Heuristics

A heuristic technique (/hjʊəˈrɪstɪk/; Ancient Greek: εὑρίσκω, 'find' or 'discover'), often called simply a heuristic, is any approach to problem solving or self-discovery that employs a practical method, not guaranteed to be optimal, perfect, or rational, but instead sufficient for reaching an immediate goal. Where finding an optimal solution is impossible or impractical, heuristic methods can be used to speed up the process of finding a satisfactory solution. Heuristics can be mental shortcuts that ease the cognitive load of making a decision.:94 Examples that employ heuristics include using a rule of thumb, an educated guess, an intuitive judgment, a guesstimate, profiling, or common sense. A heuristic technique (/hjʊəˈrɪstɪk/; Ancient Greek: εὑρίσκω, 'find' or 'discover'), often called simply a heuristic, is any approach to problem solving or self-discovery that employs a practical method, not guaranteed to be optimal, perfect, or rational, but instead sufficient for reaching an immediate goal. Where finding an optimal solution is impossible or impractical, heuristic methods can be used to speed up the process of finding a satisfactory solution. Heuristics can be mental shortcuts that ease the cognitive load of making a decision.:94 Examples that employ heuristics include using a rule of thumb, an educated guess, an intuitive judgment, a guesstimate, profiling, or common sense. Heuristics are the strategies derived from previous experiences with similar problems. These strategies rely on using readily accessible, though loosely applicable, information to control problem solving in human beings, machines, and abstract issues. The most fundamental heuristic is trial and error, which can be used in everything from matching nuts and bolts to finding the values of variables in algebra problems. Here are a few other commonly used heuristics, from George Pólya's 1945 book, How to Solve It: In psychology, heuristics are simple, efficient rules, learned or inculcated by evolutionary processes, that have been proposed to explain how people make decisions, come to judgements, and solve problems typically when facing complex problems or incomplete information. Researchers test if people use those rules with various methods. These rules work well under most circumstances, but in certain cases lead to systematic errors or cognitive biases. The study of heuristics in human decision-making was developed in the 1970s and 80s by psychologists Amos Tversky and Daniel Kahneman, although the concept was originally introduced by Nobel laureate Herbert A. Simon. Simon's original, primary object of research was problem solving which showed that we operate within what he calls bounded rationality. He coined the term 'satisficing', which denotes the situation where people seek solutions or accept choices or judgments that are 'good enough' for their purposes, but could be optimized. Rudolf Groner analyzed the history of heuristics from its roots in ancient Greece up to contemporary work in cognitive psychology and artificial intelligence, and proposed a cognitive style 'heuristic versus algorithmic thinking' which can be assessed by means of a validated questionnaire. Gerd Gigerenzer and his research group argued that models of heuristics need to be formal to allow for predictions of behavior that can be tested. They study the fast and frugal heuristics in the 'adaptive toolbox' of individuals or institutions, and the ecological rationality of these heuristics, that is the conditions under which a given heuristic is likely successful. The descriptive study of the 'adaptive toolbox' is done by observation and experiment, the prescriptive study of the ecological rationality requires mathematical analysis and computer simulation. Heuristics – such as the recognition heuristic, the take-the-best heuristic, and fast-and-frugal trees – have been shown to be effective in predictions, particularly in situations of uncertainty. It is often said that heuristics trade accuracy for effort but this is only the case in situations of risk. Risk refers to situations where all possible actions, their outcomes and probabilities are known. In the absence of this information, that is under uncertainty, heuristics can achieve higher accuracy with lower effort. This finding, known as a less-is-more effect, would not have been found without formal models. The valuable insight of this program is that heuristics are effective because of, not despite, their simplicity. Furthermore, Gigerenzer and Wolfgang Gaissmaier found that both individuals and organizations rely on heuristics in an adaptive way. Heuristics, through greater refinement and research, have begun to be applied to other theories, or be explained by them. For example: the cognitive-experiential self-theory (CEST) also is an adaptive view of heuristic processing. CEST breaks down two systems that process information. At some times, roughly speaking, individuals consider issues rationally, systematically, logically, deliberately, effortfully, and verbally. On other occasions, individuals consider issues intuitively, effortlessly, globally, and emotionally. From this perspective, heuristics are part of a larger experiential processing system that is often adaptive, but vulnerable to error in situations that require logical analysis.