Experimental
Behavioural
Economics
image courtesy US NIST
- limited numbers of subjects at once
- difficulty approximating real world incentives
- difficulty in controlling for characteristics of subjects
- difficult to avoid, e.g. "strategic playing" rather than desired behaviour
but
- empirically based
- guaranteed to approximate real human learning behaviour and rationality.. since they are human
"Zero Intelligence Agents"
due to Gode and Sunder (1993)
Unconstrained Robot Traders
Human and robot tests
Robot Traders with a bank balance
Human traders
courtesy Bibliodessy http://bibliodyssey.blogspot.com/2007/12/turk-chess-automaton-hoax.html
It turns out you can do a reasonable job of approximating human behaviour with agents acting randomly constrained by institutions...
Duffy, John, 2006 "Agent-based models and human subject experiments"
"Mechanical Turk", and stock broker Turing tests, and plain old-fashioned statisical comparison of outcomes
"Learning" agents
image courtesy http://chomskyscolorlessgreenideas.blogspot.com/2009/09/chinese-room.html
...of individual and aggregate behaviour
http://bigeyedeer.wordpress.com/2008/05/12/this-cartoon-is-a-dogs-breakfast/
Pantazi et al. BMC Medical Informatics and Decision Making 2004 4:19 doi:10.1186/1472-6947-4-19
The Duplicators by Murray Leinster (1964)
http://xkcd.com/720/
Missing: Neural networks
Evolutionary algorithms
image courtesy http://www.genetic-programming.org/
Agent-based
Computational
Economic
Models
image courtesy Renee Ting http://www.reneesbookoftheday.com/2006/10/invention-of-hugo-cabret-by-brian.html
- huge number of agents
- unbounded time scale
- freedom from boredom for agents
- "growing" a system is a form of insight
but
- learning rules may be too simple to easily calibrate against "real" behaviour
- no necessary reason that behaviours are any less arbitrary or over simple than e.g. general equilibrium economic models - still "armchair theorising"