We critically analyze Alexander Rosenberg’s argument based on the multiple realizability of biological properties that there are no biological laws. The argument is intuitive and suggestive. Nevertheless, a closer analysis reveals that the argument rests on dubious assumptions about the nature of natural selection, laws of nature, and multiple realizability. We also argue that the argument is limited in scope, since it applies to an outmoded account of laws and the applicability of the argument to other more promising accounts of (...) laws is questionable. Another concern of ours is that the relation between multiple realizability and natural selection is more complex than Rosenberg acknowledges. Finally, we claim that an explanation for why Rosenberg’s argument appears persuasive and appealing is that the argument is based on an inflated concept of multiple realizability that rests on unreliable intuitions concerning what counts as a different realization of the same property. Consequently, we argue that the argument is at best inconclusive and at worst false insofar as its implications for the existence of biological laws are concerned. (shrink)

Multiple realization was once taken to be a challenge to reductionist visions, especially within cognitive science, and a foundation of the “antireductionist consensus.” More recently, multiple realization has come to be challenged on naturalistic grounds, as well as on more “metaphysical” grounds. Within cognitive science, one focal issue concerns the role of neural plasticity for addressing these issues. If reorganization maintains the same cognitive functions, that supports claims for multiple realization. I take up the reorganization involved in language dysfunctions to (...) deal with questions concerned with multiple realization and neural plasticity. Beginning with Broca’s case for localization and the nineteenth century discussion of “reorganization,” and returning to more recent evidence for neural plasticity, I argue that, in the end, there is substantial support for multiple realization in cognitive systems; I further argue that this is wholly consistent with a recognition of methodological pluralism in cognitive science. (shrink)

Methodological reductionists practice ‘wannabe reductionism’. They claim that one should pursue reductionism, but never propose how. I integrate two strains in prior work to do so. Three kinds of activities are pursued as “reductionist”. “Successional reduction” and inter-level mechanistic explanation are legitimate and powerful strategies. Eliminativism is generally ill-conceived. Specific problem-solving heuristics for constructing inter-level mechanistic explanations show why and when they can provide powerful and fruitful tools and insights, but sometimes lead to erroneous results. I show how traditional metaphysical (...) approaches fail to engage how science is done. The methods used do so, and support a pragmatic and non-eliminativist realism. (shrink)

The view that special science properties are multiply realizable has been attacked in recent years by Shapiro, Bechtel and Mundale, Polger, and others. Focusing on psychological and neuroscientific properties, I argue that these attacks are unsuccessful. By drawing on interspecies physiological comparisons I show that diverse physical mechanisms can converge on common functional properties at multiple levels. This is illustrated with examples from the psychophysics and neuroscience of early vision. This convergence is compatible with the existence of general constraints on (...) the evolution of cognitive systems, and does not involve any ad hoc typing of coarse-grained higher level properties. The mechanisms that realize these common higher level properties are really distinct by the criteria laid down by critics of multiple readability. Finally, I present an account of how such functional properties might constitute special science kinds by playing a central explanatory role in a range of cognitive models. Behavioral science kinds in particular are the functionally defined constituents picked out by our most successful models of the multilevel systems and mechanisms that explain cognitive capacities. (shrink)

An analysis of two heuristic strategies for the development of mechanistic models, illustrated with historical examples from the life sciences. In Discovering Complexity, William Bechtel and Robert Richardson examine two heuristics that guided the development of mechanistic models in the life sciences: decomposition and localization. Drawing on historical cases from disciplines including cell biology, cognitive neuroscience, and genetics, they identify a number of "choice points" that life scientists confront in developing mechanistic explanations and show how different choices result in divergent (...) explanatory models. Describing decomposition as the attempt to differentiate functional and structural components of a system and localization as the assignment of responsibility for specific functions to specific structures, Bechtel and Richardson examine the usefulness of these heuristics as well as their fallibility—the sometimes false assumption underlying them that nature is significantly decomposable and hierarchically organized. When Discovering Complexity was originally published in 1993, few philosophers of science perceived the centrality of seeking mechanisms to explain phenomena in biology, relying instead on the model of nomological explanation advanced by the logical positivists (a model Bechtel and Richardson found to be utterly inapplicable to the examples from the life sciences in their study). Since then, mechanism and mechanistic explanation have become widely discussed. In a substantive new introduction to this MIT Press edition of their book, Bechtel and Richardson examine both philosophical and scientific developments in research on mechanistic models since 1993. (shrink)

In the past century, nearly all of the biological sciences have been directly affected by discoveries and developments in genetics, a fast-evolving subject with important theoretical dimensions. In this rich and accessible book, Paul Griffiths and Karola Stotz show how the concept of the gene has evolved and diversified across the many fields that make up modern biology. By examining the molecular biology of the 'environment', they situate genetics in the developmental biology of whole organisms, and reveal how the molecular (...) biosciences have undermined the nature/nurture distinction. Their discussion gives full weight to the revolutionary impacts of molecular biology, while rejecting 'genocentrism' and 'reductionism', and brings the topic right up to date with the philosophical implications of the most recent developments in genetics. Their book will be invaluable for those studying the philosophy of biology, genetics and other life sciences. (shrink)

The claim of the multiple realizability of mental states by brain states has been a major feature of the dominant philosophy of mind of the late 20th century. The claim is usually motivated by evidence that mental states are multiply realized, both within humans and between humans and other species. We challenge this contention by focusing on how neuroscientists differentiate brain areas. The fact that they rely centrally on psychological measures in mapping the brain and do so in a comparative (...) fashion undercuts the likelihood that, at least within organic life forms, we are likely to find cases of multiply realized psychological functions. (shrink)

Recently, Bechtel and Abrahamsen have argued that mathematical models study the dynamics of mechanisms by recomposing the components and their operations into an appropriately organized system. We will study this claim through the practice of combinational modeling in circadian clock research. In combinational modeling, experiments on model organisms and mathematical/computational models are combined with a new type of model—a synthetic model. We argue that the strategy of recomposition is more complicated than what Bechtel and Abrahamsen indicate. Moreover, synthetic modeling as (...) a kind of material recomposition strategy also points beyond the mechanistic paradigm. (shrink)

Synthetic biology is an increasingly high‐profile area of research that can be understood as encompassing three broad approaches towards the synthesis of living systems: DNA‐based device construction, genome‐driven cell engineering and protocell creation. Each approach is characterized by different aims, methods and constructs, in addition to a range of positions on intellectual property and regulatory regimes. We identify subtle but important differences between the schools in relation to their treatments of genetic determinism, cellular context and complexity. These distinctions tie into (...) two broader issues that define synthetic biology: the relationships between biology and engineering, and between synthesis and analysis. These themes also illuminate synthetic biology's connections to genetic and other forms of biological engineering, as well as to systems biology. We suggest that all these knowledge‐making distinctions in synthetic biology raise fundamental questions about the nature of biological investigation and its relationship to the construction of biological components and systems. BioEssays 30:57–65, 2008. © 2007 Wiley Periodicals, Inc. (shrink)

The interests of synthetic biologists may appear to differ greatly from those of evolutionary biologists. The engineering of organisms must be distinguished from the tinkering action of evolution; the ambition of synthetic biologists is to overcome the limits of natural evolution. But the relations between synthetic biology and evolutionary biology are more complex than this abrupt opposition: Synthetic biology may play an important role in the increasing interactions between functional and evolutionary biology. In practice, synthetic biologists have learnt to submit (...) the proteins and modules they construct to a Darwinian process of selection that optimizes their functioning. More importantly, synthetic biology can provide evolutionary biologists with decisive tools to test the scenarios they have elaborated by resurrecting some of the postulated intermediates in the evolutionary process, characterizing their properties, and experimentally testing the genetic changes supposed to be the source of new morphologies and functions. This synthetic, experimental evolution will renew and clarify many debates in evolutionary biology: It will lead to the explosion of some vague concepts as constraints, parallel evolution, and convergence, and replace them with precise mechanistic descriptions. In this way, synthetic biology resurrects the old philosophical debate about the relations between the real and the possible. (shrink)

Multiple realization historically mandated the autonomy of psychology, and its principled irreducibility to neuroscience. Recently, multiple realization and its implications for the reducibility of psychology to neuroscience have been challenged. One challenge concerns the proper understanding of reduction. Another concerns whether multiple realization is as pervasive as is alleged. I focus on the latter question. I illustrate multiple realization with actual, rather than hypothetical, cases of multiple realization from within the biological sciences. Though they do support a degree of autonomy (...) for higher levels of explanation and organization, they do not have the dire consequences critics of multiple realization fear. †To contact the author, please write to: Department of Philosophy, University of Cincinnati, P.O. Box 210374, Cincinnati, OH 45221‐0374; e‐mail: [email protected]. (shrink)

This book provides a comprehensive guide to the conceptual methodological, and epistemological problems of biology, and treats in depth the major developments in molecular biology and evolutionary theory that have transformed both biology and its philosophy in recent decades. At the same time the work is a sustained argument for a particular philosophy of biology that unifies disparate issues and offers a framework for expectations about the future directions of the life sciences. The argument explores differences between autonomist and anti-autonomist (...) views of biology. The result is a vindication of reductionism, but one that is unexpectedly hollow. For it leaves the exponents of the autonomy of biology from physical science with as much as their view of biology really requires - and rather more than the reductionist might comfortably concede. Professor Rosenberg shows how the problems of the philosophy of biology are interconnected and how their solutions are interdependent, However, this book focuses more on the direct concerns of biologists, rather than the traditional agenda of philosophers' problems about biology. This departure from earlier books on the subject results both in greater understanding and relevance of the philosophy of science to biology as a whole. (shrink)

Many scientific models in biology are how-possibly models. These models depict things as they could be, but do not necessarily capture actual states of affairs in the biological world. In contemporary philosophy of science, it is customary to treat how-possibly models as second-rate theoretical tools. Although possibly important in the early stages of theorizing, they do not constitute the main aim of modelling, namely, to discover the actual mechanism responsible for the phenomenon under study. In the paper it is argued (...) that this prevailing picture does not do justice to the synthetic strategy that is commonly used in biological engineering. In synthetic biology, how-possibly models are not simply speculations or eliminable scaffolds towards a single how-actually model, but indispensable design hypotheses for a field whose ultimate goal is to build novel biological systems. The paper explicates this by providing an example from the study of alternative genetic systems by synthetic biologist Steven Benner and his group. The case will also highlight how the method of synthesis, even when it fails, provides an effective way to limit the space of possible models for biological systems. (shrink)

Since Hilary Putnam offered multiple realization as an empirical hypothesis in the 1960s, philosophical consensus has turned against the idea that mental processes are identifiable with brain processes, and multiple realization has become the keystone of the 'antireductive consensus' across philosophy of science. Thomas W. Polger and Lawrence A. Shapiro offer the first book-length investigation of multiple realization, which serves as a starting point to a series of philosophically sophisticated and empirically informed arguments that cast doubt on the generality of (...) multiple realization in the cognitive sciences. They argue that mind-brain identities have played an important role in the growth and achievements of the cognitive sciences, and suggest that there is little prospect for multiple realization in an empirically-based theory of mind. This leads Polger and Shapiro to offer an alternative framework for understanding explanations in the cognitive sciences, as well as in chemistry, biology, and other non-basic sciences. (shrink)

One way that scientifically recognized properties are multiply realized is by “compensatory differences” among realizing properties. If a property G is jointly realized by two properties F1 and F2, then G can be multiply realized by having changes in the property F1 offset changes in the property F2. In some cases, there are scientific laws that articulate how distinct combinations of physical quantities can determine one and the same value of some other physical quantity. One moral to draw is that (...) in such cases we have the multiple realization of a single determinate, “fine grained” property instance that is exactly similar to another instance. As simple as this moral is, it has ramifications for a number of recent discussions of multiple realization in science. Taken collectively, these ramifications indicate that multiple realization by compensatory adjustments merits greater attention in the philosophy of science literature than it has hitherto received. (shrink)

When conceived as an empirical claim, it is natural to wonder how one might test the hypothesis of multiple realization. I consider general issues of testability, show how they apply specifically to the hypothesis of multiple realization, and propose an auxiliary assumption that, I argue, must be conjoined to the hypothesis of multiple realization to ensure its testability. I argue further that Bechtel and Mundale go astray because they fail to appreciate the need for this auxiliary assumption. †To contact the (...) author, please write to: Department of Philosophy, University of Wisconsin–Madison, 5185 Helen C. White Hall, 600 North Park Street, Madison, WI 53706; e‐mail: [email protected]. (shrink)

This book offers a philosophy for error-prone humans trying to understand messy systems in the real world.

Consider what the brain-state theorist has to do to make good his claims. He has to specify a physical–chemical state such that any organism (not just a mammal) is in pain if and only if (a) it possesses a brain of suitable physical–chemical structure; and (b) its brain is in that physical–chemical state. This means that the physical–chemical state in question must be a possible state of a mammalian brain, a reptilian brain, a mollusc’s brain (octopuses are mollusca, and certainly (...) feel pain), etc. At the same time, it must not be a possible (physically possible) state of the brain of any physically possible creature that cannot feel pain. Even if such a state can be found, it must be nomologically certain that it will also be a state of the brain of any extraterrestrial life that may be found that will be capable of feeling pain before we can even entertain the supposition that it may be pain. It is not altogether impossible that such a state will be found... . But this is certainly an ambitious hypothesis. (Putnam 1967/1975, p. 436) The belief that mental states are multiply realized is now nearly universal among philosophers, as is the belief that this fact decisively refutes the identity theory. I argue that the empirical support for multiple realization does not justify the confidence that has been placed in it. In order for multiple realization of mental states to be an objection to the identity theory, the neurological differences among pains, for example, must be such as to guarantee that they are of distinct neurological kinds. But the phenomena traditionally cited do not provide evidence of that sort of variation. In particular, examples of neural plasticity do not provide such evidence. (shrink)

Reductionism encompasses a set of ontological, epistemological, and methodological claims about the relation of different scientific domains. The basic question of reduction is whether the properties, concepts, explanations, or methods from one scientific domain (typically at higher levels of organization) can be deduced from or explained by the properties, concepts, explanations, or methods from another domain of science (typically one about lower levels of organization). Reduction is germane to a variety of issues in philosophy of science, including the structure of (...) scientific theories, the relations between different scientific disciplines, the nature of explanation, the diversity of methodology, and the very idea of theoretical progress, as well as to numerous topics in metaphysics and philosophy of mind, such as emergence, mereology, and supervenience. (shrink)

This paper explores the relationship between psychology and neurobiology in the context of cognitive science. Are the sciences that constitute cognitive science independent and theoretically autonomous, or is there a necessary interaction between them? I explore Fodor's Multiple Realization Thesis (MRT) which starts with the fact of multiple realization and purports to derive the theoretical autonomy of special sciences (such as psychology) from structural sciences (such as neurobiology). After laying out the MRT, it is shown that, on closer inspection, the (...) argument is either circular or self-undermining--the argument either assumes the very autonomy it seeks to demonstrate or the concluded autonomy is contradicted by the theoretical interdependence invoked by the premises of the argument. Next, I explore a concrete example of multiple realization in the explanation of animal behavior: the convergent evolution of jamming avoidance behaviors in three genera of weakly electric fish. Contrary to the image painted by the MRT, the work on these animals involves a high degree of interaction between the various levels of investigation. The fact that our understanding of electric fish behavior involves functional theories and multiple realization without the kind of disunified science that is supposed to follow from such a situation indicates that the mere fact of multiple realization cannot be the basis for an autonomous psychology. (shrink)

Although molecular biology has meant different things at different times, the term is often associated with a tendency to view cellular causation as conforming to simple linear schemas in which macro-scale effects are specified by micro-scale structures. The early achievements of molecular biologists were important for the formation of such an outlook, one to which the discovery of recombinant DNA techniques, and a number of other findings, gave new life even after the complexity of genotype–phenotype
relations had become apparent. Against this (...) background we outline how a range of scientific developments and conceptual considerations can be regarded as enabling and perhaps necessitating contemporary systems approaches. We suggest that philosophical ideas have a valuable part to play in making sense of complex scientific and disciplinary issues. (shrink)

Comparing engineering to evolution typically involves adaptationist thinking, where well-designed artifacts are likened to well-adapted organisms, and the process of evolution is likened to the process of design. A quite different comparison is made when biologists focus on evolvability instead of adaptationism. Here, the idea is that complex integrated systems, whether evolved or engineered, share universal principles that affect the way they change over time. This shift from adaptationism to evolvability is a significant move for, as I argue, we can (...) make sense of these universal principles without making any adaptationism claims. Furthermore, evolvability highlights important aspects of engineering that are ignored in the adaptationist debates. I introduce some novel engineering examples that incorporate these key neglected aspects, and use these examples to challenge some commonly cited contrasts between engineering and evolution, and to highlight some novel resemblances that have gone unnoticed. (shrink)

Synthetic biologists try to engineer useful biological systems that do not exist in nature. One of their goals is to design an orthogonal chromosome different from DNA and RNA, termed XNA for xeno nucleic acids. XNA exhibits a variety of structural chemical changes relative to its natural counterparts. These changes make this novel information‐storing biopolymer “invisible” to natural biological systems. The lack of cognition to the natural world, however, is seen as an opportunity to implement a genetic firewall that impedes (...) exchange of genetic information with the natural world, which means it could be the ultimate biosafety tool. Here I discuss, why it is necessary to go ahead designing xenobiological systems like XNA and its XNA binding proteins; what the biosafety specifications should look like for this genetic enclave; which steps should be carried out to boot up the first XNA life form; and what it means for the society at large. (shrink)

Evolutionary convergence is often appealed to in support of claims about multiple realization. The idea is that convergence shows that the same function can be realized by different kinds of structures. I argue here that the nature of convergence is more complicated than it might appear at first look. Broad claims about convergence are made by biologists during general discussions of the mechanisms of evolution. In their specialized work, though, biologists are often more limited in the claims they make. I (...) will examine a standard example to show how claims about convergence can be oversimplified. (shrink)

Forthcoming in Philosophy of Science. Despite some recent advances, multiple realization remains a largely misunderstood thesis. Consider the dispute between Lawrence Shapiro and Carl Gillett over the application of Shapiro’s recipe for deciding when we have genuine cases of multiple realization. I argue that Gillett follows many philosophers in mistakenly supposing that multiple realization is absolute and transitive. Both of these are problematic. They are tempting only when we extract the question of multiple realization from the explanatory context in which (...) it is invoked. Anchoring multiple realizability in its theoretical context provides grounds for arbitrating disagreements. Doing so, I argue, favors the view advanced by Shapiro. (shrink)

Compares classic and contemporary theories of genetics and evolution and explores the role of teleological thought in biology.

Synthetic biology is often understood in terms of the pursuit for well-characterized biological parts to create synthetic wholes. Accordingly, it has typically been conceived of as an engineering dominated and application oriented field. We argue that the relationship of synthetic biology to engineering is far more nuanced than that and involves a sophisticated epistemic dimension, as shown by the recent practice of synthetic modeling. Synthetic models are engineered genetic networks that are implanted in a natural cell environment. Their construction is (...) typically combined with experiments on model organisms as well as mathematical modeling and simulation. What is especially interesting about this combinational modeling practice is that, apart from greater integration between these different epistemic activities, it has also led to the questioning of some central assumptions and notions on which synthetic biology is based. As a result synthetic biology is in the process of becoming more “biology inspired.”. (shrink)

Synthetic biology is an increasingly high-profile area of research that can be understood as encompassing three broad approaches towards the synthesis of living systems: DNA-based device construction, genome-driven cell engineering and protocell creation. Each approach is characterized by different aims, methods and constructs, in addition to a range of positions on intellectual property and regulatory regimes. We identify subtle but important differences between the schools in relation to their treatments of genetic determinism, cellular context and complexity. These distinctions tie into (...) two broader issues that define synthetic biology: the relationships between biology and engineering, and between synthesis and analysis. These themes also illuminate synthetic biology's connections to genetic and other forms of biological engineering, as well as to systems biology. We suggest that all these knowledge-making distinctions in synthetic biology raise fundamental questions about the nature of biological investigation and its relationship to the construction of biological components and systems. (shrink)

Richard Levins’ distinction between aggregate, composed and evolved systems acquires new significance as we recognize the importance of mechanistic explanation. Criteria for aggregativity provide limiting cases for absence of organization, so through their failure, can provide rich detectors for organizational properties. I explore the use of failures of aggregativity for the analysis of mechanistic systems in diverse contexts. Aggregativity appears theoretically desireable, but we are easily fooled. It may be exaggerated through approximation, conditions of derivation, and extrapolating from some conditions (...) of decomposition illegtimately to others. Evolved systems particularly may require analyses under alternative complementary decompositions. Exploring these conditions helps us to better understand the strengths and limits of reductionistic methods. (shrink)