Ecology: Biology of Interaction. 1.11. (Supplement) The Scientific Method

Ukrainian language (latest version) / Russian language (updates stopped)

1.10. (supplement) Dynamic Typology of Biosystems

D. Shabanov, M. Kravchenko. Ecology: Biology of Interactions Section 1. Ecology and the Biosystems it Studies

1.12. (addendum) Models, their limitations and dangers

1.11. (Supplement) The Scientific Method When deciding what can be called 'ecology' and what cannot, one must remember that ecology is a science. The mode of cognition called science is much older than the scientific method. But science was transformed into a force that reshapes the world and effectively changes the way humanity exists thanks to the application of the scientific method, which can be described using the hypothetico-deductive model of scientific cognition. The rapid growth of the human population is a consequence of applying this method, through which modern medicine, agriculture and industry have developed. Thus, we exist thanks to the scientific method! The operation of the hypothetico-deductive model of scientific cognition can be described by the following algorithm. 1. Search for inconsistencies in the application of the scientific system of concepts (the model of reality constructed by science) to explain available data. Both empirical observations (more precisely — facts, hypotheses explaining empirical phenomena) and theoretical conclusions and predictions derived from the existing system of scientific concepts are tested for consistency with that system. As long as the facts and conclusions correspond to the accepted system of concepts, the subject of cognition remains at this stage. An inconsistency between empirical data or theoretical conclusions and the system of concepts requires moving to the next step of the algorithm. 2. Formulation of new hypotheses that reconcile, within the system of concepts, the facts or conclusions that prompted the move to this step. Hypotheses may be of varying scope and may either concern the particularities of specific facts or require correction of the existing system of concepts. A scientific hypothesis is one that can, under certain conditions, be refuted (falsified). 3. Predicting consequences from the proposed hypotheses (deduction). If the proposed hypothesis is correct, what predictions can be made on its basis? What facts are incompatible with it and could falsify it? Which of the predictions based on the proposed hypothesis could falsify previous views? Finding answers to these questions prompts the move to the next step. 4. Testing the predictions (in many cases — experimentally). From among the predictions made in the previous step, those that allow testing are selected. Based on the results, previous or newly proposed hypotheses may be falsified. A hypothesis that has withstood testing (is consistent with new data) is considered credible (that is, not true, but currently deserving of trust). The roots of this model go far back into human history. The search for contradictions in concepts was an important method of Socrates' dialectic (469 BC–399 BC). The importance of the first and fourth steps of the above algorithm was already understood by Ibn al-Haytham (or Alhazen), an Arab scholar of the 10th–11th century (965–1039). However, the practical development of this model is an achievement of the modern era, the accomplishment of a number of scholars including Francis Bacon (1561–1626), Galileo Galilei (1564–1642), René Descartes (1596–1650), Isaac Newton (1642–1727), Gottfried Leibniz (1646–1716), David Hume (1711–1776), Karl Popper (1902–1994), and others. Note: scientific knowledge is credible (deserving of trust) precisely because the very operation of the scientific method involves the continuous re-examination of the existing system of concepts! The details of the described scheme are the subject of intense discussion among philosophers of science. However, most of these details are probably important specifically for philosophers in terms of testing and falsifying the details of their meta-scientific (supranational) models. But in any case, for natural scientists it is important to understand the main logic of the hypothetico-deductive model (search for inconsistencies in existing concepts — formulation of hypotheses — deduction of consequences — testing and possible falsification of hypotheses). In general, the operation of the hypothetico-deductive model is a special case of the trial-and-error method. This assertion is well consistent with the information theory conclusion that any new information is the result of preserving the consequences of a random choice. Scientific knowledge grows through the selective preservation of hypotheses that have successfully passed tests which could have led to their falsification. The formulation of these hypotheses contains an element of randomness (at least at the level of the intuitive guess-finding by the authors of hypotheses). Karl Popper compared an amoeba's choice of direction of movement (occurring by trial and error) with Albert Einstein's scientific search. Einstein proved more effective than an amoeba because he applied the trial-and-error method in its most perfect form. This form includes the hypothetico-deductive method and the construction of a coherent system of scientific knowledge on the basis of a well-developed capacity for modelling. Ukrainian / Russian

1.10. (supplement) Dynamic Typology of Biosystems

D. Shabanov, M. Kravchenko. Ecology: Biology of Interactions Section 1. Ecology and the Biosystems it Studies

1.12. (addendum) Models, their limitations and dangers