Background. Most research in the area of dynamic decision making in general and stock-flow failures in particular is conducted with the help of computerized simulations as task environments or paper-based tasks of simple dynamic systems.
Purpose. The purpose of this article is to investigate if stock-flow failures persist in naturalistic decision making environments.
Methods. For this purpose, a tangible stock-flow experiment is used. It asks participants to meet a target level of water in a glass by pouring water through a funnel as fast as possible. We then compare their performance with a purely cognitive task (a manufacturing task).
Results. Participants on average did not significantly perform better in a tangible stock-flow task than in a paper-based test of a comparable task. In addition, individual performance in the tangible task cannot be related to performance in a similar paper-pencil stock-flow task.
Conclusions. An implication of this study is that tangible stock-flow tasks are as difficult for humans to control as are purely cognitive tasks. Further research should address individual differences between the two task modes (tangible vs. cognitive). A limitation of this study is the usage of only one tangible stock-flow task.
BakkenB. E. (2008). On improving dynamic decision-making: Implications from multiple-process cognitive theory. Systems Research and Behavioral Science, 25, 493-501.
2.
Booth SweeneyL.StermanJ. D. (2000). Bathtub dynamics: Initial results of a systems thinking inventory. System Dynamics Review, 16, 249-294.
3.
BrehmerB. (1992). Dynamic decision making: Human control of complex systems. Acta Psychologica, 81, 211-241.
4.
CroninM. A.GonzalezC. (2007). Understanding the building blocks of system dynamics. System Dynamics Review, 23, 1-17.
5.
CroninM. A.GonzalezC.StermanJ. D. (2009). Why don’t well-educated adults understand accumulation? A challenge to researchers, educators, and citizens. Organizational Behavior and Human Decision Processes, 108, 116-130.
6.
DörnerD. (1980). On the difficulties people have in dealing with complexity. Simulations & Gaming, 11, 87-106.
7.
DörnerD. (1996). The logic of failure: Strategic thinking for complex situations. New York, NY: Metropolitan Books.
8.
DörnerD.KreuzigH. W.ReitherF. (1994). Lohhausen. Vom Umgang mit Unbestimmtheit und Komplexität [Lohhausen. About dealing with uncertainty and complexity]. Bern, Switzerland: Huber.
9.
DuttV.GonzalezC. (2013). Reducing the linear perception of nonlinearity: Use of a physical representation. Journal of Behavioral Decision Making, 26, 51-67.
10.
EdwardsW. (1962). Dynamic decision theory and probabilistic information processing. Human Factors, 4, 59-73.
11.
ElliottT.WelshM.NettelbeckT.MillsV. (2007). Investigating naturalistic decision making in a simulated microworld: What questions should we ask?Behavior Research Methods, 39, 901-910.
12.
ForresterJ. W. (1961). Industrial dynamics. Cambridge, MA: Productivity Press.
13.
FriedmanD.CassarA.SeltenR. (2004). Economics lab: An intensive course in experimental economics. London, England: Routledge.
14.
FunkeJ. (2001). Dynamic systems as tools for analysing human judgement. Thinking & Reasoning, 7, 69-89.
15.
GonzalezC.VanyukovP.MartinM. K. (2005). The use of microworlds to study dynamic decision making. Computers in Human Behavior, 21, 273-286.
16.
GualaF. (2005). The methodology of experimental economics. Cambridge, UK: Cambridge University Press.
17.
HastieR.DawesR. M. (2001). Rational choice in an uncertain world: The psychology of judgement and decision making. Thousand Oaks, CA: Sage.
18.
KahnemanD.TverskyA. (1972). Subjective probability: A judgment of representativeness. Cognitive Psychology, 3, 430-454.
19.
KainzD.OssimitzG. (2002). Can students learn stock-flow-thinking? An empirical investigation. In System Dynamics Society (Ed.), 20th International Conference of the System Dynamics Society. Palermo, Italy. Available at http://www.systemdynamics.org/conferences/2002/index.html
20.
KleinG. (2008). Naturalistic decision making. Human Factors: The Journal of the Human Factors and Ergonomics Society, 50, 456-460.
21.
LipshitzR.KleinG.CarrollJ. S. (2006). Introduction to the special issue. Naturalistic decision making and organizational decision making: Exploring the intersections. Organization Studies, 27, 917-923.
22.
LipshitzR.KleinG.OrasanuJ.SalasE. (2001). Taking stock of naturalistic decision making. Journal of Behavioral Decision Making, 14, 331-352.
23.
MoxnesE. (1998). Not only the tragedy of the commons: Misperceptions of bioeconomics. Management Science, 44, 1234-1248.
24.
MoxnesE. (2011, March24-26). A unifying theory of local and global overshoot: From discounting to simulation. Paper presented at the 5th European System Dynamics Workshop, Frankfurt am Main, Germany.
25.
MoxnesE.JensenL. (2009). Drunker than intended: Misperceptions and information treatments. Drug and Alcohol Dependence, 105, 63-70.
26.
OssimitzG. (2002). Stock-flow-thinking and reading stock-flow-related graphs: An emperical investigation in dynamic thinking abilities. In System Dynamics Society (Ed.), 20th International Conference of the System Dynamics Society. Palermo, Italy. Available at http://www.systemdynamics.org/conferences/2002/index.html
27.
PaichM.StermanJ. D. (1993). Boom, bust, and failures to learn in experimental markets. Management Science, 39, 1439-1458.
28.
ReichertU.DörnerD. (1988). Heurismen beim Umgang mit einem “einfachen” dynamischen System [Heuristics when dealing with a “simple” dynamic system]. Sprache und Kognition, 7(1), 12-24.
29.
SenguptaK.Abdel-HamidT. K. (1993). Alternative conceptions of feedback in dynamic decision environments: An experimental investigation. Management Science, 39, 411-428.
30.
SmithV. L. (1976). Experimental economics: Induced value theory. American Economic Review, 66, 274-279.
31.
SmithV. L. (1982). Microeconomic system as an experimental science. American Economic Review, 72, 923-955.
32.
StermanJ. D. (1989). Misperceptions of feedback in dynamic decision making. Organizational Behavior and Human Decision Processes, 43, 301-335.
33.
StermanJ. D. (1994). Learning in and about complex systems. System Dynamics Review, 10, 291-330.
34.
StermanJ. D. (2002). All models are wrong: Reflections on becoming a systems scientist. System Dynamics Review, 18, 501-531.
35.
StermanJ. D. (2008). Risk communication on climate: Mental models and mass balance. Science, 322, 532-533.
36.
StermanJ. D.Booth SweeneyL. (2002). Cloudy skies: Assessing public understanding of global warming. System Dynamics Review, 18, 207-240.
37.
StermanJ. D.Booth SweeneyL. (2007). Understanding public complacency about climate change: Adults’ mental models of climate change violate conservation of matter. Climatic Change, 80, 213-238.
38.
StoutenH.HeeneA.GellynckX.PoletH. (2012). Learning from playing with microworlds in policy making: An experimental evaluation in fisheries management. Computers in Human Behavior, 28, 757-770.
39.
StrohheckerJ. (2009). A pilot study for testing the effect of stock and flow thinking on stock and flow management performance. In StrohheckerJ.GrößlerA. (Eds.), Strategisches und operatives Produktionsmanagement - Empirie und Simulation (pp. 285-305). Wiesbaden, Germany: Gabler.
40.
TabachnickB. G.FidellL. S. (2007). Using multivariate statistics (5th ed.). Boston, MA: Pearson/A&B.
41.
WittmannW. W.HattrupK. (2004). The relationship between performance in dynamic systems and intelligence. Systems Research and Behavioral Science, 21, 393-409.
42.
ZsambokC. E.KleinG. A. (1997). Naturalistic decision making. Mahwah, NJ: Lawrence Erlbaum.
