1. Optimizing Online Auction Bidding Strategies with Genetic Programming Ekaterina “Kate” Smorodkina

2. Why Optimize Bidding Strategies?  Popularity of online auctions  Limited resources (i.e. $$$)  Bidding on multiple items increases the complexity of the decision making process  Increasing number of buyers and auction listings  Difficulty in predicting the behavior of other buyers

3. Overview  Research questions and problem definition  Online auction overview and auction simulation  Strategy representation  Fitness evaluation  Evolving agents  Experiments  Future work

4. Research Questions  Is it possible to come up with one all-purpose bidding strategy for various online auction scenarios?  How successful is genetic programming in evolving bidding strategies for online auctions?

5. Online Auctions Overview  Limited time  Starting bid  Email notification  Identical items for sale  Unrestricted bidding

6. Online Auction Simulation  Item listings are randomly created with a starting bid and time limit  Agents are created with random lists of items to buy  Concurrent bidding.  Retail price on each item is known  Agents know if they no longer hold the highest bid on an item  Agents are not allowed to go over their account balance

7. Strategy Representation  Expression trees  Binary operators: +, -, /, *, %, max, min  The size of the trees is controlled by two parameters: the branching limit and the depth limit  Large trees take longer time to compute a bid  Input parameters to the expression tree

8. Input parameters to the expression  Account balance  Retail price  Current bid  Number of items on the list  Number of items missing  Sum of the retail prices on the missing items  The highest bid among all instances of the item  The lowest bid among all instances of the item

9. Agent Fitness Evaluation  Maximize the number of items obtained.  Maximize discount.  N – number of items obtained M – number of items on the list R.P – retail price H.B – highest bid

10. Evolution Cycle Modified InitializeBidEvaluate Select ReproduceBidEvaluate Compete

11. Evolving Agents  Proportional Selection  Recombination  Subtree crossover  Mutation  Competition  Elitist strategy  Termination  Fitness convergence  Mutation rate adjustment

12. Experiments to Perform  Change the environment after each auction round  Number and characteristics of items in the auction  Agents’ lists and their initial account balance  Fitness standard as a way to measure the success of the experiment

13. Results  None yet

14. Future Work  Finish this project  Expand the types of operators in the expression trees  Expand input parameters to the expression trees  Create seller agents