its efficiency. We also explain how our new one-to-n scrip system can be applied to foster cooperation in two privacy-enhancing applications. Peer-to-peer systems are typically designed around the assumption that all peers will willingly contribute resources to a global pool. They thus suffer from freeloaders, that is, participants who consume many more resources than they contribute. In this paper, we propose a general economic framework for avoiding freeloaders in peer-to-peer systems. Our system works by keeping track of the resource consumption and resource contribution of each participant. The overall standing of each participant in the system is represented by a single scalar value, called their karma. A set of nodes, called a bankset, keeps track of each node’s karma, increasing it as resources are contributed, and decreasing it as they are consumed. Our framework is resistant to malicious attempts by the resource provider, consumer, and a fraction of the members of the bank set. We illustrate the application of this framework to a peer-to-peer filesharing