Posts Tagged ‘Model Based decision making’

CARVER Matrix: Model Based Decision Making VS you’re “Gut”

April 11, 2010

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The more the subject matter of the CARVER matrix and its various potential applications expands and develops on this blog the more emails, comments and arguments I receive regarding its employment.

In the initial post in which I explained the use of the matrix, “Use The Carver Matrix for Management” and the subsequent article “Use the CARVER matrix to prioritize”, I used examples and illustrated the most simple possible equations in order to maximize the ease with which a lay person might grasp the initial concepts involved with this highly functional decision-making matrix.

In retrospect, perhaps this was a less than efficient approach in that now, people in general seem to have completely missed the point. Hence this post today.

I hope to expand some-what on the use of this matrix along with advocating the superiority of model based decision-making over your day-to-day intuition or “Common Sense” because, let’s face it, usually there is nothing “common” or “sensible” about “Common Sense” and, while your “Gut feeling’ might work wonders when choosing which type of crust you want from pizza hut on DVD night (Cheese filled), human intuition is more often than not less than ideal when making complex decisions involving complex and diverse equations with various data sets.

Why Model based decision-making?
Model Based Decision making “makes sense” because it relies on data and algorithms, not on someones random, biased and fluctuating personal feelings regarding a given subject.
There are, obviously, times when intuition seems (SEEMS, not IS) to be unstoppable. For example, sports. The sports super star can’t explain it, but he just KNEW he had to break left at a given point. He could just “feel” it.
Another good example would be psycho therapists. They meet a patient and immediately “sense” what might be wrong with them.

This is all well and good but there are factors involved here that require illumination.

  • It takes a long time to build good intuition. Chess players, for example, need 10 years of dedicated study and competition to assemble a sufficient mental repertoire of board patterns.
  • Intuition only works well in specific environments, ones that provide a person with good cues and rapid feedback .Cues are accurate indications about what’s going to happen next. They exist in poker and firefighting, but not in, say, stock markets. Despite what chartists think, it’s impossible to build good intuition about future market moves because no publicly available information provides good cues about later stock movements. Feedback from the environment is information about what worked and what didn’t. It exists in neonatal ICUs because babies stay there for a while. It’s hard, though, to build medical intuition about conditions that change after the patient has left the care environment, since there’s no feedback loop.
  • We apply intuition inconsistently. Even experts are inconsistent. One study determined what criteria clinical psychologists used to diagnose their patients, and then created simple models based on these criteria. Then, the researchers presented the doctors with new patients to diagnose and also diagnosed those new patients with their models. The models did a better job diagnosing the new cases than did the humans whose knowledge was used to build them. The best explanation for this is that people applied what they knew inconsistently — their intuition varied. Models, though, don’t have intuition. Cues are accurate indications about what’s going to happen next. They exist in poker and firefighting, but not in, say, stock markets. Despite what chartists think, it’s impossible to build good intuition about future market moves because no publicly available information provides good cues about later stock movements. Feedback from the environment is information about what worked and what didn’t. It exists in neonatal ICUs because babies stay there for a while. It’s hard, though, to build medical intuition about conditions that change after the patient has left the care environment, since there’s no feedback loop.
  • It’s easy to make bad judgments quickly. We have a many biases that lead us astray when making assessments. Here’s just one example. If I ask a group of people “Is the average price of German cars more or less than $100,000?” and then ask them to estimate the average price of German cars, they’ll “anchor” around BMWs and other high-end makes when estimating. If I ask a parallel group the same two questions but say “more or less than $30,000″ instead, they’ll anchor around VWs and give a much lower estimate. How much lower? About $35,000 on average, or half the difference in the two anchor prices. How information is presented affects what we think.

Intuition is unreliable.

The more advanced and complex the factors are that require a decision to be made, the more unreliable our decision-making process becomes when we are depending heavily on our “gut Feeling”.

Even the US military now has taken steps to remove Intuition from battle field decisions and base these on complex models and data instead.

Special Operations Forces/Tactical Decision Aids

Battelle Memorial Institute developed the Special Operations Forces Tactical Decision Aid (SOF-TDA) to provide automated calculations and reference material for the analysis of selected targets. The SOF-TDA software analyzes systems, subsystems, and components of the eight major infrastructure systems and develops a flow sheet diagramming the chosen target. The core of this project is the automation of the CARVER matrix, which allows the user to weight items identified on the flow chart with the objective parameters of the mission. This analysis examines all information on a given target, determines what parts of the target are vulnerable to attack, and assesses the subsequent down time or possible destructive effects. In FY05 the product was fielded to over 400 SOF teams.

The point is that more and more, the people who KNOW, are relying on Model based decision-making tactics and algorithms to raise the percentage chance of success.

CARVER and the confusion

Due to my initial examples of how to use the CARVER matrix being extremely, overly simple, many people have raised the following question:

“well, it takes more time to make this graph than it does to make the decision, I could have spent that time working on something.”

Well, if the decision you are trying to make is “Should I A: Go make a sandwich, B: Go return my DVD I rented or C: Go to the potty ?” Then I fully agree. You do not need the CARVER matrix. I really honestly think you can likely handle those choices on your own.

Conversely, if you are making complex decisions involving an ARRAY of potential tasks, time requirements, financial expenditures etc, then CARVER is invaluable. Once you move beyond the most basic decision-making matrix and then begin looking at components like subsystems, Mid and Long range variants and collateral effects on components completely orthogonal to the current situation it becomes clear that drafting a clear, detailed, concise and re-usable model makes all the sense in the world.

Keep in mind, we are still referring to mere personal decisions. Once we begin discussing small business choices all the way up to Anti-Terrorism the necessity for these modules becomes painfully clear and their unemployment in planning and day-to-day operations is irresponsible and in many cases, dangerous.

My next post is going to expand further on my original CARVER matrix involving management or perhaps personal prioritization in an attempt to clarify, in a more detailed, realistic way how this should be employed.

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I referenced the following articles while putting this post together.
The Future of Decision Making
Special Operations Forces, tactical decision aids.

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