ACE and EE: A Review of Some Recent Progresses

1. ACE and EE: A Review of Some Recent Progresses The 19th Annual Workshop on the Economic Science with Heterogeneous Interacting Agents (ESHIA 2014), Tianjin University, Tianjin, China June 14-19, 2014 Shu-Heng Chen, chen.shuheng@gmail.com AI-Econ Research Center Department of Economics National Chengchi University Taipei, Taiwan http://www.aiecon.org/

2. Outline • 20 Years Before • The ACE-EE Lab • Literature Reviews (Selective) • Backgrounds • Recent Progresses • Concluding Remarks

3. 20 Years Before • Gode and Sunder (1993) • ACE and Microeconomic Experiments • Smith’s Market Experiment • Zero-Intelligence Agents (Entropy-Maximizing Agents) • Arifovic (1994) • ACE and Macroeconomic Experiments • Wellford’s Cobweb Experiments • Genetic Algorithms

4. 20 Years Before • Brian Arthur (1993) • Robillard’s Two-Armed Bandit Experiments • Calibrated artificial agents with reinforcement learning

6. Gode and Sunder (1993) DA Market Buyer 1 Seller 1 Random Random Buyer 2 Seller 2 Random Random Buyer 3 Seller 3 Random Random . . . . . . Buyer N1 Seller N2 Random Random

7. Intelligent-Irrelevance Hypothesis The zero-intelligent agent is a concept of random-behaved agents, who are not purposive and are unable to learn. To trade, they simply bid (ask) randomly but are constrained by their true reservation price (zero-profit price) They showed that the market efficiency coming out of a group of zero-intelligent agent can match what we observed from human-subject experiments. Therefore, their work, to some extent, verified the long-held “intelligence-irrelevance hypothesis” in the double auction market experiments.

8. ZI Agents Replicate Human-Subject Experiments

9. Extensions Cliff (1997) showed that Gode-Sunder’s ZI agents work only for the symmetric markets, but not asymmetric markets. Cliff (1997) and Cliff and Bruten (1997) then argued that the software agents need to be smarter to match human subject experiments. They, therefore, add a little learning capability to the ZI agent, which they called ZI-Plus agent, or ZIP agent. Nevertheless, ZI agent has now been extensively used in agent-based economic and financial models (see Ladley, 2012 for a survey on this). The virtue of the device of the ZI-agent is its simplicity, and therefore, analytical tractability. Hence, it is a benchmark of ACE.

11. Smith (1962), Chart 4

12. Smith (1962), Chart 6

14. Stable Case

15. Unstable Case

16. Cobweb Experiments Experimental evidence, however, show that even under the unstable case, the cobweb model is still stable (Carlson, 1967; Wellford, 1989; Johnson and Plott, 1989). The following two figures are from Wellford (1989), reprinted in Arifovic (1994).

17. Price Dynamics from GA: Arifovic (1994)

19. Two-Armed Bandit Problem • It was conducted by Laval Robillard at Harvard in 1952-53 and reported in Bush and Mosteller (1955), “Stochastic Models for Learning.” • Brian Arthur (1991, 1993) calibrated a two-parameter reinforcement learning models using Robillard’s experiment.

20. Robillard’s Experiment Conducted by Laval Robillard at Harvard in 1952-53 (Bush and Mosteller, 1955) 2-armed bandit problem

21. Robillard’s Results Table is from Arthur (1993)

22. Fitting Performance

23. The ACE-EE Lab

24. Scaling-Up Issues of EE • Space Limits • Budget Limits • Attention Limits (Fatigue) • Experimental economics at this point has not carefully reviewed to what extent their obtained results can be sensitive to the number of agents. • One difficulty is that many experiments are not easy to be scaled-up.

25. Lux and Schornstein (2007)

26. Lux and Schornstein (2007)

28. Selective Literature Review • Chen and Tai (2006) • Duffy (2006) • Chen (2008) • Chen (2012) • Chen (2013)

32. ACE and Experimental Economics Experimental Economics Replication Inspiration of New Designs Agent-Based Computational Economics

34. Agent-Based Economic Models 84th Dahlem Workshop Gigerrenzer and Selten (2001) Cognitive Capacity Human Subject Experiments Real Economy Personality Psychology Culture

36. Four Origins of ACE • Four Origins of ACE • Theory of Markets (A long history) • Economic Tournaments (1980s) • Cellular Automata (1950s) • Experimental Economics (1990s)

37. Origins of ACE: Human Subject Experiments Care Hommes’ and his colleagues Jasmina Arifovic (1994, 1995, 1996) Gode and Suner (1993) John Duffy (2006) Agent-Based Simulations (1990s) Cellular Automata Economic Experiments (1970s) (1960s)

39. Agent-Based Financial Markets Game Experiments Zero-Intelligence Agents Reinforcement Learning Agents Belief Learning Agents Experience-Weighted Attraction (EWA) Agents Regime-Switching Agents Sophisticated (EWA) Agents Level-K Reasoning Novelty-Discovering Agents

40. Cognitive Capability: One Dimension Zero-Intelligence Agents Reinforcement Learning Agents Experience-Weighted Attractions (EWA) Agents Game Experiments Belief Learning Agents Level-K Reasoning Agents

41. Artificial Agents with Incremental Cognitive Capability

42. Backgrounds • Double Auction Markets • Working Memory Tests • Heterogeneous Agents • Genetic Programming

44. The Santa Fe DA Market • Time is discretized into alternating bid/ask (BA) and buy/sell (BS) steps. • A trading period is simply a set of S alternating BA and BS steps. • An individual DA game is divided into one or more rounds, and each rounds is further divided into one or more periods. • Transactions are cleared according to AURORA rules.

45. BA Step • The DA market opens with a BA step in which all traders are allowed to simultaneously post bids and asks. • After the monitor informs the traders • of each others' bids and asks, • the holders of the current bid (highest outstanding bid) • the holders of the current ask (lowest outstanding ask) • enter into a BS step.

46. BS Step • During the BS step, either player can accept the other player's bid or ask. • If an acceptance occurs, a transaction is executed. • If both parties accept each other's offers, the monitor randomly choose between current bid and ask to determine the transaction price.

47. AURORA Rules • The AURORA rules were inspired by similar rules by the AURORA computerized trading system developed by the Chicago Board of Trade. • AURORA rules stipulate that only the holder of current bid or current ask are allowed to trade.

49. Economic Value • Consumers: • Subjective Preference, Taste, Utility • Producers: • Technology • In simulating a market, preference and cost structure are randomly generated in a regular manner.

50. Token-Value Generation Process