Decision tree analysis

Keywords reducing uncertainty, cost-effective information, ranking sources of uncertainty, re-processing seismic, interference tests, aquifer behaviour, % uncertainty, decision tree analysis, value of information, fiscal regime, suspended wells, phased development. 

Bursi et al. (2001) reported two methods to calculate the stability of testosterone-like steroids. These were the use of a decision tree and molecular descriptors or quantum mechanical methods. For satisfactory accuracy, Bursi and colleagues (2001) had to use a 3-21G basis set with spin correction and equilibrium geometries. This required 12 hours of computation for reactants and products. Optimization of transition state geometry was also required. The simpler decision tree analysis approach indicated that descriptors such as the volumes and, to a lesser degree, the shape were important. Correlations of calculated and experimental rates of metabolism were reported. 

Danthine J.P. and Donaldson J.B. 2002. Intermediate Financial Theory. Prentice Hall, New Jersey. De Reyck B., Degraeve Z. and Vandenborre R. 2001. Project Options Valuation with Net Present Value and Decision Tree Analysis. Working Paper, London Business School. Presented at 2001 INFORMS. INFORMS Conference, Miami Beach, November. 

Cause consequence analysis was developed in Denmark to analyze risks in safety critical systems such as nuclear power stations (Stapelberg, 2009, lEC/ISO 31010, 2009). The difference to other tree analyses like Decision tree analysis is that cause-consequence is extended through the possibility to consider time delays. 

Decision tree analysis is a generic method and should be applicable to urban security issues. 

Doganavjargil, E. Fattori, M. 2008. Decision tree analysis as a tool to optimise patent current awareness bulletins. World Patent Information, 30,212-219. Dunjo, J., Fthenakis, V., Vilchez, J.A. Amaldos, J. 2010. Hazard and operability (HAZOP) analysis. A literature review. Journal of Hazardous Materials, 173, 19-32. 

Qu, Y., Adam, B.-L., Yasui, Y, Ward, M.D., Cazares, L.H., Schellhammer, P.F., Feng, Z., Semmes, O.J. and Wright, G.L., Boosted decision tree analysis of surface-enhanced laser desorption/ionization mass spectral serum profiles discriminates prostate cancer from noncancer patients. Clin. Chem., 4S, 1835-1843 (2002). 

Table 11.3. The partioning profile of case study chemicals generated from box E of the decision tree analysis.

Summarize the basic steps in the decision tree analysis methodology. 

Discuss why using expected trends for the future can lead to different supply chain decisions relative to decision tree analysis that accounts for uncertainty. 

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