Title :
Models of emerging contexts in risky and complex decision settings
Author :
Lundberg, C. Gustav
Author_Institution :
A.J. Palumbo Sch. of Bus. Adm., Duquesne Univ., Pittsburgh, PA, USA
Abstract :
Key components of the multiple constraint satisfaction frameworks are explored in a series of experiments set in complex and ambiguous domains. All cases show the prevalence and importance of a purposeful structuring of the information by the participants. The participants gradually generate coherence even without increasing information. In accordance with multiple constraint satisfaction predictions, the assessments of inferences increasingly spread apart. Also, the correlations between the dependent variable (the decision) and the independent variables, as well as between the independent variables, consistently grow stronger as the participants progress through the decision stages. The information structuring, a gradual simplification of the component structure, is captured as principal components associated with the various decision stages. Neural networks predict the judgments in the various decision stages relatively well. Finally, the role of the ongoing structuring of the underlying information was explored through the application of trained networks to data in other decision stages.
Keywords :
constraint theory; decision theory; inference mechanisms; neural nets; complex decision; decision stage; inference assessment; information structuring; multiple constraint satisfaction; neural network; risky decision; Bifurcation; Cognition; Constraint theory; Context modeling; Economic forecasting; Neural networks;
Conference_Titel :
System Sciences, 2004. Proceedings of the 37th Annual Hawaii International Conference on
Print_ISBN :
0-7695-2056-1
DOI :
10.1109/HICSS.2004.1265220