Complex Systems, Misc

A common way of characterizing Boltzmann’s explanation of thermodynamics in term of statistical mechanics is with reference to three ingredients: the dynamics, the past hypothesis, and the statistical postulate. In this paper I focus on the statistical postulate, and I have three aims. First, I wish to argue that regarding the statistical postulate as a probability postulate may be too strong: a postulate about typicality would be enough. Second, I wish to show that there is no need to postulate anything, (...) for the typicality postulate can be suitably derived from the dynamics. Finally, I discuss how the attempts to give preference to certain stochastic quantum theories (such as the spontaneous collapse theory) over deterministic alternatives on the basis that they do not need the statistical postulate fail. (shrink)

Cet article revient sur la pratique scientifique et les thèses épistémologiques de Jean-Marie Legay concernant les modèles, les simulations et les systèmes complexes. Il montre qu'il y a une cohérence entre sa thèse anti-représentationnaliste concernant les modèles et les simulations et sa caractérisation même des systèmes complexes : une simulation informatique, seule, n'est pas une expérience au sens fort car, en l'isolant, on perd la dimension complexe de toute entreprise d'expérimentation scientifique dès lors qu'il y manque le modélisateur, le terrain (...) d'inspiration originelle ainsi que les objectifs de simulation, qui en interagissant à eux trois forment un système complexe. Vouloir prendre une simulation séparée de son modélisateur et de son ancrage empirique originel pour une expérience au sens fort, vouloir la considérer à elle seule comme une captation intégrale, dans la machine, de la complexité des systèmes étudiés par ailleurs en sciences de la vie et de l'environnement, ce serait croire que l'aventure scientifique elle-même n'aurait plus désormais, avec l'essor contemporain de l'informatique de modélisation, à être considérée elle-même comme complexe. C'est bien cette seule idée qui lui paraissait déraisonnable. (shrink)

To their dismay, children look like their parents. They are not perfect copies, and over many generations some features evaporate; but even over fifty generations features relevant to an anthropologist persist. Children perhaps can find some comfort in the fact that we are not alone: organisms in general maintain remarkably stable structures through time. In What is Life? Erwin Schrödinger famously predicted the existence of the gene, but he also asked how life manages such stability in the face of thermodynamics’ (...) prescription that systems spontaneously move to increase their entropy. How does life escape the randomizing effects of the Second Law? Schrödinger of course recognized that there is no genuine conflict between life and thermodynamics. The Second Law applies only to closed, i.e., approximately energetically isolated, systems, yet living organisms are open systems. Although living creatures are not plugged into electrical sockets like your refrigerator, Schrödinger noted that organisms are dependent on the high quality energy of their environment. They maintain their structures at the expense of increased contributions of entropy (waste) to their environment. Life arises in the balance between the low entropy found in the environment and the entropy the organism itself throws off. Writing in the 1940’s, Schrödinger could see the general form of the answer to his question, but he lacked the resources to explain why these stable structures arose in the first place. (shrink)

Can stable regularities be explained without appealing to governing laws or any other modal notion? In this paper, I consider what I will call a ‘Humean system’—a generic dynamical system without guiding laws—and assess whether it could display stable regularities. First, I present what can be interpreted as an account of the rise of stable regularities, following from Strevens [2003], which has been applied to explain the patterns of complex systems (such as those from meteorology and statistical mechanics). Second, since (...) this account presupposes that the underlying dynamics displays deterministic chaos, I assess whether it can be adapted to cases where the underlying dynamics is not chaotic but truly random—that is, cases where there is no dynamics guiding the time evolution of the system. If this is so, the resulting stable, apparently non-accidental regularities are the fruit of what can be called statistical necessity rather than of a primitive physical necessity. (shrink)

Compte rendu de L. Boi, Symétries, brisures de symétries et complexité en mathématiques, physique et biologie, Peter Lang.

Emergence is much discussed by both philosophers and scientists. But, as noted by Mitchell (2012), there is a significant gulf; philosophers and scientists talk past each other. We contend that this is because philosophers and scientists typically mean different things by emergence, leading us to distinguish being emergence and pattern emergence. While related to distinctions offered by others between, for example, strong/weak emergence or epistemic/ontological emergence (Clayton, 2004, pp. 9–11), we argue that the being vs. pattern distinction better captures what (...) the two groups are addressing. In identifying pattern emergence as the central concern of scientists, however, we do not mean that pattern emergence is of no interest to philosophers. Rather, we argue that philosophers should attend to, and even contribute to, discussions of pattern emergence. But it is important that this discussion be distinguished, not conflated, with discussions of being emergence. In the following section we explicate the notion of being emergence and show how it has been the focus of many philosophical discussions, historical and contemporary. In section 3 we turn to pattern emergence, briefly presenting a few of the ways it figures in the discussions of scientists (and philosophers of science who contribute to these discussions in science). Finally, in sections 4 and 5, we consider the relevance of pattern emergence to several central topics in philosophy of biology: the emergence of complexity, of control, and of goal-directedness in biological systems. (shrink)

Recent discussions of emergence in physics have focussed on the use of limiting relations, and often particularly on singular or asymptotic limits. We discuss a putative example of emergence that does not fit into this narrative: the case of phonons. These quasi-particles have some claim to be emergent, not least because the way in which they relate to the underlying crystal is almost precisely analogous to the way in which quantum particles relate to the underlying quantum field theory. But there (...) is no need to take a limit when moving from a crystal lattice based description to the phonon description. Not only does this demonstrate that we can have emergence without limits, but also provides a way of understanding cases that do involve limits. (shrink)

The Emergic Cognitive Model (ECM) is a unified computational model of visual filling-in based on the Emergic Network architecture. The Emergic Network was designed to help realize systems undergoing continuous change. In this thesis, eight different filling-in phenomena are demonstrated under a regime of continuous eye movement (and under static eye conditions as well). -/- ECM indirectly demonstrates the power of unification inherent with Emergic Networks when cognition is decomposed according to finer-grained functions supporting change. These can interact to raise (...) additional emergent behaviours via cognitive re-use, hence the Emergic prefix throughout. Nevertheless, the model is robust and parameter free. Differential re-use occurs in the nature of model interaction with a particular testing paradigm. -/- ECM has a novel decomposition due to the requirements of handling motion and of supporting unified modelling via finer functional grains. The breadth of phenomenal behaviour covered is largely to lend credence to our novel decomposition. -/- The Emergic Network architecture is a hybrid between classical connectionism and classical computationalism that facilitates the construction of unified cognitive models. It helps cutting up of functionalism into finer-grains distributed over space (by harnessing massive recurrence) and over time (by harnessing continuous change), yet simplifies by using standard computer code to focus on the interaction of information flows. Thus while the structure of the network looks neurocentric, the dynamics are best understood in flowcentric terms. Surprisingly, dynamic system analysis (as usually understood) is not involved. An Emergic Network is engineered much like straightforward software or hardware systems that deal with continuously varying inputs. Ultimately, this thesis addresses the problem of reduction and induction over complex systems, and the Emergic Network architecture is merely a tool to assist in this epistemic endeavour. -/- ECM is strictly a sensory model and apart from perception, yet it is informed by phenomenology. It addresses the attribution problem of how much of a phenomenon is best explained at a sensory level of analysis, rather than at a perceptual one. As the causal information flows are stable under eye movement, we hypothesize that they are the locus of consciousness, howsoever it is ultimately realized. (shrink)

Consciousness has been the bone of contention for philosophers throughout centuries. Indian philosophy largely adopted lived experience as the starting point for its explorations of consciousness. For this reason, from the very beginning, experience was an integral way of grasping consciousness, whose validity as a tool was considered self-evident. Thus, in Indian philosophy, the question was not to move from the brain to mind but to understand experience of an individual and how such an experience is determined through mental structures (...) (and secondarily, the preoccupation with the brain and its relation to the mind). In contrast, cognitive science (the study of mind and cognition through 1 interdisciplinary methods, with emphasis on computational methods) found its debates soaked in discussion which primarily involved the brain and mind. Experience was not considered a primary source of information and its validity had to be established to consider it a source of information of mind. With the rise of physicalism and realization that mental states are correlative to brain states, the body was virtually neglected from involvement in understanding the mind and the attempts to reduce mind to the brain were rampant. The inability to explain subjective experience of an individual through neuroscientific findings alone has urged philosophers to explore other ways of understanding the ontology of mind. Over the last few years, embodied cognition and enactive approach have brought back the body as a central participant in this debate, providing fertile grounds to explain the relation of brain, body and mind. This paper proposes that we understand the brain as a complex system from which the mind emerges. This emergence is marked by the development of novel property of self-consciousness in human beings. The mind is a process which is embedded throughout the body and thus, the body acts as an actualizing medium for the individual. Thus, the brain is a necessary condition for the mind to be while the mind is embedded throughout the body. The brain and mind are in reciprocal causal relationship with one another, as is the body and environment with one another. In this paper, embodied cognition is understood through principles of Merleau Ponty's idea of embodiment, than through Andy Clark and Francis Varela's alone. (shrink)

Large sets of elements interacting locally and producing specific architectures reliably form a category that transcends the usual dividing line between biological and engineered systems. We propose to call them morphogenetically architected complex systems (MACS). While taking the emergence of properties seriously, the notion of MACS enables at the same time the design (or “meta-design”) of operational means that allow controlling and even, paradoxically, programming this emergence. To demonstrate our claim, we first show that among all the self-organized systems studied (...) in the field of Artificial Life, the specificity of MACS essentially lies in the close relation between their emergent properties and functional properties. Second, we argue that to be a MACS a system does not need to display more than weak emergent properties. Third, since the notion of weak emergence is based on the possibility of simulation, whether computational or mechanistic via machines, we see MACS as good candidates to help design artificial self-architected systems (such as robotic swarms) but also harness and redesign living ones (such as synthetic bacterial films). (shrink)

The presented study introduces a new theoretical model of collapse for social, cultural, or political systems. Based on the current form of quantum anthropology conceptualized by Heidi Ann Russell, further development of this field is provided. The new theoretical model is called the spiral model of collapses, and is suggested to provide an analytical framework for collapses in social, cultural, and political systems. The main conclusions of this study are: 1) The individual crises in the period before a collapse of (...) social, cultural, and political systems form the trajectory of a conical helix similar to a vortex. 2) The occurrences of crises in the period before a collapse have the shape of the trajectory on the surface of the circular cone with a convex wall narrowing up to its peak. The shape of this cone is based on the Fibonacci sequence coiled into the three-dimensional space. 3) The constant circular movement along the trajectory of crises can occur in exceptional situations in the development of social, cultural, and political systems; however, such a state is always temporary. In such cases, the trajectory of the crisis does not follow the Fibonacci sequence, but the shape of a regular helix. Remaining on the trajectory of a regular helix in the long-term is highly improbable for social, cultural, and political systems. 4) The creation of new potentialities after the final collapse of a system is explained by the conception of topological inversion, when the heretofore embodied part of the energy-information field returns to the global, wave-particle energy-information potential. 5) The global, wave-particle energy-information potential is a source of energy-information for future embodiments in the sense of the future collapses of wave functions. (shrink)

Recenzja książki: Andrzej Pelczar, Czas i dynamika. O czasie w równaniach różniczkowych i układach dynamicznych, OBI--Kraków, Biblos-Tarnów 2003, ss. 117.

This doctoral dissertation investigates the notion of physical necessity. Specifically, it studies whether it is possible to account for non-accidental regularities without the standard assumption of a pre-existent set of governing laws. Thus, it takes side with the so called deflationist accounts of laws of nature, like the humean or the antirealist. The specific aim is to complement such accounts by providing a missing explanation of the appearance of physical necessity. In order to provide an explanation, I recur to fields (...) that have not been appealed to so far in discussions about the metaphysics of laws. Namely, I recur to complex systems’ theory, and to the foundations of statistical mechanics. The explanation proposed is inspired by how complex systems’ theory has elucidated the way patterns emerge, and by the probabilistic explanations of the 2nd law of thermodynamics. More specifically, this thesis studies how some constraints that make no direct reference to the dynamics can be a sufficient condition for obtaining in the long run, with high probability, stable regular behavior. I hope to show how certain metaphysical accounts of laws might benefit from the insights achieved in these other fields. According to the proposal studied in this thesis, some regularities are not accidental not in virtue of an underlying physical necessity. The non-accidental character of certain regular behavior is only due to its overwhelming stability. Thus, from this point of view the goal becomes to explain the stability of temporal patterns without assuming a set of pre-existent guiding laws. It is argued that the stability can be the result of a process of convergence to simpler and stable regularities from a more complex lower level. According to this project, if successful, there would be no need to postulate a (mysterious) intermediate category between logical necessity and pure contingency. Similarly, there would be no need to postulate a (mysterious) set of pre-existent governing laws. Part I of the thesis motivates part II, mostly by arguing why further explanation of the notions of physical necessity and governing laws should be welcomed (chapter 1), and by studying the plausibility of a lawless fundamental level (chapters 2 and 3). Given so, part II develops the explanation of formation of simpler and stable behavior from a more complex underlying level. (shrink)

A collection of essays by experts in the field, exploring how nature works to produce systems of increasing complexity from simple components, and how our understanding of this phenomenon of emergence can lead us to a deeper appreciation of both our humanity and our relationship with God.

The future of human computation benefits from examining tasks that agents already perform and designing environments to give those tasks computational significance. We call this natural human computation. We consider the possible future of NHC through the lens of Swarm!, an application under development for Google Glass. Swarm! motivates users to compute the solutions to a class of economic optimization problems by engaging the attention dynamics of crowds. We argue that anticipating and managing economies of attention provides one of the (...) most tantalizing future applications for NHC. (shrink)

Mechanistic frameworks of investigation and explanation dominate the cognitive, neural, and psychological sciences. In this dissertation, I argue that mechanistic frameworks cannot, in principle, explain some kinds of cognition. In its place, I argue that complexity science has methods and theories more appropriate for investigating and explaining some cognitive phenomena. -/- I begin with an examination of the term 'cognition.' I defend the idea that "cognition" has been a moving target of investigation in the relevant sciences. As such it is (...) not historically true that there has been a thoroughly entrenched and agreed upon conception of "cognition." Next, I take up mechanistic frameworks. Although 'mechanism' is an umbrella term for a set of loosely related characteristics, there are common features: linearity, localization, and component dominance. I then describe complexity science, with emphasis on its utilization of dynamical systems modeling. Next, I discuss two phenomena that typically fall under the purview of complexity science: nonlinearity and interaction dominance. -/- A complexity science framework guided by the theory of self-organized criticality and utilizing the methods of dynamical systems modeling can surmount a number of challenges that face mechanistic frameworks when investigating some kinds of cognition. The first challenge is epistemic and concerns the inadequacy of mechanistic frameworks to facilitate the comprehensibility of massive amounts of data across various scales and areas of inquiry. I argue that complexity science is more appropriate for making big data comprehensible when investigating cognition, particularly across disciplines. I demonstrate this via an approach called nested dynamical modeling (NDM). NDM can facilitate comprehensibility of large amounts of data obtained from various scales of investigation by eliminating irrelevant degrees of freedom of that system as relates to the target of investigation. -/- The second shortcoming concerns ontological blind spots within mechanistic frameworks. Cognitive phenomena like extended cognition often fail to meet most, if not all, of the criteria assumed by many mechanistic approaches, especially component dominance. I argue that research guided by the notion of interaction dominance allows for extended cognition to be a real, empirically supported phenomenon within complex systems frameworks. In this chapter I discuss some of my experimental work on extended cognitive systems. -/- The search for mechanisms can be a reasonable starting position when attempting to explain natural phenomena in the life sciences. However, too strict of an adherence to theoretical and methodological commitments such as linearity, localization, and component dominance can result in intractable epistemic challenges and ontological blind spots. Complexity science has theories and methods to overcome such challenges in the investigation of cognition. (shrink)

A dynamical system is called chaotic if small changes to its initial conditions can create large changes in its behavior. By analogy, we call a dynamical system structurally chaotic if small changes to the equations describing the evolution of the system produce large changes in its behavior. Although there are many definitions of “chaos,” there are few mathematically precise candidate definitions of “structural chaos.” I propose a definition, and I explain two new theorems that show that a set of models (...) is structurally chaotic if it contains a chaotic function. I conclude by discussing the relationship between structural chaos and structural stability. (shrink)

L’osservazione della natura con l’intento di capire l’origine della varietà di forme e fenomeni in cui si manifesta ha origini remote. All’inizio il rapporto con i fenomeni naturali era dominato da sentimenti quali paura e stupore che conducevano a supporre l’esistenza di entità sfuggenti alla percezione diretta che permeavano gli elementi animandoli. Ecco dunque che la magia rappresenta l’elemento dominante della filosofia naturale primitiva caratterizzata da una unicità degli eventi e dalla impossibilità di capirli e dominarli in quanto frutto della (...) volontà di essenze a noi estranee e non governabili. Con il nascere della civiltà ed il suo progredire il tempo dedicato ai lavori necessari per il sostentamento e la sopravvivenza diminuì e nella ripartizione dei compiti alcuni individui potevano dedicare parte del loro tempo alla osservazione della natura ed alla sua interpretazione in termini non trascendenti. Nella natura, intesa come tutto ciò che ci circonda composto da esseri viventi e da materia inorganica nelle sua varie aggregazioni sulla terra e nel cosmo, ciò che attirò l’attenzione fin dall’inizio furono furono i fenomeni regolari e periodici come i moti della luna, dei pianeti e delle stelle. Nel contempo dopo una spinta iniziale dettata da esigenze pratiche come contare gli oggetti o misurare i campi, la matematica si era sviluppata autonomamente e si rivelò idonea a descrivere in termini quantitativi i moti dei corpi celesti. La terra era al centro dell’universo mentre il moto degli altri corpi celesti risultava da una composizione di moti circolari uniformi. Questa visione geocentrica e pitagorica (armonia delle sfere) dell’universo ha prevalso fino agli albori della scienza moderna, anche se una descrizione eliocentrica, basata su validi argomenti, era stata proposta. Per quanto riguarda la struttura della materia i presocratici avevano già proposto i quattro elementi mentre gli atomisti avevano ricondotto tutto ad entità elementari primigenie, il cui aggregarsi e disgregarsi da luogo a tutti gli stati e le molteplici forme della materia. Queste intuizioni si ritrovano nella fisica moderna che contempla quattro stati di aggregazione, che hanno come unico sostrato comune gli atomi. La fisica moderna nasce con Galileo e Newton, la cui dinamica si sviluppa a partire dalle leggi di Keplero che descrivono il moto dei pianeti nel sistema eliocentrico, per potersi poi applicare ad un qualunque sistema materiale. Pertanto nei due secoli successivi si ritenne che un modello meccanico potesse essere sviluppato per un qualunque sistema fisico e quindi per l’universo intero la cui evoluzione doveva essere matematicamente prevedibile. Per i fenomeni termici tuttavia vennero formulate leggi ad hoc come quelle della termodinamica che mostrano come i processi macroscopici siano irreversibili in contrasto con le leggi della meccanica. Si deve a Boltzmann1 il tentativo di ricondurre la termodinamica alla meccanica per un gran numero di particelle dei cui moti disordinati viene data una lettura di carattere statistico. L’aumento della entropia e la irreversibilità seguono dalla ipotesi di caos molecolare ossia che i moti siano così disordinati che si perde rapidamente memoria dello stato iniziale. L’idea di introdurre una misura di probabilità nel contesto della meccanica sembra antitetica con la natura stessa della teoria rivolta fino ad allora allo studio di sistemi con moti regolari, reversibili e prevedibili individualmente su tempi lunghi. Tuttavia l’analisi probabilistica diventa essenziale per lo studio di sistemi caratterizzati da forti instabilità, e da orbite irregolari per i quali la previsione richiede una conoscenza della condizioni iniziali con precisioni fisicamente non raggiungibili. Combinando la evoluzione deterministica della meccanica di Newton o di Hamilton con la descrizione statistica attraverso una opportuna misura invariante di probabilità nello spazio delle fasi, nasce la teoria dei sistemi dinamici2 che consente di descrivere non solo i sistemi ordinati o i sistemi caotici ma anche tutti quelli che vedono coesistere in diverse proporzioni ordine e caos e che presentano una straordinaria varietà di strutture geometriche e proprietà statistiche, tanto da fornire almeno se non proprio un quadro teorico per lo meno metafore utili per la descrizione dei sistemi complessi. Anche se non c’è unanime consenso ci sembra appropriato definire complessi non tanto sistemi caratterizzati da interazioni non lineari tra i suoi componenti e da proprietà emergenti, che rientrano a pieno titolo nel quadro dei sistemi dinamici, ma piuttosto i sistemi viventi o quelli di vita artificiale che ne condividono le proprietà essenziali3. Tra queste possiamo certamente annoverare la capacità di gestire la informazione e di replicarsi, consentendo tramite un meccanismo di mutazione e selezione di dare origine a strutture di crescente ricchezza strutturale e dotate di capacità cognitive sempre più elaborate. Una teoria dei sistemi complessi non esiste ancora, anche se la teoria degli automi sviluppata da Von Neumann4 e la teoria della evoluzione di Darwin5 ne possono fornire alcuni pilastri importanti. Recentemente la teoria delle reti è stata utilizzata con successo per descrivere le proprietà statistiche delle connessioni tra gli elementi costitutivi (nodi) di un sistema complesso6. Le connessioni che non sono né completamente casuali né completamente gerarchiche, consentono una sufficiente robustezza rispetto a malfunzionamenti o danneggiamenti dei nodi unita a un adeguato livello di organizzazione per consentirne un funzionamento efficiente. Nei sistemi fisici il modello base è un insieme di atomi o molecole interagenti, che danno luogo a strutture diverse quali un gas, un liquido o un cristallo, come risultato di proprietà emergenti. Nello stesso modo per i sistemi complessi possiamo proporre un sistema automi interagenti come modello base. Le molteplici forme che il sistema assume anche in questo caso vanno considerate come proprietà emergenti del medesimo sostrato al mutare delle condizioni esterne e frutto delle i replicazioni, ciascuna delle quali introduce piccole ma significative varianti. Questa è la grande differenza tra un sistema fisico ed un sistema complesso: il primo fissate le condizioni esterne ha sempre le medesime proprietà, il secondo invece cambia con il fluire del tempo perché la sua organizzazione interna muta non solo al cambiare di fattori ambientali ma anche con il succedersi delle generazioni. C’è dunque un flusso di informazione che cresce con il tempo e che consente agli automi costituenti ed alla intera struttura di acquisire nuove capacità. Questo aumento di ordine e ricchezza strutturale avviene naturalmente a spese dell’ambiente circostante, in modo che globalmente i la sua entropia cresce in accordo con la seconda legge della termodinamica. In assenza di una teoria formalizzata paragonabile a quella dei sistemi dinamici, per i sistemi complessi si possono fare osservazioni e misure, sia puntuali sui costituenti elementari e sulle loro connessioni, sia globali sull’intero sistema, oppure costruire modelli suscettibili di essere validati attraverso la simulazione. Se di un sistema si riesce infatti a fornire una descrizione sufficientemente dettagliata, è poi possibile osservare come questo si comporti traducendo le regole in algoritmi e costruendo quindi una versione virtuale, anche se semplificata del sistema stesso. Il passaggio più difficile è il confronto tra il sistema simulato ed il sistema vero, che passa necessariamente attraverso la valutazione di una numero limitato di parametri che ne caratterizzino e proprietà. La codifica del progetto è una proprietà cruciale dei sistemi complessi perché questa si realizza con un dispendio di massa ed energia incomparabilmente più piccolo rispetto a quello necessario per realizzare l’intera struttura; nello stesso tempo apportare piccole modifiche ad un progetto è rapido ed economico. In questo processo che comporta la continua introduzione di varianti si aprono molteplici strade e con lo scorrere del tempo si realizza una storia in modo unico e irripetibile. Anche il susseguirsi di eventi fisici caratterizzati da processi irreversibili e dalla presenza di molteplici biforcazioni da origine ad una storia che non si può percorrere a ritroso, né riprodurre qualora fossimo in grado ripartire dalle stesse condizioni iniziali. Tuttavia esiste una differenza profonda tra la storia di un sistema fisico come il globo terrestre e la storia della vita. La prima registra i molteplici cambiamenti che ha subito la superficie del nostro pianeta ove montagne e mari nascono e scompaiono senza un chiaro disegno soggiacente. La storia della vita è caratterizzata da una progressiva crescita della ricchezza strutturale e funzionale e accompagnata da una crescita della complessità progettuale. La rappresentazione di questa storia prende la forma di un albero con le sue ramificazioni che mostra la continua diversificazione delle strutture e la loro evoluzione verso forme sempre più avanzate. La direzione in cui scorre il tempo è ben definita: le strutture affinano le capacità sensoriali mentre cresce la potenza degli organi che elaborano la informazione. Un sistema complesso è anche caratterizzato da un molteplicità di scale, tanto più alta quanto più si sale sulla scala evolutiva. La ragione è che il procedere verso strutture sempre più elaborate avviene utilizzando altre strutture come mattoni per cui l’immagine che si può fornire è quella di una rete di automi a più strati: partendo dal basso una rete con le sue proprietà emergenti diventa il nodo di una rete di secondo livello, cioè un automa di secondo livello che interagisce con altri automi dello stesso tipo e così via. Nei sistemi inorganici, dove non esiste un progetto, si distinguono di norma solo due livelli, quello dei costituenti elementari e quello su scala macroscopica. I sistemi fisici sono riconducibili a poche leggi universali, che governano i costituenti elementari della materia, ma il passaggio dalla descrizione dalla piccola alla grande scala è impervio e consentito soltanto dalla simulazione numerica, quando ci allontaniamo dalle situazioni più semplici caratterizzate da un equilibrio statistico. I limiti che il disegno riduzionista incontra già per i sistemi fisici diventano decisamente più forti nel caso dei sistemi complessi. (shrink)

The term “Complex Systems Biology” was introduced a few years ago [Kaneko, 2006] and, although not yet of widespread use, it seems particularly well suited to indicate an approach to biology which is well rooted in complex systems science. Although broad generalizations are always dangerous, it is safe to state that mainstream biology has been largely dominated by a gene-centric view in the last decades, due to the success of molecular biology. So the one gene - one trait approch, which (...) has often proved to be effective, has been extended to cover even complex traits. This simplifying view has been appropriately criticized, and the movement called systems biology has taken off. Systems biology [Noble, 2006] emphasizes the presence of several feedback loops in biological systems, which severely limit the range of validity of explanations based upon linear causal chains (e.g. gene → behaviour). Mathematical modelling is one the favourite tools of systems biologists to analyze the possible effects of competing negative and positive feedback loops which can be observed at several levels (from molecules to organelles, cells, tissues, organs, organisms, ecosystems). Systems biology is by now a well-established field, as it can be inferred by the rapid growth in number of conferences and journals devoted to it, as well as by the existence of several grants and funded projects.Systems biology is mainly focused upon the description of specific biological items, like for example specific organisms, or specific organs in a class of animals, or specific genetic-metabolic circuits. It therefore leaves open the issue of the search for general principles of biological organization, which apply to all living beings or to at least to broad classes. We know indeed that there are some principles of this kind, biological evolution being the most famous one. The theory of cellular organization also qualifies as a general principle. But the main focus of biological research has been that of studying specific cases, with some reluctance to accept (and perhaps a limited interest for) broad generalizations. This may however change, and this is indeed the challenge of complex systems biology: looking for general principles in biological systems, in the spirit of complex systems science which searches for similar features and behaviours in various kinds of systems. The hope to find such general principles appears well founded, and I will show in Section 2 that there are indeed data which provide support to this claim. Besides data, there are also general ideas and models concerning the way in which biological systems work. The strategy, in this case, is that of introducing simplified models of biological organisms or processes, and to look for their generic properties: this term, borrowed from statistical physics, is used for those properties which are shared by a wide class of systems. In order to model these properties, the most effective approach has been so far that of using ensembles of systems, where each member can be different from another one, and to look for those properties which are widespread. This approach was introduced many years ago [Kauffman, 1971] in modelling gene regulatory networks. At that time one had very few information about the way in which the expression of a given gene affects that of other genes, apart from the fact that this influence is real and can be studied in few selected cases (like e.g. the lactose metabolism in E. coli). Today, after many years of triumphs of molecular biology, much more has been discovered, however the possibility of describing a complete map of gene-gene interactions in a moderately complex organism is still out of reach. Therefore the goal of fully describing a network of interacting genes in a real organism could not be (and still cannot be) achieved. But a different approach has proven very fruitful, that of asking what are the typical properties of such a set of interacting genes. Making some plausible hypotheses and introducing some simplifying assumptions, Kauffman was able to address some important problems. In particular, he drew attention to the fact that a dynamical system of interacting genes displays selforganizing properties which explain some key aspects of life, most notably the existence of a limited number of cellular types in every multicellular organism (these numbers are of the order of a few hundreds, while the number of theoretically possible types, absent interactions, would be much much larger than the number of protons in the universe). In section 3 I will describe the ensemble based approach in the context of gene regulatory networks, and I will show that it can describe some important experimental data. Finally, in section 4 I will discuss some methodological aspects. (shrink)

Il tema della complessità della scienza è stato da qualche decennio oggetto di una vasta letteratura sia sul versante più strettamente scientifico, sia sul piano filosofico. Un argomento emerso con notevole interesse ha riguardato un aspetto della complessità inteso come rinuncia a una generalizzazione dei procedimenti assiomatico-deduttivi come metodo generale della ricerca scientifica. È stata espressa la convinzione che la fisica pre-relativistica sia stata fondata prevalentemente sul trionfo di questo metodo, sulla scia, fra l’altro, della gloriosa tradizione dei Principia newtoniani. (...) Pur riconoscendo la sua imponenza storica e concettuale, che ha condotto spesso a una sua identificazione con la stessa tradizione della scienza occidentale, la mia ricerca storica ha evidenziato una posizione antagonista presente nelle idee di Newton, e ripresa da due grandi fisici ottocenteschi, Ampere e Maxwell, posizione consistente in un ricorso alla cosiddetta deduzione dai fenomeni, un metodo di ricerca che rappresenta un’importante alternativa rispetto al metodo assiomatico-deduttivo. Nei primi decenni del nuovo secolo, si impongono poi progressivamente le idee di Einstein, che sul problema del metodo presentano un’irrisolta problematica. La celebrazione del metodo assiomatico-deduttivo si contrappone ad una lode dell’osservazione dei fenomeni e della riflessione sugli esperimenti, una variazione sul tema della deduzione dai fenomeni. Seppure i contributi del grande scienziato alle teorie derivate dagli assiomi del campo generalizzato non portarono a teorie conclusive, le sue idee in merito contribuirono enormemente alla moderna riflessione storico-epistemologica sulla scienza. (shrink)

The influence of many factors on ecological success in common pool resource management is still unclear. This may be due to methodological issues. These include causal complexity, a lack of large-N-studies and nonlinear relationships between factors. We address all three issues with a new methodological approach, artificial neural networks, which is discussed in detail. It allows us to develop a model with comparably high predictive power. In addition, two success factors are analyzed: legal security and institutional fairness. Both factors show (...) a positive impact on success in irrigation and fisheries supporting the view that there are sector-independent success factors. (shrink)

In this paper we aim to show that phenomenal consciousness is realized by a particular level of brain operational organization and that understanding human consciousness requires a description of the laws of the immediately underlying neural collective phenomena, the nested hierarchy of electromagnetic fields of brain activity – operational architectonics. We argue that the subjective mental reality and the objective neurobiological reality, although seemingly worlds apart, are intimately connected along a unified metastable continuum and are both guided by the universal (...) laws of the physical world such as criticality, self-organization and emergence. (shrink)

In this paper, a number of traditional models related to the percolation theory has been considered by means of new computational methodology that does not use Cantor’s ideas and describes infinite and infinitesimal numbers in accordance with the principle ‘The part is less than the whole’. It gives a possibility to work with finite, infinite, and infinitesimal quantities numerically by using a new kind of a compute - the Infinity Computer – introduced recently in [18]. The new approach does not (...) contradict Cantor. In contrast, it can be viewed as an evolution of his deep ideas regarding the existence of different infinite numbers in a more applied way. Site percolation and gradient percolation have been studied by applying the new computational tools. It has been established that in an infinite system the phase transition point is not really a point as with respect of traditional approach. In light of new arithmetic it appears as a critical interval, rather than a critical point. Depending on “microscope” we use this interval could be regarded as finite, infinite and infinitesimal short interval. Using new approach we observed that in vicinity of percolation threshold we have many different infinite clusters instead of one infinite cluster that appears in traditional consideration. (shrink)

Many biological processes and objects can be described by fractals. The paper uses a new type of objects – blinking fractals – that are not covered by traditional theories considering dynamics of self-similarity processes. It is shown that both traditional and blinking fractals can be successfully studied by a recent approach allowing one to work numerically with infinite and infinitesimal numbers. It is shown that blinking fractals can be applied for modeling complex processes of growth of biological systems including their (...) season changes. The new approach allows one to give various quantitative characteristics of the obtained blinking fractals models of biological systems. (shrink)

An attempt to critically analyse the claims of the theory of self-organization of complex systems (synergetics) to the interdisciplinary generalizations and the universal efficacy of its models is made in the paper. The grounds for transfer of synergetic models to different disciplinary fields are under discussion. It is argued that synergetics is a mental scheme or a heuristic approach to exploring the complex behaviour of systems, rather than a universal key to solving concrete scientific problems. The prospects for development and (...) the possible future of synergetics in the coming decades are estimated. Copyright 2002 John Wiley & Sons, Ltd. (shrink)

The hope of finding new methods of predicting the course of historical processes could be connected with the recent developments of the theory of self-organisation, also called synergetics. It provides us with knowledge of constructive principles of co-evolution of complex social systems, co-evolution of countries and geopolitical regions being at different stages of development, integration of the East and the West, the North and the South. Due to the growth of population on the Earth in blow-up regime, the general and (...) local instability of development is increasing. The social world seems to go towards a unity, a sustainable commonwealth through pulsations rather than monotonously. It goes via alternation of disintegration, even if partial, and getting more powerful integrations of structures. There is a path of multiple reduction of temporal and material expenses, a path of a resonant excitation of desirable and — no less important — feasible in a given social system structure. In the case of the right topology of integration of geopolitical structures an arising whole organisation can accelerate its tempo of evolution. 1999 Elsevier Science Ltd. All rights reserved. (shrink)

This paper explores the connection between the 2nd thermodynamics and the emergence and evolution of life on Earth. 60 years ago, Erwin Schrodinger understood that the thermodynamically-open nature of living systems exempted them from the constraints imposed by the second law, but it was not clear why such systems should exist at all. Now we’re coming to realize that, not only are open systems ubiquitous, but they are likely, and perhaps even necessary. Some open systems are characterized as dissipative, and (...) they emerge as the system they are embedded in attempts to return closer to thermodynamic equilibrium. The emergence of life itself is a response of the surrounding system to the thermodynamic imperative of the second law. The stability and efficiency of metabolic processes over evolutionary time, as well as properties of entire ecosystems emerged to counter the effects of energy gradients applied to them. (shrink)

Replies to Kevin de Laplante’s ‘Certainty and Domain-Independence in the Sciences of Complexity’ (de Laplante, 1999), defending the thesis of J. Franklin, ‘The formal sciences discover the philosophers’ stone’, Studies in History and Philosophy of Science, 25 (1994), 513-33, that the sciences of complexity can combine certain knowledge with direct applicability to reality.