Heuristic Reasoning

The advancement of knowledge is the big goal in human understanding. To get it, we often have to push beyond the frontier of knowledge, where our understanding dissolves and where new, strange entities appear. These require bold explorations and the consequent discoveries are not idle mind games, but crucial tools for our future life. And to have a method for carrying out these explorations is essential.

Three decades ago Laudan posed the challenge: Why should the logic of discovery be revived? This paper tries to answer this question arguing that the logic of discovery should be revived, on the one hand, because, by Gödel’s second incompleteness theorem, mathematical logic fails to be the logic of justification, and only reviving the logic of discovery logic may continue to have an important role. On the other hand, scientists use heuristic tools in their work, and it may be useful to study such tools systematically in order to improve current heuristic tools or to develop new ones. As a step towards reviving the logic of discovery, the paper follows Aristotle in asserting that logic must be a tool for the method of science, and outlines an approach to the logic of discovery based on the analytic method and on ampliative inference rules.

In my opinion, it is only in the framework of a study concerning abductive inference that we can correctly and usefully grasp the cognitive status of heuristics. To this aim, it is useful to see heuristics in the perspective of the so-called fill-up and cutdown problems, which characterize abductive cognition. Abduction is a procedure in which something that lacks classical explanatory epistemic virtue can be accepted because it has virtue of another kind: [9] contend (GW-Model) that abduction presents an ignorance-preserving or (ignorance-mitigating) character. The question is: are abductive heuristic strategies always ignorance preserving? To better reframe the cognitive status of heuristics I will take advantage of my eco-cognitive model (EC-model) of abduction. I contend that, through abductive heuristics, knowledge can be enhanced, even when abduction is not considered an inference to the best explanation in the classical sense of the expression, that is an inference necessarily characterized by an empirical evaluation phase, or inductive phase, as Peirce called it. Hintikka maintains, implicity agreeing with the perspective on abduction as ignorance-preserving, that the true justification of a rule of abductive inference is a strategic one, but this strategic justification does not warrant any specific step of the whole process. I argue, taking advantage of a distinction between static and dynamic aspects of scientific inquiry, that this does mean that every abductive guess heuristically reached is damned to be ignorance-preserving if evidentially inert. When Hintikka contends that the abductive steps which lead to intermediate models cannot have “warrants” at the level of strategic justification, and also at the level of non strategic justification, in my perspective we can relieve ourselves of this burden of epistemic sufferance just acknowledging we are dealing with creative models. If we only see models in empirical science in the light of the future achieved empirical success, we obviously see them just as provisional guesses, devoid of justification and still and intrinsically looking for it. On the contrary, they are occasionally justified by themselves—abductively—just because creative, and so constitutive of a fruitful epistemic “heuristic cognitive travel”. Finally, I will illustrate that also in deduction the presence of abductive heuristic events coincides with their knowledge-enhancing character: here too these strategic aspects reflect the pure—productive—conjectural element of abductive inference and its capacity to guessing right.

How can we speed up both basic and translational scientific research without major new financial investment? One way is to speed up the process by which good proposals are funded. Another is to do a better job of identifying research that is potentially transformative. There are internal institutional barriers as well as sluggish and conservative policies in place in many government funding agencies, universities, and private firms, policies that are risk-averse and characterized by short-term accounting. While perhaps calling for transformational research, their selection procedures promote normal basic research and translational research instead. This chapter proposes that progress can be made by giving increased weight to heuristic appraisal—appraisal of the future promise of proposed research—and correspondingly less weight to confirmational appraisal—the logical and probabilistic relations between theories and data sets already on the table. Emphasis on the latter, as studied by traditional confirmation theory, is a legacy of logical positivism. Adapting a form of scenario planning from the business community is one positive suggestion.

This paper is concerned with the problem of why scientific revolutions begin. It considers first Kuhn’s view that a revolution is started by a build-up of anomalies in the old paradigm. This view is criticized on historical grounds by considering the examples of the Einsteinian revolution and the Copernican revolution. It is argued that there was no significant build-up of anomalies in the old paradigm just before the beginning of these revolutions. An alternative view is then put forward that the start of a revolution has to be explained in terms of technology and practical problems (or tech for short). There are two patterns: (i) tech first in which technological advances lead to new discoveries and these lead to the onset of the revolution, and (ii) tech last in which the need to solve an urgent practical problem produces a challenge to the old paradigm. If this challenge is successful, the new paradigm leads to a solution of the practical problem and so to technological advance. The tech first pattern is illustrated by the example of the chemical revolution, and the tech last pattern by the example of the development of the germ theory of disease. It is then argued that scientific revolutions can exhibit a combination of tech first and tech last, and this is illustrated by the Copernican revolution. In the final section of the paper, it is shown that the ‘tech first/tech last’ theory explains why the Copernican revolution occurred in Europe in the 16th and 17th centuries, and not in the ancient Greek world (with Aristarchus), or in China in the 16th and 17th centuries.

Many philosophers of science consider scientific disagreement to be a major promoter of scientific progress. However, we lack an account of the epistemically and heuristically appropriate response scientists should have towards opposing positions in peer disagreements. Even though some scientific pluralists have advocated a notion of tolerance, the implications of this notion for one’s epistemic stance and, more generally, for the scientific practice have been insufficiently explicated in the literature. In this paper we explicate a characteristic tension in which disagreeing scientists are situated and on this basis we propose a notion of epistemic tolerance.

Research on innovative economic and organizational decision making processes is reviewed using epistemological criteria, showing that an array of effective, logically sound, and in that sense ‘rational’ heuristics can be specified—different from the repertory of ‘behavioral’, potentially ‘biasing’, heuristics usually considered. Two case studies of innovative decision making under uncertainty are then presented, on new product development (a major project for reducing traffic pollution) and entrepreneurial decision making (protocol analyses of financial angels’ investing decisions); showing that the heuristics applied do resemble more the ‘slow and safe’ heuristics of scientific discovery, rather than the ‘fast and frugal’ heuristics of everyday life. A third case analyzes decision making on military flights, addressing the question of whether heuristics can be ‘fast and rational’ simultaneously. Results suggest that they can, and help in identifying the rather unexplored rational heuristics sustaining ‘highly reliable’ action under risk.

In this paper I argue that the effective way to account for the behavior of stock market prices is to put in use a dynamic approach—bottom-up, local, non-axiomatic, heuristic. To this end, I provide an analysis of the generation of four main hypotheses used to explain stock market prices (SMP). In particular I show how the means of generating these hypotheses is essential to assessing their efficiency and plausibility. In formulating a hypothesis, a selection of features of SMP is made for incorporation in a theory. This selection may be expressed mathematically in most of the cases. An examination of these means of generation can show us why some of these hypotheses are successful and efficient and some not, and can also shed light on the extent to which a particular hypothesis can be usefully applied. Thus the study of the means of generation of hypotheses will offer us a guide to formulating new hypotheses in a reliable and cogent fashion.