ICML 2012 Workshop on Markets, Mechanisms and Multi-Agent Models

July 1st, 2012. Informatics Forum, University of Edinburgh. Chaired by Amos Storkey, Jacob Abernethy and Jenn Wortman Vaughan.

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 explored these connections from many different directions.