קובי גל

Many environments in which people and computer agents interact involve deploying resources to accomplish tasks and satisfy goals. This paper investigates the way that the context in which decisions are made affects the behavior of people and the performance of computer agents that interact with people in such environments. It presents experiments that measured negotiation behavior in two different types of settings. One setting was a task context that made explicit the relationships among goals, (sub)tasks and resources. The other setting was a completely abstract context in which only the payoffs for the decision choices were listed. Results show that people are more helpful, less selfish, and less competitive when making decisions in task contexts than when making them in completely abstract contexts. Further, their overall performance was better in task contexts. A predictive computational model that was trained on data obtained in the task context outperformed a model that was trained under the abstract context. These results indicate that taking context into account is essential for the design of computer agents that will interact well with people.