Experiments in Human Multi-Issue Negotiation: Analysis and Support
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
Learning on opponent's preferences to make effective multi-issue negotiation trade-offs
ICEC '04 Proceedings of the 6th international conference on Electronic commerce
Opponent modelling in automated multi-issue negotiation using Bayesian learning
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
The handbook of negotiation and culture
The handbook of negotiation and culture
Mind and heart of the negotiator, second edition, the
Mind and heart of the negotiator, second edition, the
An agent architecture for multi-attribute negotiation
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Modelling trade and trust across cultures
iTrust'06 Proceedings of the 4th international conference on Trust Management
| Hi-index | 0.00 |
Cross-cultural negotiation poses special challenges. In this paper an agent-based simulation model is presented that tackles these challenges. The model represents a trade network for a good with a hidden quality attribute. Agents have culture scripts that are based on Hofstede's dimensions of culture. They have visible group membership and status attributes that are used by their hidden cultural rules of behaviour. They bargain according to the ABMP bargaining model that has a utility function consisting of expected gain, quality, and risk. The paper presents the model and shows results of test runs. These test runs have face validity when compared with real negotiations. Formal tests of correspondence between the model and the trade game on which is it based have yet to be conducted. Extensions will make it a useful tool for training traders who engage in cross-cultural bargaining. The present version is helping to explain the behaviours of actors in international trade networks. It proves that Hofstede's dimensions can be used to generate agents that are believable negotiators.