Abstract
Researchers in cultural evolutionary theory (CET) have recently proposed the foundation of a new field of research in cultural evolution named ‘epistemic evolution’. Drawing on evolutionary epistemology’s early studies, this programme aims to study science as an evolutionary cultural process. The paper discusses the way CET’s study of science can contribute to the philosophical debate and, vice versa, how the philosophy of science can benefit from the adoption of a cultural evolutionary perspective. Here, I argue that CET’s main contribution to an evolutionary model of scientific growth comes from the application of ‘population thinking’ to science. Populationism offers a ‘variation based’ understanding of scientists’ epistemic and socio-epistemic criteria that is able to better accommodate the variegated preferences that intervene in scientific epistemic decisions. A discussion of the so called theory choice context is offered as an example of the way a populationist approach can shed new light on the operation of scientists’ epistemic choices.
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Notes
For a general overview of evolutionary linguistic see Gontier (2012).
I take the ‘CET’ acronym from Ramsey and De Block (2015). However, these authors limit CET to what Acerbi and Mesoudi (2015) called the ‘standard cultural evolutionary approach’ typified by Boyd, Richerson, Henrich, and others, at the exclusion of the ‘cultural attractor’ orientation defended by Sperber, Claidière, and others. (For a clarification on the often convoluted debate between the two schools of cultural evolution see Acerbi and Mesoudi 2015). In this paper I will mostly focus on transmission biases which are often presented as the ‘selective’ factors of cultural evolution in the standard approach. However, I will also discuss cultural attraction particularly for what concerns the role of ‘reconstructive learning’ in science. As such, the notion of CET endorsed here is wider than the one presented by Ramsey and De Block and not limited to the standard evolutionary approach. In particular, from the point of view of this paper, it is important to stress that both the standard and the cultural attraction schools agree on the centrality of population thinking in its application to cultural evolution (Claidière et al. 2014). This paper aligns with this emphasis on populationism, discussing its possible extension to the domain of scientific evolution.
To be fair, the founding father of evolutionary epistemology is the physicist and philosopher Ernst Mach (1838–1916) who was the first to explicitly formulate the analogy between biological and scientific evolution.
As I will clarify in Sect. 3, CET’s ‘transmission biases’ are preferences or dispositions grounded in human cognition that positively select a given cultural variant increasing its frequency in a pool of competing alternatives. Attractors are biases that model cultural traits and can be selective or non-selective depending on the circumstances. Drift and migration are evolutionary but non-selective forces.
The notion of ‘cultural adaptation’ is problematic. Although references to the adaptive character of culture are frequent in CET’s authors, their notion of ‘cultural adaptation' does not go far beyond a rather vague definition of ‘good fit for the environment’. Gould and Lewontin defined cultural adaptation as a “form of non-Darwinian adaptation [...] with heritability imposed by learning” (Gould and Lewontin 1979, 592–593). Henrich and McElreath (2003) give the example of the ‘bush bread' of the Australian aborigines to exemplify what cultural adaptation is. The production of this bread, made from the toxic spores of a fern, requires individuals to learn a complex technique to detoxify the spores. As Henrich and McElreath put it: “The mental representations that allow individuals to detoxify the fern spores [...] do not come coded in their genes, nor are these continually relearned by each individual via trial-and-error experimentation or deduced solely by fitness oriented cost–benefit analysis. Instead, such adaptations result from and embody the cumulative effects of the efforts, experiments, errors, insight, and interactions of many individuals across generations” (Henrich and McElreath 2003, 123–124). According to this characterization, cultural information is adaptive because (1) manifests a good fit to the harsh Australian desert enhancing chances of survival and (2) it cannot be retrieved or ‘evoked’ (this is the term used by evolutionary psychologists) from our cognitive apparatus. Instead, the adaptive character of the cultural trait has emerged from recursive imitation and modifications. The impression is that this is a rather imprecise notion of cultural adaptation but characterizations of this type are prevalent in CET. A more precise definition of this concept should be linked to the notion of cultural fitness recently discussed by Ramsey and De Block (2015).
As Mesoudi put it: “Whereas cultural evolution does not appear to resemble neo-Darwinian evolution, with its strict assumptions of blind mutation and particulate, non-Lamarckian inheritance, cultural evolution can still be described as Darwinian, given the evidence reviewed above that it exhibits the basic Darwinian properties of variation, competition, and inheritance. Variation that is non-random is still variation, and inheritance that is Lamarckian and non-particulate is still inheritance. [...] What is needed is a theory of Darwinian cultural evolution that explicitly incorporates non-neo-Darwinian microevolutionary processes such as blending inheritance, Lamarckian inheritance of acquired characteristics, and non-random variation, as well as other processes that may have no parallel whatsoever in biological microevolution” (Mesoudi 2011, 46–47).
For a tentative evolutionary account of mathematics that aligns with the emphasis on cultural evolution presented here see Mercier (2006).
It is also important to note that Mayr never explicitly endorsed the application of population thinking to cultural evolution.
In this paper, I talk of cultural traits as cultural ideas endorsing an ‘ideational’ conception of cultural evolution (Durham 1991). However, it has been noted that CET is still divided over a definition of ‘cultural trait’. Some defend an ‘internalist’ position (e.g. information stored in brains) while others opt for an ‘externalist’ view (e.g. information stored in artifacts) (Acerbi and Mesoudi 2015). In this respect, Mesoudi states that “artifacts, speech sounds, and stated beliefs are the outward behavioural expression of information stored in the brain and as such are the cultural equivalents of phenotypic traits such as height or skin color” (Mesoudi 2011, 42).
A scientific repertoire is described by Ankeny and Leonelli as a “well-aligned assemblages of skills, behaviors, and material, social, and epistemic components that groups may use to practice certain kind of science” (Ankeny and Leonelli 2016, 20). This notion is broad enough to encompass not only conceptual and material elements such as theories, technologies and methods but also the know-how necessary to coordinate these elements in the acquisition of the resources, capacities, and expertise (Ankeny and Leonelli 2016, 20). Here, I treat scientific repertoires as cultural traits in the domain of scientific evolution. I leave aside, for the moment, the aforementioned debate between ‘internalists’ and ‘externalists’ conceptions of cultural traits.
CET seems to often use the ‘force’ analogy to refer to the causes of cultural evolution. However, it is worth mentioning that a number of philosophers of biology have recently questioned the force analogy favouring a purely statistical interpretation of evolutionary theory. According to this view, evolutionary biology does not provide causal explanations in terms of actions of specific causal forces (such as drift or selection and so on). Rather, evolutionary biology explains simply by citing the statistical properties of populations (Walsh 2007). On the contrary, CET’s conception seems to better align with the traditional view of evolutionary theory as a theory of forces (Sober 1984; Stephens 2010) even though the emphasis on populationism might accommodate a statisticalist understanding as well. (For a recent defence of the ‘force’ analogy in CET and especially cultural attraction see Buskell 2017).
‘Textbook science’ is a term used to describe Kuhn’s emphasis on science’s peculiar educational environment where scientists are conditioned, through the use of textbooks and ‘exemplars’, to conform to the general consensus of their field.
A bias based on prestige might have also be operative in the early evaluation of Darwin’s Origin given that Darwin was already a well-established and respected naturalist upon the publication of his main work.
For example, Lavoisier formulating his new theory of combustion states: “If all of chemistry can be explained in a satisfactory manner without the help of phlogiston, that is enough to render it infinitely likely that the principle does not exist, that it is a hypothetical substance, a gratuitous supposition. It is, after all, a principle of logic not to multiply entities unnecessarily” (Lavoisier as cited in Baker 2016).
I will go back to discuss the context of theory choice in the next section.
The extent to which ‘guided variation’ and ‘cultural attraction’ overlap is still a matter of debate (see Acerbi and Mesoudi 2015).
Another example of a cultural attractor is an individual that learns the habit of smoking an average number of cigarettes from an average smoker (e.g. ten cigarettes). Cultural attraction could make the trait converge on one of the two preferences: either smoking zero or twenty cigarettes. This biased non-random convergence in the space of possibilities that shapes cultural traits is called cultural attraction (Claidière and Sperber 2007).
Examples of exemplars are Aristotle’s analysis of motion, Ptolemy’s measurements of a planet’s positions and Lavoisier’s method of balance (Bird 2018). Exemplars of this type seem to fit Sperber’s emphasis on cultural attraction because, over time, many aspects of the original theories are omitted, changed, or extrapolated from the original theoretical background and adapted to new scientific evidence and standards.
Other selective scenarios are also possible. All different permutations are possible in the interplay of different evolutionary forces.
Even if Kuhn was lacking a fully-fledged populationist understanding of the way scientists’ standards operate in epistemic evolution, Wray (2011) has recently stressed the centrality of an evolutionary social epistemology in Kuhn’s work, something that has been largely ignored by earlier commentators.
A similar model is offered by Thoma (2015). Her agent-based ‘epistemic landscape’ model aims to improve Weisberg and Muldoon’s model adding the parameter of flexibility to the simulation. In short, Thoma argues that scientists are not confined to locally explore areas of the landscape in proximity of their previous trials but they are rather flexible on the choice of new research methods and informed on work different from their own. As such, her revisited model includes these parameters and concludes that the division of labour is beneficial in those cases where scientists are flexible and informed about other scientists’ research.
For example, the models suggest that explorative and more risky research is more costly under certain circumstances. As such, modelling might offer some clues on the ideal balance between the number of followers and mavericks; this, of course, might have policy oriented implications for the allocation of funding (Weisberg and Muldoon 2009).
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Fadda, A. Population Thinking in Epistemic Evolution: Bridging Cultural Evolution and the Philosophy of Science. J Gen Philos Sci 52, 351–369 (2021). https://doi.org/10.1007/s10838-020-09497-4
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DOI: https://doi.org/10.1007/s10838-020-09497-4