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

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Category MARKETS 2012

Deadline: April 20, 2012 | Date: July 01, 2012

Venue/Country: Edinburgh, U.K.

Updated: 2012-03-20 09:21:16 (GMT+9)

Call For Papers - CFP

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.storkeyed.ac.uk)

Jacob Abernethy (jaberseas.upenn.edu)

Jenn Wortman Vaughan (jenncs.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.