Working Papers Home
2015 Working Papers
2014 Working Papers
2013 Working Papers
2012 Working Papers
2011 Working Papers
2010 Working Papers
2009 Working Papers
2008 Working Papers
2007 Working Papers
2006 Working Papers
2005 Working Papers
2004 Working Papers
2003 Working Papers
2002 Working Papers
2001 Working Papers
2000 Working Papers
Search All Papers
JEL Classification
Past Working Papers (Prior to 2000)
Office of Research
Home Page
Information on
Submitting a Paper
"Hidden Markov Models of Strategic Information Control"
Bart Taub
First Author :
Bart Taub
Economics
University of Illinois at Urbana-Champaign
1206 S. Sixth Street, M/C 706
Champaign, IL 61820
USA
b-taub@uiuc.edu
http://www.business.uiuc.edu/faculty/taub.html
Abstract :
A stochastic process impinges on an agent and a principal in distinct ways. From the agent’s perspective the process is noise that interferes with his perception of productivity states, leading him to sometimes take actions that are in retrospect mistaken. From the principal’s perspective the noise is in fact the productivity of the agent’s action, and he would like to coordinate the agent’s actions with the process.
The principal is not able to provide direct material payoffs to the agent in order to induce this coordination. He is however allowed to communicate with the agent. If he fully communicates the state of the noise process, the agent will eliminate all response to it, thus vitiating the principal’s interests. If he communicates nothing, the agent’s reactions to the noise are random, and will be in synchrony with the principal’s interests only by accident. The principal can send a Pareto-improving signal however. Such a signal requires that the agent from his perspective make mistakes, and fails to fully coordinate actions with states from the principal’s perspective.
The strategic use of information is modeled using a hidden Markov model framework. In this framework, the state of a Markov process is unobservable, but it drives a signal that is correlated with it. This framework allows the agent’s optimization problem to be simplified using a measure change (which may be familiar to some readers as a Girsanov transformation). The simplified representation of the agent’s problem then becomes a set of constraints for the principal. The key methodological innovation here is that the informativeness of the signal is directly controllable by the principal. The informativeness is represented by elements of a matrix, reducing the information strategy to choosing elements of the matrix.
Manuscript Received : 2001
Manuscript Published : 2001
This abstract has been viewed 2722 times.
Click here to view the
full text
of this paper.