Skip to main navigation menu Skip to main content Skip to site footer

The Use of Game Theory for Making Rational Decisions in Business Negations: A Conceptual Model

Abstract

Objective: The objective of this paper is a comparative analysis of the world literature on game theory and its applicability for rational decision-making in negotiations and creation of a model supporting strategic decisions in negotiations. Research Design & Methods: Systematic, comparative, logical analysis and synthesis of the scientific literature. In order to create an algorithm of negotiations statements on theory of graphs, game theory and theory of heuristic algorithm were applied. Findings: The article proposes an algorithm which combines the game theory approach with heuristic algorithms in order to reflect the specifics of negotiations better. Such an algorithm can be used to support strategic decisions in negotiations and is useful for better understanding of the strategic management of negotiating processes. Implications & Recommendations: The proposed mathematical algorithm for the strategy formulation of international business negotiations can be used in electronic business negotiations, both as a standalone tool, or as partially requiring support by the negotiator. Contribution & Value Added: The game theory methods support rational solutions in business negotiations, as they enable to analyse the interacting forces. This is particularly relevant in international business negotiations, where participants from different cultures can be faced with numerous uncertainties.

Keywords

game theory, negotiation, rational, strategic decisions and negotiations support, heuristic negotiation model

PDF

Author Biography

Kęstutis Peleckis

doc. dr. Kęstutis Peleckis, profesorius

Vilnius Gediminas Technical University, Faculty of Business Management, Lecturer and PhD student of Department of International Economics and Business Management. Scientific interests: negotiation strategy, competitiveness, multiculturalism.


References

  1. Annabi, A., Breton, M., & François, P. (2012). Game theoretic analysis of negotiations under bankruptcy. European Journal of Operational Research, 221(3), 603-613. doi: 10.1016/j.ejor.2012.04.002.
  2. Arsene, C. T. C., Gabrys, B., & Al-Dabass, D. (2012). Decision support system for water distribution systems based on neural networks and graphs theory for leakage detection. Expert Systems with Applications, 39(18), 13214-13224. doi: 10.1016/j.eswa.2012.05.080.
  3. Aurangzeb, M., & Lewis, F. L. (2014). Internal structure of coalitions in competitive and altruistic graphical coalitional games. Automatica, 50(2), 335-348. doi: 10.1016/j.automatica.2013.11.002.
  4. Azar, O. (2014). The default heuristic in strategic decision making: When is it optimal to choose the default without investing in information search?. Journal of Business Research, 67(8), 1744-1748. doi: 10.1016/j.jbusres.2014.02.021.
  5. Baarslag, T., Hindriks, K., & Jonker, C. (2014). Effective acceptance conditions in real-time automated negotiation. Decision Support Systems, 60(1), 68-77. doi: 10.1016/j.dss.2013.05.021.
  6. Basel, J., & Brühl, R. (2011). Concepts of Rationality in Management Research. From Un-bounded Rationality to Ecological Rationality No. 57.
  7. Bergroth, L. (2006). Duomenu strukturu poveikis algoritmo efektyvumui ieskant dvieju seku bendra ilgiausia poseki. Vadyba, mokslo tiriamieji darbai, 29, Vakaru Lietuvos verslo kolegija. Klaipeda.
  8. Bivainis, J. (2011). Vadyba studentams: mokomoji knyga. Vilniaus Gedimino technikos universitetas. Vilnius: Technika.
  9. Chuah, S., Hoffmann, R., & Larner, J. (2014). Chinese values and negotiation behaviour: A bargaining experiment. International Business Review, 23(6), 1203-1211. doi: 10.1016/j.ibusrev.2014.05.002.
  10. Darvish, M., Yasaei, M. & Saeedi, A. (2009). Application of the graph theory and matrix methods to contractor ranking. International Journal of Project Management, 27(6), 610-619. doi: 10.1016/j.ijproman.2008.10.004.
  11. De Bruin, B. (2009). Overmathematisation in game theory: pitting the Nash Equilibrium Refinement Programme against the Epistemic Programme. Studies in History and Philosophy of Science, 40(3), 290-300. doi: 10.1016/j.shpsa.2009.06.005.
  12. Deng, X., Zheng, X., Su, X., Chan, F. T. S, & Hu, Y. (2014). Rehan Sadiq, Yong Deng, An evidential game theory framework in multi-criteria decision making process. Applied Mathematics and Computation, 244(1), 783-793. doi: 10.1016/j.amc.2014.07.065.
  13. Felinksas, G. (2007). Euristinių metodų tyrimas ir taikymas ribotų išteklių tvarkaraščiams optimizuoti. Daktaro disertacija. Kaunas: Vytauto Didžiojo universitetas.
  14. Frederick, D. (2010). Two Concepts of Rationality. Libertarian Papers, 2(5), 1-21.
  15. Ginevičius, R., Suhajda, K., Petraškevičius, V., & Šimkūnaitė, J. (2014). Lithuanian Experience of Quantitative Evaluation of Socioeconomic Systems Position by Multicriteria Methods. Procedia - Social and Behavioral Sciences, 110, 952-960. doi: 10.1016/j.sbspro.2013.12.941.
  16. Hao, J., Song, S., Leung, H., & Ming, Z. (2014). An efficient and robust negotiating strategy in bilateral negotiations over multiple items. Engineering Applications of Artificial Intelligence, 34, 45-57. doi: 10.1016/j.engappai.2014.05.008.
  17. Houser, D., & McCabe, K. (2014). Experimental Economics and Experimental Game Theory, In: Glimcher, P. W. & Fehr, E. (Eds.), Neuroeconomics (pp.19-34). 2nd edition, San Diego: Academic Press.
  18. Katkus, K. (2006). Hibridinis genetinis algoritmas komivojažieriaus uždaviniui. Kaunas: Kauno technologijos universitetas.
  19. Kozina, A. (2012). Multiparty Negotiations - Research Problems Formulation. In: Nalepka, A. & Ujwary-Gil, A. (Eds.), Business and Non-Profit Organizations Facing Increased Competition and Growing Customers’ Demands (pp.45-56). Nowy Sącz: Wyższa Szkoła Biznesu – National-Louis University.
  20. Kozina, A. (2014). Managerial Roles and Functions in Negotiation Process. Business, Management and Education, 12(1), 94-108.
  21. Kelly, A. (2003). Decision Making Using Game Theory. An Introduction for Managers. Cambridge University Press.
  22. Lin, R., Gal, U., Kraus, S., & Mazliah, Y. (2014). Training with automated agents improves people's behavior in negotiation and coordination tasks. Decision Support Systems, 60, 1-9. doi: 10.1016/j.dss.2013.05.015.
  23. Lourenzutti, R., & Krohling, R. A. (2014). The Hellinger distance in Multicriteria Decision Making: An illustration to the TOPSIS and TODIM methods. Expert Systems with Applications, 41(9), 4414-4421. doi: 10.1016/j.eswa.2014.01.015.
  24. Lova, A., Maroto, C., & Tormos, P. (2000). A multicriteria heuristic method to improve resource allocation in multiproject scheduling. European Journal of Operational Research, 127(2), 408-424. doi: 10.1016/S0377-22179900490-7.
  25. Mandow, L., & Pérez de la Cruz, J.L. (2003). Multicriteria heuristic search. European Journal of Operational Research, 150(2), 253-280, doi: 10.1016/S0377-22170200517-9.
  26. Marey, O., Bentahar, J., Asl, E. K., Mbarki, M., & Dssouli, R. (2014). Agents' Uncertainty in Argumentation-based Negotiation: Classification and Implementation. Procedia Computer Science, 32, 61-68. doi: 10.1016/j.procs.2014.05.398.
  27. Marey, O., Bentahar, J., Dssouli, R., & Mbarki, M. (2014). Measuring and analyzing agents' uncertainty in argumentation-based negotiation dialogue games. Expert Systems with Applications, 41(2), 306-320. doi: 10.1016/j.eswa.2013.07.005.
  28. Mockus, J. (2010). On simulation of optimal strategies and Nash equilibrium in the financial market context. Journal of Global Optimization, 48(1), 129-143.
  29. Nassiri-Mofakham, F., Nematbakhsh, M. A., Ghasem-Aghaee, N., & Baraani-Dastjerdi, A. (2009). A heuristic personality-based bilateral multi-issue bargaining model in electronic commerce. International Journal of Human-Computer Studies, 67(1), 1-35.
  30. Oderanti, F. O., Li, F., & De Wilde, P. (2012). Application of strategic fuzzy games to wage increase negotiation and decision problems. Expert Systems with Applications, 39(12), 11103-11114. doi: 10.1016/j.eswa.2012.03.060.
  31. Pancerz, K., & Lewicki, A. (2014). Encoding symbolic features in simple decision systems over ontological graphs for PSO and neural network based classifiers. Neurocomputing, 144, 338-345. doi: 10.1016/j.neucom.2014.04.038.
  32. Plukas, K., Mačikėnas, E., Jarašiūnienė, B., & Mikuckienė, I. (2004). Taikomoji diskrečioji matematika. Kaunas: Technologija.
  33. Pooyandeh, M., & Marceau, D. J. (2014). Incorporating Bayesian learning in agent-based simulation of stakeholders' negotiation. Computers, Environment and Urban Systems, 48, 73-85. doi: 10.1016/j.compenvurbsys.2014.07.003.
  34. Rufo, M.J., Martín, J., & Pérez, C.J. (2014). Adversarial life testing: A Bayesian negotiation model. Reliability Engineering & System Safety, 131, 118-125. doi: 10.1016/j.ress.2014.06.007.
  35. Segundo, G. A. S., Krohling, R. A., & Cosme, R. C. (2012). A differential evolution approach for solving constrained min-max optimization problems. Expert Systems with Applications, 39(18), 13440-13450. doi: 10.1016/j.eswa.2012.05.059.
  36. Shoham, Y., & Brown, K. L. (2009). Multiagent Systems, Algorithmic, Game Theoretic, and Logical Foundations. Cambridge: University Press.
  37. Suh, S., & Park, N. (2010). The Charter Fixing Negotiation Procedure with Asymmetric Impatience in a Game Theory Framework: Case Studies in Coal and Ore Transactions. The Asian Journal of Shipping and Logistics, 26(2), 247-261. doi: 10.1016/S2092-5212(10)80004-5.
  38. Tamošiūnas, L. (2011). Euristinių paieškos algoritmų tyrimas ir taikymas atviro kodo geografinėse informacinėse sistemose. Magistro darbas. Kauno Technologijos Universitetas.
  39. Wang, P., Reinelt, G., Gao, P., & Tan, Y. (2011). A model, a heuristic and a decision support system to solve the scheduling problem of an earth observing satellite constellation. Computers & Industrial Engineering, 61(2), 322-335. doi: 10.1016/j.cie.2011.02.015.
  40. Wibowo, S., & Deng, H. (2013). Consensus-based decision support for multicriteria group decision making. Computers & Industrial Engineering, 66(4), 625-633. doi: 10.1016/j.cie.2013.09.015.
  41. Wibowo, S., & Deng, H. (2013). Consensus-based decision support for multicriteria group decision making. Computers & Industrial Engineering, 66(4), 625-633. doi: 10.1016/j.cie.2013.09.015.
  42. Wilken, R., Jacob, F., & Prime, N. (2013). The ambiguous role of cultural moderators in intercultural business negotiations. International Business Review, 22(4), 736-753. doi: 10.1016/j.ibusrev.2012.12.001.
  43. Xu, H., Kilgour, D. M., Hipel, K. W., & McBean, E. A. (2013). Theory and application of conflict resolution with hybrid preference in colored graphs. Applied Mathematical Modelling, 37(3), 989-1003. doi: 10.1016/j.apm.2012.03.009.
  44. Yu, F., Kaihara, T., & Fujii, N. (2013). Coalition Formation Based Multi-item Multi-attribute Negotiation of Supply Chain Networks. Procedia CIRP, 7, 85-90. doi: 10.1016/j.procir.2013.05.015.
  45. Yu, X., & Xu, Z. (2012). Graph-based multi-agent decision making. International Journal of Approximate Reasoning, 53(4), 502-512. doi: 10.1016/j.ijar.2011.12.002.
  46. Yuan, H., & Ma, H. (2012). Game Analysis in the Construction Claim Negotiations. Procedia Engineering, 28, 586-593. doi: 10.1016/j.proeng.2012.01.773.
  47. Zavadskas, E. A., Kaklauskas, A., Trinkunas, V., Tupenaite, L., Cerkauskas, J., & Kazokaitis, P. (2012). Recommender System to Research Students' Study Efficiency. Procedia - Social and Behavioral Sciences, 51, 980-984. doi: 10.1016/j.sbspro.2012.08.273.
  48. Zavadskas, E. K., Antucheviciene, J., Šaparauskas, J., & Turskis, Z. (2013). Multi-criteria Assessment of Facades’ Alternatives: Peculiarities of Ranking Methodology. Procedia Engineering, 57, 107-112. doi: 10.1016/j.proeng.2013.04.016.
  49. Zhang, K. Z. K., Zhao, S. J., Cheung, C. M. K., & Lee, M. K. O. (2014). Examining the influence of online reviews on consumers' decision-making: A heuristic-systematic model. Decision Support Systems, 67, 78-89. doi: 10.1016/j.dss.2014.08.005.

Downloads

Download data is not yet available.

Similar Articles

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 > >> 

You may also start an advanced similarity search for this article.