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Achieving Social Optimality with Influencer Agents

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Complex Sciences (Complex 2012)


In many multi-agent systems (MAS), it is desirable that the agents can coordinate with one another on achieving socially optimal outcomes to increase the system level performance, and the traditional way of attaining this goal is to endow the agents with social rationality [7] - agents act as system utility maximizers. However, this is difficult to implement when we are facing open MAS domains such as peer-to-peer network and mobile ad-hoc networks, since we do not have control on all agents’ behaviors in such systems and each agent usually behaves individually rationally as an individual utility maximizer only. In this paper, we propose injecting a number of influencer agents to manipulate the behaviors of individually rational agents and investigate whether the individually rational agents can eventually be incentivized to coordinate on achieving socially optimal outcomes. We evaluate the effects of influencer agents in two common types of games: prisoner’s dilemma games and anti-coordination games. Simulation results show that a small proportion of influencer agents can significantly increase the average percentage of socially optimal outcomes attained in the system and better performance can be achieved compared with that of previous work.

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© 2013 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering

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Hao, J., Leung, Hf. (2013). Achieving Social Optimality with Influencer Agents. In: Glass, K., Colbaugh, R., Ormerod, P., Tsao, J. (eds) Complex Sciences. Complex 2012. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 126. Springer, Cham.

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-03472-0

  • Online ISBN: 978-3-319-03473-7

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