Toward Strategic Human Skill Development Through Human and Agent Interaction: Improving Negotiation Skill by Interacting with Bargaining Agent

Atsushi Otaki; Kiyohiko Hattori; Keiki Takadama

doi:10.20965/jaciii.2010.p0831

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JACIII Vol.14 No.7 pp. 831-839

doi: 10.20965/jaciii.2010.p0831

(2010)

Paper:

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Toward Strategic Human Skill Development Through Human and Agent Interaction: Improving Negotiation Skill by Interacting with Bargaining Agent

Atsushi Otaki, Kiyohiko Hattori, and Keiki Takadama

The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, Tokyo 182-8585, Japan

Received:

April 16, 2010

Accepted:

July 16, 2010

Published:

November 20, 2010

Keywords:

human skill development, agent, interaction bargaining game, e-learning

Abstract

This paper focuses on developing human skills through interaction between a human player and a computer agent, and explores its strategic method through experiments on the bargaining games where human players negotiate with computer agents. Specifically, human players negotiate with three types of agents: (a) strong/weak attitude agents making aggressive/defensive proposals in advantageous/disadvantageous situations; (b) fair agents making fair proposals; and (c) the “human-like” agents making mutually agreeable proposals as the number of games increases. Analysis of the human subject experiments has revealed the three major implications: (1) human players negotiating with the strong/weak attitude agents obtain the largest profit overall; (2) human players negotiating with “human-like” agents win many games; and (3) no relationship exists between profit maximization and a win of the games.

Cite this article as:

A. Otaki, K. Hattori, and K. Takadama, “Toward Strategic Human Skill Development Through Human and Agent Interaction: Improving Negotiation Skill by Interacting with Bargaining Agent,” J. Adv. Comput. Intell. Intell. Inform., Vol.14 No.7, pp. 831-839, 2010.

Data files:

References

[1] R. M. Axelrod, “The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration,” Princeton University Press, 1997.
[2] N. Gilbert and F. G. Troitzsch, “Simulation for the Social Scientist,” Open University Press, 2005.
[3] A. Oshima, “The Progress and Issues of IT based Human Resource Development in Japanese corporations,” The 14th Symposium on Social Information Systems Symposium, pp. 99-104, 2008. (in Japanese)
[4] CIPD, “People Management and Technology: Progress and Potential,” pp. 14-28. 2005.
[5] P. Emma and T. Shuan, “Technology in HRM: The Means to Become Strategic Business Partner?,” in Storey John (Ed.), Human Resource Management, Thomson Learning, pp. 235-249, 2007.
[6] A. Rubinstein, “Perfect Equilibrium in a Bargaining Model,” Econometrica, Vol.50, No.1, pp. 97-109, 1982.
[7] A. Muthoo, “Bargaining Theory with Applications,” Cambridge University Press, 1999.
[8] M. J. Osborne and A. Rubinstein, “A Course in Game Theory,” MIT Press, 1994.
[9] G. Kersten and S. Noronha, “Negotiation and the Web: User’s Perceptions and Acceptance,” IIASA, IR-98-002, 1998.
[10] G. Kersten and S. Noronha, “Supporting International Negotiation with a WWW-based System,” IIASA, IR-97-49, 1997.
[11] H. Raiffa, “The Art and Science of Negotiation,” The Belknap Press of Harvard University press, 2002.
[12] T. Kawai, Y. Koyama, and K. Takadama, “Modeling Sequential Bargaining Game Agents Towards Human-like Behaviors: Comparing Experimental and Simulation Results,” The FirstWorld Congress of the Int. Federation for Systems Research (IFSR’05), pp. 164-166. 2005.
[13] K. Takadama, T. Kawai, and T. Koyama, “Micro- and Macro-Level Validation in Agent-Based Simulation: Reproduction of Human-Like Behaviors and Thinking In a Sequential Bargaining Game,” J. of Artificial Societies and Social Simulation (JASSS), Vol.11, No.2, 2008.
[14] C. J. C. H. Watkins and P. Dayan, “Technical note: Q-learning,” Machine Learning, Vol.8, pp. 55-68.
[15] R. S. Sutton, and A. G. Bart, “Reinforcement Learn-ing –An Introduction–,” The MIT Press, 1998.

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] R. M. Axelrod, “The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration,” Princeton University Press, 1997.

[2] [2] N. Gilbert and F. G. Troitzsch, “Simulation for the Social Scientist,” Open University Press, 2005.

[3] [3] A. Oshima, “The Progress and Issues of IT based Human Resource Development in Japanese corporations,” The 14th Symposium on Social Information Systems Symposium, pp. 99-104, 2008. (in Japanese)

[4] [4] CIPD, “People Management and Technology: Progress and Potential,” pp. 14-28. 2005.

[5] [5] P. Emma and T. Shuan, “Technology in HRM: The Means to Become Strategic Business Partner?,” in Storey John (Ed.), Human Resource Management, Thomson Learning, pp. 235-249, 2007.

[6] [6] A. Rubinstein, “Perfect Equilibrium in a Bargaining Model,” Econometrica, Vol.50, No.1, pp. 97-109, 1982.

[7] [7] A. Muthoo, “Bargaining Theory with Applications,” Cambridge University Press, 1999.

[8] [8] M. J. Osborne and A. Rubinstein, “A Course in Game Theory,” MIT Press, 1994.

[9] [9] G. Kersten and S. Noronha, “Negotiation and the Web: User’s Perceptions and Acceptance,” IIASA, IR-98-002, 1998.

[10] [10] G. Kersten and S. Noronha, “Supporting International Negotiation with a WWW-based System,” IIASA, IR-97-49, 1997.

[11] [11] H. Raiffa, “The Art and Science of Negotiation,” The Belknap Press of Harvard University press, 2002.

[12] [12] T. Kawai, Y. Koyama, and K. Takadama, “Modeling Sequential Bargaining Game Agents Towards Human-like Behaviors: Comparing Experimental and Simulation Results,” The FirstWorld Congress of the Int. Federation for Systems Research (IFSR’05), pp. 164-166. 2005.

[13] [13] K. Takadama, T. Kawai, and T. Koyama, “Micro- and Macro-Level Validation in Agent-Based Simulation: Reproduction of Human-Like Behaviors and Thinking In a Sequential Bargaining Game,” J. of Artificial Societies and Social Simulation (JASSS), Vol.11, No.2, 2008.

[14] [14] C. J. C. H. Watkins and P. Dayan, “Technical note: Q-learning,” Machine Learning, Vol.8, pp. 55-68.

[15] [15] R. S. Sutton, and A. G. Bart, “Reinforcement Learn-ing –An Introduction–,” The MIT Press, 1998.