Taxonomies, Laws, And Methods And Types Of Scientific Discovery

Research on computational scientific discovery aims to develop computer systems which produce results that, if a scientist did the same, we would refer to as discoveries. Of course, if we hope to develop computational methods for scientific discovery, we must be more specific about the nature of such discoveries and how they relate to the broader context of the scientific enterprise. The term science refers both to scientific knowledge and the process of acquiring such knowledge. It involves any logical field of study that relates to observed phenomena (as opposed to mathematics) and that involves claims which can be tested empirically (as opposed to philosophy). We will attempt to characterize science more fully later in the chapter, but one
The creation of new taxonomies, laws, and theories, as well as revising and improving existing ones, make up the bulk of scientific discovery, making them some of the key activities in science.
– Taxonomies: define or describe concepts for a domain, along with specialization relations among them. A prototypical example is the taxonomy for biological organisms, which are grouped into species, genera, families, and so forth, but similar structures play important roles in particle physics, chemistry, astronomy, and many other sciences. Taxonomies specify the concepts and terms used to state laws and theories.

– Laws: summarize relations among observed variables, objects, or events. For example, Black’s heat law states that mixing two substances produces a temperature increases in one substance and a decrease in the other until they reach equilibrium. The law also describes a precise numeric relationship between the initial and final temperatures. The first statement is qualitative in form, whereas the latter is quantitative. Some laws may be quite general, whereas others may be very
In this paradigm, one uses mental operators to transform one knowledge state into another, invoking rules of thumb to select from applicable operators, choose among candidate states, and decide when one has found an acceptable solution. Newell et al. (1958) proposed this framework as both a theory of human problem solving and an approach to building computer programs with similar abilities. Simon (1966) suggested that, despite the mystery normally attached to scientific discovery, one might explain it in similar terms. He noted that scientific theories can be viewed as knowledge states, and that mental operations can transform them in response to observations. He even outlined an approach to explaining creative phenomena such as scientific insight using these and other established psychological mechanisms. Simon’s early papers on this topic only outlined an approach to modeling discovery as problem-space search, but they set a clear research agenda that is still being explored