Does visualization enhance complex problem solving? The effect of causal mapping on performance in the computer-based microworld Tailorshop

  • Michael Öllinger
  • Stephanie Hammon
  • Michael von Grundherr
  • Joachim Funke
Development Article

Abstract

Causal mapping is often recognized as a technique to support strategic decisions and actions in complex problem situations. Such drawing of causal structures is supposed to particularly foster the understanding of the interaction of the various system elements and to further encourage holistic thinking. It builds on the idea that humans make use of mental maps to represent their environment and to make predictions about it. However, a profound theoretical underpinning and empirical research of the effects of causal mapping on problem solving is missing. This study compares a causal mapping approach with more common problem solving techniques utilizing the standardized computer-simulated microworld Tailorshop. Results show that causal mapping leads to a worse performance in managing the Tailorshop and was not associated with increased knowledge about the underlying system’s structure. We conclude that the successful representation of the causal structure and the control of a complex scenario require the concerted interplay of cognitive skills that go beyond drawing causal maps.

Keywords

Complex problem solving Causal maps Causal mapping Problem representation 

References

  1. Barth, C. M., & Funke, J. (2010). Negative affective environments improve complex problem solving performance. Cognition and Emotion, 24(7), 1259–1268. doi:10.1080/02699930903223766.CrossRefGoogle Scholar
  2. Berry, D. C., & Broadbent, D. E. (1987). The combination of explicit and implicit learning processes in task control. Psychological Research, 49(1), 7–15.CrossRefGoogle Scholar
  3. Blech, C., & Funke, J. (2006). Zur Reaktivität von Kausaldiagramm-Analysen beim komplexen Problemlösen. Zeitschrift Für Psychologie, 214(4), 185–195. doi:10.1026/0044-3409.214.4.185.CrossRefGoogle Scholar
  4. Brown, S. M. (1992). Cognitive mapping and repertory grids for qualitative survey research: Some comparative observations. Journal of Management Studies, 29(3), 287–307. doi:10.1111/j.1467-6486.1992.tb00666.x.CrossRefGoogle Scholar
  5. Bryson, J. M., Ackermann, F., Eden, C., & Finn, C. B. (2004). Visible thinking: Unlocking causal mapping for practical business results. Hoboken: Wiley.Google Scholar
  6. Clariana, R. B., Engelmann, T., & Yu, W. (2013). Using centrality of concept maps as a measure of problem space states in computer-supported collaborative problem solving. Educational Technology Research and Development, 61(3), 423–442.CrossRefGoogle Scholar
  7. Danner, D., Hagemann, D., Holt, D., Hager, M., Schankin, A., Wüstenberg, S., & Funke, J. (2011). Measuring performance in dynamic decision making. Journal of Individual Differences, 32(4), 225–233.CrossRefGoogle Scholar
  8. Dörner, D. (1997). The logic of failure. Recognizing and avoiding error in complex situations. New York: Basic Books.Google Scholar
  9. Dörner, D., Kreuzig, H. W., Reither, F., & Stäudel, T. (1983). Lohhausen. Vom Umgang mit Unbestimmtheit und Komplexität. Bern: Huber.Google Scholar
  10. Doyle, J. K. (1997). The cognitive psychology of systems thinking. System Dynamics Review, 13(3), 253–265. doi:10.1002/(SICI)1099-1727(199723)13:3<253:AID-SDR129>3.0.CO;2-H.CrossRefGoogle Scholar
  11. Doyle, J. K., & Ford, D. N. (1998). Mental models concepts for system dynamics research. System Dynamics Review, 14(1), 3–29. doi:10.1002/(SICI)1099-1727(199821)14:1<3:AID-SDR140>3.0.CO;2-K.CrossRefGoogle Scholar
  12. Doyle, J. K., & Ford, D. N. (1999). Mental models concepts revisited: some clarifications and a reply to Lane. System Dynamics Review, 15(4), 411–415. doi:10.1002/(SICI)1099-1727(199924)15:4<411:AID-SDR181>3.0.CO;2-R.CrossRefGoogle Scholar
  13. Eden, C. (1988). Cognitive mapping. European Journal of Operational Research, 36(1), 1–13. doi:10.1016/0377-2217(88)90002-1.CrossRefGoogle Scholar
  14. Eden, C., Ackermann, F., & Cropper, S. (1992). The analysis of cause maps. Journal of Management Studies, 29(3), 309–324. doi:10.1111/j.1467-6486.1992.tb00667.x.CrossRefGoogle Scholar
  15. Eden, C., & Huxham, C. (1988). Action-oriented strategic management. The Journal of the Operational Research Society, 39(10), 889–899. doi:10.2307/2583040.CrossRefGoogle Scholar
  16. Eden, C., Jones, S., & Sims, D. (1979). Thinking in organizations. New York: Macmillan.Google Scholar
  17. Faul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007). G* Power 3: A flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behavior Research Methods, 39(2), 175–191.CrossRefGoogle Scholar
  18. Fiol, C. M., & Huff, A. S. (1992). Maps for managers: Where are we? Where do we go from here? Journal of Management Studies, 29(3), 267–285. doi:10.1111/j.1467-6486.1992.tb00665.x.CrossRefGoogle Scholar
  19. Forrester, J. W. (1961). Industrial dynamics. Portland: Productivity Press.Google Scholar
  20. Frensch, P., & Funke, J. (1995). Complex problem solving: The European perspective. Hillsdale: Lawrence Erlbaum Associates.Google Scholar
  21. Funke, J. (1983). Einige Bemerkungen zu Problemen der Problemlöseforschung oder: Ist Testintelligenz doch ein Prädiktor? Diagnostica, 29, 283–302.Google Scholar
  22. Funke, J. (2001). Dynamic systems as tools for analysing human judgement. Thinking and Reasoning, 7(1), 69–89. doi:10.1080/13546780042000046.CrossRefGoogle Scholar
  23. Funke, J. (2003). Problemlösendes Denken. Stuttgart: Kohlhammer.Google Scholar
  24. Funke, J. (2012). Complex problem solving. In N. M. Seel (Ed.), Encyclopedia of the sciences of learning (pp. 682–685). Heidelberg: Springer.Google Scholar
  25. Gobert, J. D., & Clement, J. J. (1999). Effects of student generated diagrams versus student-generated summaries on conceptual understanding of causal and dynamic knowledge in plate tectonics. Journal of Research in Science Teaching, 36(1), 39–53. doi:10.1002/(SICI)1098-2736(199901)36:1<39:AID-TEA4>3.0.CO;2-I.CrossRefGoogle Scholar
  26. Huff, A. S. (Ed.). (2002). Mapping strategic knowledge. London: Sage publishing.Google Scholar
  27. Hutchins, E. (1990). The technology of team navigation. In J. Galegher, R. E. Kraut, & C. Egido (Eds.), Intellectual teamwork: Social and technical bases of collaborative work (pp. 191–220). Hillsdale: Lawrence Erlbaum.Google Scholar
  28. Hyerle, D. (2009). Visual tools for transforming information into knowledge. Thousand Oaks: Corwin Press.Google Scholar
  29. Kersting, M. (1991). Wissensdiagnostik beim Problemlösen. Entwicklung und erste Bewährungskontrolle eines kontentvalide konstruierten problemspezifischen Wissenstests [Diploma Thesis]. Berlin: Freie Universität Berlin.Google Scholar
  30. Kersting, M., & Süß, H. M. (1995). Kontentvalide Wissensdiagnostik und Problemlösen: Zur Entwicklung, testtheoretischen Begründung und empirischen Bewährung eines problemspezifischen Diagnoseverfahrens. Zeitschrift Für Pädagogische Psychologie, 9, 83–93.Google Scholar
  31. Klocke, U. (2004). Folgen von Machtausübung und Einflussnahme für Wissenszuwachs und Effektivität in Kleingruppen. Berlin: dissertation.de.Google Scholar
  32. Kluge, A. (2004). Wissenserwerb für das Steuern komplexer Systeme [Knowledge aquisition for the control of complex systems]. Lengerich: Pabst Science Publishers.Google Scholar
  33. Kluwe, R. H. (1979). Wissen und Denken. Stuttgart: Kohlhammer.Google Scholar
  34. Lane, D. C. (2008). The emergence and use of diagramming in system dynamics: a critical account. Systems Research and Behavioral Science, 25(1), 3–23. doi:10.1002/sres.826.CrossRefGoogle Scholar
  35. Larkin, J. H., & Simon, H. A. (1987). Why a diagram is (sometimes) worth ten thousand words. Cognitive Science, 11(1), 65–100. doi:10.1016/S0364-0213(87)80026-5.CrossRefGoogle Scholar
  36. Liepmann, D. (Ed.). (2007). Intelligenz-Struktur-Test 2000 R (2nd ed.). Göttingen: Hogrefe.Google Scholar
  37. Lovett, M. C., & Anderson, J. R. (1996). History of success and current context in problem solving: Combined influences on operator selection. Cognitive Psychology, 31(2), 168–217.CrossRefGoogle Scholar
  38. Luchins, A. S. (1942). Mechanization in problem solving–the effect of Einstellung. Psychological Monographs, 54(248), 1–95.Google Scholar
  39. Mayer, R. E., & Anderson, R. B. (1991). Animations need narrations: An experimental test of a dual-coding hypothesis. Journal of Educational Psychology, 83(4), 484–490. doi:10.1037/0022-0663.83.4.484.CrossRefGoogle Scholar
  40. Meyer, B., & Scholl, W. (2009). Complex problem solving after unstructured discussion: Effects of information distribution and experience. Group Processes and Intergroup Relations, 12(4), 495–515. doi:10.1177/1368430209105045.CrossRefGoogle Scholar
  41. Michie, S., & Abraham, C. (2004). Interventions to change health behaviours: Evidence-based or evidence-inspired? Psychology and Health, 19(1), 29–49. doi:10.1080/0887044031000141199.CrossRefGoogle Scholar
  42. Montibeller, G., & Belton, V. (2006). Causal maps and the evaluation of decision options: A review. The Journal of the Operational Research Society, 57(7), 779–791.CrossRefGoogle Scholar
  43. Newell, A., & Simon, H. A. (1972). Human problem solving. Englewood Cliffs: Prentice Hall.Google Scholar
  44. Öllinger, M., & Goel, V. (2010). Problem Solving. In B. Glatzeder, V. Goel, & A. von Müller (Eds.), Towards a Theory of Thinking (pp. 3–21). Berlin-Heidelberg: Springer. Retrieved from http://dx.doi.org/10.1007/978-3-642-03129-8_1
  45. Öllinger, M., Jones, G., & Knoblich, G. (2008). Investigating the effect of mental set on insight problem solving. Experimental Psychology, 55(4), 269–282.CrossRefGoogle Scholar
  46. Paivio, A. (1971). Imagery and cognitive processes. New York: Holt, Rinehart & Winston.Google Scholar
  47. Paivio, A. (1986). Mental representations. A dual coding approach. New York: Oxford University Press.Google Scholar
  48. Parmenides Foundation (2011). Parmenides Eidos Suite ® (Version 7.8) [computer software] Google Scholar
  49. Plate, R. (2010). Assessing individuals’ understanding of nonlinear causal structures in complex systems. System Dynamics Review, 26(1), 19–33. doi:10.1002/sdr.432.CrossRefGoogle Scholar
  50. Preußler, W. (1998). Strukturwissen als Voraussetzung für die Steuerung komplexer dynamischer Systeme. Zeitschrift Für Experimentelle Psychologie, 45(3), 218–240.Google Scholar
  51. Putz-Osterloh, W., & Lüer, G. (1991). Über die Vorhersagbarkeit komplexer Problemlöseleistungen durch Ergebnisse in einem Intelligenztest. Zeitschrift Für Experimentelle Und Angewandte Psychologie, 28, 309–334.Google Scholar
  52. Reitman, W. R. (1964). Heuristic decision procedures, open constraints, and the structure of Ill-defined problems. In M. W. Shelly & G. L. Bryan (Eds.), Human judgments and optimality (pp. 282–315). New York: John Wiley and Sons.Google Scholar
  53. Rigas, G., Carling, E., & Brehmer, B. (2002). Reliability and validity of performance measures in microworlds. Intelligence, 30(5), 463–480. doi:10.1016/S0160-2896(02)00121-6.CrossRefGoogle Scholar
  54. Scavarda, A. J., Bouzdin-Chameeva, T., Goldstein, S. M., Hays, J. M., & Hill, A. V. (2004). A review of the causal mapping practice and research literature. Presented at the Second World Conference on POM and 15th Annual POM Conference, Cancun, Mexico.Google Scholar
  55. Shaft, T. M., & Vessey, I. (2006). The role of cognitive fit in the relationship between software comprehension and modification. MIS Quarterly, 30(1), 29–55.Google Scholar
  56. Sterman, J. D. (2000). Business dynamics. Systems thinking and modeling for a complex world. Boston: McGraw-Hill.Google Scholar
  57. Süß, H.-M. (1996). Intelligenz, Wissen und Problemlösen. Kognitive Voraussetzungen für erfolgreiches Handeln bei computersimulierten Problemen. Göttingen: Hogrefe.Google Scholar
  58. Süß, H.-M., Kersting, M., & Oberauer, K. (1993). Zur Vorhersage von Steuerungsleistungen an computersimulierten Systemen durch Wissen und Intelligenz [On the predictability of control performance on computer-simulated systems by knowledge and intelligence]. Zeitschrift Für Differentielle Und Diagnostische Psychologie, 14, 189–203.Google Scholar
  59. Van Meter, P., & Garner, J. (2005). The promise and practice of learner-generated drawing: Literature review and synthesis. Educational Psychology Review, 17(4), 285–325. doi:10.1007/s10648-005-8136-3.CrossRefGoogle Scholar
  60. Vessey, I. (1991). Cognitive fit: A theory-based analysis of the graphs versus tables literature. Decision Sciences, 22(2), 219–240. doi:10.1111/j.1540-5915.1991.tb00344.x.CrossRefGoogle Scholar
  61. White, L. (2006). Evaluating problem-structuring methods: Developing an approach to show the value and effectiveness of PSMs. The Journal of the Operational Research Society, 57(7), 842–855.CrossRefGoogle Scholar
  62. Wittmann, W., & Hattrup, K. (2004). The relationship between performance in dynamic systems and intelligence. Systems Research and Behavioral Science, 21(4), 393–409. doi:10.1002/sres.653.CrossRefGoogle Scholar
  63. Wittmann, W., Süß, H.-M., & Oberauer, K. (1996). Determinanten komplexen Problemlösens. Retrieved November 3, 2011, from www.psychologie.uni-mannheim.de/psycho2_alt/publi/ps/ber09.pdf
  64. Zhang, J. (1997). The nature of external representations in problem solving. Cognitive Science, 21(2), 179–217. doi:10.1207/s15516709cog2102_3.CrossRefGoogle Scholar
  65. Zhang, J., & Norman, D. A. (1994). Representations in distributed cognitive tasks. Cognitive Science, 18(1), 87–122.CrossRefGoogle Scholar

Copyright information

© Association for Educational Communications and Technology 2015

Authors and Affiliations

  • Michael Öllinger
    • 1
    • 2
  • Stephanie Hammon
    • 1
    • 3
  • Michael von Grundherr
    • 1
    • 4
  • Joachim Funke
    • 3
  1. 1.Parmenides Center for the Study of ThinkingParmenides FoundationPullach/MunichGermany
  2. 2.Psychological DepartmentLudwig-Maximilians-UniversityMunichGermany
  3. 3.Institute of PsychologyRuprecht-Karls-University of HeidelbergHeidelbergGermany
  4. 4.Philosophical DepartmentLudwig-Maximilians-UniversityMunichGermany

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