markets 2012 - ICML Workshop on Markets, Mechanisms, and Multi-Agent Models: Examining the Interaction of Machine Learning and Economics

ICML Workshop on Markets, Mechanisms, and Multi-Agent Models:
Examining the Interaction of Machine Learning and Economics
Edinburgh: June 30 or July 1, 2012 (to be determined)
http://icml2012marketswkshop.pbworks.com/

Important Dates:

Deadline for submissions: 20 April 2012
Notification of Acceptance: 18 May 2012
Organisers:

Amos Storkey (a.storkey-AT-ed.ac.uk)
Jacob Abernethy (jaber-AT-seas.upenn.edu)
Jenn Wortman Vaughan (jenn-AT-cs.ucla.edu)
Overview:

Many of society’s greatest accomplishments are in large part due to
the facility of markets. Markets and other allocation mechanisms have
become necessary tools of the modern age, and they have been key to
facilitating the development of complex structures, advanced
engineering, and a range of other improvements to our collective
capabilities. Much work in economics has been done to demonstrate that
markets can, in aggregate, function very well even when the individual
participants are noisy, irrational or myopic.
In terms of aims and benefits, the design of machine learning
techniques has much in common with the development of market
mechanisms: information aggregation, maximal efficiency, scalability,
and, more recently, decentralization. Current machine learning
algorithms are often single goal methods, built from simple
homogeneous units by one person or individual groups. Perhaps looking
to the organisations of economies may help in moving beyond the
current centralised design of most machine learning methods. Allowing
agents with different opinions, approaches or methods to enter and
leave the market, to interact, and to adapt to changes can have many
benefits. For example it may enable us to develop methods that provide
continuous improvement on complex problems, reuse results by improving
on previous outcomes rather than building bigger models from scratch,
and adapting to changes.
There are many relationships between machine learning methods,
Bayesian decision theory, risk minimisation, economics, statistical
physics and information theory that have been known for some time.
There are also many open questions regarding the full nature and
impact of these connections. This workshop will explore these
connections from many different directions.
Various Topics:

More detailed descriptions of each of these topics can be found on the
website.
1) Prediction markets as a tool for learning and aggregation.
2) Learning in problems of mechanism design.
3) Prediction and learning in ad auctions.
4) Online trading, portfolio selection, etc. in financial engineering.
5) Relating Market Mechanisms and Machine Learning Methods.
6) Transactional Communication in Multi-agent Systems.
Feel free to email the organizers regarding additional topics.
Submission Instructions:

We are soliciting contributions for talks and for posters. Submissions
should take the form of a abstract limited to 4 pages plus references.
At least one page of this should be dedicated to describing the
relationship of this work to other work in both Economics/Finance and
in this area of Machine Learning.
In addition if you wish to be considered for a talk, you should submit
a further description of what the motivation and content of your talk
will be (in one page or less).
Please see the website for full submission instructions.