Abstract
The methodological advances in science are above all associated with enhancing scientific knowledge by means of reliable processes. This requires the analysis of levels of reality, complexity and approaches to scientific method. In this regard, scientific processes can be procedures and methods. The procedures contribute to the initial stages of the inquiry and can complement the rigorous methods. Meanwhile, the methods enlarge our knowledge according to well-established ways or follow research processes whose reliability has been tested.
Scientific research needs methods that deal with objects and problems, whose diversity offers reasons for the unfeasibility of a universal method for science and poses problems for methodological imperialism. The existence of levels of reality (micro, meso, and macro) and the features of complexity, structural and dynamic complexity, pave the way for methodological diversity. Thus, empirical sciences show different approaches to scientific method, such as the differences between natural sciences and social sciences, and also the novelty of the sciences of the artificial in comparison with the social sciences. Consequently, the relations of the scientific methods with the levels of reality and complexity require a deeper view than the conceptions already available.
This paper has been developed within the framework of the project FFI2016-79728-P, supported by the Spanish Ministry of Economics and Competitiveness (AEI).
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Notes
- 1.
On the present views on how to characterize “heuristic,” see Chow (2015).
- 2.
This presupposes that there is a connection between methodology of science and ontology of science, which certainly has a close relationship with epistemology. In this regard, it is important to emphasize that the ontology of science is not something merely defended from perspectives of scientific realism but also from other philosophical positions, including anti-realist viewpoints. Thus, “even strongly empiricist approaches advocate a conception of scientific ontology: an ontology of observable objects, events, processes, and properties” (Chakravartty 2017, 41; see also 59–60 and 63).
- 3.
In this regard, it seems odd to claim that “the epithet ‘intelligible’ applies to theories, not to phenomena” (de Regt 2017, 12; see also pp. 45 and 88).
- 4.
Some of the conceptions in favor of monism, reductionism and methodological universalism, especially those of a directly logical-methodological kind, do not pay attention to historicity. But historicity is a key factor in understanding scientific change, complexity and problems related to scientific prediction, cf. Gonzalez, W. J. (2015b, 25, 29, 56n, 62, 77–78, 91, 103, 133, 185, 222–223, 249, 257, 267, 279n, 308 and 310).
- 5.
Cognitive rationality with practical rationality and evaluative rationality are the three main spheres of rational deliberation, cf. Rescher (1988, 2–3). These three spheres of rationality may be intertwined in science. Moreover, this epistemic, practical and evaluative intertwining can be seen in the current efforts to find an effective treatment for Covid-19 patients and an adequate vaccine to avoid future problems. For pragmatism, they involve three ranges of philosophical concern, cf. Rescher (2019, 58).
- 6.
On the characterization of the sciences of the artificial, see Simon (1996).
- 7.
Convergent and selective prospects for methodology of science have been discussed in conceptions related to scientific realism. On these views, see Gonzalez (2020a).
- 8.
Regarding psychology, see for example Gigerenzer and Gaissmaier (2011).
- 9.
In the case of economics, this is particularly clear. See Gonzalez (2014).
- 10.
- 11.
- 12.
- 13.
These values play a role in preferring a type of methodological conception (pragmatic, pluralist, instrumentalist, etc.), but they also influence the configuration of the kind of social impact considered, which can be realistic, relativistic, constructivist, etc.
- 14.
“There are numerous ways of generating economic forecasts. Many are a mix of science — based on rigorously tested econometric systems — and judgment, occasioned by unexpected events: the future is not always like the present or the past” (Hendry and Ericsson 2001, 186).
- 15.
“Although progress is being made, we are still some way from a position where the model answers can be accepted without further human intervention. This is standard international practice. McNees surveyed the large U.S. forecasting organizations in 1981; they attributed between 20 and 50% of the final forecast to judgmental adjustments (…). Adjustments are made in the light of other information, commonsense judgements, past model error, and a knowledge of its deficiencies. The useful exercise of this judgement is not limited to the specialists. Non-specialists may also make a valuable contribution providing that the issues are put to them clearly” (Burns 1986, 104).
- 16.
For complex systems, it is feasible to have a kind of methodological pluralism in terms of “having different models for different features of a phenomenon” (Morrison 2015, 7). But this involves the polyhedral character of the reality studied, whether natural, social or artificial.
- 17.
Obviously, “the abstract nature of mathematics can nevertheless yield concrete physical information” (Morrison 2015, 4).
- 18.
- 19.
- 20.
Crossdisciplinarity is characterized by problems that are discussed using methodologies that, in principle, come from disciplines that are not thematically related. This is the case with a discipline at the micro level, such as genetics, and one at the macro level, such as environmental science, which intersect in the conservation genetics. Thus, thematic barriers are crossed in crossdisciplinarity, but not in principle methodologies. Meanwhile, methodologies are combined in interdisciplinarity, because a common point of encounter is sought from different starting points.
- 21.
This kind of science deals with designs and is different from the social sciences, even though there might be dual sciences — artificial and social — as happens with communication sciences, cf. Gonzalez (2008b).
- 22.
This downward or descendent approach seems odd, insofar as it suggests the ideas of immersion, submersion or submergence.
- 23.
Cf. Gonzalez (2015b, v, vii-viii, 2, 4, 6, 10n-11, 13, 18–21, 25, 30, 32–40, 47, 56, 60, 64, 69, 71, 77, 93, 114, 127, 129, 140, 150–151, 159, 165, 173, 184, 215–216, 218, 221, 249–250, 254–256, 264, 272, 275, 277–278, 288–289, 304, 308, 317–322, and 324–338).
- 24.
Cf. Gonzalez (2012a). The analysis made in this section and the next one is based on this paper.
- 25.
Descriptive models are characteristic of basic science, whereas prescriptive models are used in applied science.
- 26.
This feature of diversity is especially highlighted by methodological pluralism, but is also indicated by methodological pragmatism, when it connects the various goals sought with effectiveness in the research process. Cf. Gonzalez (2020e).
- 27.
Formal sciences, such as mathematics, commonly have specific methodological considerations, even though “quasi-empiricist” approaches and naturalist conceptions have searched for methodological similarities with empirical sciences.
- 28.
In addition, the degree of complexity matters, especially for modeling. Thus, in the case of computer simulation, “the system can be modelled at various levels of complexity, ranging from very simple models that don’t include any interactions to more complicated modelling that encompasses physics and engineering models, with the more complicated type giving rise to a greater probability errors” (Morrison 2015, 272–273).
- 29.
On complexity from a dynamic point of view, see Gonzalez (2013b).
- 30.
“The prospects for the emergence of an effective complex system are much greater if it has a nearly-decomposable architecture” (Simon 2001, 82).
- 31.
These categories of structural and dynamic can be used to articulate lists of kinds of complexity such as “multilevel organization, multicomponent causal interactions, plasticity in relation to context variation, and evolved contingency,” Mitchell (2009, 21).
- 32.
On infosphere see Floridi (2014).
- 33.
The analysis of these elements is made in Gonzalez (2015b, especially chapter 7, 171–199), where there are more details about these issues.
- 34.
In the case of the sciences of the artificial, the dynamics is analyzed in Gonzalez (2013b).
- 35.
- 36.
This is the perspective that deals with domains of phenomena that previously were not generally perceived as “economic,” but are now analyzed in economic terms. See Mäki (2009, 352).
- 37.
- 38.
On Fogel — Nobel Prize winner in 1993 — and the methodology of the “new history,” see Gonzalez (1996, 25–111; especially, 29, 37, 74–75, 86, 90–91, 95, 105, and 107).
- 39.
Buchanan was awarded the Nobel Prize in economics in 1986. Regarding his methodological views, and in particular his approach to prediction in economics, see Gonzalez (2006a, 89–90 and 100–101).
- 40.
On the internal and external complexity, see Gonzalez (2012b).
- 41.
The analysis of the relationship between economics and the Internet shows that it is a multivariate relationship. Thus, there are nuances in the role of economics depending on whether it is the scientific side, the technologicalfacet or the social dimension of the network of networks. Cf. Gonzalez (2019).
- 42.
- 43.
- 44.
The Royal Swedish Academy of Sciences, The Nobel Prize in Physics 2016, https://www.nobelprize.org/nobel_prizes/physics/laureates/2016/press.html (accessed on 1.12.2016). “The three Laureates’ use of topological concepts in physics was decisive for their discoveries. Topology is a branch of mathematics that describes properties that only change step-wise. Using topology as a tool, they were able to astound the experts. (…) We now know of many topological phases, not only in thin layers and threads, but also in ordinary three-dimensional materials. Over the last decade, this area has boosted frontline research in condensed matter physics, not least because of the hope that topological materials could be used in new generations of electronics and superconductors, or in future quantum computers. Current research is revealing the secrets of matter in the exotic worlds discovered by this year’s Nobel Laureates.” The Royal Swedish Academy of Sciences (2016).
- 45.
Ontological emergence “asserts that genuinely novel objects and properties emerge even within the domain of physics, and it rejects the idea that only the level of fundamental physics is real” (Humphreys 2016, xvii).
- 46.
On causality and causal explanation, see Gonzalez (2018b).
- 47.
These three features of causality appear in books such as Woodward (2003).
- 48.
“‘In ordinary English, a random event is one without order, predictability or pattern. The word connotes disaggregation, falling apart, formless anarchy, and fear.’ This quote from the late Stephen J. Gould (1993) illustrates one reason why many nonbiologists — even highly educated ones — may feel uncomfortable with Darwinian evolution: Darwinian evolution centrally involves chance or randomness” (Wagner 2012, 95).
- 49.
- 50.
On the sciences of the artificial from the perspective of the sciences of design, the most influential book is Simon’s volume mentioned already, whose third edition was published in 1996. An analysis of the case of economics is in Gonzalez (2008a).
- 51.
The role of normativity, see Spohn (2011).
- 52.
On this issue, see Hacking (1999).
- 53.
The ongoing discussions on causality (such as actual causation, causal selection — one or several causes — and causal importance) might lead to differences between the cases of natural phenomena and social events. In addition, the features of causal importance might be of a different kind in natural phenomena than in social events. This concerns several aspects: (i) the causal responsibility (what produces the effect and makes the trait in the effect possible), (ii) the difference in the making of the effect, either actual or potential, and (iii) the causal specificity. Thus, natural intervention in physical phenomena and the agent intervention in economic events can have different characteristics.
- 54.
“Prediction is not the only exercise with which economics is concerned. Prescription has always been one of the major activities in economics, and it is natural that this should have been the case. Even the origin of the subject of political economy, of which economics is the modern version, was clearly related to the need for advice on what is to be done in economic matters. Any prescriptive activity must, of course, go well beyond pure prediction, because no prescription can be made without evaluation and an assessment of the good and the bad,” Sen (1986, 3).
- 55.
In the sphere of medicine, there is a clear connection between methodological problems and ethical values, which affect applied science and the application of science. In this regard, see Worrall (2006).
- 56.
A very innovative author in the sphere of the sciences of the artificial was Alan Turing, a key figure in the development of the Artificial Intelligence. See Hodges (2014).
- 57.
- 58.
- 59.
José Ortega y Gasset used this concept for the philosophy of technology, Ortega y Gasset ([1933] 1997, 23, 24 and 60). To some extent, it is a feature that is also valid for artificial designs, insofar as they enlarge the possibilities of what is natural in the human agents and societies in order to get new aims.
- 60.
Simon insisted on the feature of synthesis for the sciences of the artificial. See Simon (1996, 4–5).
- 61.
A development of this approach within the framework of scientific realism can be found in Gonzalez (2020b).
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Gonzalez, W.J. (2020). Levels of Reality, Complexity, and Approaches to Scientific Method. In: Gonzalez, W.J. (eds) Methodological Prospects for Scientific Research. Synthese Library, vol 430. Springer, Cham. https://doi.org/10.1007/978-3-030-52500-2_2
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DOI: https://doi.org/10.1007/978-3-030-52500-2_2
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