9  Heuristics in corporate decision making

Consider the following problem from Tversky and Kahneman (1982):

Linda is 31 years old, single, outspoken, and very bright. She majored in philosophy. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in anti-nuclear demonstrations.

Please rank the following statements by their probability, using 1 for the most probable and 8 for the least probable:

  • Linda is a teacher in elementary school.
  • Linda works in a bookstore and takes Yoga classes.
  • Linda is active in the feminist movement.
  • Linda is a psychiatric social worker.
  • Linda is a member of the League of Women Voters.
  • Linda is a bank teller.
  • Linda is an insurance salesperson.
  • Linda is a bank teller and is active in the feminist movement.

When experimental subjects are asked this question, they tend to rank the option “Linda is a bank teller and is active in the feminist movement” higher than “Linda is a bank teller”. Given that “Linda is a bank teller” must logically be more probable, what are subjects doing?

It was this sort of puzzle that inspired Daniel Kahneman and Amos Tversky to lay the foundations of behavioural economics. They explained errors of this type by people’s use of “heuristics”, or rules of thumb, when making decisions. Heuristics lessen the information processing demands of making decisions. They often produce useful answers, but are not foolproof.

Three of the most famous heuristics developed by Kahneman and Tversky are the availability heuristic, representative heuristic, and anchoring and adjustment. While most of the research on heuristics in decision making focuses on individuals, these heuristics have clear applications to corporate decision making.

9.1 Availability

People tend to weight their judgements toward more recent terms or concepts that are readily available in memory. In determining the probability or frequency of an event, the more available events will be assessed as more probable. For example, when Tversky and Kahneman (1973) asked about the relative frequency of words starting with the letter K compared to those with K as the third letter, people assume relatively more of the former as words starting with K are easier to recall.

9.1.1 Corporate decision making example

Bazerman and Moore (2013) argue that in performance appraisals, managers will give more weight to performance immediately before the evaluation than the previous nine months of the evaluation period. Vivid examples of the employee’s behaviour will also receive more attention than more commonplace incidents.

9.2 Representativeness

People judge a person, object or event, they assess their similarity with category prototypes. For instance, when determining the probability that A belongs to class B, we ask to what extent A resembles B. The classic example of the representativeness heuristic is when it leads to the conjunction fallacy, as in the “Linda problem” above.

9.2.1 Corporate decision making example

The representativeness heuristic leads to people to ignore the base rate of an event, the probability of it generally occurring, and focus on the representativeness of their situation. For instance, entrepreneurs spend too little time considering the base rate for business failures, instead focusing on visions of their own personal success.

9.3 Anchoring and adjustment

When people are making a judgement, they often start at an initial anchor and then adjust toward the answer (Tversky and Kahneman (1974)). Once the anchor is set, all future judgements are made in relation to that anchor. The use of this heuristic can be biased as often the anchor is weekly (or not at all) relevant and people insufficiently adjust away from the anchor.

9.3.1 Corporate decision making example

Joyce and Biddle (1981) asked auditors whether there was significant executive level fraud in more or less than 10 of each 1000 firms audited by the Big Four accounting firms. They were then asked their estimate of the level of fraud. Auditors who were asked these questions estimated the level at around a third of those who were first asked whether the level of fraud was present in more or less than 200 of 1000 firms before providing their estimate. Each set of auditors anchored on the initial number and insufficiently adjusted when giving their numerical estimate.