Cognitive sciences

Early experimental work in Gestalt psychology in Germany dates the beginning of the study of problem solving (e.g., Karl Duncker) to 1935 with his book *Zur Psychologie des produktiven Denkens (The Psychology of Productive Thinking).[13] This experimental work continued into the 1960s and early 1970s, with investigations carried out with a relatively simple (but novel to the participants) number of laboratory problem-solving tasks. problems.[14][15]​.

The innovative choice of simple tasks was based on a clearly defined optimal solution "Optimization (mathematical)") for short periods of time for their solution, which made it possible for the researchers to trace the participants' steps in the problem-solving process. The researchers' underlying assumption was that simple tasks, such as the Towers of Hanoi, correspond to the main "real" properties in the problems and therefore the characteristic of the cognitive process in participants' attempts to solve simple problems would be the same as for "real world" problems. Simple problems were used for reasons of convenience with the expectation that generalizations would be possible within more complex problems. Perhaps the best-known work in this line of exploration is the research of Allen Newell and Herbert A. Simon.[16] Other experts have shown that the principle of data decomposition improves problem-solving ability and allows for better judgment.[17]

Computer science and algorithms

In computer science and in the part of artificial intelligence that deals with algorithms ("algorithmics"), problem solving encompasses a series of techniques known as algorithms, heuristics, root cause analysis (RCA), among others. In these disciplines, problem solving is part of a broader process that encompasses problem determination, data deduplication, analysis, diagnosis, and repair.

Engineering

Problem solving is used in engineering when products or processes fail, and corrective action is required to prevent further failures. It can also be applied to a product or process before a failure event occurs, that is, when a potential problem can be predicted and analyzed, and mitigation is applied so the problem never actually occurs. Techniques such as failure mode and effects analysis can be used to proactively reduce the probability of problems occurring.

Forensic engineering is an important failure analysis technique that involves tracing product defects and failures. Corrective action can be used to avoid future errors. Reverse engineering seeks to discover the original problem-solving logic used in the development of a product through its use.

clinical psychology

Laboratory tasks alone can be useful in explaining the logical steps and reasoning behind problem solving, however, they often omit the complexity and emotional valence of "real world" problems. In clinical psychology, researchers have focused on the role of emotions in problem solving,[11]​ it has been shown that poor emotional control can alter focus on the target task and impede problem solving.[12]​ Within this conceptualization, human problem solving is composed of two related processes:

Working with people who suffer from frontal lobe injuries, neuropsychologists have discovered that deficiencies in emotional control and reasoning can be remedied, improving the ability of injured people to successfully solve everyday problems.[12]​.

Cognitive sciences

Early experimental work in Gestalt psychology in Germany dates the beginning of the study of problem solving (e.g., Karl Duncker) to 1935 with his book *Zur Psychologie des produktiven Denkens (The Psychology of Productive Thinking).[13] This experimental work continued into the 1960s and early 1970s, with investigations carried out with a relatively simple (but novel to the participants) number of laboratory problem-solving tasks. problems.[14][15]​.

The innovative choice of simple tasks was based on a clearly defined optimal solution "Optimization (mathematical)") for short periods of time for their solution, which made it possible for the researchers to trace the participants' steps in the problem-solving process. The researchers' underlying assumption was that simple tasks, such as the Towers of Hanoi, correspond to the main "real" properties in the problems and therefore the characteristic of the cognitive process in participants' attempts to solve simple problems would be the same as for "real world" problems. Simple problems were used for reasons of convenience with the expectation that generalizations would be possible within more complex problems. Perhaps the best-known work in this line of exploration is the research of Allen Newell and Herbert A. Simon.[16] Other experts have shown that the principle of data decomposition improves problem-solving ability and allows for better judgment.[17]

Computer science and algorithms

In computer science and in the part of artificial intelligence that deals with algorithms ("algorithmics"), problem solving encompasses a series of techniques known as algorithms, heuristics, root cause analysis (RCA), among others. In these disciplines, problem solving is part of a broader process that encompasses problem determination, data deduplication, analysis, diagnosis, and repair.

Engineering

Problem solving is used in engineering when products or processes fail, and corrective action is required to prevent further failures. It can also be applied to a product or process before a failure event occurs, that is, when a potential problem can be predicted and analyzed, and mitigation is applied so the problem never actually occurs. Techniques such as failure mode and effects analysis can be used to proactively reduce the probability of problems occurring.

Forensic engineering is an important failure analysis technique that involves tracing product defects and failures. Corrective action can be used to avoid future errors. Reverse engineering seeks to discover the original problem-solving logic used in the development of a product through its use.