Many rational decision making models assume that people have unlimited time, knowledge and computational power when making decisions (Gigerenzer and Goldstein 1996). Literature reveals that people’s estimations are biased in relation to normative standards (Hogarth 1981). Recognising that humans in fact make inferences about the world under limited time and knowledge has lead to the development of models of bounded rationality by Simon (1955, 1957, 1979) and the introduction of heuristics and biases by Tversky and Kahneman (1974, 1981,1986).
Poor decisions can be traced back to failures made in the Decision Making process when alternatives were not clearly defined, or the required information was not collected (Hammond et al 1998). Faulty decisions can also result from the decision maker. The limitations of the human mind in terms of information processing and computing capacities have been widely acknowledged (Miller 1956, Hardman and Harries 2002).
In order to cope with the complicated tasks of assessing probabilities and predicting values, people use heuristic principles to reduce complexity (Tversky and Kahneman 1974, Hardman and Harries 2002, Maule and Hodgkinson 2002). Heuristics are understood as “rules of thumb” or by-pass in thinking (Gore 1995).
Tversky and Kahneman (1974) identify the following heuristics. In the
representativeness trap, people judge the probability that A originates from B to be high when A is highly representative of B and tend to ignore prior
probabilities, sample sizes, concepts of chance and regression as well as predictability. When caught in the availability trap, people assess the probability of an event by the ease with which occurrences can be remembered and thus rely retrievability and imaginability in their judgments. The trap of adjustment and anchoring makes people to do estimates by starting from an initial value and adjust it to the final result. They are likely to face biases in the evaluation of
conjunctive and disjunctive events as well as in the assessment of subjective probability distributions. In business, past events or trends form the most common type of anchors (Hammond et al 1998). DeKay et al (2009) show, that when people favour one decision alternative over another, they evaluate the information as being more consistent with the preferred alternative than is warranted. This is confirmed by Tversky et al (1980).
These findings are documented by a large body of empirical work initiated by Tversky and Kahneman (Shafir 1999). Criticism on the kind of experiments applied has been placed by Lopes (1991). Kahneman (1991) commented on such criticism in a personal view. Critique on the heuristics and biases approach placed by Gigernzer (1991) has been acknowledged and returned by
Kahneman and Tversky (1996). Cosmides and Toby (1996) challenge the view of Kahneman and Tversky that humans are not capable of statistical operations and are thus applying judgmental heuristics. Tversky and Kahneman (1981, 1986) also introduced the “framing effect”. The way a decision problem is formulated or “framed” can strongly influence people’s decision (Hammond et al 1998). This has been demonstrated by Tversky and Kahneman (1981; p.453) in their “Asian disease problem”. Most framing issues are a variation of this classic example where people are presented with two choice options of which one represents a certain outcome, whereas the other represents an uncertain outcome of equal expected value (Kuhberger 1998, Zickar and Highouse 1998).
Beside the heuristics established by Tversky and Kahneman, Hammond et al (1998) identify common traps in Decision Making in business environments. In the Status-Quo Trap, decision makers tend to choose alternatives that maintain the status quo. A possible explanation to this behaviour is seen in the human desire to protect the ego from damage. Leaving the status quo requires taking action and thus responsibility which in turn gives room to criticism and regret. In the Sunk-Cost Trap, people make choices in a way that past choices are justified, even when past choices no longer seem correct, mostly because they are unwilling to admit to a poor decision taken in the past. Decision makers caught in the Confirming-Evidence Trap tend to subconsciously decide what they want to do before they figure out why they want to do it. Thus decision
makers are likely to seek information that supports their choice, rather than data that contradicts it. This behaviour is also reported by Schwenk (1984) in the context of strategic Decision Making.
In forecasting, decision makers face the overconfidence trap, i.e. they tend to be overconfident regarding their forecast accuracy. See also Gigerenzer et al (1991), Russo and Shoemaker (1992) and Dougherty et al (1997) on this issue.
On the other hand Kruger and Evans (2004) demonstrate that people tend to underestimate the duration to complete tasks and projects. The
overcautiousness trap leads to adjustment of forecasts just to be on the safe side, whereas the recallability trap results in predictions about future events that can be overly influenced by dramatic past events. These traps do not only work in isolation but can amplify one another.
In strategic decision making Das and Teng (1999) identify four key biases decision makers are likely to face. First, decision makers tend to bring their previously established beliefs into Decision Making situations and focus on selected objectives that suit their personal interests. Doing so they are most likely biased regarding their perception of the environment and the actual decision problem. Also, decision makers only focus on a limited number of alternatives that can reach a set target. Further, decision makers usually do not rely on the magnitude of probabilities but on the value of possible outcomes.
Finally, decision makers tend to view their success probability higher than it actually is. They also consider the outcomes of decisions to be completely manageable by them.
Applying heuristics means amongst others, neglecting possibly relevant information (Payne et al 1992). As Payne et al (1992) state that the use of heuristics can nevertheless lead to good solutions and can also save essential cognitive energy. Marewski et al (2010) also show that humans do not need to apply complex cognitive processes to good inferences and judgments.