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Philosophies 2019, 4(3), 50; https://doi.org/10.3390/philosophies4030050
2. Systemism, Emergence and Medicine
The mass of E. coli can be described by describing one E. coli and then saying “grow 109 of them”, [while] describing a fruit fly requires describing all of its cell types and their interactions in chemistry, space and time.
2.2. Bunge’s Ontological Systemism
- (S1) Everything, whether concrete or abstract, is a system or an actual or potential component of a system;
- (S2) Systems have systemic (emergent) features that their components lack.
The conceptual or empirical analysis of a concrete system, from atom to body to society, consists in identifying its composition, environment, structure, and mechanism. These components may be schematically defined as follows:
- Composition = Set of constituents on a given level (molecular, cellular, etc.)
- Environment = Immediate surrounding (family, workplace etc.)
- Structure = Set of bonds among the components (ligaments, hormonal signals, etc.)
- Mechanism = Process(es) that maintain(s) the system as such (cell division, metabolism, circulation of the blood, etc.)
3. Bunge’s Epistemology
- (S3) All problems should be approached in a systemic rather than in a sectorial fashion;
- (S4) All ideas should be put together into systems (theories); and
- (S5) The testing of anything, whether idea or artifact, assumes the validity of other items, which are taken as benchmarks, at least for the time being.
[O]nly RCTs allow researchers to find out whether a medical treatment is effective. (If preferred, this method is used to find out whether propositions of the form “This therapy is effective” are true or false.) (p. 143)
The aim of an experiment, contrary to that of an observation or a measurement, is to garner empirical data relevant to a hypothesis, to test it and find out its degree of factual (or empirical) truth (true, true within such an error, or false). When there is a theory (hypothetico-deductive system of propositions) referring to the same facts, the hypothesis that undergoes an empirical test can also be assigned a theoretical truth value. In both cases, the truth in question is factual, not formal or mathematical. (p. 131)
A datum becomes evidence when confronted with a hypothesis: in this case it either confirms or weakens the hypothesis to some extent. (p. 28)
4. A Critique of Bunge’s Medical Philosophy
- Humans are open systems
- (C1) The confirmation of hypotheses is inherently stochastic and must be distinguished from the notion of objective truth; only comparisons between hypotheses in light of background knowledge and their ability to explain the observed data are objective
- (C2) RCTs are neither necessary nor sufficient to establish the truth of a causal claim
- (C3) Testing of causal hypotheses requires taking into account background knowledge and the context within which an intervention is applied
4.1. Evidence and Confirmation as Separate Concepts
Variations in regularity are generally specified probabilistically or stochastically, as random processes occurring in the ontic domain. Probability is a measure of the likelihood of an event occurring. The re-conceptualization of stochastic event regularities using the concepts of probability, might be styled ‘whenever event x, then on average event y’.
It is natural to assume that the “propensity” of a model to generate a particular sort or set of data represents a causal tendency on the part of natural objects being modeled to have particular properties or behavioral patterns and this tendency or “causal power” is both represented and explained by a corresponding hypothesis.
It is well known that HIV infection is a necessary cause of AIDS: no HIV, no AIDS. In other words, having AIDS implies having HIV, though not the converse. Suppose now that a given individual b has been proved to be HIV-positive. A Bayesian will ask what is the probability that b has or will eventually develop AIDS. To answer this question, the Bayesian assumes that the Bayes’ theorem applies, and writes down this formula: P(AIDS|HIV) = P(HIV|AIDS). P(AIDS)/P(HIV), where an expression of the form P(A) means the absolute (or prior) probability of A in the given population, whereas P(A|B) is read (or interpreted) as “the conditional probability of A given (or assuming) B.”
If the lab analysis shows that b carries the HIV, the Bayesian will set P(HIV) = 1. And, since all AIDS patients are HIV carriers, he will also set P(HIV|AIDS) = 1. Substituting these values into Bayes’ formula yields P(AIDS|HIV) = P(AIDS). But this result is false, since there are persons with HIV but no AIDS. What is the source of this error? It comes from assuming tacitly that carrying HIV and suffering from AIDS are random facts, hence subject to probability theory. The HIV-AIDS connection is causal, not casual; HIV infection is only a necessary cause of AIDS. In conclusion, contrary to what Bayesians (and rational-choice theorists) assume, it is wrong to assign probabilities to all facts. Only random facts, as well as facts picked at random, have probabilities.
4.2. RCTs and the Truth Claim
The gold standard or “truth” view does harm when it undermines the obligation of science to reconcile RCTs results with other evidence in a process of cumulative understanding.
For example, since the mid-20th century, it has been known that lung cancer and smoking are strongly correlated, but only laboratory experiments on the action of nicotine and tar on living tissue have succeeded in testing (and confirming) the hypothesis that there is a definite causal link underneath the statistical correlation: we now know definitely that smoking may cause lung cancer.
4.3. RCTs and Background Knowledge
The bird infers, on repeated evidence, that when the farmer comes in the morning, he feeds her. The inference serves her well until Christmas morning, when he wrings her neck and serves her for dinner. Though this chicken did not base her inference on an RCT, had we constructed one for her, we would have obtained the same result that she did. Her problem was not her methodology, but rather that she did not understand the social and economic structure that gave rise to the causal relations that she observed.
It seems that an alternative realistic perspective on this question [whether antioxidant vitamins can prevent cardiovascular disease] is again ignored in favour of what purports to be an unassailable scientific observation of the results from RCTs. Here, once more, the effect of ignoring differences in mechanisms and contexts may be to close down research in this area prematurely.
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Bunge makes no further distinction between traditional, complementary, and alternative medicine, which is problematic, since all three refer to different concepts of medical systems. However, a detailed criticism of this conflation is not our concern here.
Bunge speaks of emergentism instead of emergence as in his former publications.
He broadly distinguishes three kinds of causal hypotheses: (i) Null hypotheses of no association between two putatively causally connected variables. (ii) General hypotheses of the form “X is a cause of that disease” or “the mechanism of X is Y”. (iii) Particular hypotheses such as “that individual is likely to suffer from that disease”.
This sounds like an argument by definition. Bunge postulates a meaning for “probability” and then concludes that the Bayesian conception is absurd. However, we are not going to pursue this point further here.
Bunge seems to have missed that Bayesians of the personalist stripe are ontological determinists and epistemic probabilists.
We use the term positivist-empiricist referring to a conjunction of concepts of logical positivism and Humean empiricism. The former rejects any reference to unobservable (metaphysical) entities, while the latter describes the scientific endeavor to test causal hypotheses by finding quantitative associations between observed events. Indeed, the literature on the philosophical and methodological foundations of EBM emphasizes statistical methodologies and avoids any reference to a particular ontology [2,19].
More precisely, we can speak of the degree of belief in the truth of the hypothesis; this unifies Bunge’s arguments about hypotheses being confirmed and hypotheses being assigned truth values.
The data generating process is covered by the auxiliary hypotheses which can be about mechanisms. These auxiliaries serve as links between theoretical entities of the system under study and observable features in nature, in this way generating observable predictions .
The distinction between the Real and the Empirical is borrowed from Critical Realism which itself exhibits many features of systems thinking .
An example is a recent case report of a patient with chronic kidney disease experiencing severe lactic acidosis from taking the widely prescribed anti-diabetic drug metformin . In the case report, the authors state their background knowledge as follows: “In the setting of dehydration with resultant acute kidney injury, metformin can accumulate, leading to type B lactic acidosis, especially in the presence of other nephrotoxic agents (ACEi and loop diuretics)”. They conclude to “use this patient as an example of the population that actually needs dosing adjustments”, a theoretical generalization supported by the evidence obtained from observations on this single patient.
See Klement  for such a qualitative medical application of the evidence/confirmation distinction.
Actually, the very significance of knowing the average treatment effect for medical practice may be questioned. See Subramanian et al.  for a brief commentary.
See chapter 5 in Judea Pearl’s “Book of Why”  for a detailed historical summary on how causation between smoking and lung cancer became established, including the argument that a “smoking gene” might be the underlying confounding factor.
Ketone bodies are produced under conditions of low insulin levels such as during fasting or very low carbohydrate intake; in this respect, ketogenic diets can mimic fasting without necessarily restricting energy intake. Ketone bodies are transported through the blood-brain barrier by monocarboxylate transporters; this transport mechanism is therefore completely insulin- and GLUT-independent.
Critical realism maintains the naïve realistic view of an independently existing world of objects and structures giving rise to events that do and do not occur, while at the same time acknowledging the epistemological limitations of our observations and knowledge that are relative to our time period and culture . Emergence and the concept of systems are central themes in critical realism , similar to Bunge’s systemism.
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