1. 1 Decision Making under Uncertainty

2. 2 The maximin criterion A decision table for the food manufacturer (Daily profits) Demand (no. of batches) 1 2 Course of action Produce 1 batch$200 $200 Produce 2 batches –$600 $400

3. 3 The Expected Monetary Value (EMV) criterion Another decision table for the food manufacturer (Daily profits) Demand (no. of batches) 12 Probability0.30.7 Course of action Produce 1 batch$200$200 Produce 2 batches –$600$400

4. 4 Calculating expected profits Produce one batch: Expected daily profit = (0.3  $200) + (0.7  $200) = $200 Produce two batches: Expected daily profit = (0.3  –$600) + (0.7  $400) = $100

5. 5 Sensitivity analysis

6. 6 Limitations of the EMV criterion It assumes that the decision maker is neutral to risk It assumes a linear value function for money It considers only one attribute - money

7. 7 Single-attribute utility: A decision tree for the conference organizer

8. 8 Applying utilities to the conference organizer’s decision

9. 9 A utility function for the conference organizer - indicating she is risk averse

10. 10 Interpreting utility functions

11. 11 The drug company research department’s problem

12. 12 Utility function for product development time

13. 13 Allais’s paradox

14. 14 Multi-attribute Utility

15. 15 Utility independence Attribute A is utility independent of attribute B, if the decision maker’s preferences for gambles involving different levels of A, but the same level of B, do not depend on the level of attribute B…

16. 16 Utility independence

17. 17 Utility functions for overrun time and project cost

18. 18 The project manager’s utilities for overrun and cost OverrunCost of (weeks)Utilityproject ($)Utility 01.0 50 0001.00 10.9 60 0000.96 30.6 80 0000.90 60.0120 0000.55 140 0000.00

19. 19 Multi-attribute utility function u(x 1,x 2 ) =k 1 u(x 1 ) + k 2 u(x 2 ) + k 3 u(x 1 )u(x 2 ) where: k 3 = 1– k 1 – k 2

20. 20 Determining k 1

21. 21 Determining k 2

22. 22 The project manager’s decision tree with utilities