scholarly journals Learning to Reach Agreement in a Continuous Ultimatum Game

2008 ◽  
Vol 33 ◽  
pp. 551-574 ◽  
Author(s):  
S. De Jong ◽  
S. Uyttendaele ◽  
K. Tuyls

It is well-known that acting in an individually rational manner, according to the principles of classical game theory, may lead to sub-optimal solutions in a class of problems named social dilemmas. In contrast, humans generally do not have much difficulty with social dilemmas, as they are able to balance personal benefit and group benefit. As agents in multi-agent systems are regularly confronted with social dilemmas, for instance in tasks such as resource allocation, these agents may benefit from the inclusion of mechanisms thought to facilitate human fairness. Although many of such mechanisms have already been implemented in a multi-agent systems context, their application is usually limited to rather abstract social dilemmas with a discrete set of available strategies (usually two). Given that many real-world examples of social dilemmas are actually continuous in nature, we extend this previous work to more general dilemmas, in which agents operate in a continuous strategy space. The social dilemma under study here is the well-known Ultimatum Game, in which an optimal solution is achieved if agents agree on a common strategy. We investigate whether a scale-free interaction network facilitates agents to reach agreement, especially in the presence of fixed-strategy agents that represent a desired (e.g. human) outcome. Moreover, we study the influence of rewiring in the interaction network. The agents are equipped with continuous-action learning automata and play a large number of random pairwise games in order to establish a common strategy. From our experiments, we may conclude that results obtained in discrete-strategy games can be generalized to continuous-strategy games to a certain extent: a scale-free interaction network structure allows agents to achieve agreement on a common strategy, and rewiring in the interaction network greatly enhances the agents' ability to reach agreement. However, it also becomes clear that some alternative mechanisms, such as reputation and volunteering, have many subtleties involved and do not have convincing beneficial effects in the continuous case.

2016 ◽  
Vol 40 (2) ◽  
pp. 504-513 ◽  
Author(s):  
Lei Chen ◽  
Kaiyu Qin ◽  
Jiangping Hu

In this paper, we investigate a tracking control problem for second-order multi-agent systems. Here, the leader is self-active and cannot be completely measured by all the followers. The interaction network associated with the leader–follower multi-agent system is described by a jointly connected topology, where the topology switches over time and is not strongly connected during each time subinterval. We consider a consensus control of the multi-agent system with or without time delay and propose two categories of neighbour-based control rules for every agent to track the leader, then provide sufficient conditions to ensure that all agents follow the leader, and meanwhile, the tracking errors can be estimated. Finally, some simulation results are presented to demonstrate our theoretical results.


2021 ◽  
Author(s):  
Zhenwei Liu ◽  
Ali Saberi ◽  
Anton A. Stoorvogel ◽  
Donya Nojavanzadeh

2020 ◽  
Vol 34 (05) ◽  
pp. 7047-7054 ◽  
Author(s):  
Nicolas Anastassacos ◽  
Stephen Hailes ◽  
Mirco Musolesi

Social dilemmas have been widely studied to explain how humans are able to cooperate in society. Considerable effort has been invested in designing artificial agents for social dilemmas that incorporate explicit agent motivations that are chosen to favor coordinated or cooperative responses. The prevalence of this general approach points towards the importance of achieving an understanding of both an agent's internal design and external environment dynamics that facilitate cooperative behavior. In this paper, we investigate how partner selection can promote cooperative behavior between agents who are trained to maximize a purely selfish objective function. Our experiments reveal that agents trained with this dynamic learn a strategy that retaliates against defectors while promoting cooperation with other agents resulting in a prosocial society.


2021 ◽  
Vol 152 ◽  
pp. 104927
Author(s):  
Zhenwei Liu ◽  
Donya Nojavanzadeh ◽  
Dmitri Saberi ◽  
Ali Saberi ◽  
Anton A. Stoorvogel

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