Abstract
Current studies of complex problem-solving do not commonly evaluate the regulatory role of such personality-based variables as tolerance for uncertainty, risk-readiness, and patterns for coping with decisional conflict. This research aims to establish the contribution of those traits into individual parameters of complex problem-solving strategies. The study was conducted on 53 healthy individuals 17 to 29 years old (M = 20.42; SD = 2.34). Our own computerized complex problem task “The Anthill” was developed for this research. We identified five measurable parameters of the participants’ problem-solving strategies: preferred orientational level (POL); orientational level variability (OLV); class quotas‘ range (R); mean and median quotas shift (MS and MeS); and abrupt changes of strategy (AC). Psychodiagnostic methods included: new questionnaire of tolerance/intolerance for uncertainty; personal decision-making factors questionnaire; Melbourne Decision Making Questionnaire; Subjective Risk Intelligence Scale; Eysencks’ Impulsiveness Scale. The study showed the role of tolerance for uncertainty, risk-readiness, negative attitude toward uncertainty, and decision-making styles in the regulation of complex problem-solving strategies. Specifically, procrastination, tolerance for uncertainty, and risk-readiness were significant predictors of individual strategy indicators, such as POL, OLV, and MeS. Thus, personality traits were shown to regulate resource allocation strategies and the required level of orientation in a complex problem.
Highlights
The study showed the role of tolerance for uncertainty, risk-readiness, negative attitude toward uncertainty, and decision-making styles in the regulation of complex problem-solving strategies
The term “complex problem” and the concept behind it became an essential part of scientific discourse during the 1980s
The studies of decision-making strategies in sequential choice problems [21,26] demonstrated tolerance/intolerance for uncertainty and risk-readiness‘ contribution to the participant’s‘ activity regulation. We suggested that those variables could be involved in the regulation of activity when solving complex problems as well
Summary
The term “complex problem” and the concept behind it became an essential part of scientific discourse during the 1980s. Most researchers strongly agreed that human problem-solving capabilities could not be fully scientifically understood based on studies of simple problems only. Laboratory data obtained in such studies significantly lacked ecological validity because of several reasons. Common tasks, used in laboratory experiments on thinking and decision-making, had little to do with problems that people faced and had to solve in real life. It became more and more evident that problem-solving in real life was connected with the actualization of different mental processes (rational thinking) and regulated by personality and psychological factors, such as motivation, self-esteem, personal involvement in the problem, etc., [1]. Several research traditions emerged in that field, that had little in common [1]. Among currently existing approaches toward complex problem-solving, the more prominent are Naturalistic Decision Making (NDM), Dynamic Decision Making (DDM), Implicit
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