Common Approach to Problem Solving and Decision-Making
While some decisions individuals make are relatively automatic (i.e., easy matching tasks, reflex reactions, etc.) the majority involve deliberate and relatively slow cognitive reasoning and thoughtful judgment. Often, there are no certain answers to the problems and questions one faces, or at least no faultless way of deciding whether a particular type of solution is the correct approach. Thus, individuals base their reasoning on a variety of common problem-solving strategies – some more effective than others.
Heuristics represent one commonly employed approach to problem solving and decision-making. Simply put, heuristics represent informal guidelines or rules of thumb that work under some circumstances but are not guaranteed to yield the correct answer. Heuristics can be contrasted with algorithms, which are detailed and complex specific rules or solutions guaranteed to furnish the correct answer or solve the problem if followed correctly (Ashcraft, 2002; Matlin, 2002). According to researchers Newell and Simon (1972), the complexity and relative ambiguity of most problems necessitates that we rely heavily upon the use of heuristics.
Kahneman and Tversky (1982) focused their research on reasoning heuristics and biases that characterize individual’s everyday conjecture about uncertain events. Specifically, they studied how individuals predict the likelihood of future events, how they categorize events or occurrences, and how various biases and judgments can be accounted for by the reasoning process (Eysenck & Keane, 2005; Matlin, 2002). Their research indicated that “people’s decision-making heuristics are well adapted to handle a wide range of problems”, but also that “these same heuristics become a liability when they are applied beyond their range” (Matlin, 2002, p. 412). Clinical diagnosis and associated treatment decisions (including medication) represent a form of real-world problem solving that because of the many variables involved require the use of heuristics. Often, the heuristics used in these circumstances yield accurate conclusions. However, in some cases, the common pitfalls associated with decision-making heuristics render errors in judgment.
The representative heuristic refers to “a judgment rule in which an estimate of the probability of an event is determined by one of two features: how similar the event is to the population of events it came from or whether the event seems similar to the process that produced it. In other words, we judge whether event A belongs to class B based on the extent to which A is representative of B, the degree to which it resembles B, or the degree to which it resembles the kind of process that B is known to be” (Ashcraft, 2002, p. 468). In short, the representative heuristic involves examining a single case or small number of examples and judging it as representative of a whole class of outcomes.
The availability heuristic “evaluates the frequency of classes or the probability of events by the ease with which relevant instances come to mind” (Ashcraft, 2002, p. 473). In short, when estimates of likelihood or frequency are necessary, the judgments are influenced by the ease with which relevant examples can be remembered. Like other heuristics, this is often a reliable process of reasoning and problem solving. However, it is also subject to bias, particularly if memory contains information that is inaccurate, incomplete, unrepresentative, or influenced by factors that do not reflect objective frequency. In particular, the familiarity bias – the tendency to judge events as more frequent or important because they are more familiar in memory – is commonly related to the ease of recall (Aschcraft, 2002).
With the anchoring and adjustment heuristic, decisions are based on accessing familiar or known aspects of information, referred to as anchors, and subsequently adjusting from this reference point to reach a final conclusion (Matlin, 2002; Sternberg, 2003). This heuristic, like others, is both typically accurate and subject to bias. Because the initial anchor is a relatively subjective judgment, the entire thought system can be skewed if this initial assumption is relatively inaccurate. Often, this anchor depends on the availability heuristic - i.e., it is most likely that information that is more available will serve as the anchor. Furthermore, both the belief-bias effect - which states that one relies heavily on established beliefs - and the confirmation bias – which states that one tends to confirm rather than reject a current hypothesis – heavily influence the starting point and subsequent adjustments one makes in the process of drawing conclusions (Matlin, 2002; Sternberg, 2003).
Although most of the research on heuristics focuses on the errors in judgment, it is important to remember that these cognitive tools represent efficient and relatively effective means of dealing with a large amount of complex information. Through these top-down processing models, individuals can quickly access relevant information and instinctively draw conclusions and solve problems that would otherwise require more complex and time consuming reasoning processes. Thus, while the processes of human reasoning are not infallible, they represent a streamlined compromise between accuracy and efficiency that enables us to work our way through the complex experiences that compose our daily lives.
Ashcraft, M. H. (2002). Cognition. Upper Saddle River, NJ: Prentice Hall.
Eysenck, M. W. & Keane, M. T. (2005). Cognitive psychology: A student’s handbook, (5th ed.). New York, NY: Psychology Press.
Matlin, M. W. (2002). Cognition, (5th ed.). New York, NY: Harcourt College Publishers.
Newell, A. & Simon, H. A. (1972). Human problem solving. Englewood Cliffs, NJ: Prentice Hall.
Sternberg, R. J. (2003). Cognitive psychology, (3rd ed.). Belmont, CA: Thompson/Wadsworth.
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