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How to Improve Dynamic Decision Making? Practice and Promise

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Complex Decision Making

Part of the book series: Understanding Complex Systems ((UCS))

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References

  • Bakken, B. E. (1993). Learning and transfer of understanding in dynamic decision environments. Unpublished doctoral dissertation. MIT, Boston.

    Google Scholar 

  • Bakken, B., Gould, J. & Kim, D. (1994). Experimentation in learning organizations: A management flight simulator approach. In J. Morecroft & J. Sterman (Eds.) Modeling for learning organizations (pp. 243–266). Portland, Or.: Productivity Press.

    Google Scholar 

  • Beckmann, J. F. & Guthke, J. (1995). Complex problem solving, intelligence, and learning ability. In P. Frensch & J. Funke (Eds.), Complex problem solving: The European perspective (pp. 177–200). NJ: Lawrence Erlbaum Associates Publishers.

    Google Scholar 

  • Benbasat, I. & Dexter, A. S. (1985). An experimental evaluation of graphical and color-enhanced information presentation Management Science, 31, 1348–1364.

    Google Scholar 

  • Berry, D. C. & Broadbent, D. E. (1987). The combination of explicit and implicit learning processes in task control. Psychological Research, 49, 7–16.

    Article  Google Scholar 

  • Berry, D. C. & Broadbent, D. E. (1988). Interactive tasks and the implicit-explicit distinction. British Journal of Psychology, 79, 251–271.

    Google Scholar 

  • Berry, D. C. (1991). The role of action in implicit learning. Quarterly Journal of Experimental Psychology 43A, 8–906.

    Google Scholar 

  • Bjorkman, M. (1972). Feed forward and feedback as determinants of knowledge and policy: Notes on neglected issue. Scandinavian J. Psychology, 13, 152–158.

    Google Scholar 

  • Brehmer, B. & Allard, R. (1991). Dynamic decision making: The effects of task complexity and feedback delay. In J. Rasmussen, B. Brehmer & J. Leplat (Eds.) Distributed decision making: Cognitive models for cooperative work (pp. 319–334). New York: Wiley.

    Google Scholar 

  • Brehmer, B. (1990). Strategies in real-time dynamic decision making. In R. M. Hogarth (Ed.), Insights in decision making (pp. 262–279). Chicago: University of Chicago Press.

    Google Scholar 

  • Brehmer, B. (1995). Feedback delays in complex dynamic decision tasks. In P. Frensch & J. Funke (Eds.) Complex problem solving: The European perspective (pp. 103–130). NJ: Lawrence Erlbaum Associates Publishers.

    Google Scholar 

  • Breuer, K. & Kummer, R. (1990). Cognitive effects from process learning with computer-based simulations. Computers in Human Behavior, 6, 69–81.

    Article  Google Scholar 

  • Briggs, P. (1990). Do they know what they are doing? An evaluation of word-processor user’s implicit and explicit task-relevant knowledge, and its role in self-directed learning. International Journal of Man-Machine Studies, 32, 385–298.

    Article  Google Scholar 

  • Broadbent, B. & Aston, B. (1978). Human control of a simulated economic system. Ergonomics, 21, 1035–1043.

    Article  Google Scholar 

  • Carey, J., Galletta, D., Kim, J., Te’eni, D., Wildemuth, B., & Zhang, P. (2004). The role of human-computer interaction in management information systems curricula: A call to action. Communications of the Association for Information Systems, 13, 357–379.

    Google Scholar 

  • Collins, A. (1991). Cognitive apprenticeship and instructional technology. In L. Idol & B. F. Jones (Eds.) Educational values and cognitive instruction: Implication for reform (pp. 11–139). New York: Elsevier Science.

    Google Scholar 

  • Conant, R. & W. Ashby. (1970). Every good regulator of a system must be a model of the system. International Journal of System Science, 1, 89–97.

    Article  Google Scholar 

  • Cox, R. J. (1992). Exploratory learning from computer-based systems. In S. Dijkstra, H. P. M. Krammer, & J. J. G. van Merrienboer (Eds.) Instructional models in computer-based learning environments (pp. 405–419). Berlin, Heidelberg: Springer-Verlag.

    Google Scholar 

  • Crookall, D., Martin, A., Saunders, D., & Coote, A. (1987). Human and computer involvement in simulation. Simulation & Games: An International Journal of Theory, Design, and Research, 17(3), 345–375.

    Google Scholar 

  • Davidsen, P. I. & Spector, J. M. (1997). Cognitive complexity in system dynamics based learning environments. International system dynamics conference. Istanbul, Turkey: Bogacizi University Printing Office.

    Google Scholar 

  • Davidsen, P. I. (1996). Educational features of the system dynamics approach to modelling and simulation. Journal of Structural Learning, 12(4), 269–290.

    Google Scholar 

  • Davidsen, P. I. (2000). Issues in the design and use of system-dynamics-based interactive learning environments. Simulation & Gaming, 31(2), 170–177.

    Article  Google Scholar 

  • Davis, F. D. & Kottermann, J . E. (1994). User perceptions of decision support effectiveness: Two productions planning experiment. Decision Sciences, 25(1), 57–78.

    Article  Google Scholar 

  • Davis, F. D. & Kottermann, J . E. (1994). Determinants of decision rule use in a production planning task. Organizational Behavior and Human Decision Processes, 63, 145–159.

    Article  Google Scholar 

  • Diehl, E. & Sterman, J. D. (1995). Effects of feedback complexity on dynamic decision making. Organizational Behavior and Human Decision Processes, 62(2), 198–215.

    Article  Google Scholar 

  • Dörner, D. & Pfeifer, E. (1992). Strategic thinking, strategic errors, stress, and intelligence. Sprache & Kognition,11, 75–90.

    Google Scholar 

  • Dörner, D. (1980). On the difficulties people have in dealing with complexity. Simulations and Games, 11, 8–106.

    Google Scholar 

  • Dörner, D., Kreuzig, H. W., Reither, F., & Staudel, T. (Eds.). (1983). Lohhausen: On dealing with uncertainty and complexity. Bern, Switzerland: Hans Huber.

    Google Scholar 

  • Edwards, W. (1962). Dynamic decision theory and probabilistic information processing. Human Factors, 4, 59–73.

    Google Scholar 

  • Elsom-Cook, M. T. (1993). Environment design and teaching intervention. In D. M. Town, T. de Jong, & H. Spada. (Eds.) Simulation-based experiential learning (pp. 165–176). Berlin: Springer-Verlag.

    Google Scholar 

  • Forrester, J. W. (1961). Industrial dynamics. Cambridge, MA: Productivity Press.

    Google Scholar 

  • Funke, J. (1995). Experimental Research on Complex Problem Solving. In P. Frensch & J. Funke (Eds.) Complex problem solving: The European perspective (pp. 3–25). NJ: Lawrence Erlbaum Associates Publishers.

    Google Scholar 

  • Funke, J. & Muller, H. (1988). Active control and prediction as determinants of system identification and system control. Sprache & Kognition, 7, 176–186.

    Google Scholar 

  • Gagné, R. M. (1985). The conditions of learning (4th edition). New York: Holt, Rinehart, & Winston.

    Google Scholar 

  • Gibson, F. P. (2000). Feedback delays: How can decision makers learn not to buy a new car every time the garage is empty? Organizational Behavior and Human Decision Processes, 83, 141–166.

    Article  Google Scholar 

  • Gonzalez, M., Machuca, J., & Castillo, J. (2000). A transparent-box multifunctional simulator of competing companies. Simulation & Gaming, 31(2), 240–256.

    Article  Google Scholar 

  • Goodyear, P. (1992). The provision of tutorial support for learning with computer-based simulations. In E. Corte, M. Lin, H. Mandal, & L. Verschaffel. (Eds.) Computer-based learning environments and problem solving (pp. 391–409). Berlin: Springer-Verlag.

    Google Scholar 

  • Gröbler, A, Maier, F. H., & Milling, P. M. (2000). Enhancing learning capabilities by providing transparency in transparency. Simulation & Gaming, 31(2), 257–278.

    Article  Google Scholar 

  • Gröbler, A. (1998). Structural transparency as an element of business simulators. Paper presented at 16th International System Dynamics Conference, Quebec City, Canada.

    Google Scholar 

  • Grubler H., Renkal A., Mandal H., & Reiter, W. (1993). Exploration strategies in an economics simulation game. In D. M. Town, T. de Jong, & H. Spada. (Eds.) Simulation-based experiential learning (pp. 225–233). Berlin: Springer-Verlag.

    Google Scholar 

  • Hayes, N. A. & Broadbent, D. E. (1988). Two modes of learning for interactive tasks. Cognition, 28, 249–276.

    Article  Google Scholar 

  • Hogarth, R. M. (1981). Beyond discrete biases: Functional and dysfunctional aspects of judgmental heuristics. Psychological Bulletin, 9(2), 197–217.

    Article  Google Scholar 

  • Homer, J. B., Hirsch, G.B., 2006. System dynamics modeling for public health: Background and opportunities. American Journal of Public Health, 96 (3), 452–458.

    Article  Google Scholar 

  • Hsiao, N. (1999). In search of theories of dynamic decision making: A literature review. Paper presented at the International System Dynamics Conference, Wellington, New Zealand.

    Google Scholar 

  • Hsiao, N. (2000). Exploration of outcome feedback for dynamic decision making. Unpublished doctoral dissertation, State University of New York at Albany, Albany.

    Google Scholar 

  • Huber, G. P. (1984). The nature and design of post-industrial organization. Management Science, 30, 928–951.

    Google Scholar 

  • Huber, O. (1995). Complex problem solving as multistage decision making. In P. Frensch & J. Funke (Eds.) Complex problem solving: The European perspective (pp. 151–173). NJ: Lawrence Erlbaum Associates Publishers.

    Google Scholar 

  • Issacs, W. & Senge, P. (1994). Overcoming limits to learning in computer-based learning environments. In J. Morecroft and J. Sterman (Eds.) Modeling for learning organizations (pp. 267–287). Portland, Or.: Productivity Press.

    Google Scholar 

  • Jansson, A. (1995). Strategies in Dynamic Decision making: Does Teaching Heuristic Strategies By Instructors Affect Performance? In J. Caverni, M. Bar-Hillel, F.Barron, & H. Jungermann (Eds.) Contributions to decision making-I (pp. 213–253). Amsterdam: Elsevier.

    Google Scholar 

  • Kerstholt, J. H. (1996). The effect of information costs on strategy selection in dynamic tasks. Acta Psychologica, 94, 273–290.

    Article  Google Scholar 

  • Kerstholt, J. H. & Raaijmakers, J. G. W. (1997). Decision making in dynamic task environments. In R. Ranyard, R. W. Crozier & O. Svenson (Eds.) Decision making: Cognitive models and explanations (pp. 205–217). New York, NY: Routledge.

    Google Scholar 

  • Keys, J. B. & Wolfe, J. (1990). The role of management games and simulations in education and research. Journal of Management, 16, 307–336.

    Article  Google Scholar 

  • Kleinmuntz, D. (1985). Cognitive heuristics and feedback in a dynamic decision environment. \underline{Management Science}, 31, 680–701.

    Google Scholar 

  • Kleinmuntz, D. & Thomas, J. (1987). The value of action and inference in dynamic decision making. Organizational Behavior and Human Decision Processes, 39, 341–364.

    Article  Google Scholar 

  • Kriz, W. C., 2003. Creating effective learning environments and learning organizations through gaming simulation design. Simulation & Gaming, 34(4), 495–511.

    Article  Google Scholar 

  • Lane, D. C. (1995). On a resurgence of management simulations and games. Journal of the Operational Research Society, 46, 604–625.

    Article  Google Scholar 

  • Langley, P. A. & Morecroft, J. D. W. (1995). Learning from microworlds environments: A summary of the research issues. In G. P. Richardson & J. D. Sterman (Eds.) System Dynamics’ 96. Cambridge, MA: System Dynamics Society.

    Google Scholar 

  • Ledrman, L. C. (1992). Debriefing: towards a systematic assessment of theory and practice. Simulations & Gaming, 23(2), 145–160.

    Article  Google Scholar 

  • Machuca, J. A. D., Ruiz, J. C., Domingo, M. A., & Gonzalez, M. M. (1998). 10 years of work on transparent-box business simulation. Paper presented at 16th International System Dynamics Conference, Quebec City, Canada.

    Google Scholar 

  • Mackinnon, A. J. & A.J. Wearing. (1980). Complexity and decision making. Behavioral Science, 25(4), 285–292.

    Article  Google Scholar 

  • Maxwell, T. A. (1995). Decisions: Cognitive styles, mental models, and task performance. Unpublished doctoral dissertation, State University of New York at Albany, Albany.

    Google Scholar 

  • Moxnes, E. (2000). Not only the tragedy of the commons: Misperceptions of feedback and policies for sustainable development. System Dynamics Review, 16(4): 325–348.

    Article  Google Scholar 

  • Moxnes, E. (1998). Not only the tragedy of the commons: Misperceptions of bioeconomics. Management Science, 44, 1234–1248.

    Google Scholar 

  • Njoo, M. & de Jong, T. (1993). Supporting exploratory learning by offering structured overviews of hypotheses. In D. M. Town, T. de Jong, & H. Spada. (Eds.) Simulation-based experiential learning (pp. 207–223). Berlin: Springer-Verlag.

    Google Scholar 

  • Paich, M & J. D. Sterman. (1993). Boom, bust, and failures to learn in experimental markets. Management Science, 39, 1439–1458.

    Article  Google Scholar 

  • Putz-Osterloh, W. Bott, B., & Koster, K. (1990). Modes of learning in problem solving – Are they transferable to tutorial systems. Computers in Human Behaviors, 6, 83–96.

    Article  Google Scholar 

  • Qudrat-Ullah, H. 2005a. ‘‘Improving Dynamic Decision Making through HCI Design Principles’’, In C. Ghaoui, (Ed.), The Encyclopedia of Human Computer Interaction, USA: Information Science Publishing, pp. 311–316.

    Google Scholar 

  • Qudrat-Ullah, H., 2005b. MDESRAP: a model for understanding the dynamics of electricity supply, resources, and pollution. International Journal of Global Energy Issues, 23(1), 1–14.

    Article  Google Scholar 

  • Qudrat-Ullah, H. (2002). Decision making and learning in complex dynamic environments. Unpublished doctoral dissertation, National University of Singapore, Singapore.

    Google Scholar 

  • Qudrat-Ullah, H. (2001). Improving Decision Making and Learning in Dynamic Task: An Experimental Investigation. Paper presented at Annual Faculty Research Conference, NUS Business School, National University of Singapore, Singapore.

    Google Scholar 

  • Rapoport, A. (1975). Research paradigms for studying dynamic decision behavior. In D. Wendt & C. A. J. Vlek (Eds.) Utility, probability, and human decision making (pp. 349–375). Dordrecht, Holland: Reidel.

    Google Scholar 

  • Sanderson, P. M. (1989). Verbalizable knowledge and skilled task performance: Association, dissociation, and mental Model. Journal of Experimental Psychology: Learning Memory and Cognition, 15, 729–739.

    Article  Google Scholar 

  • Sengupta, K & Abdel-Hamid, T. (1993). Alternative concepts of feedback in dynamic decision environments: An experimental investigation. Management Science, 39, 411–428.

    Google Scholar 

  • Spector, J. M. (2000). System dynamics and interactive learning environments: Lessons learned and implications for the future. Simulation & Gaming, 31(4), 528–535.

    Article  Google Scholar 

  • Stanley, W. B., R. C. Mathews, R. R. Buss, & S. Kolter-Cope. (1989). Insight without awareness: On the interaction of verbalization, instruction and practice in a simulated process control task. The Quarterly Journal of Experimental Psychology, 41A(3), 553–577.

    Google Scholar 

  • Stienwachs, B. (1992). How to facilitate a debriefing. Simulation & Gaming, 23(2), 186–195.

    Article  Google Scholar 

  • Sterman, J. D. (1989a). Modeling managerial behavior: Misperceptions of feedback in a dynamic decision making experiment. Management Science, 35, 321–339.

    Google Scholar 

  • Sterman, J. D. (1989b). Misperceptions of feedback in dynamic decision making. Organizational Behavior and Human Decision Processes, 43, 301–335.

    Article  Google Scholar 

  • Sterman, J. D. (1994). Learning in and around complex systems. System Dynamics Review, 10 (2–3), 291–323.

    Article  Google Scholar 

  • Sternberg, R. J. (1995). Expertise in complex problem solving: A comparison of alternative conceptions. In P. Frensch & J. Funke (Eds.) Complex problem solving: The European perspective (pp. 3–25). NJ: Lawrence Erlbaum Associates Publishers.

    Google Scholar 

  • Trees, W. S., Doyle, J. K., & Radzicki, M. J. (1996). Using cognitive styles typology to explain differences in dynamic decision making in a computer simulation game environment. Paper presented at the International System Dynamics Conference.

    Google Scholar 

  • Wolf, J. (1990). The evaluation of computer-based business games. In J. Gentry (Ed.) Guide to business gaming and experiential learning (pp. 279–300). London: Nichols.

    Google Scholar 

  • Yang, J. (1997). Give me the right goals, I will be a good dynamic decision maker. International System Dynamics Conference (pp. 709–712). Istanbul, Turkey: Bogacizi University Printing Office.

    Google Scholar 

  • Young, S. H., Chen, C. P., Wang, S. & Chen, C. H. (1997). An experiment to study the relationship between decision scope and uncontrollable positive feedback loops. International System Dynamics Conference (pp. 15–20). Istanbul, Turkey: Bogacizi University Printing Office.

    Google Scholar 

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Authors and Affiliations

  1. School of Administrative Studies, York University, Canada

    Mustafa Karakul & Hassan Qudrat-Ullah

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  1. Mustafa Karakul
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  2. Hassan Qudrat-Ullah
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Editors and Affiliations

  1. School of Administrative Studies, York University, 4700 Keele St., Toronto, ON M3J IP3, Canada

    H. Qudrat-Ullah

  2. Learning Systems Institute Instructional Systems Program, Florida State University, C 4622 University Center, Tallahassee, FL 32306-2540, USA

    J.M. Spector

  3. Department of Geography System Dynamics Program, University of Bergen, Fosswinkelsgate 6, 5007 Bergen, Norway

    P.I. Davidsen

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Karakul, M., Qudrat-Ullah, H. (2008). How to Improve Dynamic Decision Making? Practice and Promise. In: Qudrat-Ullah, H., Spector, J., Davidsen, P. (eds) Complex Decision Making. Understanding Complex Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73665-3_1

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