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Behavioral research suggests that human learning in some multi-agent systems can be predicted with surprisingly simple “foresight-free” models. The current note discusses the implications of this research, and its relationship to the observation that social interactions tend to complicate learning.
I lay out a slight refinement of Shoham et al.'s taxonomy of agendas that I consider sensible for multiagent learning (MAL) research. It is not intended to be rigid: senseless work can be done within these agendas and additional sensible agendas may arise. Within each agenda, I identify issues and suggest directions. In the computational agenda, direct algorithms are often more efficient, but MAL...
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