Can you think like an expert? Of course, and you most likely do this already in some ways. In the 1970’s when computers were being developed (and actually before the internet), some studies looked at human memory and problem solving approaches. The most coveted information was on how “experts” in various fields approached problems and how their vast stores of knowledge were organized in their memories. What did they find?
How Expert Memories Are Organized
These studies found that the knowledge of experts was highly organized. There were many categories and subcategories to what they knew, so they could recognize patterns and scenarios which dictated how they interpreted new information and situations. Also, their knowledge was hierarchical in nature, which helped fill in the subcategories and their related details.
Problem solving, a more dynamic and fluid process when compared to memory, was used in a specific way by the experts studied. What these studies found was that experts spent most of their time on problem representation. That is, they first conceptualized the problem in the most accurate terms possible, and you can see this in modern day experts. For example, if you had pain in your leg and went to your doctor, he or she would work to understand whether the problem was due to structure versus function. Do you have a broken leg? Or, do you have a blood flow issue? Being able to effectively conceptualize the real issue will then dictate the solution. The advantage of this? It reduces the basic trial-and-error type of problem solving that consists of randomly trying solutions and hoping that they are effective.
Experts Break Down Fuzzy Problems In To Concrete Parts
Two general types of problems that are cited in research are ill-defined problems and well-defined problems. A well-defined problem has concrete parameters and the answer is certain, such as in a jigsaw puzzle: The answer is there for sure, and there is only one real answer. Ill-defined problems, like most real-life issues, have many possible answers or scenarios, and the factors can often be very large or vague. An effective problem solving strategy, often used by experts, is to transform a broad ill-defined scenario in to one that is well-defined, or in to well-defined parts. In this way, a vague and larger problem can be broken down in to solvable parts, and the overall problem can be worked to its end in steps or stages.
How They Know All Of This
Much of this research was done by Herbert Simon, a researcher in Artificial Intelligence at Carnegie Mellon, when the first versions of modern computers were being developed in the 1970’s. He also a variety of topics, such as memory, decision making, problem solving, and other areas that have implications for many fields today. Simon also developed the term “heuristics,” which means a rule of thumb that experts use to gain approximations to problem solutions. The benefit of heuristics is that it saves a great deal of time and effort by finding a close solution first, rather than spend much effort at trial-and-error problem solving which is very inefficient. Simon was a Nobel prize laureate, economist, and computer scientist who contributed to many fields.
Simon teamed up with Allen Newell to do further research, and interestingly Simon was Newell’s departmental advisor. Newel and simon worked together for a long time, researching many aspects of computer science and artificial intelligence. Together with computer programmer J.C. Shaw they founded the first real artificial intelligence program, and are often credited with fostering the birth of artificial intelligence.