Reference class forecasting and the corresponding limitations
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| − | 1. Introduction | + | == Level 2 == 1. Introduction |
| − | 2. Background | + | == Level 2 == 2. Background |
| − | 3. Disadvantages of false estimations and their reasons | + | == Level 2 == 3. Disadvantages of false estimations and their reasons |
| − | + | == Level 3 == 3.1 Optimism bias | |
| − | + | == Level 3 == 3.2 Strategic misrepresentation | |
| − | 4. RCF method and its application in infrastructure projects | + | == Level 2 == 4. RCF method and its application in infrastructure projects |
| − | 5. Limitations of the Reference Class Forecast method | + | == Level 2 == 5. Limitations of the Reference Class Forecast method |
| − | 3. Frequentism | + | == Level 3 == 5.1 Frequentism |
| − | + | == Level 3 == 5.2 Subjectivity of reference classes | |
| − | 5. Acquisition of data | + | == Level 3 == 5.3 Acquisition of data |
| − | 6. A new approach – Combining RCF, Overconfidence Theory and Expert Judgements | + | == Level 2 == 6. A new approach – Combining RCF, Overconfidence Theory and Expert Judgements |
| − | 7. Conclusion | + | == Level 2 == 7. Conclusion |
| − | 8. References | + | == Level 2 == 8. References |
Reference class forecasting is a method aiming for an improved accuracy of estimations regarding projects’ costs and benefits. Since projects are inherent in almost every business sector and their growing size, it becomes and important aspect of management to provide adequate forecasts. The RCF method does so by following a three step plan where a reference class of similar projects is established at first. Then a probability distribution for certain variables is calculated and lastly, the project is benchmarked against the reference class and corrective actions for the estimations are taken. Hereby, the forecaster is asked to take an “outside view” and his psychological biases are bypassed. Nevertheless, the setting of the reference class is subjective and therefore susceptible to new biases. Furthermore, a pure empirical data analysis does not take the individual circumstances of the specific project into account. To overcome this problems, the RCF method, overconfidence theory and expert judgements can be combined. Here, experts create Min/Max scenarios and these are then combined with the probability distribution of the reference class. By this, the accuracy of the forecast can be increased, which is the overall aim of the RCF method. | Reference class forecasting is a method aiming for an improved accuracy of estimations regarding projects’ costs and benefits. Since projects are inherent in almost every business sector and their growing size, it becomes and important aspect of management to provide adequate forecasts. The RCF method does so by following a three step plan where a reference class of similar projects is established at first. Then a probability distribution for certain variables is calculated and lastly, the project is benchmarked against the reference class and corrective actions for the estimations are taken. Hereby, the forecaster is asked to take an “outside view” and his psychological biases are bypassed. Nevertheless, the setting of the reference class is subjective and therefore susceptible to new biases. Furthermore, a pure empirical data analysis does not take the individual circumstances of the specific project into account. To overcome this problems, the RCF method, overconfidence theory and expert judgements can be combined. Here, experts create Min/Max scenarios and these are then combined with the probability distribution of the reference class. By this, the accuracy of the forecast can be increased, which is the overall aim of the RCF method. | ||
Revision as of 16:47, 13 September 2016
== Level 2 == 1. Introduction == Level 2 == 2. Background == Level 2 == 3. Disadvantages of false estimations and their reasons == Level 3 == 3.1 Optimism bias == Level 3 == 3.2 Strategic misrepresentation == Level 2 == 4. RCF method and its application in infrastructure projects == Level 2 == 5. Limitations of the Reference Class Forecast method == Level 3 == 5.1 Frequentism == Level 3 == 5.2 Subjectivity of reference classes == Level 3 == 5.3 Acquisition of data == Level 2 == 6. A new approach – Combining RCF, Overconfidence Theory and Expert Judgements == Level 2 == 7. Conclusion == Level 2 == 8. References
Reference class forecasting is a method aiming for an improved accuracy of estimations regarding projects’ costs and benefits. Since projects are inherent in almost every business sector and their growing size, it becomes and important aspect of management to provide adequate forecasts. The RCF method does so by following a three step plan where a reference class of similar projects is established at first. Then a probability distribution for certain variables is calculated and lastly, the project is benchmarked against the reference class and corrective actions for the estimations are taken. Hereby, the forecaster is asked to take an “outside view” and his psychological biases are bypassed. Nevertheless, the setting of the reference class is subjective and therefore susceptible to new biases. Furthermore, a pure empirical data analysis does not take the individual circumstances of the specific project into account. To overcome this problems, the RCF method, overconfidence theory and expert judgements can be combined. Here, experts create Min/Max scenarios and these are then combined with the probability distribution of the reference class. By this, the accuracy of the forecast can be increased, which is the overall aim of the RCF method.
