I offer an analysis of the concept of scientific innovation. When research is innovated, highly novel and useful elements of investigation begin to spread through a scientific community, resulting from a process which is neither due to blind chance nor to necessity, but to a minimal use of rationality. This, however, leads to tension between two claims: (1) scientific innovation can be explained rationally; (2) no existing account of rationality explains scientific innovation. There are good reasons to maintain (1) and (2), but it is difficult for both claims to be accepted simultaneously by a rational subject. In particular, I argue that neither standard nor bounded theories of rationality can deliver a satisfactory explanation of scientific innovations.
Ofrezco aquí un análisis del concepto de innovación científica. Cuando se innova en la investigación, los elementos de investigación altamente novedosos y útiles comienzan a extenderse a través de una comunidad científica como resultado de un proceso que no se debe ni al ciego azar ni a la necesidad, sino a un uso mínimo de la racionalidad. Esto, sin embargo, genera tensiones entre dos afirmaciones: (1) la innovación científica puede ser explicada racionalmente; (2) ninguna explicación existente de la racionalidad da cuenta de la innovación científica. Hay buenas razones para mantener (1) y (2), pero es difícil que ambas afirmaciones sean aceptadas simultáneamente por un sujeto racional. En particular, sostengo que ni la teoría estándar de la racionalidad ni la teoría de la racionalidad acotada pueden ofrecer una explicación satisfactoria de las innovaciones científicas.
Scientists are often asked to promote innovation and aid society by, for instance, novel drugs and therapies, means of communication, ways of making technical devices more energy efficient, or methods for teaching mathematics to schoolchildren. Increasingly, they are also invited (if not urged) to innovate science itself. Academic institutions, grant agencies, and governments all encourage researchers to devise novel questions, methods, concepts, models, theories, goals, instruments, and even research institutions. But while the terminology of innovation is now widely used, all too often this is purely rhetorical rather than reflective. Here, I aim to foster philosophical debate concerning scientific innovation. The scientific system should be self-critical when it comes to the language required for, and used in, grant applications, research practice guidelines and reports of results, as well as the public dissemination and assessment of research. It should be clear, with all the necessary caveats, what we mean when we qualify research as “innovative”.
For starters, consider the following examples. The National Endowment for the Humanities (NEH), an independent US Government agency, offers Digital Humanities Advancement Grants, “leading to innovative work that can scale to enhance research, teaching, and public programming in the humanities.” It also aims to encourage “innovative collaborations between museum or library professionals and humanities professionals to advance preservation of, access to, use of, and engagement with digital collections and services.”
Finally, the European Union’s 8th “Framework Programme for Research and Innovation”, also called “Horizon 2020”
So we can see that the language of innovation has moved from technology and the economy into science itself. However, none of the agencies or institutions tells us what innovative science really is; or, more importantly, what rules and tools can and should be used to foster and assess innovation
Several important questions naturally arise here. The first is the obvious one: What
This might have been different. Before
Kuhn did
In the remainder of this essay, I try to build the foundations for further work. First, I spell out minimal features of scientific innovation, thus addressing the conceptual question. Second, that analysis leads to a puzzle related to the three other questions. I will explain that puzzle, especially with respect to the explanatory question, and discuss some attempts at solving it. In my view, the puzzle partly explains why criteria of innovativeness are so hard to spell out. That should inspire us to be more cautious when using the term.
For reasons that are well known, it is difficult—if not impossible—to give a complete conceptual analysis of ‘scientific innovation’. It is doubtful that the phenomenon has any essence that we could identify in terms of necessary and sufficient conditions, certainly not from the armchair using thought experiments that would provide us with rational insight into that essence. Does the innovation that results from a new laboratory instrument used in cancer research have much in common with the blending of theories from different disciplines (which, for example, made the discovery of DNA possible), or with a novel method for the long-term preservation of the original copy of Thomas Jefferson’s
Alternatively, if ‘scientific innovation’ is ill suited for complete conceptual analysis, and if it is inexact, then one might think that an account of it needs to be given in terms of what Carnap (1950, 1-18) has called an “explication”. As he famously argued, inexact concepts such as ‘probability’ must be explicated in ways that reveal and rectify deficiencies of our ordinary understanding of them: one needs to work towards “the transformation of an inexact, prescientific concept, the
It is useful to situate the notion of innovation within a broader network of related concepts. Often, no clear distinction is drawn between the
Clearly, this point is already a rectification that may be seen as going beyond ordinary language (or, more precisely, the language of grant agencies). However, there is at least one argument for this clarification: it builds on the theoretical fruitfulness of this conceptual choice. We can now better describe past attempts to change aspects of research that did not succeed, or at least not immediately, in the marketplace of scientific ideas. Consider applying the method of experimentation to psychology. This was first proposed in the eighteenth century by a mostly forgotten German philosopher-scientist, Johann Gottlob Krüger (1756), and practiced by only a few individuals at that time. Scientists back then already studied phenomena such as visual thresholds, the blind spot, the moon illusion, or the temporal persistence of visual and tactile perception. However, many scientists did not view these as
This point also turns scientific innovation into a (partially, though essentially) social affair. One might object to this by distinguishing between “real” and “alleged” innovation: perhaps Copernicus’ heliocentric model of the world or Frege’s new logic
One evident consequence of understanding innovation in this way helps to avoid another conceptual confusion. As with innovation in the economy or in technology, an innovative change in science should be more than incremental (Nickles 2015); while it need not be revolutionary in the Kuhnian sense, involving gestalt switches or world changes
The second point is that the language of innovation is applied to a number of different entities, including: concepts, problems, methods or rules of scientific reasoning, theories, goals of inquiry, and institutions. These are all possible bearers of the predicates “is innovative research” or “does not lead to a research innovation” (and similarly, to carry over the previous point, of the predicates: “is (not) an invention”; “is (not) widely diffused”). The statements of funding agencies and Kuhn’s language agree on this flexibility. One consequence of this is that innovation differs from discoveries; the latter being specific cognitive achievements or research outputs: the discovery of Uranus, of two types of electricity, of nuclear fission, of Martin Heidegger’s “Black Booklets” (bad news!), or the effects of external temperature as well as of alcohol consumption on reasoning about the trolley problem (being in a cold environment allegedly enhances utilitarian responses, but so does drinking alcohol … at least according to certain studies).
A related point is that ‘innovation’, unlike ‘discovery’, is not an achievement term (Nickles 2003, 59). If you have discovered some process, entity, or fact, there is no way of undiscovering it. An exception to this may be the notorious case of Pluto. In 2006, after heated debate, the International Astronomical Union (IAU) voted that Pluto would no longer be a planet but, adding injury to injustice, should be reclassified as a “dwarf planet” (which was only partly redressed when being “plutoed” became the American word of the year in 2006). It would certainly, however, be an overreaction to say that this case undermines the basic intuition that the discovery of X is an epistemic achievement. The object called ‘Pluto’ was discovered in 1930; what the IAU did was to create a new concept and reclassify that same object between those of planet and small Solar System body (Dick 2006). Innovation, however, because it forms part of the cycles via which the sciences renovate the intellectual, practical, and institutional framework of research, can indeed be undone. How? Well, while a certain innovation is useful in one setting or context, it may lose its utility under different circumstances—when a new type of instrument appears, such as the advanced confocal laser microscope that may soon replace traditional methods in dermapathology due to its enhanced speed and accuracy of diagnosis, and the help it offers in the treatment of skin cancer (Gareau et al. 2017). Furthermore, an innovative updating of instruments may become judged to be merely perceived rather than real. For instance, among neuroscientists, it is often claimed that positron emission tomography (PET) and fMRIs improve on preceding methods, such as the classical and widely used electroencephalogram (EEG), for studying the neural processes that underlie or realize mental processes. However, the epistemic innovativeness of PET and fMRIs has been questioned. Perhaps expectations are too high; or perhaps we have learnt that each different tool has its relative strengths and limits concerning the different processes and questions we are interested in (Roesler 2005; Bösel 2007). Our judgments concerning innovations are typically more open than in genuine discovery.
A third point follows closely upon the last remark: inventions and innovations are frequently context-relative (cf. Nickles 2003). What counts as innovation in research in one context may not count as it in another. When the ERC requires a proposal to be innovative, this is not in an absolute sense but only relative to the researcher’s field. Along these lines, there were almost no departments of history of science in Germany before the early 1990s, so when the MPG founded the MPI for History of Science in 1994, it was an innovation for the German academic system. This was despite the fact that in the USA, the history of science was already better organized and widespread at that time. This is also what we see with the innovative introduction of experiment and measurement into psychology in the nineteenth century, although these methods had been used in physics throughout the early modern period.
We must not, however, overstate the point about context-relativity. While much scientific innovation is context-relative, some is not. When measurement or experiment was used for the first time in science, the innovation was absolute. When
A fourth point is crucial in paving the way for discussion of the explanatory, evaluative, and science policy questions introduced at the outset. It is this: innovation does not just happen, neither is it unavoidable. It is neither the result of mere chance, nor necessary in a strong sense. Nickles (2003) steers a middle course between these extremes by claiming that innovation is the outcome of an adaptive process of blind variation (BV) plus selective retention (SR). In addition to “blind” variation, Nickles also speaks (2003, 56) of “undirected” or “random” processes. However, he is careful to point out that BV and SR can be, and often are, “directed”, due to constraints resulting from previous steps in evolutionary development. Biological species do not evolve in a purely random way, since variation is always a variation of a type that existed before. BV+SR can therefore lead to a certain directedness and can “have positive methodological significance” (Nickles 2003, 61).
However, is BV+SR the
Another point that supports the idea that scientific innovation involves instances of deliberate means–ends reasoning is that innovation typically involves
So these are my considerations in response to the conceptual question. (1) We should distinguish between the stages of invention, innovation, and diffusion. Scientific innovation requires inventions that are useful for changing scientific research practices or systems in non-incremental ways. (2) Unlike ‘discovery’, ‘innovation’ is not an achievement term: it applies to elements that make non-incremental, useful changes possible; but it does not necessarily itself consist of specific research results. (3) Innovation is often, though not always, context-relative. (4) Innovation is often developed purposefully and implies violations or revisions of previously established rules of science. In this sense, innovation presupposes (at least minimal) rationality.
I do not claim that these points fully define ‘scientific innovation’, if only because the term refers to a phenomenon without a clear essence. Furthermore, I do not maintain that the account provides a full explication in Carnap’s sense: while it delivers on the desiderata of sufficient similarity to the currently used meaning of ‘scientific innovation’, of fruitfulness (e.g., for understanding certain aspects of the history of science better), and perhaps also of simplicity, I do not maintain that the account is as exact as it might be. The concept studied here has fuzzy boundaries and moreover it results from actions, social rules, and interpretations concerning that concept which might change. Notwithstanding this, the account reflects how the concept is used and prevents some misuses by distinguishing it from related concepts such as invention, discovery, or revolution in science.
As we have now seen, processes of scientific invention and innovation typically involve a certain amount of deliberation and reasoning. Consider, in light of this deceptively unproblematic claim, the three further fundamental questions introduced in Section I: theoretical as well as practical questions concerning scientific innovation. They can now be restated as follows:
The
The
The
That is, by what kind of deliberations, considerations, or reasons can innovation be explained, evaluated, and fostered? These are obviously complex and thorny issues. Answers will vary substantively depending on the specific cases or (real or alleged) scientific innovation, on the type of entities that are being innovated (theories, methods, instruments, goals, or institutions), on the degree of innovativeness (context-relative or absolute), and also on the validity of claims of innovativeness. These complexities mean that I cannot provide a full answer here; indeed, even building a conceptual scheme that categorizes them all is a task that has yet to be carried out. However, I wish to show that we are facing an interesting dilemma that needs to be clarified before any of the three questions can be addressed.
The dilemma can be stated with the following two propositions:
(1) Scientific innovation can be rationally explained.
(2) No existing account of rationality explains scientific innovation.
This problem concerns the
Two main premises speak in favor of proposition (1). First, there should be ways of explaining how scientific innovation comes about. Innovation is not mysterious: we should not accept explanations that refer to supernatural influences (Nickles, 2003, p. 54). Second, however, it is not sufficient merely to explain causally how an innovative theory or method came about. We also need to understand why it was (viewed as) a
Following on from the previous point, one therefore needs an appropriate account of rationality to address the explanatory question (and,
Let us consider the SAR first. According to this conception, one has to answer epistemic questions as an
Now consider a past funding proposal. The scientists submitted it with the expectation of a fruitful course of
A similar claim can be made when we turn, for a brief moment, from explanatory to evaluative considerations. The committee evaluating a funding application is, at the time of their decision, in a similar situation: the research has not yet been conducted. Committee members have to take a decision concerning a highly uncertain future. When the ERC (as quoted in Section I) demands that applicants present “high-risk, high-gains” proposals, it does not understand what it is asking for. There is even a trilemma here. First, by demanding high-risk ideas, the ERC assumes that scientists can make probability estimates, which they cannot. Second, if the ERC really intended to demand
Thus, a rational explanation of the development of a new theory or method, or a novel institution, cannot imply that the proponent somehow justified its adequacy in a way that would follow the SAR rules. Furthermore, if we
Nickles therefore rightly criticizes the attempts of grant agencies to apply approximations or surrogates of “logics of confirmation” to proposals, thereby ignoring the fact that such “logics” are backward-looking, not forward-looking normative theories of justification:
“In frontier research contexts, one will rarely have a partition of states of nature and their probabilities, and one is likely to be unclear about the ultimate goal and hence the preference ranking and utilities.” (Nickles 2016, 35)
There is another crucial point against using the SAR. When considering the conceptual explication of innovation, I argued that innovation must be distinguished from invention. Only inventions that are useful in one way or another should be considered as possibly leading to innovation, and only if they spread are they truly innovating. So, the
What then can we say about the BAR? This notion of rationality uses FFHs as its rules—such as “tit for tat” (i.e., cooperate first, then imitate your partner’s last reaction; Axelrod, 1984), the “recognition heuristic” (when you have two options and know nothing but the name of one of them, assume that this option is what you are looking for; Goldstein & Gigerenzer 2002), imitate the majority (Boyd & Richerson 2005); to name but a few. These have been shown not only to be what we actually use, but also to be efficacious for many real-life problems, such as whom to marry, which job to take, or how to buy a new TV set; all of which are characterized by uncertainty and computational intractability. Based on a limited number of cues, FFHs use simple algorithms which, after a finite sequence of search steps, without weighing cues or calculating probabilities and utilities, deliver determinate answers. They work well, sometimes even better than optimizing rules, whenever there is a suitable and reliable connection between the reasoner’s mind and environment (Gigerenzer & Sturm 2012)
Once again, however, there are problems here. First, proponents of the BAR themselves have emphasized limits to the applicability of the account:
“Some higher-order processes, such as the creative processes involved in the development of scientific theories or the design of sophisticated artifacts, are most likely beyond the purview of fast and frugal heuristics.” (Todd & Gigerenzer 2000, 740)
One reason for this is the following. FFHs work well when there is an objective answer which is hard or costly or even impossible for individual reasoners to figure out using only internal computation. However, in the case of many if not most instances of scientific innovation, no such objective answer exists. We have ideas or tools with the potential to lead to genuine innovation, but whether disseminating these throughout a community will prove to be useful or effective cannot be determined by simple heuristics, no matter how well they serve us in our daily decisions. We can certainly bet on the advantages of, say, transferring experimental or simulation methods to a new field of research, because they previously performed well elsewhere; but the devil is in the details. For instance, experimenting with human subjects poses new challenges, since they function differently from physical systems. Again, applying models and rules of evolutionary biology to psychology has seemed promising to defenders of evolutionary psychology; yet, its success has been limited (Richardson 2007). We can try out new approaches, but we cannot claim to know in advance that they
Note, however, that all these considerations mostly relate to the evaluative and the science policy questions: the skepticism concerns the issue of whether one can use FFHs to predict or help bring about creative insight. What about the explanatory question that is under discussion here? In this case we encounter a second set of problems for the BAR. To begin with, we must distinguish
Let us assume, nonetheless, that in specific cases we can show that scientists have applied heuristics. This is indeed possible. Then another problem has to be addressed: What is the relevant environment? Is that environment stable enough to assume that the heuristic will work in such a way that we can view it as a
To sum up the discussion so far: propositions (1) and (2) are both highly plausible, but also incompatible with one another. Admittedly, I have not
Now, how to deal with this puzzle? One may weaken (1) as follows:
(1*) (Only)
Thus, maybe we should not think that all scientific innovation requires a rational explanation; maybe sometimes we can only give a purely causal explanation. This would fly in the face of the fourth conceptual point; but I am willing to grant that there are different cases. Still, the problem remains: even if one merely claims that some innovation can be rationally explained, we still need an account of rationality for these cases.
Another way to avoid the dilemma is by questioning the meaning of
(2) No existing account of rationality explains scientific innovation.
After all, this does not mean the same as:
(2*) No account of rationality can explain scientific innovation rationally.
A truly deep, and insuperable dilemma or incompatibility between (1) (or (1*)) and (2) only exists if we take (2) to mean the same as (2*); but we do not have to do that. But then another challenge arises: What do we have to change in, or add to, our understanding of rationality? What would make research innovation through some invention a rational affair? Whatever it is, it has to incorporate the following two points with which I will conclude.
First, a conflation of discovery and innovation could easily lead to the expectation that it is possible to rationally explain innovation in terms of rules of (or good or even ideal)
Second, research can be innovative in
This helps to avoid hindsight fallacy in explanations of past scientific innovation. Moreover, the point also has consequences for the evaluative and the science policy questions. We may be able to learn from the past insofar as past rules of scientific reasoning resemble those we adhere to today; but perhaps we can also learn from a closer look at how past rules were violated or revised as inventions began to innovate, until they became completely disseminated throughout a scientific community. At the same time, it should be clear that past rational steps in innovative science only limit the future to a certain extent.
As shown, the popular but imprecise language of scientific innovation requires clarification. We should view innovation as one stage within a larger process from invention to diffusion; and accept that innovation is a consequence of those inventions that are recognized as useful for changing research practices or systems in non-incremental ways. Furthermore, unlike ‘discovery’, ‘innovation’ is not an achievement term; and it applies to elements that make possible, but do not by themselves establish or guarantee, correct research outputs. Innovation is often, though not always, context-relative; and importantly, we assume that an innovation is deliberately developed and accepted, given that it implies violations or revisions of established rules of science: it is not an outcome of blind chance. In this sense, innovation presupposes (at least minimal) rationality. However, what notion of rationality can help us to explain, evaluate, and foster scientific innovation is a thorny issue; and I am inclined to think that abstract or general theories of rationality will not resolve it. I have shown this for the case of rational explanations; similar problems will arise when we consider the evaluative and the science policy questions. All this should make us question the widespread talk of ‘innovation’. Despite our clear interest in seeing innovation in all fields of research lead to the advancement of science, calls for research proposals and offers of funding should be formulated in more reflective ways, and perhaps avoid direct demands for innovation; and their resolution should not hinge on excessively optimistic claims concerning our ability to predict and steer the future direction of the scientific enterprise.
For comments and discussions, I am grateful to David Casacuberta, Anna Estany, Catherine Herfeld, Thomas Nickles, and two anonymous referees. Christopher Evans helped to improve the language of this paper, and gave several highly useful recommendations concerning content too. This work was supported by the Spanish Ministry for the Economy, Industry and Competitiveness (MINECO) through the research project
Thomas Sturm is an ICREA Research Professor in philosophy and history of science at the
Address: ICREA, Pg. Lluís Companys 23 (08010 Barcelona), Spain. Dept. de Filosofia, Universitat Autònoma de Barcelona (08193 Bellaterra-Barcelona), Spain. Email: Thomas.Sturm@uab.cat
See https://www.neh.gov/grants/odh/digital-humanities-advancement-grants. – For the US National Science Foundation, see Nickles, 2015, pp. 69-71.
See http://www.germaninnovation.org/about-us/gcri-partner-institutions/max-planck-gesellschaft
Currently (2019) more than 80 MPIs exist. While more than 40 Institutes have been closed since the MPG started its work, shutting one down is difficult because they develop a life of their own. So, closing sometimes also takes the form of integrating institutes or rebranding them.
See https://ec.europa.eu/info/horizon-europe-next-research-and-innovation-framework-programme_en
See http://ec.europa.eu/programmes/horizon2020/en/what-horizon-2020
http://ec.europa.eu/research/horizon2020/pdf/press/fact_sheet_on_horizon2020_budget.pdf
See http://ec.europa.eu/programmes/horizon2020/en/h2020-section/excellent-science
One attempt to solve this problem, at least partially, is to mechanize assessments or decision-making by introducing
A hint for further research: a search on Google Ngram reveals that the general English word ‘innovation’ was used from the Renaissance up until it was forgotten in the 18th century, but it became more popular again beginning in the 1950s.
The distinction between “convergent” and “divergent” thinking is not the same as the famous one between “normal” and “revolutionary” science that forms the backbone of
One such confusion—between innovation and revolution—can be found in Nickles (2013).
Among the few exceptions are: Nickles 2003, 2015, 2016; Smith 2003; Kostoff 2006; Estany & Herrera 2016. For the literature on innovation in other areas, see e.g., Shavinina 2003 (scientific innovation is dealt with in chapters by Nickles, Holton, Shavinina and Dasgupta).
When Carnap originally introduced the methodology of explication for the case of probability, he pointed out that this concept possesses irreducibly different meanings that need to be isolated from one another in order to make the right one precise for a specific theoretical purpose—here, the development of an account of confirmation of hypotheses. For his immediate purpose, only the “logical” meaning of probability mattered, i.e., the concept of probability as it applies to
One innovation might later be replaced by another (think of the horse wagon, the car, the hybrid car, the self-steering car, etc.). In between, an invention will have to follow innovation; but this does not break the basic order of each invention-to-innovation cycle: an artificial entity
At the same time, this distinction between invention and innovation differs from that between descriptive and evaluative questions. It is one thing to say that a new concept was invented by a scientist, and then began to become accepted communitywide, and another to judge such an invention-to-innovation cycle to be
There were a few authors in the eighteenth century, such as Johann Nicolas Tetens, who already viewed themselves as psychologists and who experimented with the mind (albeit with simple tools, and without a plausible theory of psychological experimentation; see Sturm 2006) in approximately the same way as current psychologists do.
As one anonymous referee has noted, Kuhn and later Kuhn scholars toned down the notion of revolution, so that what appear to be radical revolutions only seem so because our history of science is not sufficiently fined-grained (Kitcher 1993); or there could be gradual revolutions (Anderson, Barker & Chen 2006). I cannot enter into this here; suffice it to say that, as I explain in Section II.2, many different items can be subject to innovation—and perhaps if several (e.g., theories, instruments, and reasoning standards) are changed
Nakamura et al. 2014; Duke & Bègue 2015. Both studies indicate that decreased empathy predicts utilitarian choices better than increased deliberation. So, if you are sitting in a cold room with lots of vodka in you, it seems almost unavoidable that you will decide on trolley paths like a true utilitarian would. Yet, one needs to add important qualifications here: presumably, if you are in a cold environment, your feeling cold leads to less empathy, while if you are drunk, you will feel warmer than you actually are, and then by virtue of the former correlation, you should be more empathetic. I suggest cognitive scientists should consider this important complexity in future research proposals.
Schumpeter (1934, 66) distinguished between product innovation as “the introduction of a new good … or a new quality of a good” and process innovation as the “introduction of a new method of production”. One might think, accordingly, that discovery, being the primary goal and product of research, is a kind of product innovation. However, we have to distinguish between discovery as such and recognition of it. According to the first conceptual point made above, innovation follows invention, and ‘innovation’ refers to the very first stages of the dissemination of an invention into a market. Therefore, applying Schumpeter’s distinction between product and process innovation to science correctly means that we would, strictly speaking, refer to the early stages of the recognition of a discovery, not to the discovery itself. Of course, some sociologists of science would object. Thus, Schaffer (1994) claims that we cannot distinguish between the two: the communitywide recognition of a discovery is all there is to discovery. But, apart from the fact that this is a
I am here building on Bennett’s (1964) analysis of a basic aspect of rationality which he illustrates through the distance between the quasi-language of bees and human linguistic behavior.
I use the term ‘minimal rationality’ here without committing myself to a specific set of
Thus, for the evaluative problem, the dilemma would be between: (1-E) scientific innovation can be evaluated on the basis of rational considerations; and (2-E) no existing account of rationality can justify the evaluation of scientific innovation. For the science policy problem, the dilemma would be between: (1-S) scientific innovation can be fostered by means of rational considerations; and (2-S) no existing account of rationality can help foster scientific innovations. Some important changes would have to be made, as we are changing from descriptive to normative domains, and rational considerations in each of them can be
For more on plausible theories of probability, see Gillies 2000.
It is important that the normative adequacy of FFHs requires that there is an objective answer that one can look up in a lexicon (say, in the case of city sizes) or determine in some other way. This in turn might involve norms of the SAR; see Sturm, 2019. Here, I ignore this complication, since I focus on FFHs as possible rational explanations.