Idealization and the Aims of Science shows just how crucial idealization is to science and why it matters. Beginning with the acknowledgment of our status as limited human agents trying to make sense of an exceedingly complex world, Angela Potochnik moves on to explain how science aims to depict and make use of causal patterns—a project that makes essential use of idealization. She offers case studies from a number of branches of science to demonstrate the ubiquity of idealization, shows how causal patterns are used to develop scientific explanations, and describes how the necessarily imperfect connection between science and truth leads to researchers’ values influencing their findings. The resulting book is a tour de force, a synthesis of the study of idealization that also offers countless new insights and avenues for future exploration.
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Introduction: Doing Science in a Complex World
All scientific research has been accomplished by human agents, for human ends. Each scientist is an individual human being, with a unique combination of characteristics, concerns, and background. The science these individuals pursue is aimed to provide greater human understanding of the world we inhabit and of ourselves and to further human projects of construction, manipulation, and control. Increasingly, philosophers of science have focused on accounting for scientific practice as it actually occurs: the often-messy and never-completed project of limited human beings, pursued for human ends. This stands in contrast to philosophical approaches to science popular in the past that attempted to transcend the messiness of science and the limitations of its current practitioners. One of those approaches is "rationally reconstructing" science to demonstrate its logical or epistemic basis, setting aside historical contingencies and any other deviations from the logically and epistemically ideal. Rudolf Carnap (1928) famously employed this approach in Der logische Aufbau der Welt. A second philosophical approach that attempts to transcend science's imperfections is, instead of accounting for today's actual science, aiming to predict what a future, more perfect science will look like. This strategy was explicitly employed by Paul Oppenheim and Hilary Putnam (1958), who developed a "working hypothesis" that all of science will ultimately be grounded explicitly in microphysics.
Along with philosophers' increasing focus on actual scientific practice, there is also greater attention paid to how complexity influences science. Quite many scientists and philosophers of science have been impressed by the complexity of the world we inhabit and investigate, and appreciation for this complexity increasingly shapes scientific approaches as well as philosophical accounts of science. Phenomena occur in seemingly endless variety andpermutations. Simple accounts of these phenomena generally meet with only limited success, and approaches to studying complex systems have proliferated across many fields of science. Philosophers have variously called this world disordered, complex, dappled, and unsimple (Dupré 1993; Bechtel and Richardson 1993; Cartwright 1999; Wimsatt 2007; Mitchell 2012b). These philosophers and others have attempted to articulate the implications of this complexity for our theories of science and metaphysics, with wide-ranging results. Our best scientific laws may, strictly speaking, be false (Cartwright 1983). Our categories, including scientific categories, may not "carve nature at its joints" (Dupré 1993). Phenomena may not actually be law governed or predictable (Cartwright 1999). Scientific practices may vary widely, consisting mainly of fallible, heuristic procedures (Wimsatt 2007). The influence of different causes on a phenomenon may not be separable, even in principle (Mitchell 2012b).
These two observations — that science is ultimately the project of limited human beings and that the world we inhabit is incredibly complex — together constitute the starting point of my investigation in this book. Both are familiar ideas in today's philosophy of science. Nonetheless, tracing out their full implications leads to surprising conclusions, conclusions that conflict with a variety of widely held philosophical ideas about science. Most basically, a science practiced by limited human beings in a complex world results in widespread idealization. Idealizations are assumptions made without regard for whether they are true, generally with full knowledge that they are false. Classic examples are the assumption of a frictionless plane in physics and the assumption of perfectly rational agents in economics. Despite their falsity, idealizations appear in most every scientific project and product, for a range of purposes, and they are not eliminated or even controlled for in the ways we might expect. This widespread idealization outstrips what most philosophers are willing to accept. Accordingly, the full scope of the use of idealizations in science has wide-ranging implications for our best theories of science. In this book, I investigate the implications of idealization for what science shows us about the world, levels of organization and the relationship among fields of science, the nature of scientific explanations, the role of human values in science, and even the very aims of science. In this chapter, I begin by considering in greater depth these observations of science as a human project and of the complexity of the world.
1.1 Science by Humans
In The Descent of Man, Charles Darwin (1871) observed of animals that "the males are almost always the wooers" and that "the female, on the other hand, with the rarest exceptions, is less eager than the male ... she is coy." This idea, that in most species males are aggressive and sexually promiscuous while females are passive and sexually selective, has been widely held by biologists, from Darwin up to today. It is one of the primary ideas of what is called sexual selection theory, one part of evolutionary theory. Male elephant seals fight each other for dominance over harems of females. Peacocks display their colorful trains in the hopes that a peahen will choose to mate with them instead of other peacocks.
A. J. Bateman (1948) provided an explanation for this phenomenon, based on his research on Drosophila (fruit flies). According to what has been dubbed Bateman's principle, males stand to produce more offspring as a result of more matings, whereas in most species, females gain few if any additional offspring from multiple matings. If this is true, then males who mate many times are evolutionarily advantaged over other males, and male promiscuity and aggression in securing mates are expected to evolve, but the same is not true for females. Figure 1.1 shows Bateman's representation of the data from his fruit fly research. He ran two experiments, and in both, the number of offspring increased more dramatically for male fruit flies who mated more often than they did for female fruit flies who mated more often. Bateman says,
This would explain why ... there is nearly always a combination of an undiscriminating eagerness in the males and a discriminating passivity in the females. Even in a derived monogamous species (e.g. man) this sex difference might be expected to persist as a rule. (365)
And so, biologists widely accept that most male animals, but not female animals, have evolved to be promiscuous and aggressive and that Bateman's principle explains why: it is evolutionary advantageous for males, but not females, to mate many times.
However, it has been argued that this view of the natural world, based on Darwin's ideas from the late nineteenth century, is also infused with Victorian moral sentiment. Jonathan Knight (2002), for example, says that Bateman's work "has been used to extend dated preconceptions about human sexual behaviour to the entire animal kingdom, sometimes to the detriment of scientific knowledge" (254). Knight shows that, while many biologists still endorse Bateman's work as basically sound, most also emphasize that the situation is often much more complicated than Bateman recognized. He notes one biologist's analysis that "the reason Bateman's observation became 'Bateman's principle' is that it appealed to people's intuition about the behaviour of individuals" (256). Figure 1.2 is a much more recent representation of Bateman's principle by Krasnec et al. (2012). Comparison between this figure and Bateman's original figure shows how Bateman's principle has taken on a life of its own. First, the relationship depicted in this treatment is more sharply defined than what Bateman presented based on his data. Second, this treatment posits a relationship to overall fitness, which is a stronger claim than Bateman's finding regarding fertility, just one component of fitness. And so, we find both a codification of Bateman's principle and, increasingly, the recognition that this simple principle does not reflect the full complexity of sexual behavior in animals. A few biologists even reject Bateman's principle entirely, along with Darwin's original observation of aggressive males and coy females, as grounded entirely in scientists' values and expectations instead of sound evidential reasoning (Roughgarden 2004, 2009; Gowaty et al. 2012).
This is one example of how scientific findings can be informed by human expectations. Darwin's observations of the natural world bear a striking resemblance to the social norms of Victorian England, and Bateman's research was later taken to underwrite a general principle in part because it accorded with researchers' intuitions. Research informed by human expectations can still be well founded, and it can still lead to successful results. Darwin's idea of aggressive males and coy females may mirror the expectations for men and women in Victorian England, but it is nonetheless the case that male elephant seals tend to fight one another while female elephant seals do not and that the results of those fights determine male seals' access to mating opportunities. There are also examples of researchers' expectations leading to straightforwardly bad science. Stephen Jay Gould (1996) shows that this is so for research purported to show a genetic basis for IQ differences between races, sexes, or social classes. As a second example, Naomi Oreskes and Erik Conway (2011) analyze how a single group of scientists misled the public about tobacco research and environmental issues ranging from acid rain to global climate change, apparently motivated by their political leanings. But even more legitimate research like Bateman's work, as well as scientific results that are even more widely accepted, still bears the mark of human expectations, human concerns, and the limits of human observation.
Features of the natural world that are not anticipated might escape notice entirely. The primatologist Sarah Blaffer Hrdy (1986) shows that data running contrary to the generalization about male promiscuity and female coyness in animals were available many years before any researchers began to question that generalization. In particular, female promiscuity is quite common among primates. Hrdy argues that it was thus not newly available data but a shift in the researchers themselves that led to a recognition of the limits of the promiscuity/coyness generalization. She says,
I seriously question whether it could have been just chance or just historical sequence that caused a small group of primatologists in the 1960s, who happened to be mostly male, to focus on male-male competition and on the number of matings males obtained, while a subsequent group of researchers, including many women (beginning in the 1970s), started to shift the focus to female behaviors. (Hrdy 1986, 159)
Hrdy hypothesizes that this new focus on female behaviors led researchers to finally recognize female promiscuity in primates. This in turn inspired, for the first time, a search for explanations for female promiscuity, instead of merely dismissing those behaviors as anomalies. There is still no agreed-upon evolutionary explanation for female promiscuity. Many biologists think the existing theoretical framework is accurate but needs to be employed differently for cases like the primates (Clutton-Brock 2009). Other biologists think the whole theoretical framework inspired by Darwin's observations of differences between male and female animals needs to be abandoned (Roughgarden 2009). For present purposes, the important point is that, in this case, different individuals with different life experiences performing the science resulted in a significantly altered focus. The changed focus in turn led to an emphasis on different types of interactions and the observation of different patterns.
In philosophy, there is increasing recognition of the myriad ways in which human expectations, concerns, and limitations influence scientific practice. Feminist philosophy of science has attended to this influence for quite some time, including especially the role of social values on the process and products of science. A primary goal of this line of research is to address the question of how social influences on science can be reconciled with scientific objectivity. The current focus in philosophy of science on scientific models, including on the use of abstractions and idealizations, is another area where the limitations of human scientists are considered to be relevant. Wimsatt (2007), for instance, explicitly addresses how idealized models are the result of science produced by limited human agents. Finally, there is a growing literature in philosophy of science that directly addresses the relationship between science and broader social concerns. Special journal issues on the topics of socially relevant and socially engaged philosophy of science (Fehr and Plaisance 2010; Potochnik 2014) address this trend.
The extent of human influences on science makes it especially important for philosophy of science to account for science as it is actually conducted. The more science is recognized to be the activity of limited human beings, the more its characteristics can be expected to diverge from an idealized rational reconstruction or a hypothesized, perfect end state. Indeed, there is no reason to think that our actual scientific practice and products bear any particularly strong resemblance to a science philosophers would hold to be ideal or to an end state that philosophers would hold to be perfect. As Catherine Elgin (2010) puts the point, "Science is a human achievement — a product of human endeavor. As such, it is ineluctably connected to the ways we access the world" (446). Instead of finding a way to fit our actual scientific practices into a predetermined mold based on antecedent commitments or expectations of science's goals and end point, philosophy of science must take its lead from observations about how science produced by real human beings finds success and what kind of success this is. This is the tack I take throughout this book. Such an approach to philosophy of science is a natural outgrowth of a naturalized epistemology, where our philosophical theories of knowledge are informed by how science in fact proceeds. In this case, the idea is that our philosophical theories of science itself should reflect how science in fact proceeds.
One might have the concern that, if philosophy of science aims to account for actual scientific practice — or merely aims to account for actual scientific practice — this would threaten to eliminate any grounds for making normative claims about science. There is, after all, a traditional dichotomy with normative philosophical claims about science on one side and descriptive claims of sociology and history of science on the other. It may seem that providing an account of actual scientific practice lands us on the wrong side of this divide to make any normative claims about science, any prescriptions for how science should proceed. Yet we require a normative account of science in order to articulate epistemically meaningful standards for scientific practice and to distinguish between successful and unsuccessful scientific results in an epistemically relevant way. Limiting ourselves to purely descriptive claims about science would, it seems, undermine philosophy's ability to adjudicate debates about the proper aims, scope, and success of science. It would also in effect eliminate philosophy of science as an enterprise distinct from sociology and history of science.
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Table of ContentsPreface
1 Introduction: Doing Science in a Complex World
1.1 Science by Humans
1.2 Science in a Complex World
1.3 The Payoff: Idealizations and Many Aims
2 Complex Causality and Simplified Representation
2.1 Causal Patterns in the Face of Complexity
2.1.1 Causal Patterns
2.1.2 Causal Complexity
2.2 Simplification by Idealization
2.2.1 Reasons to Idealize
2.2.2 Idealizations’ Representational Role
2.2.3 Rampant and Unchecked Idealization
3 The Diversity of Scientific Projects
3.1 Broad Patterns: Modeling Cooperation
3.2 A Specific Phenomenon: Variation in Human Aggression
3.3 Predictions and Idealizations in the Physical Sciences
3.4 Surveying the Diversity
4 Science Isn’t after the Truth
4.1 The Aims of Science
4.1.1 Understanding as Science’s Epistemic Aim
4.1.2 Separate Pursuit of Science’s Aims
4.2 Understanding, Truth, and Knowledge
4.2.1 The Nature of Scientific Understanding
4.2.2 The Role of Truth and Scientific Knowledge
5 Causal Pattern Explanations
5.1 Explanation, Communication, and Understanding
5.2 An Account of Scientific Explanation
5.2.1 The Scope of Causal Patterns
5.2.2 The Crucial Role of the Audience
5.2.3 Adequate Explanations
6 Levels and Fields of Science
6.1 Levels in Philosophy and Science
6.2 Going without Levels
6.2.1 Against Hierarchy
6.2.2 Prizing Apart Forms of Stratification
6.3 The Fields of Science and How They Relate
7 Scientific Pluralism and Its Limits
7.1 The Entrenchment of Social Values
7.2 How Science Doesn’t Inform Metaphysics
7.3 Scientific Progress
List of Figures
List of Tables