Problem-Solving, Decision-Making, and Intelligence


The topics of problem-solving, creativity, decision-making, reasoning and intelligence are closely related, to the point of overlapping. For instance, scholars still face difficulties in unanimously defining the constructs of creativity and intelligence (Jaarsveld & Lachmann, 2017). Smith et al. (2009) defined creativity as “anything novel with a potential of value or utility” (as cited in Goldstein, 2019, p. 377). However, such a utilitarian approach to creativity does not describe a creative aspect of art or music since these spheres do not provide an objective, measurable value. Intelligence has once been perceived as an ability to solve well-defined problems through algorithms, such as proving a theorem (Jaarsveld et al., 2012). This approach has been challenged by Kaufmann (2013), who argued that distinguishing intelligence from creativity disservices both (as cited in Silvia, 2015). Given these circumstances, it is necessary to provide a theoretical clarification, so employers and team leaders could select staff members suitable for achieving specific professional goals.

Problem-solving and Creativity

Problem-solving strategies largely depend on the type of problem and thinking necessary for solving it. Well-defined problems, which have clear initial and end states, are usually solved through convergent thinking. Cropley (2006) defined convergent thinking as applying logical and conventional search to produce an already known answer (as cited in Jaarsveld & Lachmann, 2017). On the contrary, divergent thinking produces new approaches and brings novel, unusual, or surprising answers to unknown, ill-defined problems (Jaarsveld & Lachmann, 2017). Whereas convergent thinking relies on pure intelligence, divergent thinking utilizes creativity.

Creative problem-solving has a theoretical explanation that breaks it down into distinctive stages. According to Basadur et al. (2020), the process of creative problem-solving consists of four stages linked by brain networks (as cited in Goldstein, 2019, p. 378). First, the brain generates a problem or recognizes its existence; next, it formulates the problem and develops ideas. At the third stage, the brain evaluates all generated ideas and selects the most appropriate solution. Finally, the selected solution becomes an actual product; this stage may take multiple cycles of trials and errors (Goldstein, 2019). Therefore, creative problem-solving should be perceived as a lengthy process that involves practice and goes far beyond idea generation. One cannot become competent and solve problems creatively without practicing and making mistakes.

Therefore, nurturing creativity in problem-solving requires meeting two critical conditions. Most importantly, creative problem-solving demands a right for trying and making errors in the process. Goldstein (2019) provided a case of Jorge Odon, a car mechanic who invented a childbirth assistance device based on the idea of getting a cork out of the bottle without breaking it. Odon’s work took years of development in order to turn an idea into a functional product. Secondly, inhibition, a process of limiting cognition to goal-relevant information, is counter-productive to creativity (Radel et al., 2015). For example, soccer is a simple game where the team which scores more goals wins. However, elite young soccer players also showed better results in creativity tests (Vestberg, 2017). In this regard, inhibiting talents with strict playing discipline would eliminate their advantage in creativity over the average players. Overall, one should realize that creativity is a valuable ability, but at the same time, not mandatory for solving well-defined problems.

Decision-making and Reasoning

It is difficult to deny that humans are not always rational in their decisions. However, even the wrong, irrational human decisions are still based on certain rationale. Goldstein (2019) defined two conclusion- and decision-making methods: inductive and deductive reasoning. Inductive reasoning derives judgments from observations, whereas deductive reasoning utilizes syllogisms and general logic (Goldstein, 2019). Both methods explain how a particular decision was made; however, they are both vulnerable to cognitive distortions, which may lead to errors in decision-making.

Decisions based on deductive reasoning are subjected to heuristics, rules of thumb, which the human brain applies to save time and energy. While heuristics often result in correct decisions, they may sometimes lead to undesirable consequences. For example, groupthink bias stemming from the confirmation heuristic led to the Challenger space shuttle crash in 1986 (Murata et al., 2015). More specifically, the group interested in Challenger’s launch disregarded potential risks associated with cold weather and applied pressure on the O-ring (rubber seal) manufacturer. The group created an illusion of unanimity and invulnerability that ultimately led to O-ring rupture and Challenger’s crash during the launch procedure.

Deductive reasoning based on drawing conclusions from the premises can also be flawed. In particular, belief bias deprives the arguments based on deductive reasoning from impartiality. Goldstein (2019) provided an example of a statement, which can be shaped into the following syllogism:

  1. All Congress members from New York oppose the new tax law;
  2. Some of the Congress members who oppose the new tax law take bribes;
  3. Some Congress members from New York take bribes.

This example shows how belief bias can lead to false and negative conclusions about a whole group. In this regard, one should try to eliminate biases in reasoning, especially if working in a diverse population setting. The American Psychological Association (APA, 2017) Code of conduct explicitly states that psychologists must be aware of and respect differences based on age, gender, national origin, and other group identity factors. Therefore, heuristics- and belief-based flaws in reasoning may lead to significant ethical conflicts and reputational risks.

Human and Artificial Intelligence

Human intelligence is a multifaceted construct, which can be defined in several ways. For the sake of clarity, this section will use the traditional definition of intelligence as a problem-solving ability (Simon and Newel, 1971, as cited in Jaarsveld et al., 2012). Therefore, human intelligence is an individual’s ability to solve problems by applying convergent and divergent thinking. The latter part is important, as Jaarsveld et al. (2012) and multiple other scholars perceived creativity, associated with divergent thinking, as an element of intelligence. As such, it is possible to claim that human intelligence, at least its creative aspect, can be enhanced through practical experience. Furthermore, Carroll (1982) discussed the development of the IQ measure and argued that intelligence consists of at least seven independent factors (ac cited in Jaarsveld & Lachmann, 2017). In this regard, intelligence should not be limited to the hereditary dimension.

The subject of artificial intelligence (AI) plays an important part in discussions about the future of humanity. In 1956, Herb Simon and Alan Newel created a real “thinking machine” that used humanlike reasoning to solve problems (Goldstein, 2019, p. 15). In this regard, AI development may have significant socioeconomic and ethical implications, as AI will continue to improve in logical and creative reasoning. At the same time, AI already possesses a colossal advantage in computing power compared to the human brain. Therefore, many human employees will likely lose their jobs since human competitive advantage over machines will become non-existent.


Problem-solving, creativity, decision-making, and intelligence are overlapping constructs, often indistinguishable even for scholars. For instance, intelligence was traditionally defined as the problem-solving ability, and only recently have scholars started attributing creativity to it. Intelligence substantially affects decision-making, especially reasoning behind decisions. Regardless of theoretical aspects, it is important to understand that intelligence, especially its creative aspect, can be enhanced through practical experience. Finally, it is necessary to avoid bias in reasoning in order to prevent the emergence of severe ethical issues.


American Psychological Association. (2017). Ethical principles of psychologists and Code of conduct. Web.

Goldstein, B. E. (2019). Cognitive psychology: Connecting mind, research, and everyday experience (5th ed.). Cengage.

Jaarsveld, S., Lachmann, T., & van Leeuwen, C. (2012). Creative reasoning across developmental levels: Convergence and divergence in problem creation. Intelligence, 40(2), 172-188. Web.

Jaarsveld, S., & Lachmann, T. (2017). Intelligence and creativity in problem solving: The importance of test features in cognition research. Frontiers in Psychology, 8, 134. Web.

Murata, A., Nakamura, T., & Karwowski, W. (2015). Influence of cognitive biases in distorting decision making and leading to critical unfavorable incidents. Safety, 1(1), 44-58. Web.

Radel, R., Davranche, K., Fournier, M., & Dietrich, A. (2015). The role of (dis) inhibition in creativity: Decreased inhibition improves idea generation. Cognition, 134, 110-120. Web.

Silvia, P. J. (2015). Intelligence and creativity are pretty similar after all. Educational Psychology Review, 27(4), 599-606. Web.

Vestberg, T., Reinebo, G., Maurex, L., Ingvar, M., & Petrovic, P. (2017). Core executive functions are associated with success in young elite soccer players. PloS One, 12(2), e0170845. Web.

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