Why Management Science
We all of us use models to arrive at decisions. They range in sophistication from the "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 the right conditions , easily lead us astray. This would not be so terrible if these "right conditions" were rare. 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 of the truffle hunter. We confine ourselves to three topics. They are chosen for the richness of their content and the ubiquity of their application. We focus on evaluating uncertainty explicitly, understanding the dynamic nature of decision-making, using historical data and limited information effectively, simulating complex systems, and allocating scarce resources. We confine ourselves to three basic models. They are chosen for the richness of their content and the ubiquity of their application. Since all 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.
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 are pleased to 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 before one warts and all. Further, as we
will see in the course, they are capable of tracking and handling a multitude of
interactions which are well beyond the cognitive powers of most humans.
- Objection #3: The data requirements of quantitative models are
This objection had some merit 20 years ago. Now, with the state of
modern computing, its a justification for laziness. One of the useful features
of a quantitative model is that it tells the decision maker what information s/he
should be collecting.
- Objection #4: Quantitative models ignore the context .
Yes. However, there is no law that obliges one to follow the
recommendations of a quantitative model that is not appropriate to the context.
Second, ignoring the context can be a good thing. Frequently, the context of a
decision is irrelevant but does not appear to be so to the decision maker. A
quantitative model often captures the essence of a decision, say, the choice
between a gamble and a sure thing. It does not matter whether the context is the
stock market, a medical diagnosis or wildcatting. One of the things this allows
for is the transferability of insight from one setting to another. An example of
this is the use by hotels of the same models and principles that airlines use
for yield management. A more revealing example comes from experiments on auction
behavior. The subjects were managers responsible for submitting bids on
construction projects. The goal was to see whether these managers made bids in
accordance with the optimal bids of the standard (quantitative models) auction
model. As long as the auctions were couched in the context of the construction
industry , essentially, yes. However, when they were presented with same auction
scenarios, but the context changed (to say oil leases) they did remarkable
badly. Why? The bids they made were arrived at through the use of rules of thumb
that were specific to their industry. When the context switched, their rules of
thumb became useless. One might argue that this not important as few people are
going to switch industries in mid career. Not so. Suppose that the rules of
thumb are tied to characteristics of the industry at a particular time. As time
changes, those characteristics change, until eventually those rules become
useless. (Note: this is also the reason for having a course on modeling that
is not tied to a particular functional area of business).
Perhaps the most surprising thing that you will learn from this course is
that simple quantitative models frequently provide profound qualitative insights
into the process being modeled. Thus, one may wish to build a model not so much
to decide what to do, but test and refine ones intuition about what is going on.