Gradwohl DECS433 W2010
DECS-433: Decision Making under Uncertainty
Ronen Gradwohl r-gradwohl@kellogg.northwestern.edu Jacobs 545
(847) 467-0943
This version: December 10, 2009
Description
We all use models to arrive at decisions. These models range in sophistication from “everyone but me is a fool” to the full rationality assumed in financial markets.
Unfortunately, the models we tend to use are not very good. They can, under some conditions, easily lead us astray. This would not be so terrible if these “conditions” were rare. However, they are not. This course is about how to make good models as well as how to distinguish a good model from a bad one. In the study of models and how they relate to decision making we try to find the middle ground between the perspective of the parachutist and that of the truffle hunter. We focus on evaluating uncertainty, understanding the dynamic nature of decision-making, using historical data and limited information effectively and simulating complex systems. Since the topics are quantitative in nature it may be useful to summarize and dispose of the usual objections against them.
• Objection #1: Not everything can be reduced to numbers.
True. But a great deal of importance can.
• Objection #2: Workable quantitative models cannot capture the complexities of real life.
1
So what? The question is not whether a particular quantitative model accurately represents reality but whether there is an alternative model that is more accurate. How do we know that a decision arrived at by what we call instinct (or intuition, experience, etc.) has really acknowledged all the complexities of reality? Indeed, one of the beauties of quantitative models is their explicitness. Like Cromwell’s portrait they appear warts and all.
• Objection #3: The data requirements of quantitative models are prohibitive.
This objection had some merit 20 years ago. Given the state of modern computing, it is an excuse for laziness. One of the useful features of a quantitative model is
Ronen Gradwohl r-gradwohl@kellogg.northwestern.edu Jacobs 545
(847) 467-0943
This version: December 10, 2009
Description
We all use models to arrive at decisions. These models range in sophistication from “everyone but me is a fool” to the full rationality assumed in financial markets.
Unfortunately, the models we tend to use are not very good. They can, under some conditions, easily lead us astray. This would not be so terrible if these “conditions” were rare. However, they are not. This course is about how to make good models as well as how to distinguish a good model from a bad one. In the study of models and how they relate to decision making we try to find the middle ground between the perspective of the parachutist and that of the truffle hunter. We focus on evaluating uncertainty, understanding the dynamic nature of decision-making, using historical data and limited information effectively and simulating complex systems. Since the topics are quantitative in nature it may be useful to summarize and dispose of the usual objections against them.
• Objection #1: Not everything can be reduced to numbers.
True. But a great deal of importance can.
• Objection #2: Workable quantitative models cannot capture the complexities of real life.
1
So what? The question is not whether a particular quantitative model accurately represents reality but whether there is an alternative model that is more accurate. How do we know that a decision arrived at by what we call instinct (or intuition, experience, etc.) has really acknowledged all the complexities of reality? Indeed, one of the beauties of quantitative models is their explicitness. Like Cromwell’s portrait they appear warts and all.
• Objection #3: The data requirements of quantitative models are prohibitive.
This objection had some merit 20 years ago. Given the state of modern computing, it is an excuse for laziness. One of the useful features of a quantitative model is