Unless one embraces activities as foundational, understanding activities in mechanisms requires an account of the means by which entities in biological mechanisms engage in their activities—an account that does not merely explain activities in terms of more basic entities and activities. Recent biological research on molecular motors exemplifies such an account, one that explains activities in terms of free energy and constraints. After describing the characteristic “stepping” activities of these molecules and mapping the stages of those (...) steps onto the stages of the motors’ hydrolytic cycles, researchers pieced together from images of the molecules in different hydrolyzation states accounts of how the chemical energy in ATP is transformed in the constrained environments of the motors into the characteristic activities of the motors. We argue that New Mechanism’s standard set of analytic categories—entities, activities, and organization—should be expanded to include constraints and energetics. Not only is such an expansion required descriptively to capture research on molecular motors but, more importantly from a philosophical point of view, it enables a non-regressive account of activities in mechanisms. In other words, this expansion enables a philosophical account of mechanistic explanation that avoids a regress of entities and activities “all the way down.” Rather, mechanistic explanation bottoms out in constraints and energetics. (shrink)

Modeling mechanisms is central to the biological sciences – for purposes of explanation, prediction, extrapolation, and manipulation. A closer look at the philosophical literature reveals that mechanisms are predominantly modeled in a purely qualitative way. That is, mechanistic models are conceived of as representing how certain entities and activities are spatially and temporally organized so that they bring about the behavior of the mechanism in question. Although this adequately characterizes how mechanisms are represented in biology textbooks, (...) contemporary biological research practice shows the need for quantitative, probabilistic models of mechanisms, too. In this paper we argue that the formal framework of causal graph theory is well-suited to provide us with models of biological mechanisms that incorporate quantitative and probabilistic information. On the ba-sis of an example from contemporary biological practice, namely feedback regulation of fatty acid biosynthesis in Brassica napus, we show that causal graph theoretical models can account for feedback as well as for the multi-level character of mechanisms. However, we do not claim that causal graph theoretical representations of mechanisms are advantageous in all respects and should replace common qualitative models. Rather, we endorse the more balanced view that causal graph theoretical models of mechanisms are useful for some purposes, while being insufficient for others. (shrink)

Autoinhibition is a design principle realized in many molecular mechanisms in biology. After explicating the notion of a design principle and showing that autoinhibition is such a principle, we focus on how researchers discovered instances of autoinhibition, using research establishing the autoinhibition of the molecular motors kinesin and dynein as our case study. Research on kinesin and dynein began in the fashion described in accounts of mechanistic explanation but, once the mechanisms had been discovered, researchers discovered that they (...) exhibited a second phenomenon, autoinhibition. The discovery of autoinhibition not only reverses the pattern in terms of which philosophers have understood mechanism discovery but runs counter to the one phenomenon-one mechanism principle assumed to relate mechanisms and the phenomena they explain. The ubiquity of autoinhibition as a design principle, therefore, necessitates a philosophical understanding of mechanisms that recognizes how they can participate in more than one phenomenon. Since mechanisms with this design are released from autoinhibition only when they are acted on by control mechanisms, we advance a revised account of mechanisms that accommodates attribution of multiple phenomena to the same mechanism and distinguishes them from other processes that control them. (shrink)

Living organisms act as integrated wholes to maintain themselves. Individual actions can each be explained by characterizing the mechanisms that perform the activity. But these alone do not explain how various activities are coordinated and performed versatilely. We argue that this depends on a specific type of mechanism, a control mechanism. We develop an account of control by examining several extensively studied control mechanisms operative in the bacterium E. coli. On our analysis, what distinguishes a control mechanism from (...) other mechanisms is that it relies on measuring one or more variables, which results in setting constraints in the control mechanism that determine its action on flexible constraints in other mechanisms. In the most basic arrangement, the measurement process directly determines the action of the control mechanism, but in more complex arrangements signals mediate between measurements and effectors. This opens the possibility of multiple responses to the same measurement and responses based on multiple measurements. It also allows crosstalk, resulting in networks of control mechanisms. Such networks integrate the behaviors of the organism but also present a challenge in tailoring responses to particular measurements. We discuss how integrated activity can still result in differential, versatile, responses. (shrink)

Although quantum mechanics can accurately predict the probability distribution of outcomes in an ensemble of identical systems, it cannot predict the result of an individual system. All the local and global hidden variable theories attempting to explain individual behavior have been proved invalid by experiments (violation of Bell’s inequality) and theory. As an alternative, Schrodinger and others have hypothesized existence of free will in every particle which causes randomness in individual results. However, these free will theories have failed to quantitatively (...) explain the quantum mechanical results. In this paper, we take the clue from quantum biology to get the explanation of quantum mechanical distribution. Recently it was reported that mutations (which are quantum processes) in DNA of E. coli bacteria instead of being random were biased in a direction such that the chance of survival of the bacteria is increased. Extrapolating it, we assume that all the particles including inanimate fundamental particles have a will and that is biased to satisfy the collective goals of the ensemble. Using this postulate, we mathematically derive the correct spin probability distribution without using quantum mechanical formalism (operators and Born’s rule) and exactly reproduce the quantum mechanical spin correlation in entangled pairs. Using our concept, we also mathematically derive the form of quantum mechanical wave function of free particle which is conventionally a postulate of quantum mechanics. Thus, we prove that the origin of quantum mechanical results lies in the will (or consciousness) of the objects biased by the collective goal of ensemble or universe. This biasing by the group on individuals can be called as “coherence” which directly represents the extent of life present in the ensemble. So, we can say that life originates out of establishment of coherence in a group of inanimate particles. (shrink)

We advance an account that grounds cognition, specifically decision-making, in an activity all organisms as autonomous systems must perform to keep themselves viable—controlling their production mechanisms. Production mechanisms, as we characterize them, perform activities such as procuring resources from their environment, putting these resources to use to construct and repair the organism's body and moving through the environment. Given the variable nature of the environment and the continual degradation of the organism, these production mechanisms must be regulated (...) by control mechanisms that select when a production is required and how it should be carried out. To operate on production mechanisms, control mechanisms need to procure information through measurement processes and evaluate possible actions. They are making decisions. In all organisms, these decisions are made by multiple different control mechanisms that are organized not hierarchically but heterarchically. In many cases, they employ internal models of features of the environment with which the organism must deal. Cognition, in the form of decision-making, is thus fundamental to living systems which must control their production mechanisms. (shrink)

In the first part of this article we survey general similarities and differences between biological and social macroevolution. In the second (and main) part, we consider a concrete mathematical model capable of describing important features of both biological and social macroevolution. In mathematical models of historical macrodynamics, a hyperbolic pattern of world population growth arises from non-linear, second-order positive feedback between demographic growth and technological development. This is more or less identical with the working of the collective learning (...) mechanism. Based on diverse paleontological data and an analogy with macrosociological models, we suggest that the hyperbolic character of biodiversity growth can be similarly accounted for by non-linear, second-order positive feedback between diversity growth and the complexity of community structure, suggesting the presence within the biosphere of a certain analogue of the collective learning mechanism. We discuss how such positive feedback mechanisms can be modelled mathematically. (shrink)

Biological theory demands a clear organism concept, but at present biologists cannot agree on one. They know that counting particular units, and not counting others, allows them to generate explanatory and predictive descriptions of evolutionary processes. Yet they lack a unified theory telling them which units to count. In this paper, I offer a novel account of biological individuality, which reconciles conflicting definitions of ‘organism’ by interpreting them as describing alternative realisers of a common functional role, and then (...) defines individual organisms as essentially possessing some mechanisms that play this role. (shrink)

We argue that genuine biological autonomy, or described at human level as free will, requires taking into account quantum vacuum processes in the context of biological teleology. One faces at least three basic problems of genuine biological autonomy: (1) if biological autonomy is not physical, where does it come from? (2) Is there a room for biological causes? And (3) how to obtain a workable model of biological teleology? It is shown here that the (...) solution of all these three problems is related to the quantum vacuum. We present a short review of how this basic aspect of the fundamentals of quantum theory, although it had not been addressed for nearly 100 years, actually it was suggested by Bohr, Heisenberg, and others. Realizing that the quantum mechanical measurement problem associated with the “collapse” of the wave function is related, in the Copenhagen Interpretation of quantum mechanics, to a process between self-consciousness and the external physical environment, we are extending the issue for an explanation of the different processes occurring between living organisms and their internal environment. Definitions of genuine biological autonomy, biological aim, and biological spontaneity are presented. We propose to improve the popular two-stage model of decisions with a biological model suitable to obtain a deeper look at the nature of the mind-body problem. In the newly emerging picture biological autonomy emerges as a new, fundamental and inevitable element of the scientific worldview. (shrink)

Mechanisms, in the prominent biological sense of the term, are historical entities. That is, whether or not something is a mechanism for something depends on its history. Put differently, while your spontaneously-generated molecule-for-molecule double has a heart, and its heart pumps blood around its body, its heart does not have a mechanism for pumping, since it does not have the right history. My argument for this claim is that mechanisms have proper functions; proper functions are historical entities; (...) so, mechanisms are historical entities, too. This thesis runs against the mainstream new mechanist way of thinking about mechanisms, where mechanisms are generally thought of in an ahistorical way. After arguing for this thesis, I draw out some consequences for philosophy of science and metaphysics. (shrink)

This selective review explores biologically inspired learning as a model for intelligent robot control and sensing technology on the basis of specific examples. Hebbian synaptic learning is discussed as a functionally relevant model for machine learning and intelligence, as explained on the basis of examples from the highly plastic biological neural networks of invertebrates and vertebrates. Its potential for adaptive learning and control without supervision, the generation of functional complexity, and control architectures based on self-organization is brought forward. Learning (...) without prior knowledge based on excitatory and inhibitory neural mechanisms accounts for the process through which survival-relevant or task-relevant representations are either reinforced or suppressed. The basic mechanisms of unsupervised biological learning drive synaptic plasticity and adaptation for behavioral success in living brains with different levels of complexity. The insights collected here point toward the Hebbian model as a choice solution for “intelligent” robotics and sensor systems. Keywords: Hebbian learning; synaptic plasticity; neural networks; self-organization; brain; reinforcement; sensory processing; robot control . (shrink)

Leuridan (2011) questions whether mechanisms can really replace laws at the heart of our thinking about science. In doing so, he enters a long-standing discussion about the relationship between the mech-anistic structures evident in the theories of contemporary biology and the laws of nature privileged especially in traditional empiricist traditions of the philosophy of science (see e.g. Wimsatt 1974; Bechtel and Abrahamsen 2005; Bogen 2005; Darden 2006; Glennan 1996; MDC 2000; Schaffner 1993; Tabery 2003; Weber 2005). In our view, (...) Leuridan misconstrues this discussion. His weak positive claim that mechanistic sciences appeal to generalizations is true but uninteresting. His stronger claim, that all causal claims require laws, is unsupported by his arguments. Though we proceed by criticizing Leuridan’s arguments, our greater purpose is to embellish his arguments in order to show how thinking about mechanisms enriches and transforms old philosophical debates about laws in biology and provides new insights into how generalizations afford prediction, explanation and control. (shrink)

The aim of this study is to justify the belief that there are biological normative mechanisms that fulfill non-trivial causal roles in the explanations (as formulated by researchers) of actions and behaviors present in specific systems. One example of such mechanisms is the predictive mechanisms described and explained by predictive processing (hereinafter PP), which (1) guide actions and (2) shape causal transitions between states that have specific content and fulfillment conditions (e.g. mental states). Therefore, I am (...) guided by a specific theoretical goal associated with the need to indicate those conditions that should be met by the non-trivial theory of normative mechanisms and the specific models proposed by PP supporters. In this work, I use classical philosophical methods, such as conceptual analysis and critical reflection. I also analyze selected studies in the field of cognitive science, cognitive psychology, neurology, information theory and biology in terms of the methodology, argumentation and language used, in accordance with their theoretical importance for the issues discussed in this study. In this sense, the research presented here is interdisciplinary. My research framework is informed by the mechanistic model of explanation, which defines the necessary and sufficient conditions for explaining a given phenomenon. The research methods I chose are therefore related to the problems that I intend to solve. In the introductory chapter, “The concept of predictive processing”, I discuss the nature of PP as well as its main assumptions and theses. I also highlight the key concepts and distinctions for this research framework. Many authors argue that PP is a contemporary version of Kantianism and is exposed to objections similar to those made against the approach of Immanuel Kant. I discuss this thesis and show that it is only in a very general sense that the PP framework is neo-Kantian. Here we are not dealing with transcendental deduction nor with the application of transcendental arguments. I argue that PP is based on reverse engineering and abduction inferences. In the second part of this chapter, I respond to the objection formulated by Dan Zahavi, who directly accuses this research framework of anti-realistic consequences. I demonstrate that the position of internalism, present in the so-called conservative PP, does not imply anti-realism, and that, due to the explanatory role played in it by structural representations directed at real patterns, it is justified to claim that PP is realistic. In this way, I show that PP is a non-trivial research framework, having its subject, specific methods and its own epistemic status. Finally, I discuss positions classified as the socalled radical PP. In the chapter “Predictive processing as a Bayesian explanatory model” I justify the thesis according to which PP offers Bayesian modeling. Many researchers claim that the brain is an implemented statistical probabilistic network that is an approximation of the Bayesian 7 rule. In practice, this means that all cognitive processes are to apply Bayes' rule and can be described in terms of probability distributions. Such a solution arouses objections among many researchers and is the subject of wide criticism. The purpose of this chapter is to justify the thesis that Bayesian PP is a non-trivial research framework. For this purpose, I argue that it explains certain phenomena not only at the computational level described by David Marr, but also at the level of algorithms and implementation. Later in this chapter I demonstrate that PP is normative modeling. Proponents of the use of Bayesian models in psychology or decision theory argue that they are normative because they allow the formulation of formal rules of action that show what needs to be done to make a given action optimal. Critics of this approach emphasize that such thinking about the normativity of Bayesian modeling is unjustified and that science should shift from prescriptive to descriptive positions. In a polemic with Shira Elqayam and Jonathan Evans (2011), I show that the division they propose into prescriptivism and Bayesian descriptivism is apparent, because, as I argue, there are two forms of prescriptivism, i.e. the weak and the strong. I argue that the weak version is epistemic and can lead to anti-realism, while the strong version is ontic and allows one to justify realism in relation to Bayesian models. I argue that a weak version of prescriptivism is valid for PP. It allows us to adopt anti-realism in relation to PP. In practice, this means that you can explain phenomena using Bayes' rule. This does not, however, imply that they are Bayesian in nature. However, the full justification of realism in relation to the Bayesian PP presupposes the adoption of strong prescriptivism. This position assumes that phenomena are explained by Bayesian rule because they are Bayesian as such. If they are Bayesian in nature, then they should be explained using Bayesian modeling. This thesis will be substantiated in the chapters “Normative functions and mechanisms in the context of predictive processing” and “Normative mechanisms and actions in predictive processing”. In the chapter “The Free Energy Principle in predictive processing”, I discuss the Free Energy Principle (hereinafter FEP) formulated by Karl Friston and some of its implications. According to this principle, all biological systems (defined in terms of Markov blankets) minimize the free energy of their internal states in order to maintain homeostasis. Some researchers believe that PP is a special case of applying this principle to cognition, and that predictive mechanisms are homeostatic mechanisms that minimize free energy. The discussion of FEP is important due to the fact that some authors consider it to be important for explanatory purposes and normative. If this is the case, then FEP turns out to be crucial in explaining normative predictive mechanisms and, in general, any normative biological mechanisms. To define the explanatory possibilities of this principle, I refer to the discussion of its supporters on the issue they define as the problem of continuity and discontinuity between life and mind. A critical analysis of this discussion and the additional arguments I have formulated have allowed me to revise the explanatory ambitions of FEP. I also reject the belief that this principle is necessary to explain the nature of predictive mechanisms. I argue that the principle formulated and defended by Friston is an important research heuristic for PP analysis. 8 In the chapter “Normative functions and mechanisms in predictive processing”, I start my analyzes by formulating an answer to the question about the normative nature of homeostatic mechanisms. I demonstrate that predictive mechanisms are not homeostatic. I defend the view that a full explanation of normative mechanisms presupposes an explanation of normative functions. I discuss the most important proposals for understanding the normativity of a function, both from a systemic and teleosemantic perspective. I conclude that the non-trivial concept of a function must meet two requirements which I define as explanatory and normative. I show that none of the theories I have invoked satisfactorily meets both of these requirements. Instead, I propose a model of normativity based on Bickhard's account, but supplemented by a mechanistic perspective. I argue that a function is normative when: (1) it allows one to explain the dysfunction of a given mechanism; (2) it contributes to the maintenance of the organism's stability by shaping and limiting possible relations, processes and behaviors of a given system; and when (3) (according to the representational and predictive functions) it enables explaining the attribution of logical values of certain representations / predictions. In such an approach, a mechanism is normative when it performs certain normative functions and when it is constitutive for a specific action or behavior, despite the fact that for some reason it cannot realize it either currently or in the long-term. Such an understanding of the normativity of mechanisms presupposes the acceptance of the epistemic hypothesis. I argue that this hypothesis is not cognitively satisfactory, and therefore the ontic hypothesis should be justified, which is directly related to adopting the position of ontic prescriptivism. For this reason, referring to the mechanistic theory of scientific explanations, I formulate an ontical interpretation of the concept of a normative mechanism. According to this approach, a mechanism or a function is normative when they perform such and such causal roles in explaining certain actions and behaviors. With regard to the normative properties of predictive mechanisms and functions, this means that they are the causes of specific actions an organism carries out in the environment. In this way, I justify the necessity of accepting the ontic hypothesis and rejecting the epistemic hypothesis. The fifth chapter, “Normative mechanisms and actions in predictive processing”, is devoted to the dark room problem and the related exploration-exploitation trade-off. A dark room is the state that an agent could be in if it minimized the sum of all potential prediction errors. I demonstrate that, in accordance with the basic assumption of PP about the need for continuous and long-term minimization of prediction errors, such a state should be desirable for the agent. Is it really so? Many authors believe it is not. I argue that the test of the value of PP is the possibility of a non-trivial solution of this problem, which can be reduced to the choice between active and uncertainty-increasing exploration and safe and easily predictable exploitation. I show that the solution proposed by PP supporters present in the literature does not enable a fully satisfactory explanation of this dilemma. Then I defend the position according to which the full explanation of the normative mechanisms, and, subsequently, the solution to the dilemma of exploration and exploitation, involves reference to the existence of constraints present in the environment. The constraints 9 include elements of the environment that make a given mechanism not only causal but also normative. They are therefore key to explaining the predictive mechanisms. They do not only play the role of the context in which the mechanism is implemented, but, above all, are its constitutive component. I argue that the full explanation of the role of constraints in normative predictive mechanisms presupposes the integration of individual models of specific cognitive phenomena, because only the mechanistic integration of PP with other models allows for a non-trivial explanation of the nature of normative predictive mechanisms that would have a strong explanatory value. The explanatory monism present in many approaches to PP makes it impossible to solve the problem of the dark room. Later in this chapter, I argue that the Bayesian PP is normative not because it enables the formulation of such and such rules of action, but because the predictive mechanisms themselves are normative. They are normative because they condition the choice of such and such actions by agents. In this way, I justify the hypothesis that normative mechanisms make it possible to explain the phenomenon of agent motivation, which is crucial for solving the dark room problem. In the last part of the chapter, I formulate the hypothesis of distributed normativity, which assumes that the normative nature of certain mechanisms, functions or objects is determined by the relations into which these mechanisms, functions or objects enter. This means that what is normative (in the primary sense) is the relational structure that constitutes the normativity of specific items included in it. I suggest that this hypothesis opens up many areas of research and makes it possible to rethink many problems. In the “Conclusion”, I summarize the results of my research and indicate further research perspectives. (shrink)

Is it possible to know anything about life we have not yet encountered? We know of only one example of life: our own. Given this, many scientists are inclined to doubt that any principles of Earth’s biology will generalize to other worlds in which life might exist. Let’s call this the “N = 1 problem.” By comparison, we expect the principles of geometry, mechanics, and chemistry would generalize. Interestingly, each of these has predictable consequences when applied to biology. The surface-to-volume (...) property of geometry, for example, limits the size of unassisted cells in a given medium. This effect is real, precise, universal, and predictive. Furthermore, there are basic problems all life must solve if it is to persist, such as resistance to radiation, faithful inheritance, and energy regulation. If these universal problems have a limited set of possible solutions, some common outcomes must consistently emerge. In this chapter, I discuss the N = 1 problem, its implications, and my response (Mariscal 2014). I hold that our current knowledge of biology can justify believing certain generalizations as holding for life anywhere. Life on Earth may be our only example of life, but this is only a reason to be cautious in our approach to life in the universe, not a reason to give up altogether. In my account, a candidate biological generalization is assessed by the assumptions it makes. A claim is accepted only if its justification includes principles of evolution, but no contingent facts of life on Earth. (shrink)

I show how archaeologists have two problems. The construction of scenarios accounting for the raw data of Archaeology, the material remains of the past, and the explanation of pre-history. Within Archaeology, there has been an ongoing debate about how to constrain speculation within both of these archaeological projects, and archaeologists have consistently looked to biological mechanisms for constraints. I demonstrate the problems of using biology, either as an analogy for cultural processes or through direct application of biological (...) principles to material remains. This is done through setting out the requirements of a Darwinian Archaeology, and then measuring various approaches against these requirements. This approach leads to the conclusion that archaeologist's explanations of the past must include within their formulations an account of human cognitive capacities within their explanatory framework. The limits of our understanding of the human past will be the limits of our understanding of Homo sapiens. (shrink)

In the first part of this article we survey general similarities and differences between biological and social macroevolution. In the second (and main) part, we consider a concrete mathematical model capable of describing important features of both biological and social macroevolution. In mathematical models of historical macrodynamics, a hyperbolic pattern of world population growth arises from non-linear, second-order positive feedback between demographic growth and technological development. Based on diverse paleontological data and an analogy with macrosociological models, we suggest (...) that the hyperbolic character of biodiversity growth can be similarly accounted for by non-linear, second-order positive feedback between diversity growth and the complexity of community structure. We discuss how such positive feedback mechanisms can be modelled mathematically. (shrink)

According to new mechanists, mechanisms explain how specific biological phenomena are produced. New mechanists have had little to say about how mechanisms relate to the organism in which they reside. A key feature of organisms, emphasized by the autonomy tradition, is that organisms maintain themselves. To do this, they rely on mechanisms. But mechanisms must be controlled so that they produce the phenomena for which they are responsible when and in the manner needed by the (...) organism. To account for how they are controlled, we characterize mechanisms as sets of constraints on the flow of free energy. Some constraints are flexible and can be acted on by other mechanisms, control mechanisms, that utilize information procured from the organism and its environment to alter the flexible constraints in other mechanisms so that they produce phenomena appropriate to the circumstances. We further show that control mechanisms in living organisms are organized heterarchically—control is carried out primarily by local controllers that integrate information they acquire as well as that which they procure from other control mechanisms. The result is not a hierarchy of control but an integrated network of control mechanisms that has been crafted over the course of evolution. (shrink)

The new mechanists and the autonomy approach both aim to account for how biological phenomena are explained. One identifies appeals to how components of a mechanism are organized so that their activities produce a phenomenon. The other directs attention towards the whole organism and focuses on how it achieves self-maintenance. This paper discusses challenges each confronts and how each could benefit from collaboration with the other: the new mechanistic framework can gain by taking into account what happens outside individual (...) mechanisms, while the autonomy approach can ground itself in biological research into how the actual components constituting an autonomous system interact and contribute in different ways to realize and maintain the system. To press the case that these two traditions should be constructively integrated we describe how three recent developments in the autonomy tradition together provide a bridge between the two traditions: (1) a framework of work and constraints, (2) a conception of function grounded in the organization of an autonomous system, and (3) a focus on control. (shrink)

Biologists often study particular biological systems as models of a phenomenon of interest even if they already know that the phenomenon is produced by diverse mechanisms and hence none of those systems alone can sufficiently represent it. To understand this modeling practice, the present paper provides an account of how multiple model systems can be used to study a phenomenon that is produced by diverse mechanisms. Even if generalizability of results from a single model system is significantly (...) limited, generalizations concerning specific aspects of mechanisms often hold across certain ranges of biological systems, which enables multiple model systems to jointly represent such a phenomenon. Comparing mechanisms that operate in different biological systems as examples of the same phenomenon also facilitates characterization and investigation of individual mechanisms. I also compare my account with two existing accounts of the use of multiple model systems and argue that my account is distinct from and complementary to them. (shrink)

Psychological construction represents an important new approach to psychological phenomena, one that has the promise to help us reconceptualize the mind both as a behavioral and as a biological system. It has so far been developed in the greatest detail for emotion, but it has important implications for how researchers approach other mental phenomena such as reasoning, memory, and language use. Its key contention is that phenomena that are characterized in (folk) psychological vocabulary are not themselves basic features of (...) the mind, but are constructed from more basic psychological operations. The framework of mechanistic explanation, currently under development in philosophy of science, can provide a useful perspective on the psychological construction approach. A central insight of the mechanistic account of explanation is that biological and psychological phenomena result from mechanisms in which component parts and operations do not individually exhibit the phenomena of interest but function together in an orchestrated and sometimes in a complex dynamical manner to generate it. While at times acknowledging the compatibility of the mechanist approach with constructionist approach (Lindquist, Wager, Kober, Bliss-Moreau, & Barrett, 2012), proponents of the constructionist approach have at other times pitched their approach as anti-mechanist. For example, Barrett (2009) claims that the psychological constructionist approach rejects machines as the primary metaphor for understanding the mind, instead favoring a recipe metaphor; constructionism also purportedly rejects the “mechanistic” picture of causation, which it portrays as linear or sequential in nature (see also Barrett, Wilson-Mendenhall, & Barsalou, in press). While some mechanistic accounts do fit this description, we will see that the mechanisms generating phenomena can be complex and dynamic, producing phenomena far less stereotypic and more adaptive than people often associate with machines. Our goal, however, is not just to render constructionism and mechanism compatible. Philosophers of science have been examining the nature of mechanistic explanation in biology with the goal of gaining new insights into the operation of science. We will identify some of the places where the mechanistic account can shed new light on the constructionist project. (shrink)

The rapidly increasing interest in the quantum properties of living matter stimulates a discussion of the fundamental properties of life as well as quantum mechanics. In this discussion often concepts are used that originate in philosophy and ask for a philosophical analysis. In the present work the classic philosophical tradition based on Aristotle and Aquinas is employed which surprisingly is able to shed light on important aspects. Especially one could mention the high degree of unity in living objects and the (...) occurrence of thorough qualitative changes. The latter are outside the scope of classical physics where changes are restricted to geometrical rearrangement of microscopic particles. A challenging approach is used in the philosophical analysis as the empirical evidence is not taken from everyday life but from 20th century science (quantum mechanics) and recent results in the field of quantum biology. In the discussion it is argued that quantum entanglement is possibly related to the occurrence of life. Finally it is recommended that scientists and philosophers should be open for dialogue that could enrich both. Scientists could redirect their investigation, as paradigm shifts like the one originating from philosophical evaluation of quantum mechanics give new insight about the relation between the whole en the parts. Whereas philosophers could use scientific results as a consistency check for their philosophical framework for understanding reality. (shrink)

This chapter introduces the main research schools and paradigms along which the field of evolutionary biology has been developing. Evolutionary thinking was originally founded upon the Neo-Darwinian paradigm that combines the teachings of traditional Darwinism with those of the Modern Synthesis. The Neo-Darwinian paradigm has since further diversified into the Micro-, Meso-, and Macroevolutionary schools, and it has also started to integrate the school of Ecology. Together, these schools establish the paradigm called Ecological Evolutionary Developmental Biology (Eco-Evo-Devo). A final school (...) studies Reticulate Evolution as it occurs by means of symbiosis, symbiogenesis, lateral gene transfer, infective heredity, and hybridization. This chapter reviews the major tenets and points of differentiation that exist between these distinct evolution schools. The chapter ends by looking into how evolution can be defined and how distinct units, levels, and mechanisms underlie theorizing on evolutionary hierarchies and evolutionary causation. The following chapter examines how the different evolution schools are implemented into the symbolic sciences. (shrink)

There are two fundamentally distinct kinds of biological theorizing. "Formal biology" focuses on the relations, captured in formal laws, among mathematically abstracted properties of abstract objects. Population genetics and theoretical mathematical ecology, which are cases of formal biology, thus share methods and goals with theoretical physics. "Compositional biology," on the other hand, is concerned with articulating the concrete structure, mechanisms, and function, through developmental and evolutionary time, of material parts and wholes. Molecular genetics, biochemistry, developmental biology, and physiology, (...) which are examples of compositional biology, are in serious need of philosophical attention. For example, the very concept of a "part" is understudied in both philosophy of biology and philosophy of science. ;My dissertation is an attempt to clarify the distinction between formal biology and compositional biology and, in so doing, provide a clear philosophical analysis, with case studies, of compositional biology. Given the social, economic, and medical importance of compositional biology, understanding it is urgent. For my investigation, I draw on the philosophical fields of metaphysics and epistemology, as well as philosophy of biology and philosophy of science. I suggest new ways of thinking about some classic philosophy of science issues, such as modeling, laws of nature, abstraction, explanation, and confirmation. I hint at the relevance of my study of two kinds of biological theorizing to debates concerning the disunity of science. (shrink)

The scientific status of evolutionary theory seems to be more or less perennially under question. I am not referring here (just) to the silliness of young Earth creation- ism (Pigliucci 2002; Boudry and Braeckman 2010), or even of the barely more intel- lectually sophisticated so-called Intelligent Design theory (Recker 2010; Brigandt this volume), but rather to discussions among scientists and philosophers of science concerning the epistemic status of evolutionary theory (Sober 2010). As we shall see in what follows, this debate (...) has a long history, dating all the way back to Darwin, and it is in great part rooted in the fundamental dichotomy between what French biologist and Nobel laureate Jacques Monod (1971) called chance and necessity—i.e., the inevitable and inextricable interplay of deterministic and stochastic mechanisms operating during the course of evolution. (shrink)

The presumptions underlying quantum mechanics make it relevant to a limited range of situations only; furthermore, its statistical character means that it provides no answers to the question ‘what is really going on?’. Following Barad, I hypothesise that the underlying mechanics has parallels with human activities, as used by Barad to account for the way quantum measurements introduce definiteness into previously indefinite situations. We are led to consider a subtle type of order, different from those commonly encountered in the discipline (...) of physics, and yet comprehensible in terms of concepts considered by Barad and Yardley such as oppositional dynamics or ‘intra-actions’. The emergent organisation implies that nature is no longer fundamentally meaningless. Agencies can be viewed as dynamical systems, so we are dealing with models involving interacting dynamical systems. The ‘congealing of agencies’ to which Barad refers can be equated to the presence of regulatory mechanisms restricting the range of possibilities open to the agencies concerned. (shrink)

This paper adds to the philosophical literature on mechanistic explanation by elaborating two related explanatory functions of idealisation in mechanistic models. The first function involves explaining the presence of structural/organizational features of mechanisms by reference to their role as difference-makers for performance requirements. The second involves tracking counterfactual dependency relations between features of mechanisms and features of mechanistic explanandum phenomena. To make these functions salient, we relate our discussion to an exemplar from systems biological research on the (...) mechanism for countering heat shock—the heat shock response system—in Escherichia coli bacteria. This research also reinforces a more general lesson: ontic constraint accounts in the literature on mechanistic explanation provide insufficiently informative normative appraisals of mechanistic models. We close by outlining an alternative view on the explanatory norms governing mechanistic representation. (shrink)

Are living organisms--as Descartes argued--just machines? Or is the nature of life such that it can never be fully explained by mechanistic models? In this thought-provoking and controversial book, eminent geophysicist Walter M. Elsasser argues that the behavior of living organisms cannot be reduced to physico-chemical causality. Suggesting that molecular biology today is at the same point as Newtonian physics on the eve of the quantum revolution, Elsasser lays the foundation for a theoretical biology that points the way toward a (...) natural philosophy of organic life. Explicitly repudiating "vitalism" (the notion that the laws of nature need to be modified when applied to living organisms), Elsasser argues instead that the structural complexity of even a single living cell is "transcomputational"--that is, beyond the power of any imaginable system to compute. Beginning from this insight, Elsasser leads the reader through a step-by-step process that ultimately arrives at the conclusion that living and non-living matter are separated by "a no-man's land of irrationality." Trained in Germany as a physicist, Elsasser first pondered the implications of quantum mechanics for biology as early as 1951. The more closely he studied the inherent complexity of life, the more skeptical he became of the reductionist view of organisms as tiny machines. "An organism," he concluded, "is a source of causal chains which cannot be traced beyond a terminal point because they are lost in the unfathomable complexity of the organism." Like the physicist who works within the bounds of an unfathomable universe, Elsasser argues, the biologist must seek answers within a system that is no less unfathomable. (shrink)

The paper deals with two central issues in the philosophy of neuroscience and psychiatry, namely those of the nature and the major kinds and types of psychopathological mechanisms. Contrary to a widespread view, I argue that mechanisms are not kinds of systems but kinds of processes unfolding in systems or between systems. More precisely, I argue that psychopathological mechanisms are sets of actions and interactions between brain-systems or circuits as well as between the latter and other systems (...) in one's body and external environment, both physical and social, involved in human psychopathology. According to the kinds of properties of the interacting systems or their component-parts, psychopathological mechanisms may be physical, chemical, biological, psychological, social, or, typically, mixed ones. Furthermore, I focus on two main kinds of psychopathological mechanisms involved in the causation of mental disorders, namely the pathogenetic and pathophysiological ones, stressing the importance of their careful distinction for the integrative understanding of otherwise disparate and apparently incommensurable psychiatric research findings. I illustrate my analysis with an example drawn from contemporary research on the mechanisms of acute psychosis. Finally, I stress the relevance of psychopathological mechanisms to a more scientifi cally-grounded classifi cation of mental disorders. (shrink)

In this paper I address an important question in Aristotle’s biology, What are the causal mechanisms behind the transmission of biological form? Aristotle’s answer to this question, I argue, is found in Generation of Animals Book 4 in connection with his investigation into the phenomenon of inheritance. There we are told that an organism’s reproductive material contains a set of "movements" which are derived from the various "potentials" of its nature (the internal principle of change that initiates and (...) controls development). These movements, I suggest, function as specialized vehicles for communicating the parts of the parent’s heritable form during the act of reproduction. After exploring the details of this mechanism, I then take up Aristotle’s theory of inheritance proper. At the heart of the theory are three general principles (or 'laws') that govern the interactions between the maternal and paternal movements, the outcome of which determines the pattern of inheritance for the offspring. Although this paper is primarily aimed at providing a detailed analysis of Aristotle’s account of inheritance, the results of that analysis have implications for other areas of Aristotle’s biology. One of the most interesting of these is the question of whether Aristotle’s biology is anti-evolutionary (as traditionally assumed) or whether (as I argue) it leaves room for a theory of evolution by natural selection, even if Aristotle himself never took that step. (shrink)

In the first part of this article we survey general similarities and differences between biological and social macroevolution. In the second (and main) part, we consider a concrete mathematical model capable of describing important features of both biological and social macroevolution. In mathematical models of historical macrodynamics, a hyperbolic pattern of world population growth arises from non-linear, second-order positive feedback between demographic growth and technological development. Based on diverse paleontological data and an analogy with macrosociological models, we suggest (...) that the hyperbolic character of biodiversity growth can be similarly accounted for by non-linear, second-order positive feedback between diversity growth and the complexity of community structure. We discuss how such positive feedback mechanisms can be modelled mathematically. (shrink)

While many different mechanisms contribute to the generation of spatial order in biological development, the formation of morphogenetic fields which in turn direct cell responses giving rise to pattern and form are of major importance and essential for embryogenesis and regeneration. Most likely the fields represent concentration patterns of substances produced by molecular kinetics. Short range autocatalytic activation in conjunction with longer range “lateral” inhibition or depletion effects is capable of generating such patterns (Gierer and Meinhardt, 1972). Non-linear (...) reactions are required, and mathematical criteria were derived to design molecular models capable of pattern generation. The classical embryological feature of proportion regulation can be incorporated into the models. The conditions are mathematically necessary for the simplest two-factor case, and are likely to be a fair approximation in multi-component systems in which activation and inhibition are systems parameters subsuming the action of several agents. Gradients, symmetric and periodic patterns, in one or two dimensions, stable or pulsing in time, can be generated on this basis. Our basic concept of autocatalysis in conjunction with lateral inhibition accounts for self-regulatory biological features, including the reproducible formation of structures from near-uniform initial conditions as required by the logic of the generation cycle. Real tissue form, for instance that of budding Hydra, may often be traced back to local curvature arising within an initially relatively flat cell sheet, the position of evagination being determined by morphogenetic fields. Shell theory developed for architecture may also be applied to such biological processes. (shrink)

The article presents a perspective on the scientific explanation of the subjectivity of conscious experience. It proposes plausible answers for two empirically valid questions: the ‘how’ question concerning the developmental mechanisms of subjectivity, and the ‘why’ question concerning its function. Biological individuation, which is acquired in several different stages, serves as a provisional description of how subjective perspectives may have evolved. To the extent that an individuated informational space seems the most efficient way for a given organism to (...) select biologically valuable information, subjectivity is deemed to constitute an adaptive response to informational overflow. One of the possible consequences of this view is that subjectivity might be (at least functionally) dissociated from consciousness, insofar as the former primarily facilitates selection, the latter action. (shrink)

To approach the issue of the recent proposal of an Extended Evolutionary Synthesis (EES) put forth by Massimo Pigliucci and Gerd Müller, I suggest to consider the EES as a metascientific view: a description of what’s new in how evolutionary biology is carried out, not only a description of recently learned aspects of evolution. Knowing ‘what is it to do research’ in evolutionary biology, today versus yesterday, can aid training, research and career choices, establishment of relationships and collaborations, decision of (...) funding and research policies, in order to make the field advance for the better. After reviewing the concepts associated to the EES proposal (categorized for convenience as mechanisms, measures, fields, perspectives and applications), I show their transience, and sketch out ongoing disagreements about the EES. Then I examine the deep difficulties, i.e., the enormity and complexity of the covered field, affecting the achievement of trusted metascientific views; the insufficiency of conceptual analysis to capture the substance of scientific research; the entanglement between empirical and metascientific concepts, between multiple chronologies, and between descriptive and normative intentions; and the ineliminable stakeholding of any reviewer involved in the reviewed field. I propose that disciplines such as scientometrics, ethnography, sociology, economics and history, combined with conceptual analysis, inspire a more rigorous approach to the evolutionary biology scientific community, more grounded and shared, confirming or transforming claims for ‘synthesis’ while preserving their maintenance goals. (shrink)

A new theory that naturalizes biological function is explained and compared with earlier etiological and causal role theories. Etiological theories explain functions from how they are caused over their evolutionary history. Causal role theories analyze how functional mechanisms serve the current capacities of their containing system. The new proposal unifies the key notions of both kinds of theories, but goes beyond them by explaining how functions in an organism can exist as factors with autonomous causal efficacy. The goal-directedness (...) and normativity of functions exist in this strict sense as well. The theory depends on an internal physiological or neural process that mimics an organism’s fitness, and modulates the organism’s variability accordingly. The structure of the internal process can be subdivided into subprocesses that monitor specific functions in an organism. The theory matches well with each intuition on a previously published list of intuited ideas about biological functions, including intuitions that have posed difficulties for other theories. (shrink)

Ever since the advent of molecular biology in the 1970s, mechanical models have become the dogma in the field, where a "true" understanding of any subject is equated to a mechanistic description. This has been to the detriment of the biomedical sciences, where, barring some exceptions, notable new feats of understanding have arguably not been achieved in normal and disease biology, including neurodegenerative disease and cancer pathobiology. I argue for a "mechanism-plus-X" paradigm, where mainstay elements of mechanistic models such as (...) hierarchy and correlation are combined with nomological principles such as general operative rules and generative principles. Depending on the question at hand and the nature of the inquiry, X could range from proven physical laws to speculative biological generalizations, such as the notional principle of cellular synchrony. I argue that the "mechanism-plus-X" approach should ultimately aim to move biological inquiries out of the deadlock of oft-encountered mechanistic pitfalls and reposition biology to its former capacity of illuminating fundamental truths about the world. (shrink)

The purpose of this paper is to present a general mechanistic framework for analyzing causal representational claims, and offer a way to distinguish genuinely representational explanations from those that invoke representations for honorific purposes. It is usually agreed that rats are capable of navigation because they maintain a cognitive map of their environment. Exactly how and why their neural states give rise to mental representations is a matter of an ongoing debate. I will show that anticipatory mechanisms involved in (...) rats’ evaluation of possible routes give rise to satisfaction conditions of contents, and this is why they are representationally relevant for explaining and predicting rats’ behavior. I argue that a naturalistic account of satisfaction conditions of contents answers the most important objections of antirepresentationalists. (shrink)

Kant’s theory of biology in the Critique of the Power of Judgment may be rejected as obsolete and attacked from two opposite perspectives. In light of recent advances in biology one can claim contra Kant, on the one hand, that biological phenomena, which Kant held could only be explicated with the help of teleological principles, can in fact be explained in an entirely mechanical manner, or on the other, that despite the irreducibility of biology to physico-mechanical explanations, it is (...) nonetheless proper science. I argue in response that Kant’s analysis of organisms is by no means obsolete. It reveals biology’s uniqueness in much the same way as several current theorists do. It brings to the fore the unique purposive characteristics of living phenomena, which are encapsulated in Kant’s concept of “natural end” and which must be explicated in natural terms in order for biology to become a science. I maintain that Kant’s reluctance to consider biology proper science is not a consequence of his critical philosophy but rather of his inability to complete this task. Kant lacked an appropriate theoretical framework, such as provided later by modern biology, which would enable the integration of the unique features of biology in an empirical system. Nevertheless, as I show in this paper, the conceptual problems with which Kant struggled attest more to the relevance and depth of his insights than to the shortcomings of his view. His contribution to the biological thought consists in insisting on an empirical approach to biology and in providing the essential philosophical underpinning of the autonomous status of biology. (shrink)

This paper examines the notion of the biopolitical body from the standpoint of Foucault’s logic of the security mechanism and the history he tells of vaccine technology. It then investigates how the increasing importance of the genetic code for determining the meaning and limits of the human in the field of 20th century cell biology has been a cause for ongoing transformation in the practices that currently extend vaccine research and development. I argue that these transformations mark the emergence of (...) a new kind of medical subject – the stabilized and infinitely reproducible human cell line – and that the practices and markets exploiting this new form of organism have had a destabilizing effect on the very biopolitical structures that engendered them and, in fact, mark a new way of conceiving the possibilities of cellular life. I call these new ways of organizing power that intervene in the logic of the security measure by mediating the relationship between populations and persons the microbiopolitical. (shrink)

The concept of mechanism in biology has three distinct meanings. It may refer to a philosophical thesis about the nature of life and biology (‘mechanicism’), to the internal workings of a machine-like structure (‘machine mechanism’), or to the causal explanation of a particular phenomenon (‘causal mechanism’). In this paper I trace the conceptual evolution of ‘mechanism’ in the history of biology, and I examine how the three meanings of this term have come to be featured in the philosophy of biology, (...) situating the new ‘mechanismic program’ in this context. I argue that the leading advocates of the mechanismic program (i.e., Craver, Darden, Bechtel, etc.) inadvertently conflate the different senses of ‘mechanism’. Specifically, they all inappropriately endow causal mechanisms with the ontic status of machine mechanisms, and this invariably results in problematic accounts of the role played by mechanism-talk in scientific practice. I suggest that for effective analyses of the concept of mechanism, causal mechanisms need to be distinguished from machine mechanisms, and the new mechanismic program in the philosophy of biology needs to be demarcated from the traditional concerns of mechanistic biology. (shrink)

“Teleosemantic” or “biosemantic” theories form a strong naturalistic programme in the philosophy of mind and language. They seek to explain the nature of mind and language by recourse to a natural history of “proper functions” as selected-for effects of language- and thought-producing mechanisms. However, they remain vague with respect to the nature of the proposed analogy between selected-for effects on the biological level and phenomena that are not strictly biological, such as reproducible linguistic and cultural forms. This (...) essay critically explores various interpretations of this analogy. It suggests that these interpretations can be explicated by contrasting adaptationist with pluralist readings of the evolutionary concept of adaptation. Among the possible interpretations of the relations between biological adaptations and their analogues in language and culture, the two most relevant are a linear, hierarchical, signalling-based model that takes its cues from the evolution of co-operation and joint intentionality and a mutualistic, pluralist model that takes its cues from mimesis and symbolism in the evolution of human communication. Arguing for the merits of the mutualistic model, the present analysis indicates a path towards an evolutionary pluralist version of biosemantics that will align with theories of cognition as being environmentally “scaffolded”. Language and other cultural forms are partly independent reproducible structures that acquire proper functions of their own while being integrated with organism-based cognitive traits in co-evolutionary fashion. (shrink)

Natural Computing is a field of research in Computer Science aimed at reinterpreting biological phenomena as computing mechanisms. This allows unconventional computing architectures to be proposed in which computations are performed by atoms, DNA strands, cells, insects or other biological elements. Membrane Computing is a branch of Natural Computing in which biological phenomena of interest are related with interactions between molecules inside cells. The research in Membrane Computing has lead to very important theoretical results that show (...) how, in principle, cells could be used to solve any (computable) computational problem with performances that cannot be obtained by conventional computers. However, the implementation of a cell-based computational architecture seems not easily achievable. On the other hand, models of Membrane Computing have found an alternative application to the description of biological systems, with the aim of developing simulators and other analysis tools for the study of biological problems. (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)

Denis Diderot’s natural philosophy is deeply and centrally ‘biologistic’: as it emerges between the 1740s and 1780s, thus right before the appearance of the term ‘biology’ as a way of designating a unified science of life (McLaughlin), his project is motivated by the desire both to understand the laws governing organic beings and to emphasize, more ‘philosophically’, the uniqueness of organic beings within the physical world as a whole. This is apparent both in the metaphysics of vital matter he puts (...) forth in works such as D’Alembert’s Dream (1769) and the more empirical concern with the mechanics of life in his manuscript Elements of Physiology, on which he worked during the last twenty years of his life. This ‘biologism’ obviously presents the interpreter of Diderot with some difficulties, notably as regards his materialism, given that contemporary forms of materialism have on the contrary strongly rejected notions of emergence, vitalism, teleology and any concepts appealing to unique, irreducible features of organisms. In response, some have described him as a ‘holist’ (Kaitaro) while others have emphasized his materialist, naturalist project (Bourdin, Wolfe). In what follows I examine a little-known aspect of Diderot’s articulation of his biological project: his statement in favour of epigenesis within the short but suggestive Encyclopédie article “Spinosiste.” Diderot was, of course, a partisan of epigenesis (the developmental-biological theory opposed to preformation, according to which beings develop by successive adjunction of layers of matter), but why include a statement in favour of a particular biological (or developmental) theory within an entry dealing with a philosopher, Spinoza, who does not seem to have been concerned at all with the specific properties of living beings, how they grow from embryonic to developed states, and so on? By trying to answer this question I also try and locate Diderot’s biological project in relation to what will become, in the years after his death, the project for a science called ‘biology’, with figures such as Treviranus and Lamarck. For it is not clear that the two can be easily correlated or causally linked: Diderot’s ‘epigenetic Spinozism’ is a different conceptual entity from what we find in histories of biology. (shrink)

Evolutionary psychologists often try to “bring together” biology and psychology by making predictions about what specific psychological mechanisms exist from theories about what patterns of behaviour would have been adaptive in the EEA for humans. This paper shows that one of the deepest methodological generalities in evolutionary biology—that proximate explanations and ultimate explanations stand in a many-to-many relation—entails that this inferential strategy is unsound. Ultimate explanations almost never entail the truth of any particular proximate hypothesis. But of course it (...) does not follow that there are no other ways of “bringing together” biology and psychology. Accordingly, this paper explores one other strategy for doing just that, the pursuit of a very specific kind of consilience. However, I argue that inferences reflecting the pursuit of this kind of consilience with the best available theories in contemporary evolutionary biology indicate that psychologists should have a preference for explanations of adaptive behavior in humans that refer to learning and other similarly malleable psychological mechanisms—and not modules or instincts or any other kind of relatively innate and relatively non-malleable psychological mechanism. (shrink)

Using as case studies two early diagrams that represent mechanisms of the cell division cycle, we aim to extend prior philosophical analyses of the roles of diagrams in scientific reasoning, and specifically their role in biological reasoning. The diagrams we discuss are, in practice, integral and indispensible elements of reasoning from experimental data about the cell division cycle to mathematical models of the cycle’s molecular mechanisms. In accordance with prior analyses, the diagrams provide functional explanations of the (...) cell cycle and facilitate the construction of mathematical models of the cell cycle. But, extending beyond those analyses, we show how diagrams facilitate the construction of mathematical models, and we argue that the diagrams permit nomological explanations of the cell cycle. We further argue that what makes diagrams integral and indispensible for explanation and model construction is their nature as locality aids: they group together information that is to be used together in a way that sentential representations do not. (shrink)

The INBIOSA project brings together a group of experts across many disciplines who believe that science requires a revolutionary transformative step in order to address many of the vexing challenges presented by the world. It is INBIOSA’s purpose to enable the focused collaboration of an interdisciplinary community of original thinkers. This paper sets out the case for support for this effort. The focus of the transformative research program proposal is biology-centric. We admit that biology to date has been more fact-oriented (...) and less theoretical than physics. However, the key leverageable idea is that careful extension of the science of living systems can be more effectively applied to some of our most vexing modern problems than the prevailing scheme, derived from abstractions in physics. While these have some universal application and demonstrate computational advantages, they are not theoretically mandated for the living. A new set of mathematical abstractions derived from biology can now be similarly extended. This is made possible by leveraging new formal tools to understand abstraction and enable computability. [The latter has a much expanded meaning in our context from the one known and used in computer science and biology today, that is "by rote algorithmic means", since it is not known if a living system is computable in this sense (Mossio et al., 2009).] Two major challenges constitute the effort. The first challenge is to design an original general system of abstractions within the biological domain. The initial issue is descriptive leading to the explanatory. There has not yet been a serious formal examination of the abstractions of the biological domain. What is used today is an amalgam; much is inherited from physics (via the bridging abstractions of chemistry) and there are many new abstractions from advances in mathematics (incentivized by the need for more capable computational analyses). Interspersed are abstractions, concepts and underlying assumptions “native” to biology and distinct from the mechanical language of physics and computation as we know them. A pressing agenda should be to single out the most concrete and at the same time the most fundamental process-units in biology and to recruit them into the descriptive domain. Therefore, the first challenge is to build a coherent formal system of abstractions and operations that is truly native to living systems. Nothing will be thrown away, but many common methods will be philosophically recast, just as in physics relativity subsumed and reinterpreted Newtonian mechanics. -/- This step is required because we need a comprehensible, formal system to apply in many domains. Emphasis should be placed on the distinction between multi-perspective analysis and synthesis and on what could be the basic terms or tools needed. The second challenge is relatively simple: the actual application of this set of biology-centric ways and means to cross-disciplinary problems. In its early stages, this will seem to be a “new science”. This White Paper sets out the case of continuing support of Information and Communication Technology (ICT) for transformative research in biology and information processing centered on paradigm changes in the epistemological, ontological, mathematical and computational bases of the science of living systems. Today, curiously, living systems cannot be said to be anything more than dissipative structures organized internally by genetic information. There is not anything substantially different from abiotic systems other than the empirical nature of their robustness. We believe that there are other new and unique properties and patterns comprehensible at this bio-logical level. The report lays out a fundamental set of approaches to articulate these properties and patterns, and is composed as follows. -/- Sections 1 through 4 (preamble, introduction, motivation and major biomathematical problems) are incipient. Section 5 describes the issues affecting Integral Biomathics and Section 6 -- the aspects of the Grand Challenge we face with this project. Section 7 contemplates the effort to formalize a General Theory of Living Systems (GTLS) from what we have today. The goal is to have a formal system, equivalent to that which exists in the physics community. Here we define how to perceive the role of time in biology. Section 8 describes the initial efforts to apply this general theory of living systems in many domains, with special emphasis on crossdisciplinary problems and multiple domains spanning both “hard” and “soft” sciences. The expected result is a coherent collection of integrated mathematical techniques. Section 9 discusses the first two test cases, project proposals, of our approach. They are designed to demonstrate the ability of our approach to address “wicked problems” which span across physics, chemistry, biology, societies and societal dynamics. The solutions require integrated measurable results at multiple levels known as “grand challenges” to existing methods. Finally, Section 10 adheres to an appeal for action, advocating the necessity for further long-term support of the INBIOSA program. -/- The report is concluded with preliminary non-exclusive list of challenging research themes to address, as well as required administrative actions. The efforts described in the ten sections of this White Paper will proceed concurrently. Collectively, they describe a program that can be managed and measured as it progresses. (shrink)

Biologists, historians of biology, and philosophers of biology often ask what is it to be an individual, really. This book does not answer that question. Instead, it answers a much more interesting one: How do biologists individuate individuals? In answering that question, the authors explore why biologists individuate individuals, in what ways, and for what purposes. The cross-disciplinary, dialogical approach to answering metaphysical questions that is pursued in the volume may seem strange to metaphysicians who are not biologically focused, but (...) it is adroitly achieved by the editors. Scott Lidgard (a paleontologist and marine ecologist) and Lynn K. Nyhart (a historian of biology) orchestrate a dialogue among historians of biology, philosophers of biology, and practicing biologists over 10 chapters. These are followed by three reflective commentaries written to frame the different disciplinary perspectives and to highlight the historical, biological, and philosophical themes across the chapters. The result is a volume—in structure and in content—that has much to be generously commended. Biological individuality is a hotly discussed topic, but it is also part of a series of long-standing arguments within both the history and philosophy of biology (HPB) and metaphysics. Notable and fervent debates have centered on evolution and the units of selection, predominantly on Michael T. Ghiselin’s and David L. Hull’s notion of species as individuals, Peter Godfrey-Smith’s Darwinian individuals, and Ellen Clarke’s individuating mechanisms. Lately, it has encompassed non-Darwinian individuals, symbiotic associations like Thomas Pradeu’s immunological individuals, and John Dupré and Maureen A. O’Malley’s metabolic individuals.2 The present volume is curated in a way to introduce the reader to new research in HPB that articulates these debates as well as to introduce and engage in the study of further notions of biological individuality. But its aim is more than an introduction. As the subtitle suggests, it is also intended to give the reader insight into the working together of biologists, historians of biology, and philosophers of biology in figuring out how the notion of biological individuality is instantiated. As such, the problem-centered dialogue that results does more than talk through biological individuality. It shows how the different and often divergent goals of the authors’ disciplines shape not only how they think about individuality but how they communicate this thinking in reciprocal collaboration with others in different disciplines. … cont’d…. (shrink)

Distinctions in fundamentality between different levels of description are central to the viability of contemporary decoherence-based Everettian quantum mechanics (EQM). This approach to quantum theory characteristically combines a determinate fundamental reality (one universal wave function) with an indeterminate emergent reality (multiple decoherent worlds). In this chapter I explore how the Everettian appeal to fundamentality and emergence can be understood within existing metaphysical frameworks, identify grounding and concept fundamentality as promising theoretical tools, and use them to characterize a system of explanatory (...) levels (with associated laws of nature) for EQM. This Everettian level structure encompasses and extends the ‘classical’ levels structure. The ‘classical’ levels of physics, chemistry, biology, etc. are recovered, but they are emergent in character and potentially variable across Everett worlds. EQM invokes an additional fundamental level, not present in the classical levels picture, and a novel potential role for self-location in interlevel metaphysics. When given a modal realist interpretation, EQM also makes trouble for supervenience-based approaches to levels. (shrink)

The coronavirus pandemic, like its predecessors - AIDS, Ebola, etc., is evidence of the evolutionary instability of the socio-cultural and ecological niche created by mankind, as the main factor in the evolutionary success of our biological species and the civilization created by it. At least, this applies to the modern global civilization, which is called technogenic or technological, although it exists in several varieties. As we hope to show, the current crisis has less ontological as well as epistemological roots; (...) its reason lies in the main evolutionary trends in the development of science as a social institution. It was only later that epistemological factors were transformed into existential-ontological factors associated with the asymmetry of the existence of civilization and our biosocial nature. The perception or ignorance of a risk factor as a real fact is determined by the presence or absence of knowledge about it. In other words, risk is the result of the integration of the corresponding ontological concept into the general categorical structure. The plurality of such structures is a hallmark of multidisciplinary ontologies, each of which is associated with its own factual continuum. The aim of this study was to conceptually model for support relativistic parameter of evolutionary efficiency of stable evolutionary human strategy (SESH) in its techno-rationalistic module. The meaning of this term is equivalent to the category of scientific and technological development. (shrink)