DOI: 10.14704/nq.2018.16.5.1353

A Dynamic Bounded Rationality Model for Technology Selection in Cognition Process

Li Zhou, Songlin Wang


This paper attempts to overcome the defect of traditional technology selection models: the inaccurate depiction of the dynamic decision-making in technology selection with fixed time point and static preferences. To this end, a new dynamic model was created considering the preference changing over the time. The preferences were deconstructed with discontinuous functions, and a theory was developed under bounded rationality for the preference changes in four phases of cognition. It is discovered that the decision-maker may become conservative in cognition process, leading to equilibrium evolution in conservative direction over the time. The discovery was verified through a case study on the neuroscience innovation in China. The research findings shed new light on cognition and decision-making studies and open up a new way for technology selection.


Cognition Process, Bounded Rationality, Technological Innovation, Decision-Making, Graph Model

Full Text:



Dolan RJ. Emotion, cognition and behavior. Science 2002; 298(5596): 1191-94.

Evans L, Lohse N, Summers M. A fuzzy-decision-tree approach for manufacturing technology selection exploiting experience-based information. Expert Systems with Applications 2013; 40(16): 6412-26.

Fang L, Hipel KW, Kilgour DM. Interactive decision making: the graph model for conflict resolution. John Wiley & Sons, 1993: 2-39.

Georgescu-Roegen N. The entropy law and the economic problem. Valuing the Earth: Economics, Ecology, Ethics 1993: 75-88.

Han X, Xu H. Research on Technology Transfer Conflict Based on Graph Model for Conflict Analysis. Science & Technology and Economy 2012; 25(5): 72-76.

He S, Hipel KW, Kilgour DM. Water diversion conflicts in China: a hierarchical perspective. Water Resources Management 2014; 28(7): 1823-37.

Hipel KW, Kilgour DM, Fang L. The graph model for conflict resolution. John Wiley & Sons, 2011; 2-39.

Hipel KW, Walker SB. Conflict analysis in environmental management. Environmetrics 2011; 22(3): 279-93.

Izard CE. Four systems for emotion activation: Cognitive and noncognitive processes. Psychological Review 1993; 100(1): 68-68.

Kahneman D, Tversky A. Prospect Theory: An Analysis of Decision under Risk. Econometrica 1979; XLVII, 263‒91. DOI: http://dx. doi. org/10.2307/1914185. 1979.

Khalil TM. Management of technology: The key to competitiveness and wealth creation. McGraw-Hill Science, Engineering & Mathematics, 2000: 16-59.

Khawam K, Lahoud S, Ibrahim M, Yassin M, Martin S, El Helou M, Moety F. Radio access technology selection in heterogeneous networks. Physical Communication 2016; 18: 125-39.

Kilgour DM, Hipel KW, Fang L. The graph model for conflicts. Automatica 1987; 23(1): 41-55.

Kinsara RA, Petersons O, Hipel KW and Kilgour DM. Advanced decision support for the graph model for conflict resolution. Journal of Decision Systems 2015; 24(2): 117-45.

Lee YG, Song YI. Selecting the key research areas in nano-technology field using technology cluster analysis: A case study based on National R&D Programs in South Korea. Technovation 2007; 27(1-2): 57-64.

Madani K, Hipel KW. Non-cooperative stability definitions for strategic analysis of generic water resources conflicts. Water Resources Management 2011; 25(8): 1949-77.

Mohanty RP, Venkataraman S. Use of the analytic hierarchy process for selecting automated manufacturing systems. International Journal of Operations & Production Management 1993; 13(8): 45-57.

Nash JF. Equilibrium points in n-person games. Proceedings of the national academy of sciences 1950; 36(1): 48-49.

Onar SC, Oztaysi B, Otay İ and Kahraman C. Multi-expert wind energy technology selection using interval-valued intuitionistic fuzzy sets. Energy 2015; 90: 274-285.

Phaal R, Farrukh CJP, Probert DR. Technology management process assessment: a case study. International Journal of Operations & Production Management 2001; 21(8): 1116-32.

Roy A, Karandikar A. Optimal radio access technology selection policy for LTE-WiFi network. Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks, 2015 13th International Symposium on. IEEE 2015: 291-98.

Scheiner CW, Baccarella CV, Bessant J and Voigt KI. Thinking patterns and gut feeling in technology identification and evaluation. Technological Forecasting and Social Change 2015; 101: 112-23.

Shannon CE. A mathematical theory of communication. ACM SIGMOBILE Mobile Computing and Communications Review 2001; 5(1): 3-55.

Sharot T. The optimism bias. Current Biology 2011; 21(23): R941-45.

Shen YC, Chang SH, Lin GTR and Yu HC. A hybrid selection model for emerging technology. Technological Forecasting and Social Change 2010; 77(1): 151-66.

Simon HA. Making management decisions: The role of intuition and emotion. The Academy of Management Executive (1987-1989) 1987: 57-64.

Simon HA. Models of bounded rationality: Empirically grounded economic reason. MIT press, 1982: 9-66.

Sloane EB, Liberatore MJ, Nydick RL, Luo W and Chung QB. Using the analytic hierarchy process as a clinical engineering tool to facilitate an iterative, multidisciplinary, microeconomic health technology assessment. Computers & Operations Research 2003; 30(10): 1447-65.

Tolga E, Demircan ML, Kahraman C. Operating system selection using fuzzy replacement analysis and analytic hierarchy process. International Journal of Production Economics 2005; 97(1): 89-117.

Torkkeli M, Tuominen M. The contribution of technology selection to core competencies. International Journal of Production Economics 2002; 77(3): 271-84.

Yu J, Kilgour DM, Hipel KW and Zhao M. Power asymmetry in conflict resolution with application to a water pollution dispute in China. Water Resources Research 2015; 51(10): 8627-45.

Supporting Agencies

| NeuroScience + QuantumPhysics> NeuroQuantology :: Copyright 2001-2019