Incentivising monitoring in open normative systems

We present an approach to incentivising monitoring for norm violations in open multi-agent systems such as Wikipedia. In such systems, there is no crisp definition of a norm violation; rather, it is a matter of judgement whether an agent’s behaviour conforms to generally accepted standards of behaviour. Agents may legitimately disagree about borderline cases. Using ideas from scrip systems and peer prediction, we show how to design a mechanism that incentivises agents to monitor each other’s behaviour for norm violations. The mechanism keeps the probability of undetected violations (submissions that the majority of the community would consider not conforming to standards) low, and is robust against collusion by the monitoring agents

Advanced agent technology : AAMAS 2011 Workshops : AMPLE, AOSE, ARMS, DOCM3AS, ITMAS : Taipei, Taiwan, May 2-6, 2011 : revised selected papers

The major methodologies of crowd simulation in a dynamic environments are either based on micro or macro models. These two types of models represent the trade-off between the level of detail and efficiency. The domain of pedestrian flow simulation on road networks is no exception and theories rely either on equation based model or agent based models. There is a growing interest in hybrid modeling that combines both of these types. This paper addresses the problem of combining both micro and macro models of pedestrians movement to speedup simulations. Hybrid model uses efficient macro modeling in part of the road networks that do not require a fine grained model and more detailed but less efficient micro modeling in critical locations. One key issue raised by such an approach and discussed is the consistency of the resulting hybrid model. Preliminary results presented in this article is a proof of concept that the use of hybrid model to simulate evacuation plan in road networks may be more efficient than the use of micro model alone

Incentive-compatible mechanisms for norm monitoring in open multi-agent systems

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, which apply to systems with a large number of agents, 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