Simplicity, Not Simplism: Reestablishing Occam's Razor as Paramount Scientific Necessity

1 † Formatting note: All quoted emphases are editorial. Working Paper - Version 3.1 (Dec 26, 2025) © 2025 Arseniy Vodopyanov. Licensed under CC BY 4.0 (Creative Commons Attribution). Simplicity, Not Simplism: Reestablishing Occam’s Razor as Paramount Scientific Necessity Author: Arseniy Vodopyanov 1 Pre-print archived at SSRN Electronic Journal: http://doi.org/10.2139/ssrn.5570539 Pre-print indexed on PhilPapers: https://philpapers.org/rec/VODBFP 1 ORCID: 0009-0007-1900-6073 Email: [email protected] Center for Studies in Foundational Dualism 2 Simplicity, Not Simplism Abstract Occam’s Razor (also known as the principle of parsimony ) is regularly dismissed as an obsolete vestige. One, no longer worthy of its longstanding status as a fundamental scientific principle. This paper offers an interpretation of said principle’s core preference termed Verificational Simplicity or Blind Faith Parsimony. This interpretation identifies the most scientific hypothesis as that yielding the fullest account of corresponding data while necessitating least verifiabilityaverse assumption. In light of this straightforward interpretation, it is argued that Occam’s Razor is of paramount scientific importance (being so fundamental as even to underlie virtually all other integral scientific values). Three core supporting arguments are utilized. Argument 1 highlights the too often overlooked agility with which Occam’s Razor dovetails with available corresponding data—no matter how complex. Argument 2 shows that without parsimony to block ad hoc hypotheses , falsifiability becomes lost. And since falsifiability is an essential “distinguish[er of genuine] science from non-science,” science at large thus becomes lost as well. Whereas argument 3 is focused on the enormous burden of proof that reasonable corresponding dismissals must shoulder. Keywords : Philosophy of Science, Parsimony, Verifiability, Falsifiability, Testability, Bayesian Probabilism 3 Simplicity, Not Simplism Introduction, Methods, Context Leitmotif This paper is intended for both experts and layman enthusiasts. Among its chief intentions is to delve into only as much (e.g., historical, argumentative) depth as is necessary for the core corresponding thesis to be comfortably established and buttressed against reasonable scrutiny. I.e., In keeping with the core theme of its title and subject, this paper also implicitly argues that it is unnecessary to delve very far beyond corresponding basics—in order to firmly reestablish Occam’s Razor as indeed nothing short of fundamental scientific necessity. Established over the remainder of this introduction are some basic corresponding history, terms and logical corollaries. Dismissals McFadden (2023, 8) observes: “Occam’s razor... has recently been attacked as a cultural bias without rational foundation.” He then provides a helpful corresponding summary: This criticism of the influence of Occam’s razor in science remains common. [It] has been attacked with claims that ‘its rhetorical purpose [is] as an old saw persuading us to champion the supposed virtue of simplicity.’ 44 In systems biology, for example, it has been claimed that because life is ‘irreducibly complex’ Occam’s razor has no role in model selection. 45 Recent popular science articles by influential authors have made similar claims… arguing that Occam’s razor represents the ‘tyranny of simple explanations’ 46 or that it is ‘appealing, widely believed, and deeply misleading’. 47 … Likely contribut[ing] to what has been described as ‘a worrying trend to favour unnecessarily complex interpretations’ 48 (2023, 11). Occam’s Razor Itself Oxford Reference’s Dictionary of Epidemiology (Porta, 2014) describes Occam’s Razor as: “The principle of scientific parsimony (parsimony in the sense of unwillingness to use unnecessary resources, [which is also sometimes referred to as scientific] frugality, austerity). An ancient principle often attributed to the philosopher… William of Ockham (c.1285-c.1349), who said: … assumptions to explain a phenomenon must not be multiplied beyond necessity.” Routledge’s Probability, Choice, and Reason textbook, elaborates thus: “Occam’s Razor has come to embody the method of eliminating unnecessary hypotheses. Essentially, [it] holds that 4 Simplicity, Not Simplism , the theory which explains all (or the most) while assuming the least is the most likely to be correct. … This is the principle of parsimony” (Williams 2021, 186). The textbook then expands: “Occam’s Razor… points to the simplest explanation that is consistent with the data available at a given time, but even so the simplest explanation may be ruled out as new data become available. This does not invalidate the Razor, which does not state that simpler theories are necessarily more true than more complex theories, but that when more than one theory explains the same data, the simpler should be accorded more probabilistic weight. So Occam’s Razor… is also consistent with multiplying entities which are in fact necessary to explain a phenomenon” (Williams 2021, 189). As expanded-upon in Argument 1 below, said data/evidence consistency point is absolutely crucial—leading Occam’s Razor to apply as enduringly to McFadden’s example of “systems biology,” as to any other area of science. Corollary Principles Hitchens’s Razor and Sagan Standard Two apropos principles that are overt logical corollaries of the above, are known as: a. Hitchens’s Razor: “What [is] asserted without evidence can… be dismissed without evidence” (2007, 150). b. Sagan Standard: “Extraordinary claims require extraordinary evidence” (1979, 73). 2 The two are such clear logical derivatives of Occam’s Razor, simply because the contrary practice of accepting hypotheses that instead either lack sufficient supporting data to match their extraordinariness (e.g., copper bracelets counteract arthritis) or lack any supporting data (e.g., copper bracelets cure mortality) —would indeed be overtly contrary to staying “consistent with the data available.” Probabilism/Bayesianism Similarly, the “probabilistic” application of Occam’s Razor mentioned in the above-cited Routledge textbook—likewise quickly proves to be another such logical corollary. Mainly and 2 As noted by Tressoldi (2011, 1), said principle is Sagan’s popular rewording of “Laplace’s principle[:] ‘the weight of evidence for an extraordinary claim must be proportioned to its strangeness’ (Gillispie et al., 1999).” 5 Simplicity, Not Simplism , simply so, because collapsing richer spectral data into mere binaries at the cost of explanatory accuracy—is likewise demonstrably antithetical to staying “consistent with the data available.” E.g., If despite the existence of more nuanced corresponding data, hypotheses are judged only in the binary terms of either being extraordinary or not—this would prevent any two extraordinary claims from being compared to identify which is the more extraordinary among them (and therefore, per Sagan standard, which requires more extraordinary evidence to prevail). This would make a ‘ghost’ explanation of spooky action at a distance —just as extraordinary (and therefore just as scientific) as quantum entanglement . Which would in turn, likewise be overtly antithetical to staying “consistent with the data available.” This logical demand for probabilism in effective Occam’s Razor utilization, leading naturally into the third Occam’s Razor corollary mentioned in this brief introduction— Bayesian[ probabil]ism . I.e., Per International Encyclopedia of Statistical Science: “The Bayesian paradigm is based on an interpretation of probability as a conditional measure of uncertainty which closely matches the sense of the word ‘probability’ in ordinary language. Statistical inference about a quantity of interest is described as the modification of the uncertainty about its value in the light of evidence” (Bernardo 2025). Argument 1: Data Consistency Example A: Duck, Goose The most common corresponding example is known simply as duck tes t. Description Dictionary.com elaborates: “If it looks like a duck, swims like a duck and quacks like a duck, says Occam’s razor, it’s probably a duck. Not a goose disguised as a duck that infiltrated the flock.” In Combination With Data Consistency The above is so, because the added extraordinariness of that latter goose-infiltrator hypothesis proves superfluous (and therefore unscientific) when the extraordinary evidence needed to substantiate it—is missing. Occam’s Razor compelling researchers to prioritize the more logically/evidentially straightforward (and therefore more scientific) duck hypothesis instead. As 6 Simplicity, Not Simplism , observed by Williams above however, this would cease to be the case if “new data became available” which revealed the goose hypothesis to be more logically straightforward after all. E.g., If the animal was actually verified to take off its duck costume; without which it indeed looked, acted and sounded like a goose. In light of this extraordinary new data, it is then the disguised-goose hypothesis that proves the more logically/evidentially straightforward and therefore more scientific of the two. Because it would then be even less logically/evidentially straightforward to claim that it was nonetheless a duck all along. Which would have had to successfully disguise itself as a goose who, in turn, disguised itself as a duck. Unless even more extraordinary evidence/data came to light, which rendered this even more-extraordinary hypothesis the most straightforward, plausible option. And so on. Conclusion of Example A In either case, Occam’s Razor indeed remains ever helpful and fully intact. Simply reminding researchers that, however extraordinary/complex the available corresponding data may be, whichever hypothesis matches it most verifiably—deserves to be prioritized. The degree of complexity that pertains to the corresponding body of evidence, having no effect on said principle’s applicability thereto. Said [data consistency] point likewise leads naturally to this paper’s central postulation that the sort of ‘simplicity’ that Occam’s Razor prefers, is most optimally expressed as something like: Verificational Simplicity or Blind Faith Parsimony . I.e., The most scientific hypothesis is whichever yields fullest account of corresponding data, while necessitating least 3 verifiabilityaverse assumption . An interpretation which—as detailed further below—indeed codifies said principle’s scientific fundamentality. Unless specified otherwise, more-ambibuously interpreted Occam’s Razor versions are abbreviated below as OR , while ORb refers to said principle under this particular interpretation. Example B: “Systems Biology” and ‘The Watchmaker’ The following (two-prong) example returns to the topic of “systems biology” and the infamous aforementioned irreducible complexity argument. 3 Meaning ‘lowest overall extent’ thereof, rather than merely the ‘lowest number’ of assumptions. Otherwise explanations like ‘Yahweh did it’ (a single three-word assumption invoking but a single entity) would invariably prevail. Instead however, it is the overall extent to which this (omnipotent/omniscient creator) assumption strays from corresponding evidence—that obliterates its Verificational Simplicity. 7 Simplicity, Not Simplism , Description First proposed by Michael Behe, the argument postulates that some singular biological systems are “composed of several well-matched, interacting parts that contribute to the basic function, wherein the removal of any one of the parts causes the system to effectively cease functioning” (United States District Court 2006, 240). This was argued to show that such systems could not have evolved via (ultimately random-mutation-based) Darwinian evolution and must instead have been intelligently designed. While the reason that this postulation was so vehemently opposed as to turn into a major court case, was the fear that was a “Trojan Horse” (Forrest and Gross 2004) accepting which would put the Abrahamic / creationist account on par with corresponding scientific accounts. Under ORb For the sake of argument, let us grant that this is a reasonable postulation after all—which moreover indeed points to corresponding systems having been intelligently designed by some unknown source. Even so, ORb would still very much apply and quickly rule out creationism . No differently so, than it would if a horological historian we re to discover what may be an ancient timepiece and begin the process of scientifically deciphering when, how, why and by whom the device was most likely built. This is so in the latter case, because ORb would lead our historian to deprioritize whichever hypotheses rely to the highest extent of (a) ignoring corresponding evidence and/or (b) making assumptions which stray therefrom. An approach which, in that particular case, would quickly lead to profound deprioritization of the possibility that said piece of ‘intelligent design’ was produced by an all-powerful, all-knowing ( omni-god ) entity like the Abrahamic creator. If it proves for example: (a) that tools (e.g., molds) were used in its making, (b) that it was built in steps (rather than via spontaneous creation) or (c) that it is a means to another end (e.g., timekeeping)—all such clues would immediately reveal that an engineer of finite power and knowledge is a far more likely culprit. Since omnipotent, omniscient creators are exceedingly unlikely to require tools and steps to produce their creations; much less to have any use for timekeeping devices in the first place. The same applies to the various facets of systems biology. E.g., The evolution of the biosphere unmistakably constitutes a process utilizing countless individual steps, all of which were provably executed using myriad biological tools/means (nucleotides, ribosomes, RNA, DNA, homeobox 8 Simplicity, Not Simplism , genes, retrotransposons, etc.) over several billion years and counting. This is about as far removed from spontaneous creation [of the sort that would reasonably point to an all-powerful creator] as any known phenomenon can be. Conclusion of ‘Example B’ Even if the irreducible complexity argument prevailed, optimally interpreted OR would nonetheless be perfectly capable of fulfilling its scientific function (e.g., of maintaining a firewall between unreasonable religious assertions and genuine science). In Sum of ‘Argument 1: Data Consistency’ The (data consistency) distinction between such Verificational S implicity and any other moresuperficial 4 types thereof—is both pivotal and too often overlooked in arguments against OR being scientifically fundamental. Argument 2: No Parsimony → No Falsifiability → No Science The following 3-step argument establishes corresponding fundamentality further yet. 1. No Falsifiability = No Science Despite occasional challenges (often in serving fashionable theories which themselves increasingly prove unfalsifiable), the notion that falsifiability is central to identifying pseudoscientific hypotheses—is well founded and established in mainstream academia. This is briefly corroborated below. a. The New Dictionary of the History of Ideas ’ falsifiability entry (Nickles, 2025) states: “Falsifiability [has become] the most commonly invoked ‘criterion of demarcation’ of science from nonscience.” b. McFadden (2023, 8) adds: “Philosophy of science is rarely taught as a component of scientific education, but if pushed to identify the defining feature of their disciplines most scientists generally choose the principle of falsifiability (attributable to Karl Popper). 14 ” 4Whether this superficiality arises from over-emphasizing secondary criteria like: description length, syntactic neatness, geometric/mathematical elegance, the sheer number of free: parameters, entities, assumptions, etc. 9 Simplicity, Not Simplism , c. A prominent example of corresponding contextualization is Michael Ruse’s tellingly titled “Creation Science Is Not Science” (1982, 76): “Religion [and] creation-science is not science [because genuine] science must be open to change, however confident one may feel at present. Fanatical dogmatism is just not acceptable. … If the facts speak against a theory, then it must go. A [genuine] body of science must be falsifiable.” d. Stanford Encyclopedia of Philosophy (Thornton, 2023) expands upon the corresponding position thus: “If a theory is incompatible with possible empirical observations it is scientific; conversely, a theory which is compatible with all such observations… is unscientific.” e. Cambridge dictionary adds: “Falsifiable [means] able to be proved... false. [E.g.,] All good science must be falsifiable .” Why falsifiability (as defined above) is so scientifically-essential, is further delineated via Oxford Reference. According to which, science itself is: “The systematic study of the structure and behavior of the physical and natural world through observation, experimentation, and the testing of theories against the evidence obtained” (as cited in McHugh 2024, 88). Ergo, if a theory cannot be falsified when “test[ed] against evidence”, then it simply isn’t genuinely evidence-based and therefore—isn’t scientific. Particularly so, when testable alternatives exist (as is virtually always the case). 2. No Parsimony = No Falsifiability Per Popper (2008, 731): “Falsification of a theor[y] can always be avoided by introducing an auxiliary hypothesis.” These are also referred to as ad hoc hypotheses, immunizing stratagems or instances of special pleading. Schindler (2024, 71) elaborates: When introduced to save a theory, ad hoc hypotheses would reduce the theory’s falsifiability, and therefore ‘degrees [5] of ad hoc-ness are related (inversely) to degrees of testability and significance’ (Popper, 1959). … Ad hoc hypotheses are hypotheses which may make independent predictions, but for which there [is] no support. This view is incredibly popular... (Schaffner, 1974; Leplin, 1975; Scerri & Worrall, 2001; Sober, 2008; Worrall, 2002). The following example is often invoked to motivate the view (e.g., Worral, 5 Again, it being well-established that such factors are optimally assessed (not in either/or binaries, but) in “degrees.” 16 Simplicity, Not Simplism Competing Interests and Funding The author declares no competing interests nor funding received for this work. Acknowledgments The author thanks Jeaneas Fan for her valuable proofreading contributions. AI tools (e.g., Perplexity, Venice) were used for additional proofreading and formatting suggestions. All contributions were thoroughly vetted and edited by the author. 17 Simplicity, Not Simplism References Bernardo, J. M. 2025. "Bayesian Statistics." In International Encyclopedia of Statistical Science, edited by M. Lovric. Springer. https://doi.org/10.1007/978-3-662-69359-9_59 . Carroll, Robert T. 2008. “Ad Hoc Hypothesis.” The Skeptic’s Dictionary. https://skepdic.com/adhoc.html . Archived October 1, 2025 at the Wayback Machine. 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