Designing agents aiming to negotiate with human counterparts requires additional factors. In this work, we analyze the main elements of human negotiations in a structured human experiment. Particularly, we focus on studying the effect of negotiators being aware of the other side’s gain on the bidding behavior and the negotiation outcome. We compare the negotiations in two settings where one allows human negotiators to see their opponent’s utility and the other does not. Furthermore, we study what kind of emotional state expressed and arguments sent in those setups. We rigorously discuss the findings from our experiments.

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This paper describes an agent-based, incentive-driven, and privacy-preserving information sharing framework. Main contribution of the paper is to give the data provider agent an active role in the information sharing process and to change the currently asymmetric position between the provider and the requester of data and information (DI) to the favor of the DI provider. Instead of a binary yes/no answer to the requester’s data request and the incentive offer, the provider may negotiate about excluding from the requested DI bundle certain pieces of DI with high privacy value, and/or ask for a different type of incentive. We show the presented approach on a use case. However, the proposed architecture is domain independent.@en