Real World Problem-Solving
This article describes what the human brain is doing when we define the problem (requirements and scope), plan for problem-solving (select datasets and filter or standardize and clean them for relevant information), and engage in the creative thinking process that is analysis. The author differentiates the creative process from the analytical process she terms "insight problem solving", but without creativity, the analyst would not know which methods to apply to the dataset and would have more difficulty expressing their findings in a way that is actionable for the decision-maker. To do this effectively requires a certain amount of empathy to understand what the decision-maker needs and in what format so that they can digest it most thoroughly and see the action steps needed for implementation.
It is interesting to see the problem-solving process laid out in a neurological sense when it is second nature to seasoned analysts. The author describes Tversky and Kahneman's thinking processes that allow analysts to figure out big problems while driving home in light traffic as if on autopilot. One analyst claims to solve most problems by walking away from her computer, riding her bike, or going rollerblading. Sometimes, like figuring out where we left the remote, the important things are fleeting and can only occur to us when we are doing something else. How might you have solved an important thinking problem in an unlikely place or while doing something non-analytical?
7. Conclusion
While the neural basis for problem-solving, creativity and insight have been studied extensively in the past, there is still a lack of understanding of the role of the environment in informing the problem-solving process. Current research has primarily focused on internally-guided mental processes for idea generation and evaluation. However, the type of real world problem-solving (RWPS) that is often considered a hallmark of human intelligence has involved both a dynamic interaction with the environment and the ability to handle intervening and interrupting events. In this paper, I have attempted to synthesize the literature into a unified theory of RWPS, with a specific focus on ways in which the environment can help problem-solve and the key neural networks involved in processing and utilizing relevant and useful environmental information. Understanding the neural basis for RWPS will allow us to be better situated to solve difficult problems. Moreover, for researchers in computer science and artificial intelligence, clues into the neural underpinnings of the computations taking place during creative RWPS, can inform the design the next generation of helper and exploration robots which need these capabilities in order to be resourceful and resilient in the open-world.