Contemporary complexity theory has been instrumental in providing novel rigorous definitions for some classic philosophical concepts, including emergence. In an attempt to provide an account of emergence that is consistent with complexity and dynamical systems theory, several authors have turned to the notion of constraints on state transitions. Drawing on complexity theory directly, this paper builds on those accounts, further developing the constraint-based interpretation of emergence and arguing that such accounts recover many of the features of more traditional accounts. We (...) show that the constraint-based account of emergence also leads naturally into a meaningful definition of self-organization, another concept that has received increasing attention recently. Along the way, we distinguish between order and organization, two concepts which are frequently conflated. Finally, we consider possibilities for future research in the philosophy of complex systems, as well as applications of the distinctions made in this paper. (shrink)

Climatology is a paradigmatic complex systems science. Understanding the global climate involves tackling problems in physics, chemistry, economics, and many other disciplines. I argue that complex systems like the global climate are characterized by certain dynamical features that explain how those systems change over time. A complex system's dynamics are shaped by the interaction of many different components operating at many different temporal and spatial scales. Examining the multidisciplinary and holistic methods of climatology can help us better understand the nature (...) of complex systems in general. -/- Questions surrounding climate science can be divided into three rough categories: foundational, methodological, and evaluative questions. "How do we know that we can trust science?" is a paradigmatic foundational question (and a surprisingly difficult one to answer). Because the global climate is so complex, questions like "what makes a system complex?" also fall into this category. There are a number of existing definitions of `complexity,' and while all of them capture some aspects of what makes intuitively complex systems distinctive, none is entirely satisfactory. Most existing accounts of complexity have been developed to work with information-theoretic objects (signals, for instance) rather than the physical and social systems studied by scientists. -/- Dynamical complexity, a concept articulated in detail in the first third of the dissertation, is designed to bridge the gap between the mathematics of contemporary complexity theory (in particular the formalism of "effective complexity" developed by Gell-Mann and Lloyd [2003]) and a more general account of the structure of science generally. Dynamical complexity provides a physical interpretation of the formal tools of mathematical complexity theory, and thus can be used as a framework for thinking about general problems in the philosophy of science, including theories, explanation, and lawhood. -/- Methodological questions include questions about how climate science constructs its models, on what basis we trust those models, and how we might improve those models. In order to answer questions about climate modeling, it's important to understand what climate models look like and how they are constructed. Climate model families are significantly more diverse than are the model families of most other sciences (even sciences that study other complex systems). Existing climate models range from basic models that can be solved on paper to staggeringly complicated models that can only be analyzed using the most advanced supercomputers in the world. I introduce some of the central concepts in climatology by demonstrating how one of the most basic climate models might be constructed. I begin with the assumption that the Earth is a simple featureless blackbody which receives energy from the sun and releases it into space, and show how to model that assumption formally. I then gradually add other factors (e.g. albedo and the greenhouse effect) to the model, and show how each addition brings the model's prediction closer to agreement with observation. After constructing this basic model, I describe the so-called "complexity hierarchy" of the rest of climate models, and argue that the sense of "complexity" used in the climate modeling community is related to dynamical complexity. -/- With a clear understanding of the basics of climate modeling in hand, I then argue that foundational issues discussed early in the dissertation suggest that computation plays an irrevocably central role in climate modeling. "Science by simulation" is essential given the complexity of the global climate, but features of the climate system--the presence of non-linearities, feedback loops, and chaotic dynamics--put principled limits on the effectiveness of computational models. This tension is at the root of the staggering pluralism of the climate model hierarchy, and suggests that such pluralism is here to stay, rather than an artifact of our ignorance. Rather than attempting to converge on a single "best fit" climate model, we ought to embrace the diversity of climate models, and view each as a specialized tool designed to predict and explain a rather narrow range of phenomena. Understanding the climate system as a whole requires examining a number of different models, and correlating their outputs. This is the most significant methodological challenge of climatology. -/- Climatology's role contemporary political discourse raises an unusually high number of evaluative questions for a physical science. The two leading approaches to crafting policy surrounding climate change center on mitigation (i.e. stopping the changes from occurring) and adaptation (making post hoc changes to ameliorate the harm caused by those changes). Crafting an effective socio-political response to the threat of anthropogenic climate change, however, requires us to integrate multiple perspectives and values: the proper response will be just as diverse and pluralistic as the climate models themselves, and will incorporate aspects of both approaches. I conclude by offering some concrete recommendations about how to integrate this value pluralism into our socio-political decision making framework. (shrink)

As a significant extension of our previous calculus of logical differentials and moving logic, we propose here a mathematical diagram for specifying the emergence of novelty, through the construction of some “differentials” related to cohomological computations. Later we intend to examine how to use these “differentials” in the analysis of anticipation or evolution schemes. This proposal is given as a consequence of our comments on the Ehresmann–Vanbremeersch’s work on memory evolutive systems, from the two points of view which are characterization (...) of life and use of categorical modeling. It would not be possible to conceive of the “differentials” outside the frame of a MES. Furthermore, we hope that this diagram will be useful to exhibit the “emergence of sense” in discourses, and this idea is supported here by a brief examination of how a discourse could be seen as a living system. (shrink)

I investigate the relationship between adaptation, as defined in evolutionary theory through natural selection, and the concept of emergence. I argue that there is an essential correlation between the former, and “emergence” defined in the field of algorithmic simulations. I first show that the computational concept of emergence (in terms of incompressible simulation) can be correlated with a causal criterion of emergence (in terms of the specificity of the explanation of global patterns). On this ground, I argue that emergence in (...) general involves some sort of selective processes. Finally, I show that a second criterion, concerning novel explanatory regularities following the emergence of a pattern, captures the robustness of emergence displayed by some cases of emergence (according to the first criterion). Emergent processes fulfilling both criteria are therefore exemplified in evolutionary biology by some so-called “innovations”, and mostly by the new units of fitness or new kinds of adaptations (like sexual reproduction, multicellular organisms, cells, societies) sometimes called “major transitions in evolution”, that recent research programs (Maynard-Smith and Szathmary 1995 ; Michod 1999 ) aims at explaining. (shrink)

Emergent phenomena are quite interesting and amazing, but they present two main scientific obstacles: to be rationally understood and to be mathematically modelled. In this paper we propose a powerful mathematical tool for modelling emergent phenomena by applying category theory. Furthermore, since great part of biological phenomena are emergent, we present an essay of how to access an emergence from observational data. In the mathematical perspective, we utilize constructs (categories whose objects are structured sets), their operations and their corresponding generalized (...) underlying functor (which are not necessarily faithful) to define emergence. From this definition, we elaborate several results concerning homomorphism between emergences, representability, pull-back, push-out, equalizer, product and co-product of emergences among other results. After we have presented such mathematical model, we utilize a hierarchical structure as example—from molecules to a flatworm—, in order to understand the relation between biological systems and its underlying emergence in a hierarchical model. (shrink)

Complex, multigenic biological traits are shaped by the emergent interaction of proteins being the main functional units at the molecular scale. Based on a phenomenological approach, algorithms for quantifying two different aspects of emergence were introduced (Wegner and Hao in Progr Biophys Mol Biol 161:54–61, 2021) describing: (i) pairwise reciprocal interactions of proteins mutually modifying their contribution to a complex trait (denoted as weak emergence), and (ii) formation of a new, complex trait by a set of n ‘constitutive’ proteins at (...) concentrations exceeding individual threshold values (strong emergence). The latter algorithm is modified here to take account of protein redundancy with respect to a complex trait (‘full redundancy’). Irreducibility is considered a necessary and sufficient criterion for strong biological emergence; if one constitutive protein is missing, or its concentration drops below the threshold the trait is lost. A definition based on ‘unpredictability’ is dismissed, because this criterion is irrelevant for the evolution of a complex trait, and apparent unpredictability may rather reflect our basic deficits in understanding unless we can provide an unequivocal proof for it. The phenomenological approach advocated here allows to identify hidden rules according to which strongly emergent traits may be organized. This is of high value for understanding the evolution of complex traits which seems to require the saltational advent of all constitutive proteins ‘in one turn’ to arrive at a functional trait providing for an improved fitness of the organism. Rather than being a purely random process, it may be guided by fundamental structural principles. (shrink)

Scientific theories of consciousness identify its contents with the spatiotemporal structure of neural population activity. We follow up on this approach by stating and motivating Dynamical Emergence Theory, which defines the amount and structure of experience in terms of the intrinsic topology and geometry of a physical system’s collective dynamics. Specifically, we posit that distinct perceptual states correspond to coarse-grained macrostates reflecting an optimal partitioning of the system’s state space—a notion that aligns with several ideas and results from computational neuroscience (...) and cognitive psychology. We relate DET to existing work, offer predictions for empirical studies, and outline future research directions. (shrink)

This thesis gives a philosophical assessment of a contemporary movement, influential amongst physicists, about the status of microscopic and macroscopic properties. The fountainhead for the movement was a short 1972 paper `More is Different', written by the condensed-matter physicist, Philip Anderson. Each of the chapters is concerned with themes mentioned in that paper, or subsequently expounded by Anderson and his followers. In Chapter 1, I aim to locate Anderson's existence claims for `emergent properties' within the metaphysical, epistemological and methodological doctrines (...) that identify themselves as `emergentist'. I argue, against the commentators' consensus, that the New Emergentists make claims about the metaphysical status of physical properties, and should not be read as concerned only with matters of research methodology for physics. In Chapter 2, I look at the physical examples that the New Emergentists appeal to, and propose a way of formulating their main claims within modern analytic metaphysics. I argue that it is possible to view their thesis as an updated version of `British Emergentism' a movement popular in the early years of the twentieth century. I support this contention by comparing examples of emergent properties put forward by the British and the New Emergentists. Chapter 3 is a discussion of the significance of renormalisation techniques. I attack a set of claims by Robert Batterman, who presents renormalisation as an explanatory strategy unrecognised by the philosophy of science. I separate several different methods of renormalisation analysis, and argue that he has conflated them. In Chapter 4, I discuss the theoretical representation of phase transitions in condensed matter physics; in particular, their appeal to the limit of an infinite system. I examine and refute various claims to the effect that the ineliminability of this limit in modelling phase transitions is of great metaphysical significance. I suggest a definition of phase transitions for finite systems that dissolves this illusion, and gives us reason to trust the results of the theories that only apply in the infinite limit. %I then comment on the significance of these results for a suggestion of Laura Ruetsche, that quantum statistical mechanics should be used as a guide to the interpretation of quantum field theory. Chapter 5 revisits the doctrines of the New Emergentists in order to place their views within some wider philosophical debates. I look at how their doctrines bear on issues in the interpretation of quantum mechanics, in the philosophy of mind and the philosophy of science. I close by returning to the context within physics, in which the New Emergentists originally made their presence felt: the controversy over whether elementary particle physics should enjoy greater status than other areas. (shrink)

By what empirical means can a person determine whether he or she is presently awake or dreaming? Any conceivable test addressing this question, which is a special case of the classical metaphysical doubting of reality, must be statistical (for the same reason that empirical science is, as noted by Hume). Subjecting the experienced reality to any kind of statistical test (for instance, a test for bizarreness) requires, however, that a set of baseline measurements be available. In a dream, or in (...) a simulation, any such baseline data would be vulnerable to tampering by the same processes that give rise to the experienced reality, making the outcome of a reality test impossible to trust. Moreover, standard cryptographic defenses against such tampering cannot be relied upon, because of the potentially unlimited reach of reality modification within a dream, which may range from the integrity of the verification keys to the declared outcome of the entire process. In the face of this double predicament, the rational course of action is to take reality at face value. The predicament also has some intriguing corollaries. In particular, even the most revealing insight that a person may gain into the ultimate nature of reality (for instance, by attaining enlightenment in the Buddhist sense) is ultimately unreliable, for the reasons just mentioned. At the same time, to adhere to this principle, one has to be aware of it, which may not be possible in various states of reduced or altered cognitive function such as dreaming or religious experience. Thus, a subjectively enlightened person may still lack the one truly important piece of the puzzle concerning his or her existence. (shrink)

While some branches of complexity theory are advancing rapidly, the same cannot be said for our understanding of emergence. Despite a complete knowledge of the rules underlying the interactions between the parts of many systems, we are often baffled by their sudden transitions from simple to complex. Here I propose a solution to this conceptual problem. Given that emergence is often the result of many interactions occurring simultaneously in time and space, an ability to intuitively grasp it would require the (...) ability to consciously think in parallel. A simple exercise is used to demonstrate that we do not possess this ability. Our surprise at the behaviour of cellular automata models, and the natural cases of pattern formation they mimic, is then explained from this perspective. This work suggests that the cognitive limitations of the mind can be as significant a barrier to scientific progress as the limitations of our senses. (shrink)

We propose a theoretical framework that highlights the most important consequences of complexity for the form and evolution of projects and use it to develop a typology of project complexity. This framework also enables us to deepen the understanding of how knowledge production and flexibility strategies enable project participants to address complexity. Based on this understanding, we advance a number of propositions regarding the strategies that can be most effective for different categories of complexity. These results contribute to the integration (...) of various strands in the research on project complexity and provide a roadmap for further research on the strategies for addressing it. (shrink)

In this philosophical paper, I discuss and illustrate the necessary three ingredients that together could allow a collective phenomenon to be labelled as “emergent.” First, the phenomenon, as usual, requires a group of natural objects entering in a non-linear relationship and potentially entailing the existence of various semantic descriptions depending on the human scale of observation. Second, this phenomenon has to be observed by a mechanical observer instead of a human one, which has the natural capacity for temporal or spatial (...) integration, or both. Finally, for this natural observer to detect and select the collective phenomenon, it needs to do so on account of the adaptive advantage this phenomenon is responsible for. The necessity for such a teleological characterization and the presence of natural selection drive us to defend, with many authors, the idea that emergent phenomena should belong only to biology. Following a brief philosophical plea, we present a simple and illustrative computer thought experiment in which a society of agents evolves a stigmergic collective behavior as an outcome of its greater adaptive value. The three ingredients are illustrated and discussed within this experimental context. Such an inclusion of the mechanical observer and the natural selection to which this phenomenon is submitted should underlie the necessary de-subjectivation that strengthens any scientific endeavor. I shall finally show why the short paths taken by ant colonies, the collective flying of birds and the maximum consumption of nutrients by a cellular metabolism are strongly emergent. (shrink)