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Qualitative reasoning is a reasoning that is based, not on numbers, but on a range of more abstract or sophisticated data. It is often compared to quantitative reasoning, which, by definition, is based on numerical values or statistics. Some might describe qualitative reasoning as “intuitive” reasoning, where the logical outcomes that are involved rely on less technical factors. Many also see it as a “higher level” type of reasoning than purely quantitative reasoning, which only requires specific numbers of calculations to be successful.
One of the interesting things about qualitative reasoning in the twenty-first century is that it has been applied, not just to human thought, but to artificial intelligence. Artificial intelligence is the ability of technologies to imitate human or animal thought, and to make decisions based on changing factors. In this pursuit, qualitative reasoning plays a prominent role, since part of the challenge for developing technologies is to use more than just numbers to produce logical outcomes. This is just a small part of the way that scientists are trying to help make technologies reproduce the functions of an organic brain, where modern science has produced some impressive successes, but has also met some seemingly insurmountable challenges.
Experts would define qualitative reasoning as anything that is not just based on numbers. A qualitative reasoning project might take a number of images or conditions and try to analyze them from a less technical perspective than quantitative reasoning would call for. In the early years of computers, scientists focused almost exclusively on quantitative reasoning, since even the earliest computers excelled at this kind of logical operation. In fact, while computers were able to far exceed the human capacity for quantitative reasoning, they were not originally able to match most human abilities for qualitative reasoning.
In current times, this equation is changing rapidly, as engineers have succeeded in enabling technologies to do some basic types of qualitative reasoning. For example, programmers may be able to enter some general environmental factors into a computer program, and get back some logical outcomes, even when unanticipated factors are then introduced; previous technologies would often simply lock up in such situations. This kind of “observational logic” has vast ramifications for the future of artificial intelligence; it may be that the scientists of the future will enable computers to further model human thought, and subsequently, human behaviors, which could dramatically alter the role of technology in the global society.