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The complexity of automated negotiation in a multi-issue, incomplete-information and continuous-time environment poses severe challenges, and in recent years many strategies have been proposed in response to this challenge. For the traditional evolution, strategies are studied in games assuming that “globally” negotiates with all other participates. This evaluation, however, is not suited for negotiation settings that are primarily characterized by “local” interactions among the participating agents, that is, settings in which each of possibly many participating agents negotiates only with its local neighbors rather than all other agents. A new class of negotiation games is therefore introduced that take negotiation locality (hence spatial information about the agents) into consideration. It is shown how spatial evolutionary game theory can be used to interpret bilateral negotiation results among state-of-the-art strategies.
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