Representativeness (Intuition) versus Probability (Statistical accuracy)

"Experts are led astray not by what they believe, but by how they think" 

(Kahneman, 2011, pp. 219-220).

Every day decisions are made that affect the organization as well as the workplace and the workers. For example, decisions are made during an interview process to determine who the best candidate for the job will be. Additionally, decisions are made to identify who should be promoted from within the organization. Numerous other similar types of decisions are made weekly, sometimes daily, within organizations. These decisions are made using the information at hand (resume, work related performance records, referrals, manager's evaluation, etc…) as a predictor of future performance.

Basing a decision on intuition alone has been shown to be ineffective. Utilizing the information at hand can prove to produce slightly better predictions, depending on the validity of the information. The best that one can do when faced with having to make such a decision in a short time frame is to separate your subjectivity and base your decision on the data. Kahneman (2011) supported this position: "prediction  by representativeness is not statistically optimal" (pp. 150-151).  Here Kahneman refers to 'representativeness' as the decision-makers subjectivity, the decision-makers intuitive judgements about a particular candidate. Decisions based on representativeness have been shown to be no more accurate than in random assignment. Accuracy in decision making comes when statistical evidence (empirical data) guides the decision process. Kahneman made the following recommendations when making decisions:

• Anchor your judgement of the probability of an outcome on a plausible base rate.
• Question the diagnosticity of your evidence (p. 154).

Additional guide-lines, or rules, for making predictions are provided by  Kahneman (2011):

• Errors of prediction are inevitable because the world is unpredictable.
• High subjective confidence is not to be trusted as an indicator of accuracy (p. 220).

Basing decisions on 'plausible base rates' can lead to more accurate predictions in the long-term. The flexibility here is the term plausible, utilizing the best information made available in conjunction with your current knowledge of the field will assist in making more accuracy predictions. Avoiding subjective, or representative decisions, will improve the accuracy of predictions for the short-term as well as the long-term.

Reference:

Kahneman, D. (2010). Thinking, Fast and Slow. New York, NY: Farrar, Straus, and Giroux.