Effective Decision-making: An Exploration of Alternative Frameworks MBA 230 Week 7 Jerry Estenson.

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Presentation transcript:

Effective Decision-making: An Exploration of Alternative Frameworks MBA 230 Week 7 Jerry Estenson

For me it started with Dr. Deming

Observation As you progress through your career you will be measure more by the quality of your decisions than by your intellect or potential.

Key questions Why is quality decision making so hard? – The individual decision-maker The operation of the human mind making decision The decision-maker within an organization The organization within a society How can we make those decisions better?

Decision making occurs as a reaction to an issue or a problem Decision making is a cognitive process of developing and analyzing alternatives and making a final choice Decision making

The profile of a good decision-maker Intelligent

Iceland Known for its well- educated and common sense population Three of its major banks failed because they supported its citizen’s debt-fueled spending spree

Why do we continue to make bad decisions? We are hard wired to make decisions as quickly and easily as possible. Habit and hardwired brains

Perception “A process by which individuals organize and interpret their sensory impressions in order to give meaning to their environment.” – They create their own reality Managers must handle not only what is real but what employees perceive to be real. – That is the challenge!

Decision making errors - overview Dismiss evidence in the decision making process Attempting to shortcut the decision process, we rely on impulses and rules of thumbs Shortcuts can be useful, but often lead to distortions Beware of the power of HABIT

Learning about assumptions “Assumptions can be misleading:

More common decision making errors Availability bias (Tversky & Kahneman) – Emphasizing information that is most readily at hand Escalation of commitment (Staw) – Maintaining an ineffective course of action too long – Throwing “good money after bad” Randomness error (Tversky & Kahneman) – Creating meaning out of random events

A few more decision making errors Hindsight bias (Fishhoff) – After an outcome is already known, believing that it could have been accurately predicted beforehand Confirmation bias (Plous) – Selecting and using only facts that support your decision Risk aversion (Kahneman & Tversky) – The unwillingness to accept a bargain with an uncertain payoff rather than another bargain with a more certain, but possibly lower, expected payoff

Example from your work life or in the background of your project

Fight or flight

The Range of Frameworks IntuitiveRational

Classic rational decision making blended with evidence based decision making

Rational model The “perfect world” model Offers a step-by-step approach to making decisions that maximize outcomes by examining all available alternatives

Big Data uses and abuses

Why is evidence ignored in the decision making process? Bob Sutton - Stanford It upsets the power structure People do not want to hear the truth Information issues – Too much – Disparate conclusions

Suggestions on how to work through resistance to change Bob Sutton Set aside beliefs and conventional wisdom and look to facts Unrelenting commitment to gather facts and information Keep pace with new evidence and use evidence to update practices

How will you implement your recommendation organization wide? “Just do it” Parallel approach Sequential approach

Additional Thoughts - Final evaluation and Adjustment How will “good” be defined? When will results be measured? How will process be evaluated? How will adjustments be made?

Closing general advise Recognize poor thinking and second-rate decision making in others – Then look in the mirror Consider each decision from the perspective of “others” Create a checklist (think of surgery and pre-flight; Gawande) Perform a pre-mortem (assume you are in the future, the decision was made, it created a negative outcome – Look for potential problems with decision Know what you cannot know (recognize that complex systems are complex and avoid oversimplification)