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This article was submitted to Frontiers in Quantitative Psychology and Measurement, a specialty of Frontiers in Psychology.
Edited by: Aaro Toomela, University of Tallinn, Estonia
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The value of analogical modeling in scientific theory construction has been endorsed by a number of philosophers of science (e.g., Hesse,
The need for analogical modeling stems from two features that are often present in the initial generation of scientific theories. First, as with exploratory factor analysis, for example, the generation of theories takes the form of explanatory reasoning known as
In science, increasing our knowledge of the nature of our theories' causal mechanisms by analogical modeling is achieved by using the pragmatic strategy of conceiving of these unknown mechanisms in terms of what is already familiar and well understood. Well-known examples of models that have resulted from using this strategy are the model of chromosomal inheritance, based on an analogy with a string of beads; the model of natural selection, based on an analogy with artificial selection; and computational models of the mind, based on analogies with the computer. With respect to the objects of scientific investigation, the strategy of analogical modeling can be represented in the following sequence:
PHENOMENA
In order to understand the nature of analogical modeling further, it is necessary to distinguish between a model, the source of the model, and the subject, or target, of the model (Hesse,
However, this creative role requires the source from which the model is drawn to be different from the subject that is modeled. For example, the modern computer is a well-known source for the modeling of human cognition, although our cognitive apparatus is not generally thought to be a real computer. Models in which the source and the subject are different are sometimes called
It is
In addition to developing nascent theories, the strategy of analogical modeling also serves to assess their plausibility. In evaluating the aptness of an analogical model, the analogy between its source and subject must be assessed, and for this one needs to consider the structure of analogies. The structure of analogies in models comprises a positive analogy in which the source and subject are alike, a negative analogy in which the source and subject are unlike, and a neutral analogy where we have no reliable knowledge about matched attributes in the source and subject of the model (Hesse,
Reasoning by analogy is an important form of inference, but it is difficult to characterize precisely. Because analogical reasoning results in new knowledge claims, it is ampliative, or content-increasing, a feature it shares with inductive reasoning. However, unlike arguments based on inductive inference, arguments based on analogy can produce knowledge claims about new kinds of things. Briefly, we may say that an analogy is an argument based on assumed, or known, parallels or similarities between two or more objects, properties, or events. What is known about one class of entities is employed to learn more about the other class of entities. A good analogical argument provides an understanding of the less familiar in terms of the more familiar by discerning that the two are alike in relevant respects, but not in other respects. As already mentioned, reasoning by analogy from the known functioning of computers to the less well known character of human cognitive processes is frequently undertaken in psychological research.
Analogical reasoning is important in science and obviously lies at the inferential heart of analogical modeling. It should be emphasized that abductive, or explanatory, reasoning, is an important form of scientific reasoning in its own right. Because the theories fashioned in science are often explanatory theories, the use of analogical modeling in order to develop those theories will necessarily involve combining the two forms of reasoning to produce a creative form of reasoning known as
The reasoning involved in analogical abduction can be simply stated in the form of a general argument schema as follows:
Hypothesis H about property Q was correct in situation S1. Situation S1 is like the situation S2 in relevant respects. Therefore, an analog of H might be appropriate in situation S2.
Darwin's ( The hypothesis of evolution by artificial selection was correct in cases of selective domestic breeding. Cases of selective domestic breeding are like cases of the natural evolution of species with respect to the selection process. Therefore, by analogy with the hypothesis of artificial selection, the hypothesis of natural selection might be appropriate in situations where variants are not deliberately selected for.
In formulating his theory of natural selection, Darwin took advantage of the two most important features of analogical abduction: Its ability to create, and its ability to justify. In reasoning by analogy, using known facts about artificial selection, Darwin was able to hypothesize the parallel mechanism of natural selection that explained diversity among natural species. At the same time, he was able to appeal to the epistemic worth of his carefully crafted analogy and proclaim the plausibility of his hypothesis of natural selection. Numerous creative scientists have been able to exploit the resources of analogical abduction in this manner.
An instructive example of an analogical model in psychology is Rom Harré's ( The theory of dramaturgy provides a correct account of behavior on the theatrical stage. Behavior on the theatrical stage is like a good deal of human behavior in social life. Therefore, by analogy with the theory of dramaturgy, much human social behavior might be understood and monitored as actors on life's stage.
The basic idea of the dramaturgical perspective is that we observe and hear a simulacrum of life on the stage, and that our knowledge of how this is produced provides us with a guide to the creation of real life. Goffman's dramaturgical perspective provides a detailed analytical account of the roles and rules human agents follow on life's stage combined with a “watchful consciousness” of the actor, the producer, the audience, and the critic.
As a source model, the dramaturgical model has both positive and negative analogies for there are clear similarities and differences between real life and dramatically staged acts. Regarding similarities, Goffman noted that to be understood as the person they are portraying, the actor has to act in manner that parallels what the audience would expect of that kind of person. Clearly, there are differences between stage drama and real life. The differences involve sequences of acts and actions that are at once selective, simplified, and heightened. For example, in comparison with real life, only a limited number life sequences are followed, time is compressed, and resolutions are effectively reached (Harré,
Analogical modeling in science is a risky cognitive undertaking. In practice, it involves reading widely in neighboring disciplines for potentially fruitful source models, and selecting those source models that have worked well in theories that are judged similar to the theory being developed. In addition, the methodology of modeling through analogical abduction provides useful guidance for expanding scientists' knowledge of latent causal mechanisms (see, e.g., Harré,