A well-established body of research consistently shows that people involved in multiple-issue negotiations frequently select pareto-inferior agreements that "leave money on the table." Using an evolutionary computation approach, we show how simple, boundedly rational, artificial, adaptive agents can learn to perform as effectively as humans in stylized negotiations. Furthermore, there is the promise that these agents can be integrated into practicable electronic commerce systems which would not only leave less money on the table, but also would enable new types of transactions to be negotiated cost effectively.
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