Making Collective Intelligence Work: Learning, Liquidity, and Manipulation in Markets
Dr. Sanmay Das
, Washington Univ. in St. Louis, USA
Date: Thursday, April 17, 2014
Time: 3:00 PM to 4:00 PM Note: all times are in the Eastern Time Zone
Location: Star Conference Room, MIT 32-D463
Host: Prof. Tomaso Poggio
Contact: Kathleen Sullivan, email@example.com
Relevant URL: http://lcsl.mit.edu
Speaker URL: None
TALK: Making Collective Intelligence Work: Learning, Liquidity, and Manipulation in Markets
Abstract: Collective intelligence systems, from prediction markets to Wikipedia, have the capacity to provide useful information by aggregating the wisdom of the crowd. Yet the mechanisms that govern how individuals interact in these forums can substantially affect the quality of information produced. I will discuss this issue in the context of two specific problems in prediction markets: ensuring sufficient liquidity and mitigating manipulation. The accuracy of the information reflected in market prices depends on the markets liquidity. In a liquid market, arriving traders have someone to trade with at a 'reasonable' price, so they are willing to participate and contribute their information. Liquidity provision can be framed as a reinforcement learning problem for a market-making agent, complicated by the censored nature of observations. I will describe an algorithm for solving this problem using moment-matching approximations in the belief space, and discuss theoretical results and empirical evaluation of the algorithm in experiments with trading agents and human subjects, showing that it offers several potential benefits over standard cost-function based approaches. In markets where participants influence the outcome of the events on which they are trading, concerns over manipulation naturally arise. I will present a game-theoretic model of manipulation, which gives insight into the question of how informative market prices are in the presence of manipulation opportunities, and also into how markets can affect the incentives of agents in the outside world. In addition, I will describe our experience with a field experiment related to manipulation, the Instructor Rating Markets. Time permitting, I will also briefly discuss work in my group on related issues in other types of collective intelligence systems, for example, information growth, user engagement, and manipulation in social media like Wikipedia and Reddit.
Created by Kathleen Sullivan at Monday, April 14, 2014 at 2:08 PM.