On the Benefits of Using Artificial Intelligence in the Negotiation Process

Abstract

Background. The integration of artificial intelligence (AI) into negotiation process has become a response to the challenges of today's highly complex and dynamic business environment. Growing volumes of information and the need to make strategic decisions in the face of uncertainty make traditional approaches to negotiation ineffective. Research into the impact of AI not only on the negotiation outcomes but also on the behavioral aspects of interactions between participants is particularly relevant.

Objective. To analyze the effectiveness of using AI technologies in the context of the socio-psychological aspects of negotiation process.

Study Participants. The study involved 60 people (aged 22–23 years), senior students of the Moscow Aviation Institute (National Research University).

Methods. The study implemented methods of laboratory experiment, observation, nonparametric statistical analysis using the Mann — Whitney U-test to test hypotheses in case of deviation from the normal data distribution.

Results. The study demonstrated that using AI in the negotiation process significantly increases the economic effectiveness of transactions (average benefit), contributes to the shift of dominant behavioral strategies towards cooperation and improves socio-psychological indicators of interaction (reducing tension, increasing communicative effectiveness and satisfaction with the process). In terms of the duration of negotiations and the frequency of coming to a full agreement, pronounced efficiency trends were revealed for the group with AI, which did not reach the level of statistical significance with the available sample size (n = 5 in the group) and requires confirmation in an expanded sample.

Conclusions. The presented experiment makes a reasoned contribution to the debate about the need to use AI in modern negotiation processes. The obtained data confirm that using AI in the negotiation process increases its effectiveness and transforms the socio-psychological climate of interaction during the negotiation process, shifting the emphasis from confrontation to cooperation.

References

Agrawal, A., Gans, J., Goldfarb, A. (2018). Prediction machines: The simple economics of artificial intelligence. Boston: Harvard Business Review Press.

Bazarov, T.Yu., Bazarova, K.T. (2007). Is distributed leadership possible? National Psychological Journal, (1), 123–127. (In Russ.)

Binns, R. (2018). Fairness in machine learning: Lessons from political philosophy. In the Proceedings of Machine Learning Research. Vol. 81. In: S.A. Friedler, C. Wilson, (eds.). First Conference on Fairness, Accountability and Transparency (February 23–24, 2018). (pp. 149–159). Cambridge: JMLR Publ.

Bolotova, A.K., Zhukov, Yu.M., Petrovskaya, L.A. (2024). Social communications. Psychology of communication. 2nd ed. Moscow: Yurait Publ. (In Russ.)

Campbell, D.T., Stanley, J.C. (1963). Experimental and Quasi-Experimental Designs for Research on Teaching. In: N.L. Gage, (ed.). Handbook of research on teaching (pp. 171–246). Chicago: Rand McNally Publ.

Cialdini, R. (2009). Influence: The psychology of persuasion. 4th ed. Saint Petersburg: Piter Publ. (In Russ.)

Emel'yanov, S.M. (2009). Workshop on conflictology. 2nd ed. Saint Petersburg: Piter Publ. (In Russ.)

Fisher, R., Ury, W. (1992). Getting to yes: Negotiating agreement without giving in. Moscow: Nauka Publ. (In Russ.)

Grishina, N.V. (2015). Psychology of conflict. 3rd ed. Saint Petersburg: Piter Publ. (In Russ.)

Janis, I.L. (1982). Groupthink: Psychological studies of policy decisions and fiascoes. 2nd ed. Boston: Houghton Mifflin Publ.

Jennings, N.R., Faratin, P., Lomuscio, A.R., Parsons, S., Sierra, C., Wooldridge, M. (2001). Automated negotiation: Prospects, methods and challenges. Group Decision and Negotiation, 10(2), 199–215. https://doi.org/10.1023/A:1008746126376

Kahneman, D. (2011). Thinking, fast and slow. New York: Farrar, Straus and Giroux Publ.

Kupreichenko, A.B. (2008). Psychology of trust and distrust. Moscow: Institute of Psychology of the RAS Publ. (In Russ.)

Leonov, N.I. (2013). Methods of studying conflicts and conflict behavior. Moscow: MPSU Publ. (In Russ.)

Mann, H.B., Whitney, D.R. (1947). On a test of whether one of two random variables is stochastically larger than the other. Annals of Mathematical Statistics, 18(1), 50–60. https://doi.org/10.1214/aoms/1177730491

Mell, J., Gratch, J. (2017). Grumpy and Pinocchio: Using expressive virtual agents to study human-computer negotiations. In: S. Das, E. Durfee, K. Larson, M. Winikoff, (eds.). 16th International Conference on Autonomous Agents and MultiAgent Systems (May 8–12, 2017). (pp. 401–409). South Carolina: IFAAMAS Publ.

Panferov, V.N. (2017). Methodological construct of communication. In: Social psychology of communication. (pp. 5–40). Moscow: INFRA-M Publ. (In Russ.)

Pruitt, D.G., Rubin, J.Z. (1986). Social conflict: Escalation, stalemate, and settlement. New York: Random House Publ.

Raiffa, H. (1996). The art and science of negotiation. Moscow: Mir Publ. (In Russ.)

Rubin, J.Z., Brown, B.R. (1975). The social psychology of bargaining and negotiation. New York: Academic Press Publ.

Schott, B. (2006). How to negotiate reliably, creatively, successfully. Moscow: Omega-L Publ. (In Russ.)

Tulchinskii, G.L. (2023). Meaning, subjectness and responsibility in digital communications. Chelovek = The Human Being, 34(3), 74–93. (In Russ.). https://doi.org/10.31857/S023620070026106-3

Ury, W. (2013). The power of a positive no: How to say no and still get to yes. Moscow: Alpina Business Books Publ. (In Russ.)