General model of decision making
- Identify the problem
- Define objectives
- Make a pre-decision
- Generate alternatives
- Evaluate alternatives
- Make a choice
- Implement
- Follow up
Linearity Brunswick’s model
t_score = B1x1 + B2x2 + …Bnxn
x1…xn cues: pros, cons
B1 = weights; the importance on X1 for the final decision
A linear model will outperform experts. Why?
- Tend to lack insight into their own decision making
- Tend to rely only on a few cues
- Low inter-rater agreement about the weights
- The more information it is presented, the more confident they become in their decision.
Linearity decision errors.
Beta errors
- Ease of recall errors
Decisions become biased because we “overweight” information that is easy for us to remember
- Vivid
- Recent - Contrast errors
Decisions become biased because cues influence each other through differentiation and association
Linearity decision errors.
Xi errors
- Bounded rationality
The tendency to arbitrarily simplify the choices available . Individuals can only handle about 5-7 pieces of information at any one time. - Satisficing
The tendency to satisfy some criteria while sacrificing other criteria of decision making. To reduce cognitive load, we tend to select choices that are simply adequate, instead of evaluating all options to find the optimal one. - Time constraints
Within organisations, many important decisions are made under temporal duress. Under time constrains , we tend to revert to rigid responses rather to rules of optimisation. - Distinctive decision making
Only evaluating the cues one at a time using a step-ladder technique
Linearity and Likelihood
Linearity is based on Brunswik’s lens model(Ben Franklin) while likelihood is based on a Bayesion model (Carl Jung) of a decision making.
Both models describe why human judgement is often sub-optimal.
Main difference between Brunswik and Bayesian model?
Is that the Brunswik model is focused on accuracy based on cues and betas while the Bayesian is based on probability.
Likelihood
Probability and risk are everywhere. In general, we good at understanding simple odds, but when we are given additional information, or any level of uncertainty, our ability to determine the likelihood degrades.
Two main organisational errors we make based in probability assessments
- We anchor to the positive
This means our decision can be dramatically influenced by what information is available to us or how the problem is framed - We panic when in the negative
Framing effect
Framing effect
According to Prospect Theory, individuals tend to be:
- Risk averse when they are in a positive frame
- Risk seeking when they are in a negative frame
Example of framing effect => preference reversal
A will save 200 lives or 400 people will die. First option is presented in terms of gains and second presented in terms of losses. Choice is heavily influenced by the way problem is framed.
Framing effects in organisations
- Escalation of commitment
- Goals
- Creativity
Escalation of commitment and framing. Name three requirements.
- A substantial amount of previous investment of time and money
- Clearly negative information regarding the previous choice
- Dilemma of either abandoning the previous course of action or continuing commitment
How to reduce escalation of commitment?
- Reduce the level of felt responsibility by changing the decision maker
- Reduce the need to “save face” by lowering the threat of punishment
- Task the decision to groups
Goal setting and framing
To be motivating a goal must be:
- Specific
- Hard
- Feedback
Creativity and framing
Reframing the problem => other ways to achieve the same goal
