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Software Agents in Economic Environments Robert S. Gazzale Ph.D. Candidate, Department of Economics Jeffrey MacKie Mason Professor, Dept. of Economics.

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Presentation on theme: "Software Agents in Economic Environments Robert S. Gazzale Ph.D. Candidate, Department of Economics Jeffrey MacKie Mason Professor, Dept. of Economics."— Presentation transcript:

1 Software Agents in Economic Environments Robert S. Gazzale Ph.D. Candidate, Department of Economics Jeffrey MacKie Mason Professor, Dept. of Economics & School of Information CARAT 2002/2003 April 9, 2003

2 CARAT: Gazzale & MacKie Mason Funding: Past & Present Many thanks to: CARAT NSF IBM

3 April 9, 2003 CARAT: Gazzale & MacKie Mason Collaborators Chris Brooks Computer Science, University of San Francisco Yan Chen School of Information Rajarshi Das IBM Institute for Advanced Commerce Ed Durfee AI Lab, EECS Jeff Kephart IBM Institute for Advanced Commerce

4 April 9, 2003 CARAT: Gazzale & MacKie Mason A Model of Economic Modeling Environment Outcomes

5 April 9, 2003 CARAT: Gazzale & MacKie Mason A Model of Economic Modeling Environment Outcomes

6 April 9, 2003 CARAT: Gazzale & MacKie Mason A Model of Economic Modeling Environment Outcomes

7 April 9, 2003 CARAT: Gazzale & MacKie Mason A Model of Economic Modeling: Alternative View Environment Outcome

8 April 9, 2003 CARAT: Gazzale & MacKie Mason A Model of Economic Modeling: Which Mapping? Environment Outcomes

9 April 9, 2003 CARAT: Gazzale & MacKie Mason Equilibrium: The Mapping from Environment to Outcome One Agent Environment Optimal action Non-cooperative Games Nash Equilibrium  Given what everybody else is doing, no one agent can change strategy to do better.

10 April 9, 2003 CARAT: Gazzale & MacKie Mason John Nash?

11 April 9, 2003 CARAT: Gazzale & MacKie Mason Problem: Finding Equilibria Is it solvable? If so, will agents find it? Bounded Rationality (Herbert Simon)  Cognition is not free.  Satisfice rather than optimize?

12 April 9, 2003 CARAT: Gazzale & MacKie Mason Problem: Out of equilibrium matters! Particularly if agents are boundedly rational Do we get to equilibrium? What happens on path to equilibrium?

13 April 9, 2003 CARAT: Gazzale & MacKie Mason More Problems with “Equilibrium” Which Equilibrium? If there are many equilibria, which is going to happen when?

14 April 9, 2003 CARAT: Gazzale & MacKie Mason Why Software Agents? Useful in alleviating equilibrium issues. Cheap. Present/Future of software agents in real markets Particularly where equilibrium not solvable.

15 April 9, 2003 CARAT: Gazzale & MacKie Mason Application 1: Convergence to Equilibrium Nash Equilibrium Theory: Supermodular (SPM) games played by learning agents converge to Nash Equilibrium

16 April 9, 2003 CARAT: Gazzale & MacKie Mason Application 1: Convergence to Equilibrium Nash Equilibrium Theory: Supermodular (SPM) games played by learning agents converge to Nash Equilibrium Trust me, you don’t need to know what this is!

17 April 9, 2003 CARAT: Gazzale & MacKie Mason Application 1: Convergence to Equilibrium Nash Equilibrium Theory makes no predictions if NOT Supermodular.

18 April 9, 2003 CARAT: Gazzale & MacKie Mason Application 1: Convergence to Equilibrium Nash Equilibrium Is more supermodular “better”?

19 April 9, 2003 CARAT: Gazzale & MacKie Mason Application 1: Convergence to Equilibrium Nash Equilibrium Answers to these questions important in designing markets!

20 April 9, 2003 CARAT: Gazzale & MacKie Mason Convergence to Equilibrium: Human Experiment Methodology Design game where parameter controls whether or not game is SPM Laboratory experiments with human subjects playing for real money!

21 April 9, 2003 CARAT: Gazzale & MacKie Mason Convergence to Equilibrium: Human Experiment Methodology Design game where parameter controls whether or not game is SPM Laboratory experiments with human subjects playing for real money!

22 April 9, 2003 CARAT: Gazzale & MacKie Mason Convergence to Equilibrium: Human Experiment Results Problem: Dynamics not complete with human subject experiments (60 rounds)

23 April 9, 2003 CARAT: Gazzale & MacKie Mason Convergence to Equilibrium: Human Experiment Results

24 April 9, 2003 CARAT: Gazzale & MacKie Mason Convergence to Equilibrium: Human Experiment Results Will this treatment catch-up?

25 April 9, 2003 CARAT: Gazzale & MacKie Mason Convergence to Equilibrium: Human Experiment Results Will this treatment catch-up? Will any of these pull ahead?

26 April 9, 2003 CARAT: Gazzale & MacKie Mason Software agents to complete dynamics Methodology Select various learning models Endow agents with these models Calibrate models with actual data Compare calibrated learning models Endow pool of agents with best model and let run!

27 April 9, 2003 CARAT: Gazzale & MacKie Mason Software agents to complete dynamics Use of computation power For each learning model and treatment  For each set of parameters (1100 sets) 12 agents play in each iteration for 60 rounds 1500 iterations of game 8,910,000,000 “decisions” in <6 hours! Select Parameters that most-closely fit data. For best learning model 12 agents each iteration for 1000 rounds 1500 iterations of game

28 April 9, 2003 CARAT: Gazzale & MacKie Mason Simulation Results “Never” does catch up! Pulls ahead for a short while!

29 April 9, 2003 CARAT: Gazzale & MacKie Mason Application 2: Agents Solving Difficult Problems Many problems without analytical solution Natural domain for use of computer science methods to find optimum Many are “hill-climbing” methods Economics needs to inform these solutions

30 April 9, 2003 CARAT: Gazzale & MacKie Mason A not so hard problem for an agent... No matter where we start, rather easy to get to the summit!

31 April 9, 2003 CARAT: Gazzale & MacKie Mason A more difficult landscape... Tough to get from here to here

32 April 9, 2003 CARAT: Gazzale & MacKie Mason A more difficult landscape... Made a little easier... Use economic knowledge to: reduce the search space!

33 April 9, 2003 CARAT: Gazzale & MacKie Mason A more difficult landscape... Made a little easier... select better starting values! Use economic knowledge to:

34 April 9, 2003 CARAT: Gazzale & MacKie Mason A more difficult landscape... Made a little easier... Use economic knowledge to:

35 April 9, 2003 CARAT: Gazzale & MacKie Mason A more difficult landscape... Made a little easier... Use economic knowledge to: supply gradient information!

36 April 9, 2003 CARAT: Gazzale & MacKie Mason Problem 1: Pricing Problem Firm Sells Information Goods Consumer demand uncertain Many different pricing schedules possible General rule: Higher profits from schedules that are harder to learn. What schedule? No analytical solution!

37 April 9, 2003 CARAT: Gazzale & MacKie Mason The Pricing Problem: Results Adaptive uses knowledge to move among schedules!

38 April 9, 2003 CARAT: Gazzale & MacKie Mason Problem 2: Battle of the Agents Highly complex environment Large Search space Actions of competitor warp my landscape. Result: Computer science algorithms, without economic knowledge, perform quite poorly

39 April 9, 2003 CARAT: Gazzale & MacKie Mason Computer algorithm, no economic knowledge Equilibrium

40 April 9, 2003 CARAT: Gazzale & MacKie Mason Computer algorithm, with economic knowledge Gradient info } Reduce search space } Better starting values

41 April 9, 2003 CARAT: Gazzale & MacKie Mason That’s all folks!

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