TY - JOUR
T1 - Incentive-compatible mechanisms for norm monitoring in open multi-agent systems
AU - Alechina, Natasha
AU - Halpern, Joseph Y.
AU - Kash, Ian A.
AU - Logan, Brian
N1 - Publisher Copyright:
© 2018 AI Access Foundation.All rights reserved.
PY - 2018/6/1
Y1 - 2018/6/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85049736796&partnerID=8YFLogxK
U2 - 10.1613/jair.1.11214
DO - 10.1613/jair.1.11214
M3 - Article
AN - SCOPUS:85049736796
SN - 1076-9757
VL - 62
SP - 433
EP - 458
JO - Journal of Artificial Intelligence Research
JF - Journal of Artificial Intelligence Research
ER -