# A Continuous Monitoring and Reallocation Method for Successful Decisions in Change Management
## 4 Experiments and Results  
Appropriate experiments were designed and carried out to verify the efficiency of the proposed CoMoRe method. This section presents the graphical and numerical results of experimental testing. The application of the method is studied, and the advantages that arise from the experimental results are analyzed. The developed simulation program uses the Python programming language in the Scientific Python development environment (Spyder) on Anaconda Navigator.  
According to change models, human behavior varies. As presented in Section 3, it starts with denial, which gradually grows and then manifests as increasing resistance. Then, acceptance slowly begins, and finally, the performance significantly increases.  
The experiments involved a hypothetical company that is implementing a change. In the first experiment, the company uses 10 workers for 10 corresponding tasks (\(n=10\)). In the second experiment, the number of workers and tasks are increased to \(n=100\) in order to study the performance and time for larger values. The period of change, in which human behavior in response to change was simulated, was split into 15 intervals (\(T=15\)) for both experiments.  
The performance values are measured as a percentage of the maximum initial performance, which is always assumed to be 100%. Figure 3 [51] shows the simulated curve obtained with the change management model, which reflects simulated human behavior in the phase of change. Although the simulated curve in Figure 3 shows the typical behavior of employees in a period of change, the specific values of performance are not always constant. This uncertainty in performance values was modeled in the experiment by applying a symmetric triangular probability distribution to the values of performance and by repeating the experiment numerous times by using these random performance values. The triangular distribution was selected because for the phenomenon described by the change curve, only the average performance decline is known, having been derived from many observations in the past. It is also known that these changes are not abrupt, i.e., the standard deviation of the distribution is relatively small, and the values become less probable as we move away from the mean. Furthermore, all tailed distributions are rejected because they could lead to negative performance, which is not expected for this phenomenon. Thus, the triangular distribution is the easiest and best way to describe the uncertainty in resistance to change.  
Figure 3: The simulated change curve used as the ground truth for the Classical Change Management Model [51].  
Figure 3 also presents (in green) the random performance values when the simulation was repeated 100 times. The performance change simulation procedure presented in Figure 3, which shows the performance distribution during the change process, was used as the ground-truth change curve in the following experiments and is named the Classical Change Management Model (CCMM).  
Based on the simulation of the CCMM change curve (Figure 3), employee behavior was simulated in this experiment using the parameters in Table 1[51], which shows the minimum and maximum values of resistance for every time interval, from \(t=0\) to \(t=15\). The interval limits are considered the lower and upper limits of the triangular distribution, which provided random values for resistance that were used during repetitions of the experiments. Red indicates that the resistance values are positive, meaning employees have a negative attitude toward the change. Green indicates \"negative resistance\", which, in fact, means employees have a positive attitude toward the change. By using these resistance values and Equation (9), the corresponding performance values can be calculated for the next interval of time.