[mod.ai] Seminar - Machine Learning and Economics

mitchell@RED.RUTGERS.EDU (Tom) (01/22/86)

[Forwarded from the Rutgers bboard by Laws@SRI-AI.]


ML Colloquium talk

Title: Market Traders: Intelligent Distributed Systems 
		In an Open World
Speaker: Prof. Spencer Star
	 Laval University, Quebec
Date: Friday, Jan 24
Time: 11 am
Location: Hill 423

	Professor Spencer Star is a computer scientist/economist who
works on simulating economic markets.  He will be spending the coming
year on sabbatical at Rutgers to work on incorporating a machine
learning component into his current market simulations.  He is
visiting now in order to meet the department and to get some feedback
on his current research ideas on learning.  Below is part of an
abstract from his recent paper.  [...]

				-Tom Mitchell


  Market Traders: Intelligent Distributed Systems In an Open World

Although markets are at the heart of modern microeconomics, there has
been relatively little attention paid to disequilibriun states and to
the decision-making rules used by traders within markets.  I am
interested in the procedures that traders use to determine when and
how much they will bid, how they adapt their behaviour to a changing
market environment, and the effects of their adaptive behaviour on the
market's disequilibrium path.  This paper reports on research to study
these questions with the aid of a computer program that represents a
market with interacting and independent knowledge-based traders.  The
program is callled TRADER.

In a series of experiments with TRADER I find that market efficiency
requires a minimum number of intelligent traders with a capacity to
learn, but when their knowledge is reflected in the market bids and
asks, naive traders can enter the markets and sometimes do better than
the expert traders.  Moreover, the entrance of naive traders in a
market that is already functioning efficiently does not degrade the
market's performance.  Since learning by independent agents appears to
be a key element in understanding and using open systems, the focus of
future research will be on studying learing and adaptive processes by
intelligent agents in open systems.