Experimental Economics Presentation Updated version

1. Experimental EconomicsPresentationUpdated version Cecilia and Danye Nov. 27th, 2007

2. The Question • What is the expected observed price and quantity change for Spring Break destination air tickets in the (six weeks) leading up to the Spring Break?

3. Basic Assumption • For buyer, his action is based on his assumption of the pricing strategy of sellers. • For seller, his action is based on his observation of the buyers’ actions. • This game is an interaction between buyers and sellers.

4. Possible Outcomes • Pricing trends • Going up with time • Going down with time • Going up and drop down at “last minute” • Expected observed Outcomes • Rather flat, because: if…then… • A general trend of dropping down: because sellers are concerned about “blocks of 5”.

5. Recap – Instruction Setup • Shared information: • In this experiment, there are 2 types of agents: Sellers and Buyers. • Your objective is to maximize your final Payoff. And generally speaking, the buyers face a diminishing marginal value (utility) with purchasing volume. • Each period lasts for 3 minutes. There will be 12 periods.

6. Recap – Instruction Setup • Seller’s information: • You’re representing one of them, but you don’t know yet…

7. Recap – Instruction Setup • Seller’s information: • Stock: 100 (Same for all Sellers) • Money: 0 (Same for all Sellers) • Cost per unit: Random number between100-200 • You can “ASK” any price for your goods. • The cost per unit, independently drawn for each seller, is consistent for each period, despite of how many goods you sell. • However, you will incur costs at blocks of 5 units. • You may “Cancel” your “ASK” if no buyer has accepted it, by buying back your own stock. You may only buy back your own stock (identified by blue font) but NOT other sellers’ stock. • Payoff = Total earnings – Total Costs (with units rounded up to the nearest multiple of 5)

8. Recap – Instruction Setup • Buyer’s information: • Stock: 0 (Same for all buyers) • Money: 10000 (Same for all buyers) • Value per unit: Random number between200-300 • You can “BUY” a maximum of 5 units • You cannot “ASK”. You can only take posted prices by the sellers, and you cannot re-sell your goods to other buyers. • Your value per unit, independently drawn for each buyer, decreases by 10 for every extra unit you purchase. • Your payoff is calculated as follows: • Payoff = Total value – Total amount of money spent

9. Data Analysis First 5 periods before everyone learned Sellers’ Constraints

10. Period 1

11. Period 2

12. Period 3

13. Observations • First 3 periods • Learning process, both sides rather cautious • Little fluctuation in observed prices (180-200) • General trend rather flat, with a slight drop in period 2 and slight heading up in period 3. • Numbers of transactions accomplished flat out through the 3 min process, with slightly more concentration in the beginning and the end.

14. Period 4

15. Period 5

16. Observations • Periods 4 and 5 • Both sellers and buyers begin to take risks • See more fluctuation in observed prices (140-200) • General trend: price is going down. • Sellers are more concerned to make sure they sell at blocks of 5 units. • Sellers’ personality taken into account. • The online scenario, everyone trying to click on the cheapest price

17. Data Analysis Cont. Period 6-11, after everyone learned Sellers’ Constraints

18. Period 6

19. Period 9

20. Observations • Periods 6 and 9 (similar pattern) • Buyers not buying as much in the beginning, transaction concentrated towards the end. • Drop down in average observed prices (140-180), compared with the former cap of 200. • General trend: price is going down, especially if we omit the “out-liner”/fluctuation of prices. • The fluctuation prices are higher than the general trend prices. (Low price tickets achieving more quantities)

21. Period 7

22. Period 8

23. Period 10

24. Period 11

25. Observations • Periods 7, 8, and 10 • Period 11 is not representative • Besides the previous observations, we see more fluctuation in the middle, • The fluctuation prices are lower than the general trend prices. (High price tickets achieving more quantities, especially for period 8) • Sellers’ personality taken into account. • The online scenario, everyone trying to click on the cheapest price—cheap ones always followed by high prices

26. Price Analysis

27. Quantities and Time Analysis

28. Price and Quantities for each sellers

29. Seller 1

31. Seller 2

33. Seller 3

34. Load Factors

35. Conclusion • First, it is consistent throughout our experiment that the prices have been fluctuating, with a general downward trend, but still staying rather close to 200. • Second, from a buyer’s perspective, and without the “cancel” option for buyers, the best strategy is either to wait toward the end and purchase a relatively cheap price, or to make the decision in the middle of the period, because that is the session where we see most fluctuations. • Third, from a seller’s perspective, the best strategy is mostly likely the one adopted by seller 2: starting off at a high price and steadily going downward within a given period.

36. Further Thoughts • Setup/real-life concerns • Flexibility option for buyers • Cancellation option for buyers • Increasing cost for sellers • Buyers can access historical information and can “learn”. • Real life: Prices high towards the end, because airlines keep the prices up. • Seller’s personality, game theory perspective, best response not only to buyers, but also to other sellers

37. Further Thoughts • Hardly see drop down in last minute. • No cancellation for buyers • See more fluctuation in the middle (sellers trying to make it to the “blocks of 5”) • Prices already rather low toward the end • Observations might change if the subjects know it’s an airplane scenario