BE101x: Decision Aids

A Taxonomy of Decision Support

  1. Data and Feedback
    1. Providing feedback on spending reduces the credit card overspending, feedback on garbage or energy changes behaviour
  2. Advice (from Experts or peers)
  3. Cases and Similar situations
    1. Past instances that are similar to the current one
  4. Structured model
    1. Models using the linear weighted additive format
  5. Consumption vocabulary

Advice Taking

Two experts provide a forecast you are interested in. How do you best use these two forecasts?

Thought experiment: Guessing the number of coins in a jar, suppose the correct answer is 40.

Two possibilities

Averaging is a good strategy

Two heads are better than one! Even the same head over time is better than just once

This is especially true when the two heads use different data, backgrounds and approaches.

Research by Jack Soll and Rick Larrick (Duke University) suggests, though

•People do not seem to believe in averaging

•They believe, incorrectly, that Averaging = Average performance

•Overconfident in their ability to “spot the expert”

Two scenarios

Scenario1: You could take advice from two people who have access to the same data:

1)Mr. A, who has the same background and training as you.
2)Mr. B, whose background and training is different.

Scenario2: You could take advice from two people who have the same background as you:

1)Ms. X, who has access to the same data as you.
2)Ms. Y, who has access to a different dataset.

Responses to the two scenarios

Many people choose “A” and “X” respectively.

However B and Y might be better choices.

Tips on advice-taking

  1. Seek advice from someone who has a different knowledge base than you
  2. Weight their advice equally with yours. It is tempting to overweight your own judgment and underweight theirs
  3. Get more advice-givers
  4. If all fails, give yourself advice by shiftingperspectives over time.

Two types of Decision Support Systems (DSS)

1)Model Based

2)Case Based (also called Database)

Hoch S and D Schkade (1996), Management Science

Model Based DSS

Provides the user with a score based on certain attributes or cues.

For instance:

Credit Rating = a + b1  (Debt Ratio) + b2  (Cash Flow) + b3  (Revenue Trend) + b4  (Location)

The coefficients could come from

a) expert judgments,

b) a judgment bootstrap, of

c) a regression based on past data

Case Based DSS

•The DSS retrieves past instances that are most similar to the current one using a “euclidian distance” algorithm.

•The user than then employ an anchoring and adjustment process to make a judgment or prediction.

•A sample screen might look like this:

Model vs. Case

•In stable environments, model based DSS helped more than case based

•In noisy environments, case DSS was slightly better

•In either case, a combination of both is the best

Combining Intuition with Models

•Blattberg R and S Hoch (1990), Management Science conducted several experiments comparing the performance of managerial intuition with a model’s prediction.

•Performance = Correlation between the prediction and the actual outcome

Comparing Models and Experts

•Why models are better

–Models are consistent and mathematically correct

–They are unbiased and unaffected by framing, politics and emotions

–They do not get tired

•Why expert intuition is better

–Models only know what the expert tells it

–Experts can value qualitative cues well

–Experts can change decision-making strategy as a function of environment

–Experts have access to more cues

The Results

A to F represent six different studies. The height of the bars represent performance.

In all cases, an equally weighted average outperformed both the model and the expert.

The Prescription

•Let the expert and the model make independent judgments

•Take an equally weighted average

•Food for thought: If you rely on decision support, how can you best incorporate the decision-maker’s intuition or judgment?