1. Example Does the shape of the utility function matter? A group of towns intends to develop a resort. Two possible sites have been identified. The council is currently conducting studies on the merits of the two sites. The final decision will be made next year. Land values around the two possible sites are increasing as investors speculate that property values will rise sharply in the vicinity of the new resort. 20.1 Friday, 20 November 20154:34 AM

2. Example A private real estate developer would like to build houses near the new resort. They have identified parcels of land near the two sites. Current prices (£,000) and estimated future values of developable land are summarised below. 20.2 Parcel of land near location AB Current purchase price £1,200£700 Present value if future cash flows if resort is built at this location £1,500£1,100 Present value of future sales price of parcel of land if resort is not built at this location £950£350

3. Example The developers can purchase land now either at site A only, at site B only, or at both. If land is purchased and the resort is not built at that location, the land will sold at a loss. If the land is not purchased now, it will not be available at a later date. It is assessed that there is a probability of 0.4 that the new resort is built at location A (0.6 at location B). 20.3

4. Financial Decision Tree 20.4 Generating cash flows from left to right

5. Probabilistic Decision Tree 20.5 Generating cash flows from right to left

6. Combined Decision Tree 20.6 So the selected strategy is option 2, to buy B.

7. Assessment of the Managers Utility 20.7 But does the “shape” of the utility function matter?

8. Assessment of the Managers Utility 20.8 You would accept £350, rather than take the gamble. Feed forward to the two proposed models.

9. An Exponential Utility Function 20.9 Using the values from the previous slide. The probabilities are fed into the tree.

10. Decision Tree Employing An Exponential Utility Function 20.10 So the selected strategy is option 3, to buy A and B. Cccccc ccc

11. A Power Utility Function 20.11 Using the values from before. The probabilities are fed into the tree.

12. Decision Tree Employing A Power Utility Function 20.12 Cccccc ccc

13. Experimentally Observed Utility Function – Low Stimuli 20.13 Using the previous values and the low stimuli Booij and van de Kuillen 2009 model. Where low stimuli reflect the financial levels of the decision the participants had to make.

14. Decision Tree Employing Regression For Experimentally Observed Utility Function – Low Stimuli 20.14 So the selected strategy (just) is option 4, to buy nothing.

15. Decision Tree Employing Piece Wise Interpolation For Experimentally Observed Utility Function – Low Stimuli 20.15 So the selected strategy (just) is option 4, to buy nothing.

16. Experimentally Observed Utility Function – High Stimuli 20.16 Using the previous values and the high stimuli Booij and van de Kuillen 2009 model. Where high stimuli reflect the financial levels of the decision the participants had to make.

17. Decision Tree Employing Regression For Experimentally Observed Utility Function – High Stimuli 20.17 So the selected strategy (just) is option 3, to buy A and B.

18. Decision Tree Employing Piece Wise Interpolation For Experimentally Observed Utility Function – High Stimuli 20.18 So the selected strategy (just) is option 3, to buy A and B.

19. Experimentally Observed Utility Function – High Stimuli versus Low Stimuli 20.19 But they hardly differ!

20. Assessment of the Managers Utility 20.20 But does the “shape” of the utility function matter? Yes!!