The Influence of Task Contexts on the Decision-Making of Humans and Computers

  • Ya’akov Gal
  • Barbara Grosz
  • Avi Pfeffer
  • Stuart Shieber
  • Alex Allain
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4635)


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.


Task Condition Table Condition Task Context Nash Equilibrium Strategy Abstract Context 
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  1. 1.
    Das, R., Hanson, J.E., Kephart, J.O., Tesauro, G.: Agent-human interactions in the continuous double auction. In: Nebel, B. (ed.) Proc. 17th International Joint Conference on Artificial Intelligence (IJCAI 2001) (2001)Google Scholar
  2. 2.
    Gal, Y., Pfeffer, A.: Predicting people’s bidding behavior in negotiation. In: Stone, P., Weiss, G. (eds.) Proc. 5th International Joint Conference on Multi-agent Systems (AAMAS 2006) (2006)Google Scholar
  3. 3.
    Gal, Y., Pfeffer, A., Marzo, F., Grosz, B.: Learning social preferences in games. In: Proc. 19th National Conference on Artificial Intelligence (AAAI 2004) (2004)Google Scholar
  4. 4.
    Grosz, B., Kraus, S., Talman, S., Stossel, B.: The influence of social dependencies on decision-making. Initial investigations with a new game. In: Kudenko, D., Kazakov, D., Alonso, E. (eds.) Adaptive Agents and Multi-Agent Systems II. LNCS (LNAI), vol. 3394, Springer, Heidelberg (2005)Google Scholar
  5. 5.
    Jarvenpaa, S.L.: The effect of task demands and graphical format on information processing strategies. Management Science 35(3), 285–303 (1989)CrossRefGoogle Scholar
  6. 6.
    Kahneman, D., Knetsch, J.L., Thaler, R.H.: The endowment effect, loss aversion, and status quo bias. Journal of Economic Perspectives 5, 193–206 (1991)Google Scholar
  7. 7.
    Kahneman, D., Tversky, A. (eds.): Choices, Values, and Frames. Cambridge University Press, Cambridge (2000)Google Scholar
  8. 8.
    Liberman, V., Samuels, S., Ross, L.: The name of the game: Predictive power of reputation vs. situational labels in dertermining prisoners’ dilemma game movesGoogle Scholar
  9. 9.
    Marzo, F., Gal, Y., Grosz, B., Pfeffer, A.: Social preferences in relational contexts. In: Fourth Conference in Collective Intentionality (2005)Google Scholar
  10. 10.
    Pollack, M.E.: Intelligent technology for an aging population: The use of AI to assist elders with cognitive impairment. AI Magazine 26(9) (2006)Google Scholar
  11. 11.
    Tversky, A., Kahneman, D.: The framing of decisions and the psychology of choice. Science 211, 452–458 (1981)CrossRefMathSciNetGoogle Scholar
  12. 12.
    Tversky, A., Simonson, I.Google Scholar
  13. 13.
    Vessey, I.: Cognitive fit: A theory-based analysis of the graphs versus tables literature. Decision Sciences 22(2), 219–240 (1991)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Ya’akov Gal
    • 1
    • 2
  • Barbara Grosz
    • 2
  • Avi Pfeffer
    • 2
  • Stuart Shieber
    • 2
  • Alex Allain
    • 3
  1. 1.MIT Computer Science and Artificial Intelligence Laboratory, Cambridge MA 02139 
  2. 2.Harvard School of Engineering and Applied Sciences, Cambridge MA 02138 
  3. 3.Liquid Machines, Inc., Waltham MA 02451 

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