# Development Cycle Modeling: Process Risk
## 5 Results
### Probability of Success  
It appears that SabMDE does show promise for quantifying development processes and guiding an agent's decision-making. However, these results can be extended a step further by considering the information captured in the DEP DPath in the context of the Shannon information formula (7) when restated as in (28). The DPath information is the sum of the information in each of the DPath's DNodes.  
\[H_{P}(V,R,L,skill)=\log_{2}(\frac{1}{p}) \tag{28}\]  
The probability associated with the DEP DPath information can be interpreted as the probability of successful traversal (29).  
\[p_{success}=\frac{1}{2^{H_{P}(V,R,L,skill)}} \tag{29}\]  
It follows that the probability of project failure, i.e., the risk, is given by (30).  
\[risk=p_{failure}=1-p_{success}=1-\frac{1}{2^{H_{P}(V,R,L,skill)}} \tag{30}\]  
Table 4 is an extension of Table 3 that shows the probabilities of success and failure for each DPath revealed by the \(T_{1}\) test set as calculated by (29) and (30), respectively. These examples are necessarily small ones; they must fit within the confines of this paper. However, it should be stated that DPaths can be quite long, perhaps hundreds or thousands of compositions long. Because each DNode's worst-case information value corresponds to the DSpace's structural probability at a given composition level, each DNode information value can be large. Consequently, the sum of the DNodes' information, a DPath's information, can grow quite large. In Table 3, consider the DPaths designated as \"Others\". Those paths are untested. Their DNode information values are an order of magnitude greater than the values for tested DNodes. This differential carries over to Table 4 where (29) imposes a range limit  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Test Set**} & \multirow{2}{*}{**DPath Index**} & \multicolumn{2}{c}{**Information**} & \multicolumn{2}{c}{**Probability**} \\ \cline{3-6}  & & **DPath** & **Minimum** & **Success** & **Risk** \\ \hline \multirow{2}{*}{0} & 0 & 0.00 & 0.00 & 1.00 & 0.00 \\  & Others & 0.00 & & 1.00 & 0.00 \\ \hline \multirow{6}{*}{1} & 0 & 0.00 & & 1.00 & 0.00 \\  & 1 & 0.19 & & 0.87 & 0.13 \\  & 2 & 0.20 & & 0.87 & 0.13 \\  & 3 & 0.39 & & 0.76 & 0.24 \\  & 4 & 0.20 & & 0.87 & 0.13 \\  & 5 & 0.39 & & 0.76 & 0.24 \\  & 6 & 0.39 & & 0.76 & 0.24 \\  & 7 & 0.59 & & 0.66 & 0.34 \\
1 & 8 & 0.20 & 0.00 & 0.87 & 0.13 \\  & 9 & 0.39 & & 0.76 & 0.24 \\  & 10 & 0.39 & & 0.76 & 0.24 \\  & 11 & 0.59 & & 0.66 & 0.34 \\  & 12 & 0.39 & & 0.76 & 0.24 \\  & 13 & 0.59 & & 0.66 & 0.34 \\  & 14 & 0.59 & & 0.66 & 0.34 \\  & 15 & 0.79 & & 0.58 & 0.42 \\  & Others & 8.00 & & 0.00 & 1.00 \\ \hline \hline \end{tabular}
\end{table}
Table 4: DPath success and risk probabilities for DSpace traversals using Test Sets 0 and 1.  
on the DPath information value. Because the \(p_{success}\) range is [0,1], the non-linear \(H_{p}()\) range is [\(\infty\),0]. In practice, a probability lower limit value is chosen to fix an information upper limit, \(h_{ul}\) and this limit can be used to scale raw calculated DPath information values to the range needed by (29), see (31). For example, a 0.1 probability lower limit value means a 10% chance of DPath success. That probability value corresponds to an information upper limit value of 3.3219281 according to (29).  
\[h_{scaled}=\frac{h_{ul}}{H_{p}(V,R,L,skill)} \tag{31}\]  
Table 5 is an example where information values, randomly selected from a [0, 10,000] range, were scaled with (31). Table 5 shows a set of information values that distinguish potentially successful DPaths from the remainder.  
The scaling technique works because an agent needs only to rank DPath alternatives by their merit, and for this purpose, only the relative information values are needed, not their absolute values. The scaled information allows the probability of success or the risk of failure to be calculated for any DPath.