Optimality

In this paper I examine the role of optimality reasoning in Aristotle’s natural science. By “optimality reasoning” I mean reasoning that appeals to some conception of “what is best” in order to explain why things are the way they are. We are first introduced to this pattern of reasoning in the famous passage at Phaedo 97b8-98a2, where (Plato’s) Socrates invokes “what is best” as a cause (aitia) of things in nature. This passage can be seen as the intellectual ancestor of (...) Aristotle’s own principle, expressed by the famous dictum “nature does nothing in vain but always what is best for the substance from among the possibilities concerning each kind of animal” (Progression of Animals II, 704b12-18). The paper is focused around exploring three questions that arise in connection with Aristotle’s use of this optimality principle: (1) How do we understand the concept of “the best” at work in the principle? (2) How does Aristotle conceive of “the range of possibilities”? And, finally, (3) what role does optimality reasoning play in Aristotle’s natural science? Is it a special form of demonstration in which the optimality principle functions as one of its premises, or is it a heuristic device that helps uncover those causally relevant features of a natural substances that ultimately serve as middle terms in demonstrations? In the final section I return to the comparison between Plato and Aristotle and argue that, while both see the natural world as the product of an optimizing agent and while both see this assumption as licensing a pattern of reasoning that appeals to a certain conception of “the best”, they disagree fundamentally over what the optimization agent is and how it operates. Thus, despite their general agreement, it would be a mistake to think that Aristotle simply took over Plato’s use of optimality reasoning without significant modifications. (shrink)

Optimality models are widely used in different parts of biology. Two important questions that have been asked about such models are: are they explanatory and, if so, what type of explanations do they offer? My concern in this paper is with the approach of Rice (2012, 2015) and Irvine (2015), who claim that these models provide non-causal explanations. I argue that there are serious problems with this approach and with the accounts of explanation it is intended to justify. The idea (...) behind this undertaking is to draw attention to an important issue associated with the recent pluralist stance on explanation: the rampant proliferation of theories of explanation. This proliferation supports a pluralist perspective on explanation, and pluralism encourages such a proliferation. But, if we are not careful about how we arrive at and how we justify new accounts of explanation — i.e., if we do not try to avoid the sort of problems discussed in this paper — we may end up trivializing the concept of explanation. (shrink)

Is there any way to reconcile the adaptationist’s image of natural selection as an engine of optimality with the more complex image of its dynamics we get from population genetics? This has long been an important strand in the controversy surrounding adaptationism, yet debate has been hampered by a tendency to conflate various different ways of thinking about maximization. Here I distinguish four varieties of maximization principle. I then discuss the logical relations between these varieties, arguing that, although they may (...) seem similar at face value, none entails any of the others. I then turn briefly to the status of each variety, arguing that, while each type of maximization principle faces serious problems, the problems are subtly different for each type. In the last section, I reflect on what is at stake in the debate. (shrink)

Synthetic biology offers a powerful method to design and construct biological devices for human purposes. Two prominent design methodologies are currently used. Rational design adapts the design methodology of traditional engineering sciences, such as mechanical engineering. Directed evolution, in contrast, models its design principles after natural evolution, as it attempts to design and improve systems by guiding them to evolve in a certain direction. Previous work has argued that the primary difference between these two is the way they treat variation: (...) rational design attempts to suppress it, whilst direct evolution utilizes variation. I argue that this contrast is too simplistic, as it fails to distinguish different types of variation and different phases of design in synthetic biology. I outline three types of variation and show how they influence the construction of synthetic biological systems during the design process. Viewing the two design approaches with these more fine-grained distinctions provides a better understanding of the methodological differences and respective benefits of rational design and directed evolution, and clarifies the constraints and choices that the different design approaches must deal with. (shrink)

Darwin’s claim that Natural Selection, through optimization of fitness, explains complex biological design has not yet been properly formalized. Alan Grafen’s Formal Darwinism Project aims at providing such a formalization and at demonstrating that fitness maximization is coherent with results from Population Genetics, usually interpreted as denying it. We suggest that Grafen’s proposal suffers from some limitations linked to its concept of design as optimized fitness. In order to overcome these limitations, we propose a classification of evolutionary facts based on (...) a bi-dimensional complexity space, which adds robustness to fitness as measure of the quality of a design. In this space, each point represents an organismic architecture, and movement between two points would be an evolutionary fact. Natural Selection explains movement along the fitness axis, while non-selective mechanisms explain movement on the robustness axis. Moreover, we propose a thermodynamic metaphor to draw parallelisms between notions of fitness and entropy, robustness and energy, and movement in this space and reversible and irreversible cycles. We argue that this metaphor illustrates different evolutionary processes usually overlooked by proposals of formalization of Darwinian theory, such Grafen’s Formal Darwinism Project. (shrink)

Courtesy of its free energy formulation, the hierarchical predictive processing theory of the brain (PTB) is often claimed to be a grand unifying theory. To test this claim, we examine a central case: activity of mesocorticolimbic dopaminergic (DA) systems. After reviewing the three most prominent hypotheses of DA activity—the anhedonia, incentive salience, and reward prediction error hypotheses—we conclude that the evidence currently vindicates explanatory pluralism. This vindication implies that the grand unifying claims of advocates of PTB are unwarranted. More generally, (...) we suggest that the form of scientific progress in the cognitive sciences is unlikely to be a single overarching grand unifying theory. (shrink)

Resumen -/- El objetivo de este trabajo consiste en analizar las relaciones entre los modelos de optimalidad y la selección natural. Defenderemos que esas relaciones pueden dividirse en dos tipos, en tanto hay dos tipos de explicaciones seleccionistas, que llamaremos “históricas” y “ahistóricas”. Las explicaciones históricas revelan como una población dada adquiere un rasgo que es adaptativo en ese ambiente e involucran muchas generaciones, variación, etc. Las explicaciones ahistóricas, explican por qué, en determinado momento, ciertos tipos de organismos tienen un (...) mayor éxito reproductivo que otros. Mostraremos que los modelos de optimalidad pueden jugar un rol en la determinación del explanandum de las explicaciones histórica seleccionistas, esto es, que ayudan a reconocer qué rasgos son adaptativos. Por otra parte, mostramos que los modelos de optimalidad nos permiten determinar a veces la parte del explanans de las explicaciones ahistóricas (particularmente, el concepto de fitness). -/- Abstract -/- The goal of this paper is to analyze the relations between optimality models and natural selection. We contend that these relationships can be divided into two kinds, as there are two kinds of natural selection explanations, which we call “historical” and “ahistorical”. Historical explanations reveal how a given population acquires a trait which is adaptive in its environment, and involves many generations, variations, etc. Ahistorical ones, explain why, at a given moment, certain kinds of organisms have a greater reproductive success than others. We show that optimality models can play a role in determining the explanandum of historical selectionist explanations, that is, they help us to recognize which traits are adaptive. And, on the other hand, that optimality models sometimes allow us to determine part of the explanans of ahistorical explanations (particularly, the concept of fitness). (shrink)

El objetivo de este trabajo consiste en analizar las relaciones entre los modelos de optimalidad y la selección natural. Defenderemos que esas relaciones pueden dividirse en dos tipos, en tanto hay dos tipos de explicaciones seleccionistas, que llamaremos “históricas” y “ahistóricas”. Las explicaciones históricas revelan como una población dada adquiere un rasgo que es adaptativo en ese ambiente e involucran muchas generaciones, variación, etc. Las explicaciones ahistóricas, explican por qué, en determinado momento, ciertos tipos de organismos tienen un mayor éxito (...) reproductivo que otros. Mostraremos que los modelos de optimalidad pueden jugar un rol en la determinación del explanandum de las explicaciones histórica seleccionistas, esto es, que ayudan a reconocer qué rasgos son adaptativos. Por otra parte, mostramos que los modelos de optimalidad nos permiten determinar a veces la parte del explanans de las explicaciones ahistóricas (particularmente, el concepto de fitness). The goal of this paper is to analyze the relations between optimality models and natural selection. We contend that these relationships can be divided into two kinds, as there are two kinds of natural selection explanations, which we call “historical” and “ahistorical”. Historical explanations reveal how a given population acquires a trait which is adaptive in its environment, and involves many generations, variations, etc. Ahistorical ones, explain why, at a given moment, certain kinds of organisms have a greater reproductive success than others. We show that optimality models can play a role in determining the explanandum of historical selectionist explanations, that is, they help us to recognize which traits are adaptive. And, on the other hand, that optimality models sometimes allow us to determine part of the explanans of ahistorical explanations (particularly, the concept of fitness). (shrink)

The notion that natural selection is a process of fitness maximization gets a bad press in population genetics, yet in other areas of biology the view that organisms behave as if attempting to maximize their fitness remains widespread. Here I critically appraise the prospects for reconciliation. I first distinguish four varieties of fitness maximization. I then examine two recent developments that may appear to vindicate at least one of these varieties. The first is the ‘new’ interpretation of Fisher's fundamental theorem (...) of natural selection, on which the theorem is exactly true for any evolving population that satisfies some minimal assumptions. The second is the Formal Darwinism project, which forges links between gene frequency change and optimal strategy choice. In both cases, I argue that the results fail to establish a biologically significant maximization principle. I conclude that it may be a mistake to look for universal maximization principles justified by theory alone. A more promising approach may be to find maximization principles that apply conditionally and to show that the conditions were satisfied in the evolution of particular traits. (shrink)

When visual attention is directed away from a stimulus, neural processing is weak and strength and precision of sensory data decreases. From a computational perspective, in such situations observers should give more weight to prior expectations in order to behave optimally during a discrimination task. Here we test a signal detection theoretic model that counter-intuitively predicts subjects will do just the opposite in a discrimination task with two stimuli, one attended and one unattended: when subjects are probed to discriminate the (...) unattended stimulus, they rely less on prior information about the probed stimulus’ identity. The model is in part inspired by recent findings that attention reduces trial-by-trial variability of the neuronal population response and that they use a common criterion for attended and unattended trials. In five different visual discrimination experiments, when attention was directed away from the target stimulus, subjects did not adjust their response bias in reaction to a change in stimulus presentation frequency despite being fully informed and despite the presence of performance feedback and monetary and social incentives. This indicates that subjects did not rely more on the priors under conditions of inattention as would be predicted by a Bayes-optimal observer model. These results inform and constrain future models of Bayesian inference in the human brain. (shrink)

Evolution and Rationality marks the end of a three-year project, ‘Evolution, Cooperation, and Rationality’, directed at the University of Bristol by the book’s editors, Samir Okasha and Ken Binmore. The collection draws together the editors’ pick of the papers delivered at the conferences the project hosted, and covers a wide range of topics at the intersection of evolutionary theory and the social sciences. It is a splendid anthology: timely, interdisciplinary, thematically cohesive, and full of substantive and interesting disagreements between the (...) contributors. (shrink)

The scientific study of living organisms is permeated by machine and design metaphors. Genes are thought of as the ‘‘blueprint’’ of an organism, organisms are ‘‘reverse engineered’’ to discover their func- tionality, and living cells are compared to biochemical factories, complete with assembly lines, transport systems, messenger circuits, etc. Although the notion of design is indispensable to think about adapta- tions, and engineering analogies have considerable heuristic value (e.g., optimality assumptions), we argue they are limited in several important respects. In (...) particular, the analogy with human-made machines falters when we move down to the level of molecular biology and genetics. Living organisms are far more messy and less transparent than human-made machines. Notoriously, evolution is an oppor- tunistic tinkerer, blindly stumbling on ‘‘designs’’ that no sensible engineer would come up with. Despite impressive technological innovation, the prospect of artificially designing new life forms from scratch has proven more difficult than the superficial analogy with ‘‘programming’’ the right ‘‘software’’ would sug- gest. The idea of applying straightforward engineering approaches to living systems and their genomes— isolating functional components, designing new parts from scratch, recombining and assembling them into novel life forms—pushes the analogy with human artifacts beyond its limits. In the absence of a one-to-one correspondence between genotype and phenotype, there is no straightforward way to imple- ment novel biological functions and design new life forms. Both the developmental complexity of gene expression and the multifarious interactions of genes and environments are serious obstacles for ‘‘engi- neering’’ a particular phenotype. The problem of reverse-engineering a desired phenotype to its genetic ‘‘instructions’’ is probably intractable for any but the most simple phenotypes. Recent developments in the field of bio-engineering and synthetic biology reflect these limitations. Instead of genetically engi- neering a desired trait from scratch, as the machine/engineering metaphor promises, researchers are making greater strides by co-opting natural selection to ‘‘search’’ for a suitable genotype, or by borrowing and recombining genetic material from extant life forms. (shrink)

The scientific study of living organisms is permeated by machine and design metaphors. Genes are thought of as the ‘‘blueprint’’ of an organism, organisms are ‘‘reverse engineered’’ to discover their functionality, and living cells are compared to biochemical factories, complete with assembly lines, transport systems, messenger circuits, etc. Although the notion of design is indispensable to think about adaptations, and engineering analogies have considerable heuristic value (e.g., optimality assumptions), we argue they are limited in several important respects. In particular, the (...) analogy with human-made machines falters when we move down to the level of molecular biology and genetics. Living organisms are far more messy and less transparent than human-made machines. Notoriously, evolution is an opportunistic tinkerer, blindly stumbling on ‘‘designs’’ that no sensible engineer would come up with. Despite impressive technological innovation, the prospect of artificially designing new life forms from scratch has proven more difficult than the superficial analogy with ‘‘programming’’ the right ‘‘software’’ would suggest. The idea of applying straightforward engineering approaches to living systems and their genomes— isolating functional components, designing new parts from scratch, recombining and assembling them into novel life forms—pushes the analogy with human artifacts beyond its limits. In the absence of a one-to-one correspondence between genotype and phenotype, there is no straightforward way to implement novel biological functions and design new life forms. Both the developmental complexity of gene expression and the multifarious interactions of genes and environments are serious obstacles for ‘‘engineering’’ a particular phenotype. The problem of reverse-engineering a desired phenotype to its genetic ‘‘instructions’’ is probably intractable for any but the most simple phenotypes. Recent developments in the field of bio-engineering and synthetic biology reflect these limitations. Instead of genetically engineering a desired trait from scratch, as the machine/engineering metaphor promises, researchers are making greater strides by co-opting natural selection to ‘‘search’’ for a suitable genotype, or by borrowing and recombining genetic material from extant life forms. (shrink)

In men and women sexual arousal culminates in orgasm, with female orgasm solely from sexual intercourse often regarded as a unique feature of human sexuality. However, orgasm from sexual intercourse occurs more reliably in men than in women, likely reflecting the different types of physical stimulation men and women require for orgasm. In men, orgasms are under strong selective pressure as orgasms are coupled with ejaculation and thus contribute to male reproductive success. By contrast, women's orgasms in intercourse are highly (...) variable and are under little selective pressure as they are not a reproductive necessity. The proximal mechanisms producing variability in women's orgasms are little understood. In 1924 Marie Bonaparte proposed that a shorter distance between a woman's clitoris and her urethral meatus (CUMD) increased her likelihood of experiencing orgasm in intercourse. She based this on her published data that were never statistically analyzed. In 1940 Landis and colleagues published similar data suggesting the same relationship, but these data too were never fully analyzed. We analyzed raw data from these two studies and found that both demonstrate a strong inverse relationship between CUMD and orgasm during intercourse. Unresolved is whether this increased likelihood of orgasm with shorter CUMD reflects increased penile–clitoral contact during sexual intercourse or increased penile stimulation of internal aspects of the clitoris. CUMD likely reflects prenatal androgen exposure, with higher androgen levels producing larger distances. Thus these results suggest that women exposed to lower levels of prenatal androgens are more likely to experience orgasm during sexual intercourse. . (shrink)

The value of optimality modeling has long been a source of contention amongst population biologists. Here I present a view of the optimality approach as at once playing a crucial explanatory role and yet also depending on external sources of confirmation. Optimality models are not alone in facing this tension between their explanatory value and their dependence on other approaches; I suspect that the scenario is quite common in science. This investigation of the optimality approach thus serves as a case (...) study, on the basis of which I suggest that there is a widely felt tension in science between explanatory independence and broad epistemic interdependence, and that this tension influences scientific methodology. (shrink)

The fate of optimality modeling is typically linked to that of adaptationism: the two are thought to stand or fall together (Gould and Lewontin, Proc Relig Soc Lond 205:581–598, 1979; Orzack and Sober, Am Nat 143(3):361–380, 1994). I argue here that this is mistaken. The debate over adaptationism has tended to focus on one particular use of optimality models, which I refer to here as their strong use. The strong use of an optimality model involves the claim that selection is (...) the only important influence on the evolutionary outcome in question and is thus linked to adaptationism. However, biologists seldom intend this strong use of optimality models. One common alternative that I term the weak use simply involves the claim that an optimality model accurately represents the role of selection in bringing about the outcome. This and other weaker uses of optimality models insulate the optimality approach from criticisms of adaptationism, and they account for the prominence of optimality modeling (broadly construed) in population biology. The centrality of these uses of optimality models ensures a continuing role for the optimality approach, regardless of the fate of adaptationism. (shrink)

The optimality approach to modeling natural selection has been criticized by many biologists and philosophers of biology. For instance, Lewontin (1979) argues that the optimality approach is a shortcut that will be replaced by models incorporating genetic information, if and when such models become available. In contrast, I think that optimality models have a permanent role in evolutionary study. I base my argument for this claim on what I think it takes to best explain an event. In certain contexts, optimality (...) and game-theoretic models best explain some central types of evolutionary phenomena. ‡Thanks to Michael Friedman, Helen Longino, Michael Weisberg, and especially Elliott Sober for comments on earlier drafts of this paper. †To contact the author, please write to: Department of Philosophy, Stanford University, Stanford, CA 94305-2155; e-mail: [email protected]. (shrink)

Given the recent explosion of interest in applications of evolutionary biology to understanding human psychology, we think it timely to assure better understanding of modern evolutionary theory among the psychologists who might be using it. We find it necessary to do so because of the very reducd version of evolutionary theorizing that has been incorporated into much of evolutionary psychology so far. Our aim here is to clarify why the use of a reduced version of evolutionary genetics will lead to (...) faulty science and to indicate where other resources of evolutionary biology can be found that might elevate the standard of the evolutionary component of evolutionary psychology. (shrink)

Twenty years have passed since Gould and Lewontin published their critique of ‘the adaptationist program’ – the tendency of some evolutionary biologists to assume, rather than demonstrate, the operation of natural selection. After the ‘Spandrels paper’, evolutionists were more careful about producing just-so stories based on selection, and paid more attention to a panoply of other processes. Then came reactions against the excesses of the anti-adaptationist movement, which ranged from a complete dismissal of Gould and Lewontin’s contribution to a positive (...) call to overcome the problems. We now have an excellent opportunity for finally affirming a more balanced and pluralistic approach to the study of evolutionary biology. (shrink)

My contribution to this Symposium focuses on the links between sexuality and reproduction from the evolutionary point of view.' The relation between women's sexuality and reproduction is particularly importantb ecause of a vital intersectionb etweenp olitics and biology feminists have noticed, for more than a century, that women's identity is often defined in terms of her reproductive capacity. More recently, in the second wave of the feminist movement in the United States, debates about women'si dentityh ave explicitlyi ncludeds exuality;m uch (...) feminist argument in the late 1960's and early 1970's involved an attempt to separate out an autonomous female sexuality from women's reproductive functions. It is especially relevant, then, to examine biological arguments, particularlye volutionarya rgumentst, o see what they say about whether and how women's sexuality is related to reproduction. We shall find that many evolutionarya rgumentss eem to supportt he direct linkingo f female sexualitya nd reproductionY. et I will argue that this supporti s not well-groundedI. n fact, I think evolutionarye xplanationso f female sexuality exemplify how social beliefs and social agendas can influence very basic biological explanations of fundamental physiological processes. In this paper, I shall spend some time spelling out a few examples in which assumptions about the close link between reproduction and sexuality yield misleading results, then I shall conclude with a discussion of the consequences of this case study for issues in the philosophy of science. (shrink)