In this article, network science is discussed from a methodological perspective, and two central theses are defended. The first is that network science exploits the very properties that make a system complex. Rather than using idealization techniques to strip those properties away, as is standard practice in other areas of science, network science brings them to the fore, and uses them to furnish new forms of explanation. The second thesis is that network representations are particularly helpful in explaining the (...) properties of non-decomposable systems. Where part-whole decomposition is not possible, network science provides a much-needed alternative method of compressing information about the behavior of complex systems, and does so without succumbing to problems associated with combinatorial explosion. The article concludes with a comparison between the uses of network representation analyzed in the main discussion, and an entirely distinct use of network representation that has recently been discussed in connection with mechanistic modeling. (shrink)

Applied Evolutionary Epistemology is a scientific-philosophical theory that defines evolution as the set of phenomena whereby units evolve at levels of ontological hierarchies by mechanisms and processes. This theory also provides a methodology to study evolution, namely, studying evolution involves identifying the units that evolve, the levels at which they evolve, and the mechanisms and processes whereby they evolve. Identifying units and levels of evolution in turn requires the development of ontological hierarchy theories, and examining mechanisms and processes necessitates theorizing (...) about causality. Together, hierarchy and causality theories explain how biorealities form and diversify with time. This paper analyzes how Applied EE redefines both hierarchy and causality theories in the light of the recent explosion of network approaches to causal reasoning associated with studies on reticulate and macroevolution. Causality theories have often been framed from within a rigid, ladder-like hierarchy theory where the rungs of the ladder represent the different levels, and the elements on the rungs represent the evolving units. Causality then is either defined reductionistically as an upward movement along the strands of a singular hierarchy, or holistically as a downward movement along that same hierarchy. Upward causation theories thereby analyze causal processes in time, i.e. over the course of natural history or phylogenetically, as Darwin and the founders of the Modern Synthesis intended. Downward causation theories analyze causal processes in space, ontogenetically or ecologically, as the current eco-evo-devo schools are evidencing. This work demonstrates how macroevolution and reticulate evolution theories add to the complexity by examining reticulate causal processes in space–time, and the interactional hierarchies that such studies bring forth introduce a new form of causation that is here called reticulate causation. Reticulate causation occurs between units and levels belonging to different as well as to the same ontological hierarchies. This article concludes that beyond recognizing the existence of multiple units, levels, and mechanisms or processes of evolution, also the existence of multiple kinds of evolutionary causation as well as the existence of multiple evolutionary hierarchies needs to be acknowledged. This furthermore implies that evolution is a pluralistic process divisible into different kinds. (shrink)

Network analysis as a tool for ecological interactions studies has been widely used since last decade. However, there are few studies on the factors that shape network patterns in communities. In this sense, we compared the topological properties of the interaction network between flower-visiting social wasps and plants in two distinct phytophysiognomies in a Brazilian savanna (Riparian Forest and Rocky Grassland). Results showed that the landscapes differed in species richness and composition, and also the interaction networks between wasps and (...) plants had different patterns. The network was more complex in the Riparian Forest, with a larger number of species and individuals and a greater amount of connections between them. The network specialization degree was more generalist in the Riparian Forest than in the Rocky Grassland. This result was corroborated by means of the nestedness index. In both networks was found asymmetry, with a large number of wasps per plant species. In general aspects, most wasps had low niche amplitude, visiting from one to three plant species. Our results suggest that differences in structural complexity of the environment directly influence the structure of the interaction network between flower-visiting social wasps and plants. (shrink)

Social epistemologists should be well-equipped to explain and evaluate the growing vulnerabilities associated with filter bubbles, echo chambers, and group polarization in social media. However, almost all social epistemology has been built for social contexts that involve merely a speaker-hearer dyad. Filter bubbles, echo chambers, and group polarization all presuppose much larger and more complex network structures. In this paper, we lay the groundwork for a properly social epistemology that gives the role and structure of networks their due. (...) In particular, we formally define epistemic constructs that quantify the structural epistemic position of each node within an interconnected network. We argue for the epistemic value of a structure that we call the (m,k)-observer. We then present empirical evidence that (m,k)-observers are rare in social media discussions of controversial topics, which suggests that people suffer from serious problems of epistemic vulnerability. We conclude by arguing that social epistemologists and computer scientists should work together to develop minimal interventions that improve the structure of epistemic networks. (shrink)

Abstract: This research focuses on utilizing Artificial Neural Networks (ANNs) to predict Audit Risk accurately, a critical aspect of ensuring financial system integrity and preventing fraud. Our dataset, gathered from Kaggle, comprises 18 diverse features, including financial and historical parameters, offering a comprehensive view of audit-related factors. These features encompass 'Sector_score,' 'PARA_A,' 'SCORE_A,' 'PARA_B,' 'SCORE_B,' 'TOTAL,' 'numbers,' 'marks,' 'Money_Value,' 'District,' 'Loss,' 'Loss_SCORE,' 'History,' 'History_score,' 'score,' and 'Risk,' with a total of 774 samples. Our proposed neural network architecture, consisting of (...) three layers (1 input, 1 hidden, and 1 output), forms the core of this study. While it may seem simple, the power of ANNs lies not in complexity but in their ability to uncover intricate data patterns. The model underwent rigorous training and validation, resulting in remarkable outcomes—an accuracy of 100% and an average error rate of 0.000015. In addition to performance metrics, our research investigates feature importance, revealing the key contributors to Audit Risk prediction. Notably, 'Sector_score,' 'PARA_A,' 'SCORE_A,' 'PARA_B,' 'SCORE_B,' 'TOTAL,' 'numbers,' 'marks,' 'Money_Value,' 'District,' 'Loss,' 'Loss_SCORE,' 'History,' and 'History_score' emerged as the most influential factors, shedding light on the elements crucial for precise risk assessment. This study advances Audit Risk prediction models and highlights the potential of ANNs in strengthening financial system integrity and fraud prevention. The insights gained from our findings offer practical guidance for stakeholders in the audit and financial sectors. (shrink)

We introduce the notion of complexity, first at an intuitive level and then in relatively more concrete terms, explaining the various characteristic features of complex systems with examples. There exists a vast literature on complexity, and our exposition is intended to be an elementary introduction, meant for a broad audience. -/- Briefly, a complex system is one whose description involves a hierarchy of levels, where each level is made of a large number of components interacting among themselves. The (...) time evolution of such a system is of a complex nature, depending on the interactions among subsystems in the next level below the one under consideration and, at the same time, conditioned by the level above, where the latter sets the context for the evolution. Generally speaking, the interactions among the constituents of the various levels lead to a dynamics characterized by numerous characteristic scales, each level having its own set of scales. What is more, a level commonly exhibits ‘emergent properties’ that cannot be derived from considerations relating to its component systems taken in isolation or to those in a different contextual setting. In the dynamic evolution of some particular level, there occurs a self-organized emergence of a higher level and the process is repeated at still higher levels. -/- The interaction and self-organization of the components of a complex system follow the principle commonly expressed by saying that the ‘whole is different from the sum of the parts’. In the case of systems whose behavior can be expressed mathematically in terms of differential equations this means that the interactions are nonlinear in nature. -/- While all of the above features are not universally exhibited by complex systems, these are nevertheless indicative of a broad commonness relative to which individual systems can be described and analyzed. There exist measures of complexity which, once again, are not of universal applicability, being more heuristic than exact. The present state of knowledge and understanding of complex systems is itself an emerging one. Still, a large number of results on various systems can be related to their complex character, making complexity an immensely fertile concept in the study of natural, biological, and social phenomena. -/- All this puts a very definite limitation on the complete description of a complex system as a whole since such a system can be precisely described only contextually, relative to some particular level, where emergent properties rule out an exact description of more than one levels within a common framework. -/- We discuss the implications of these observations in the context of our conception of the so-called noumenal reality that has a mind-independent existence and is perceived by us in the form of the phenomenal reality. The latter is derived from the former by means of our perceptions and interpretations, and our efforts at sorting out and making sense of the bewildering complexity of reality takes the form of incessant processes of inference that lead to theories. Strictly speaking, theories apply to models that are constructed as idealized versions of parts of reality, within which inferences and abstractions can be carried out meaningfully, enabling us to construct the theories. -/- There exists a correspondence between the phenomenal and the noumenal realities in terms of events and their correlations, where these are experienced as the complex behavior of systems or entities of various descriptions. The infinite diversity of behavior of systems in the phenomenal world are explained within specified contexts by theories. The latter are constructs generated in our ceaseless attempts at interpreting the world, and the question arises as to whether these are reflections of `laws of nature' residing in the noumenal world. This is a fundamental concern of scientific realism, within the fold of which there exists a trend towards the assumption that theories express truths about the noumenal reality. We examine this assumption (referred to as a ‘point of view’ in the present essay) closely and indicate that an alternative point of view is also consistent within the broad framework of scientific realism. This is the view that theories are domain-specific and contextual, and that these are arrived at by independent processes of inference and abstractions in the various domains of experience. Theories in contiguous domains of experience dovetail and interpenetrate with one another, and bear the responsibility of correctly explaining our observations within these domains. -/- With accumulating experience, theories get revised and the network of our theories of the world acquires a complex structure, exhibiting a complex evolution. There exists a tendency within the fold of scientific realism of interpreting this complex evolution in rather simple terms, where one assumes (this, again, is a point of view) that theories tend more and more closely to truths about Nature and, what is more, progress towards an all-embracing ‘ultimate theory’ -- a foundational one in respect of all our inquiries into nature. We examine this point of view closely and outline the alternative view -- one broadly consistent with scientific realism -- that there is no ‘ultimate’ law of nature, that theories do not correspond to truths inherent in reality, and that successive revisions in theory do not lead monotonically to some ultimate truth. Instead, the theories generated in succession are incommensurate with each other, testifying to the fact that a theory gives us a perspective view of some part of reality, arrived at contextually. Instead of resembling a monotonically converging series successive theories are analogous to asymptotic series. -/- Before we summarize all the above considerations, we briefly address the issue of the complexity of the {\it human mind} -- one as pervasive as the complexity of Nature at large. The complexity of the mind is related to the complexity of the underlying neuronal organization in the brain, which operates within a larger biological context, its activities being modulated by other physiological systems, notably the one involving a host of chemical messengers. The mind, with no materiality of its own, is nevertheless emergent from the activity of interacting neuronal assemblies in the brain. As in the case of reality at large, there can be no ultimate theory of the mind, from which one can explain and predict the entire spectrum of human behavior, which is an infinitely rich and diverse one. (shrink)

Social network structure has been argued to shape the structure of languages, as well as affect the spread of innovations and the formation of conventions in the community. Specifically, theoretical and computational models of language change predict that sparsely connected communities develop more systematic languages, while tightly knit communities can maintain high levels of linguistic complexity and variability. However, the role of social network structure in the cultural evolution of languages has never been tested experimentally. Here, we present results from (...) a behavioral group communication study, in which we examined the formation of new languages created in the lab by micro‐societies that varied in their network structure. We contrasted three types of social networks: fully connected, small‐world, and scale‐free. We examined the artificial languages created by these different networks with respect to their linguistic structure, communicative success, stability, and convergence. Results did not reveal any effect of network structure for any measure, with all languages becoming similarly more systematic, more accurate, more stable, and more shared over time. At the same time, small‐world networks showed the greatest variation in their convergence, stabilization, and emerging structure patterns, indicating that network structure can influence the community's susceptibility to random linguistic changes (i.e., drift). (shrink)

In this paper I discuss the ontological status of actants. Actants are argued as being the basic constituting entities of networks in the framework of Actor Network Theory (Latour, 2007). I introduce two problems concerning actants that have been pointed out by Collin (2010). The first problem concerns the explanatory role of actants. According to Collin, actants cannot play the role of explanans of networks and products of the same newtork at the same time, at pain of circularity. (...) The second problem is that if actants are, as suggested by Latour, fundamentally propertyless, then it is unclear how they combine into networks. This makes the nature of actants inexplicable. -/- I suggest that both problems rest on the assumption of a form of object ontology, i.e. the assumption that the ontological basis of reality consists in discrete individual entities that have intrinsic properties. I argue that the solution to this problem consists in the assumption of an ontology of relations, as suggested within the framework of Ontic Structural Realism (Ladyman & Ross, 2007). Ontic Structural Realism is a theory concerning the ontology of science that claims that scientific theories represent a reality consisting on only relation, and no individual entities. -/- Furthermore I argue that the employment of OSR can, at the price of little modification for both theories, solve both of the two problems identified by Collin concerning ANT. -/- Throughout the text I seek support for my claims by referring to examples of application of ANT to the context of networked learning. As I argue, the complexity of the phenomenon of networked learning gives us a convenient vantage point from which we can clearly understand many important aspects of both ANT and OSR. -/- While my proposal can be considered as an attempt to solve Collin's problems, it is also an experiment of reconciliation between analytic and constructivist philosophy of science. -/- In fact I point out that on the one hand Actor Network Theory and Ontic Structural Realism show an interesting number of points of agreement, such as the naturalistic character and the focus on relationality. On the other hand, I argue that all the intuitive discrepancies that originates from the Science and Technology Studies’ criticism against analytic philosophy of science are at a closer look only apparent. (shrink)

A small consortium of philosophers has begun work on the implications of epistemic networks (Zollman 2008 and forthcoming; Grim 2006, 2007; Weisberg and Muldoon forthcoming), building on theoretical work in economics, computer science, and engineering (Bala and Goyal 1998, Kleinberg 2001; Amaral et. al., 2004) and on some experimental work in social psychology (Mason, Jones, and Goldstone, 2008). This paper outlines core philosophical results and extends those results to the specific question of thresholds. Epistemic maximization of certain types does (...) show clear threshold effects. Intriguingly, however, those effects appear to be importantly independent from more familiar threshold effects in networks. (shrink)

Resting state networks comprise several brain regions that exhibit complex patterns of interaction. Switching from eyes closed (EC) to eyes open (EO) during the resting state modifies these patterns of connectivity, but precisely how these change remains unclear. Here we use functional magnetic resonance imaging to scan healthy participants in two resting conditions (viz., EC and EO). Seven resting state networks were chosen for this study: salience network (SN), default mode network (DMN), central executive network (CEN), dorsal (...) attention network (DAN), visual network (VN), motor network (MN) and auditory network (AN). We performed functional connectivity (FC) analysis for each network, comparing the FC maps for both EC and EO. Our results show increased connectivity between most networks during EC relative to EO, thereby suggesting enhanced integration during EC and greater modularity or specialization during EO. Among these networks, SN is distinctive: during the transition from EO to EC it evinces increased connectivity with DMN and decreased connectivity with VN. This change might imply that SN functions in a manner analogous to a circuit switch, modulating resting state relations with DMN and VN, when transitioning between EO and EC. (shrink)

Infinite machines (IMs) can do supertasks. A supertask is an infinite series of operations done in some finite time. Whether or not our universe contains any IMs, they are worthy of study as upper bounds on finite machines. We introduce IMs and describe some of their physical and psychological aspects. An accelerating Turing machine (an ATM) is a Turing machine that performs every next operation twice as fast. It can carry out infinitely many operations in finite time. Many ATMs can (...) be connected together to form networks of infinitely powerful agents. A network of ATMs can also be thought of as the control system for an infinitely complex robot. We describe a robot with a dense network of ATMs for its retinas, its brain, and its motor controllers. Such a robot can perform psychological supertasks - it can perceive infinitely detailed objects in all their detail; it can formulate infinite plans; it can make infinitely precise movements. An endless hierarchy of IMs might realize a deep notion of intelligent computing everywhere. (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)

Over the last decades, network-based approaches have become highly popular in diverse fields of biology, including neuroscience, ecology, molecular biology and genetics. While these approaches continue to grow very rapidly, some of their conceptual and methodological aspects still require a programmatic foundation. This challenge particularly concerns the question of whether a generalized account of explanatory, organisational and descriptive levels of networks can be applied universally across biological sciences. To this end, this highly interdisciplinary theme issue focuses on the definition, (...) motivation and application of key concepts in biological network science, such as explanatory power of distinctively network explanations, network levels, and network hierarchies. (shrink)

Urban infrastructures serve as the backbone of modern economies, mediating global exchanges and responding to urban demands. Yet, our comprehension of these complex structures, particularly within diverse socio-political terrain, remains fragmented. In bridging this knowledge gap, this study delves into “boundary objects”—entities enabling diverse stakeholders to collaborate without a comprehensive consensus. Central to our investigation is the hypothesis that oceanic infrastructural developments are instrumental in molding the interface of urban, industrial, and energy sectors within marine contexts. Our lens is (...) directed at the Gulf of Mexico, which is distinguished by its industrial depth and expansive marine grid. We highlight the Gulf Intracoastal Waterway’s (GIWW) paramount role in regional movement and the ecological facets of practices such as dredging, which is vital for transport and coastal conservation. A striking revelation of our study is the transformation of offshore structures in the Gulf into vibrant marine habitats. Emphasizing the intertwined nature of marine infrastructures, we denote oceans as pivotal platforms for impending urban expansion, especially as land resources wane. Our research aspires to validate the role played by oceans as a nexus in the urban–industrial–energy fusion. (shrink)

Philosophers have long tried to understand scientific change in terms of a dynamics of revision within ‘theoretical frameworks,’ ‘disciplinary matrices,’ ‘scientific paradigms’ or ‘conceptual schemes.’ No-one, however, has made clear precisely how one might model such a conceptual scheme, nor what form change dynamics within such a structure could be expected to take. In this paper we take some first steps in applying network theory to the issue, modeling conceptual schemes as simple networks and the dynamics of change as (...) cascades on those networks. The results allow a new understanding of two traditional approaches—Popper and Kuhn—as well as introducing the intriguing prospect of viewing scientific change using the metaphor of selforganizing criticality. (shrink)

We propose a qualitatively new kind of governance for the emerging need to efficiently guide the densely interconnected, ever more complex world development, which is based on explicit and openly presented problem solutions and their interactive implementation practice within the versatile, but unified professional analysis of complex real-world dynamics, involving both the powerful central units and the attached creative worldwide network of professional representatives. We provide fundamental and rigorous scientific arguments in favour of introduction of just that kind (...) of governance at the modern development stage, after the complexity threshold in the real world dynamics evolution (sometimes intuitively designated as "globalisation"), where the traditional spontaneous-empirical kind of development and related administrative governance lose dramatically and inevitably their efficiency and the necessity of explicit consistent understanding and guidance of complex real-world dynamics becomes evident. (shrink)

The modern computer environment has moved past the local data center with a single entry and exit point to a global network comprising many data centers and hundreds of entry and exit points, commonly referred as Cloud Computing, used by all possible devices with numerous entry and exit point for transactions, online processing, request and responses traveling across the network, making the ever complex networks even more complex, making traversing, monitoring and detecting threats over such an environment (...) a big challenge for Network forensic and investigation for cybercrimes. It has demanded in depth analysis using network tools and techniques to determine how best information can be extracted pertinent to an investigation. Data mining technique providing great aid in finding relevant clusters for predicting unusual activities, pattern matching and fraud detection in an environment, capable to deal with huge amount of data. The concept of network forensics in cloud computing requires a new mindset where some data will not be available, some data will be suspect, and some data will be court ready and can fit into the traditional network forensics model. From a network security viewpoint, all data traversing the cloud network backplane is visible and accessible by the cloud service provider. It is not possible to think now that one physical device will only have one operating system that needs to be taken down for investigation. Without the network forensics investigator, understanding the architecture of the cloud environment systems and possible compromises will be overlooked or missed. In this paper, we focus on the role of Network Forensic in a cloud environment, its mapping few of the available tools and contribution of Data Mining in making analysis, and also to bring out the challenges in this field. (shrink)

In artificial intelligence, recent research has demonstrated the remarkable potential of Deep Convolutional Neural Networks (DCNNs), which seem to exceed state-of-the-art performance in new domains weekly, especially on the sorts of very difficult perceptual discrimination tasks that skeptics thought would remain beyond the reach of artificial intelligence. However, it has proven difficult to explain why DCNNs perform so well. In philosophy of mind, empiricists have long suggested that complex cognition is based on information derived from sensory experience, often (...) appealing to a faculty of abstraction. Rationalists have frequently complained, however, that empiricists never adequately explained how this faculty of abstraction actually works. In this paper, I tie these two questions together, to the mutual benefit of both disciplines. I argue that the architectural features that distinguish DCNNs from earlier neural networks allow them to implement a form of hierarchical processing that I call “transformational abstraction”. Transformational abstraction iteratively converts sensory-based representations of category exemplars into new formats that are increasingly tolerant to “nuisance variation” in input. Reflecting upon the way that DCNNs leverage a combination of linear and non-linear processing to efficiently accomplish this feat allows us to understand how the brain is capable of bi-directional travel between exemplars and abstractions, addressing longstanding problems in empiricist philosophy of mind. I end by considering the prospects for future research on DCNNs, arguing that rather than simply implementing 80s connectionism with more brute-force computation, transformational abstraction counts as a qualitatively distinct form of processing ripe with philosophical and psychological significance, because it is significantly better suited to depict the generic mechanism responsible for this important kind of psychological processing in the brain. (shrink)

Abstract: Online book review platforms generate vast user data, making accurate rating prediction crucial for personalized recommendations. This research explores neural networks as simple models for predicting book ratings without complex algorithms. Our novel approach uses neural networks to predict ratings solely from user-book interactions, eliminating manual feature engineering. The model processes data, learns patterns, and predicts ratings. We discuss data preprocessing, neural network design, and training techniques. Real-world data experiments show the model's effectiveness, surpassing traditional methods. (...) This research can enhance user experience, book catalog organization, and aid publishers, simplifying recommendation processes and providing tailored suggestions based on user preferences. (shrink)

All the conserved detailed results of evolution stored in DNA must be read, transcribed, and translated via an RNAmediated process. This is required for the development and growth of each individual cell. Thus, all known living organisms fundamentally depend on these RNA-mediated processes. In most cases, they are interconnected with other RNAs and their associated protein complexes and function in a strictly coordinated hierarchy of temporal and spatial steps (i.e., an RNA network). Clearly, all cellular life as we know it (...) could not function without these key agents of DNA replication, namely rRNA, tRNA, and mRNA. Thus, any definition of life that lacks RNA functions and their networks misses an essential requirement for RNA agents that inherently regulate and coordinate (communicate to) cells, tissues, organs, and organisms. The precellular evolution of RNAs occurred at the core of the emergence of cellular life and the question remained of how both precellular and cellular levels are interconnected historically and functionally. RNA-networks andRNA-communication can interconnect these levels.With the reemergence of virology in evolution, it became clear that communicating viruses and subviral infectious genetic parasites are bridging these two levels by invading, integrating, coadapting, exapting, and recombining constituent parts in host genomes for cellular requirements in gene regulation and coordination aims. Therefore, a 21st century understanding of life is of an inherently social process based on communicating RNA networks, in which viruses and cells continuously interact. (shrink)

In modern days, person-computer communication systems have gradually penetrated our lives. One of the crucial technologies in person-computer communication systems, Speech Emotion Recognition (SER) technology, permits machines to correctly recognize emotions and greater understand users' intent and human-computer interlinkage. The main objective of the SER is to improve the human-machine interface. It is also used to observe a person's psychological condition by lie detectors. Automatic Speech Emotion Recognition(SER) is vital in the person-computer interface, but SER has challenges for accurate recognition. (...) In this work to resolve the above problem, automatic Speech enhancement shows that deep learning techniques effectively eliminate background noise. Using Deep leaning models for four states were created: happy, sad, angry, and intoxicated. Recurrent Neural Network (RNN) algorithm used to reduce the possibility of over fitting by randomly omitting neurons in the hidden layers. The proposed RNN method could be implemented in personal assistant systems to give better and more appropriate state-based interactions between humans. In the simulation results shows Improving accuracy, Time complexity, Error rate is also reduced to using the proposed method. -/- . (shrink)

The development of technology is unbelievably rapid. From limited local networks to high speed Internet, from crude computing machines to powerful semi-conductors, the world had changed drastically compared to just a few decades ago. In the constantly renewing process of adapting to such an unnaturally high-entropy setting, innovations as well as entirely new concepts, were often born. In the business world, one such phenomenon was the creation of a new type of entrepreneurship. This paper proposes a new academic discipline (...) of computational entrepreneurship, which centers on: (i) an exponentially growing (and less expensive) computing power, to the extent that almost everybody in a modern society can own and use that; (ii) omnipresent high-speed Internet connectivity, wired or wireless, representing our modern day’s economic connectomics; (iii) growing concern of exploiting “serendipity” for a strategic commercial advantage; and (iv) growing capabilities of lay people in performing calculations for their informed decisions in taking fast-moving entrepreneurial opportunities. Computational entrepreneurship has slowly become a new mode of operation for business ventures and will likely bring the academic discipline of entrepreneurship back to mainstream economics. (shrink)

Paul Cilliers has developed a novel post-structural approach to complexity that has influenced several writers contributing to the current complexity literature. Concomitantly however, Cilliers advocates for modelling complex systems using connectionist neural networks (rather than analytic, rule-based models). In this paper, I argue that it is dilemmic to simultaneously hold these two positions. Cilliers’ post-structural interpretation of complexity states that models of complex systems are always contextual and provisional; there is no exclusive model of complex systems. (...) This sentiment however appears at odds with Cilliers’ promotion of connectionist neural networks as the best way to model complex systems. The lesson is that those who currently follow Cilliers’ post-structural approach to complexity cannot also develop a preferred model of complex systems, and those who currently advocate for some preferred model of complex systems cannot adopt the post-structural approach to complexity without giving up the purported objectivity and/or superiority of their preferred model. (shrink)

Coherence and correspondence are classical contenders as theories of truth. In this paper we examine them instead as interacting factors in the dynamics of belief across epistemic networks. We construct an agent-based model of network contact in which agents are characterized not in terms of single beliefs but in terms of internal belief suites. Individuals update elements of their belief suites on input from other agents in order both to maximize internal belief coherence and to incorporate ‘trickled in’ elements (...) of truth as correspondence. Results, though often intuitive, prove more complex than in simpler models (Hegselmann & Krause 2002, 2006; Grim, Singer, Reade & Fisher 2015). The optimistic finding is that pressures toward internal coherence can exploit and expand on small elements of truth as correspondence is introduced into epistemic networks. Less optimistic results show that pressures for coherence can also work directly against the incorporation of truth, particularly when coherence is established first and new data is introduced later. (shrink)

Symmetry in biological and physical systems is a product of self-organization driven by evolutionary processes, or mechanical systems under constraints. Symmetry-based feature extraction or representation by neural networks may unravel the most informative contents in large image databases. Despite significant achievements of artificial intelligence in recognition and classification of regular patterns, the problem of uncertainty remains a major challenge in ambiguous data. In this study, we present an artificial neural network that detects symmetry uncertainty states in human observers. To (...) this end, we exploit a neural network metric in the output of a biologically inspired Self- Organizing Map Quantization Error (SOM-QE). Shape pairs with perfect geometry mirror symmetry but a non-homogenous appearance, caused by local variations in hue, saturation, or lightness within and/or across the shapes in a given pair produce, as shown here, a longer choice response time (RT) for “yes” responses relative to symmetry. These data are consistently mirrored by the variations in the SOM-QE from unsupervised neural network analysis of the same stimulus images. The neural network metric is thus capable of detecting and scaling human symmetry uncertainty in response to patterns. Such capacity is tightly linked to the metric’s proven selectivity to local contrast and color variations in large and highly complex image data. (shrink)

This study employs a neural network to analyze the connection between smoking, drinking, and various health-related factors using a dataset of 5148 samples. Achieving an impressive 99.94% accuracy and an average training error of 0.0016, the model identifies influential factors such as serum aminotransferases, serum creatinine, sex, weight, and triglyceride levels. These findings enhance our understanding of lifestyle choices and their impact on health. This research underscores the potential of machine learning in studying complex health phenomena.

The grammatical forms and material of the book of Revelation suggest a complex interplay of Old Testament and 1st century literature and language. As well, the book does not lack its own peculiarity and character that is unparalleled in the literate world. Various analytical tools including historical-comparative methodologies have been employed to reconstruct the linguistic paradigm of the book. Artificial intelligence and its derivatives provide alternate methods of probing this paradigm.

The nature of “the self” has been one of the central problems in philosophy and more recently in neuroscience. This raises various questions: Can we attribute a self to animals? Do animals and humans share certain aspects of their core selves, yielding a trans-species concept of self? What are the neural processes that underlie a possible trans-species concept of self? What are the developmental aspects and do they result in various levels of self-representation? Drawing on recent literature from both human (...) and animal research, we suggest a trans-species concept of self that is based upon what has been called a “core-self” which can be described by self-related processing as a specific mode of interaction between organism and environment. When we refer to specific neural networks, we will here refer to the underlying system as the “core-SELF.” The core-SELF provides primordial neural coordinates that represent organisms as living creatures—at the lowest level this elaborates interoceptive states along with raw emotional feelings while higher medial cortical levels facilitate affective-cognitive integration . Developmentally, SRP allows stimuli from the environment to be related and linked to organismic needs, signaled and processed within core-self structures within subcorical-cortical midline structures that provide the foundation for epigenetic emergence of ecologically framed, higher idiographic forms of selfhood across different individuals within a species. These functions ultimately operate as a coordinated network. We postulate that core SRP operates automatically, is deeply affective, and is developmentally and epigenetically connected to sensory-motor and higher cognitive abilities. This core-self is mediated by SCMS, embedded in visceral and instinctual representations of the body that are well integrated with basic attentional, emotional and motivational functions that are apparently shared between humans, non-human mammals, and perhaps in a proto-SELF form, other vertebrates. Such a trans-species concept of organismic coherence is thoroughly biological and affective at the lowest levels of a complex neural network, and culturally and ecologically molded at higher levels of neural processing. It allows organisms to selectively adapt to and integrate with physical and social environments. Such a psychobiologically universal, but environmentally diversified, concept may promote novel trans-species studies of the core-self across mammalian species. (shrink)

In this paper we make a simple theoretical point using a practical issue as an example. The simple theoretical point is that robustness is not 'all or nothing': in asking whether a system is robust one has to ask 'robust with respect to what property?' and 'robust over what set of changes in the system?' The practical issue used to illustrate the point is an examination of degrees of linkage between sub-networks and a pointed contrast in robustness and fragility (...) between the dynamics of (1) contact infection and (2) information transfer or belief change. Time to infection across linked sub-networks, it turns out, is fairly robust with regard to the degree of linkage between them. Time to infection is fragile and sensitive, however, with regard to the type of sub-network involved: total, ring, small world, random, or scale-free. Aspects of robustness and fragility are reversed where it is belief updating with reinforcement rather than infection that is at issue. In information dynamics, the pattern of time to consensus is robust across changes in network type but remarkably fragile with respect to degree of linkage between sub-networks. These results have important implications for public health interventions in realistic social networks, particularly with an eye to ethnic and socio-economic sub-communities, and in social networks with sub-communities changing in structure or linkage. (shrink)

Life expectancy prediction, a pivotal facet of public health and policy formulation, has witnessed remarkable advancements owing to the integration of neural network models and comprehensive datasets. In this research, we present an innovative approach to forecasting life expectancy in diverse countries. Leveraging a neural network architecture, our model was trained on a dataset comprising 22 distinct features, acquired from Kaggle, and encompassing key health indicators, socioeconomic metrics, and cultural attributes. The model demonstrated exceptional predictive accuracy, attaining an impressive 99.27% (...) and an average error of 0.0034, underscoring the potency of deep learning in tackling complex, real-world challenges. Furthermore, our study delved into feature importance analysis, identifying critical determinants such as HIV/AIDS prevalence, income composition of resources, and socioeconomic status that significantly influence life expectancy. These findings provide actionable insights for healthcare policymakers and practitioners, emphasizing the importance of addressing health disparities, promoting economic development, and implementing targeted interventions. This research bridges the gap between data-driven methodologies and global health, offering a robust predictive tool and enriching our understanding of the multifaceted dynamics that shape life expectancy across nations. (shrink)

Beyond belief change and meme adoption, both genetics and infection have been spoken of in terms of information transfer. What we examine here, concentrating on the specific case of transfer between sub-networks, are the differences in network dynamics in these cases: the different network dynamics of germs, genes, and memes. Germs and memes, it turns out, exhibit a very different dynamics across networks. For infection, measured in terms of time to total infection, it is network type rather than (...) degree of linkage between sub-networks that is of primary importance. For belief transfer, measured in terms of time to consensus, it is degree of linkage rather than network type that is crucial. Genes model each of these other dynamics in part, but match neither in full. For genetics, like belief transfer and unlike infection, network type makes little difference. Like infection and unlike belief, on the other hand, the dynamics of genetic information transfer within single and between linked networks are much the same. In ways both surprising and intriguing, transfer of genetic information seems to be robust across network differences crucial for the other two. (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)

The epistemic position of an agent often depends on their position in a larger network of other agents who provide them with information. In general, agents are better off if they have diverse and independent sources. Sullivan et al. [19] developed a method for quantitatively characterizing the epistemic position of individuals in a network that takes into account both diversity and independence; and presented a proof-of-concept, closed-source implementation on a small graph derived from Twitter data [19]. This paper reports on (...) an open-source reimplementation of their algorithm in Python, optimized to be usable on much larger networks. In addition to the algorithm and package, we also show the ability to scale up our package to large synthetic social network graph profiling, and finally demonstrate its utility in analyzing real-world empirical evidence of ‘echo chambers’ on online social media, as well as evidence of interdisciplinary diversity in an academic communications network. (shrink)

Functional decomposition is an important goal in the life sciences, and is central to mechanistic explanation and explanatory reduction. A growing literature in philosophy of science, however, has challenged decomposition-based notions of explanation. ‘Holists’ posit that complex systems exhibit context-sensitivity, dynamic interaction, and network dependence, and that these properties undermine decomposition. They then infer from the failure of decomposition to the failure of mechanistic explanation and reduction. I argue that complexity, so construed, is only incompatible with one notion of (...) decomposition, which I call ‘atomism’, and not with decomposition writ large. Atomism posits that function ascriptions must be made to parts with minimal reference to the surrounding system. Complexity does indeed falsify atomism, but I contend that there is a weaker, ‘contextualist’ notion of decomposition that is fully compatible with the properties that holists cite. Contextualism suggests that the function of parts can shift with external context, and that interactions with other parts might help determine their context-appropriate functions. This still admits of functional decomposition within a given context. I will give examples based on the notion of oscillatory multiplexing in systems neuroscience. If contextualism is feasible, then holist inferences are faulty—one cannot infer from the presence of complexity to the failure of decomposition, mechanism, and reductionism. (shrink)

Let us start by some general definitions of the concept of complexity. We take a complex system to be one composed by a large number of parts, and whose properties are not fully explained by an understanding of its components parts. Studies of complex systems recognized the importance of “wholeness”, defined as problems of organization (and of regulation), phenomena non resolvable into local events, dynamics interactions in the difference of behaviour of parts when isolated or in higher configuration, (...) etc., in short, systems of various orders (or levels) not understandable by investigation of their respective parts in isolation. In a complex system it is essential to distinguish between ‘global’ and ‘local’ properties. Theoretical physicists in the last two decades have discovered that the collective behaviour of a macro-system, i.e. a system composed of many objects, does not change qualitatively when the behaviour of single components are modified slightly. Conversely, it has been also found that the behaviour of single components does change when the overall behaviour of the system is modified. There are many universal classes which describe the collective behaviour of the system, and each class has its own characteristics; the universal classes do not change when we perturb the system. The most interesting and rewarding work consists in finding these universal classes and in spelling out their properties. This conception has been followed in studies done in the last twenty years on second order phase transitions. The objective, which has been mostly achieved, was to classify all possible types of phase transitions in different universality classes and to compute the parameters that control the behaviour of the system near the transition (or critical or bifurcation) point as a function of the universality class. This point of view is not very different from the one expressed by Thom in the introduction of Structural Stability and Morphogenesis (1975). It differs from Thom’s program because there is no a priori idea of the mathematical framework which should be used. Indeed Thom considers only a restricted class of models (ordinary differential equations in low dimensional spaces) while we do not have any prejudice regarding which models should be accepted. One of the most interesting and surprising results obtained by studying complex systems is the possibility of classifying the configurations of the system taxonomically. It is well-known that a well founded taxonomy is possible only if the objects we want to classify have some unique properties, i.e. species may be introduced in an objective way only if it is impossible to go continuously from one specie to another; in a more mathematical language, we say that objects must have the property of ultrametricity. More precisely, it was discovered that there are conditions under which a class of complex systems may only exist in configurations that have the ultrametricity property and consequently they can be classified in a hierarchical way. Indeed, it has been found that only this ultrametricity property is shared by the near-optimal solutions of many optimization problems of complex functions, i.e. corrugated landscapes in Kauffman’s language. These results are derived from the study of spin glass model, but they have wider implications. It is possible that the kind of structures that arise in these cases is present in many other apparently unrelated problems. Before to go on with our considerations, we have to pick in mind two main complementary ideas about complexity. (i) According to the prevalent and usual point of view, the essence of complex systems lies in the emergence of complex structures from the non-linear interaction of many simple elements that obey simple rules. Typically, these rules consist of 0–1 alternatives selected in response to the input received, as in many prototypes like cellular automata, Boolean networks, spin systems, etc. Quite intricate patterns and structures can occur in such systems. However, what can be also said is that these are toy systems, and the systems occurring in reality rather consist of elements that individually are quite complex themselves. (ii) So, this bring a new aspect that seems essential and indispensable to the emergence and functioning of complex systems, namely the coordination of individual agents or elements that themselves are complex at their own scale of operation. This coordination dramatically reduces the degree of freedom of those participating agents. Even the constituents of molecules, i.e. the atoms, are rather complicated conglomerations of subatomic particles, perhaps ultimately excitations of patterns of superstrings. Genes, the elementary biochemical coding units, are very complex macromolecular strings, as are the metabolic units, the proteins. Neurons, the basic elements of cognitive networks, themselves are cells. In those mentioned and in other complex systems, it is an important feature that the potential complexity of the behaviour of the individual agents gets dramatically simplified through the global interactions within the system. The individual degrees of freedom are drastically reduced, or, in a more formal terminology, the factual space of the system is much smaller than the product of the state space of the individual elements. That is one key aspect. The other one is that on this basis, that is utilizing the coordination between the activities of its members, the system then becomes able to develop and express a coherent structure at a higher level, that is, an emergent behaviour (and emergent properties) that transcends what each element is individually capable of. (shrink)

As national and state health care policy -making becomes contentious and complex, there is a need for a forum to debate and explore public concerns and values in health care, give voice to local citizens, to facilitate consensus among various stakeholders, and provide feedback and direction to health care institutions and policy makers. This paper explores the role that regional health care ethics committees can play and provides two contrasting examples of Networks involved in facilitation of public input (...) into and the development of health care policies and adoption of state-wide practices. (shrink)

This book looks at the affective-cognitive roots of how the human mind inquires into the workings of nature and, more generally, how the mind confronts reality. Reality is an infinitely complex system, in virtue of which the mind can comprehend it only in bits and pieces, by making up interpretations of the myriads of signals received from the world by way of integrating those with information stored from the past. This constitutes a piecemeal interpretation by which we assemble our (...) phenomenal reality. In perceiving the complex world and responding to it, the mind invokes the logic of affect and the logic of reason, the former mostly innate and implicit, and the latter generated consciously in explicit terms with reference to mind-independent relations between entities in nature. It is a strange combination of affect and reason that enables us to make decisions and inferences, --- the latter mostly of the inductive type --- thereby making possible the development of theories. Theories are our tool-kits for explaining and predicting phenomena, guiding us along in our journey in life. Theories, however, are defeasible, and need to be constantly updated, at times even radically. In this, the self and the soul are of enormous relevance. The former is the affect-based psychological engine driving all our mental processes, while the latter is the capacity of the conscious mind to examine and reconstruct the self by modulating repressed conflicts. If the soul remains inoperative, all our theories become misdirected and a rot spreads inexorably all around us. (shrink)

The main aim of the text is to show how a game of Go (Weiqi, baduk, Igo) can serve as a model representation of the ontological-metaphysical aspect of the actor–network theory (ANT). An additional objective is to demonstrate in return that this ontological-metaphys⁠ical aspect of ANT represented on Go/Weiqi game model is able to highlight the key aspect of this theory—onto-methodological praxis.

In adversarial machine learning, attackers add carefully crafted perturbations to input, where the perturbations are almost imperceptible to humans, but can cause models to make wrong predictions. In this paper, we did comprehensive review of some of the most recent research, advancement and discoveries on adversarial attack, adversarial sampling generation, the potency or effectiveness of each of the existing attack methods, we also did comprehensive review on some of the most recent research, advancement and discoveries on adversarial defense strategies, the (...) effectiveness of each defense methods, and finally we did comparison on effectiveness and potency of different adversarial attack and defense methods. We came to conclusion that adversarial attack will mainly be blackbox for the foreseeable future since attacker has limited or no knowledge of gradient use for NN model, we also concluded that as dataset becomes more complex, so will be increase in demand for scalable adversarial defense strategy to mitigate or combat attack, and we strongly recommended that any neural network model with or without defense strategy should regularly be revisited, with the source code continuously updated at regular interval to check for any vulnerability against newer attack. (shrink)

The Protein Ontology (PRO; http://proconsortium.org) formally defines protein entities and explicitly represents their major forms and interrelations. Protein entities represented in PRO corresponding to single amino acid chains are categorized by level of specificity into family, gene, sequence and modification metaclasses, and there is a separate metaclass for protein complexes. All metaclasses also have organism-specific derivatives. PRO complements established sequence databases such as UniProtKB, and interoperates with other biomedical and biological ontologies such as the Gene Ontology (GO). PRO relates to (...) UniProtKB in that PRO’s organism-specific classes of proteins encoded by a specific gene correspond to entities documented in UniProtKB entries. PRO relates to the GO in that PRO’s representations of organism-specific protein complexes are subclasses of the organism-agnostic protein complex terms in the GO Cellular Component Ontology. The past few years have seen growth and changes to the PRO, as well as new points of access to the data and new applications of PRO in immunology and proteomics. Here we describe some of these developments. (shrink)

In the book, the author defines the evolution as a continuous and nonlinear complex of the structure of matter, interaction types and environments of existence; analyzes existing in modern science and philosophy approaches to the study of the process of evolution, degree of development factors and causes of evolution. Unifying interdisciplinary research in cosmology, evolution, biology, neuroscience and philosophy, the author presents his vision of the evolution model of «Evolving matter», which allows us to consider not only the laws (...) of the transition space vacuum in neural networks but also to see our Universe as a complex, heterogeneous organization. Interdisciplinary amount of information on the theory of evolution is systematized and a new method of world perception is proposed in the book. (shrink)

Gao, Barzel, and Barabási attempted to devise a method to identify universal resilience in systems. This target was missed due to simplification in the methodology and to the confounding of the system-functions resilience and robustness. These two system-functions are distinct in both their form and in the roles they play in systems. There are many different kinds of both robustness and resilience. To clarify the difference between robustness and resilience, a short guide is provided consisting of six examples of robustness (...) and six examples of resilience. There is a concluding discussion concerning the manner in which robustness and resilience are confounded in the paper “Universal Resilience Patterns in Complex Networks.” -/- . (shrink)

Is any unified theory of brain function possible? Following a line of thought dat- ing back to the early cybernetics (see, e.g., Cordeschi, 2002), Clark (in press) has proposed the action-oriented Hierarchical Predictive Coding (HPC) as the account to be pursued in the effort of gain- ing the “Grand Unified Theory of the Mind”—or “painting the big picture,” as Edelman (2012) put it. Such line of thought is indeed appealing, but to be effectively pursued it should be confronted with experimental (...) findings and explana- tory capabilities (Edelman, 2012). The point we are making in this note is that a brain with predictive capa- bilities is certainly necessary to endow the agent situated in the environment with forethought or foresight, a crucial issue to outline the unified account advocated by Clark. But the capacity for fore- thought is deeply entangled with the capacity for emotions and when emotions are brought into the game, cogni- tive functions become part of a large-scale functional brain network. However, for such complex networks a consistent view of hierarchical organization in large-scale functional networks has yet to emerge (Bressler and Menon, 2010), whilst heterarchical organization is likely to play a strategic role (Berntson et al., 2012). This raises the necessity of a multilevel approach that embraces causal relations across levels of explanation in either direc- tion (bottom–up or top–down), endorsing mutual calibration of constructs across levels (Berntson et al., 2012). Which, in turn, calls for a revised perspective on Marr’s levels of analysis framework (Marr, 1982). In the following we highlight some drawbacks of Clark’s proposal in address- ing the above issues. (shrink)

A strong motivation for the human genome project was to relate biological features to the structure and function of small sets of genes, and ideally to individual genes. However, it is now increasingly realized that many problems require a "systems" approach emphasizing the interplay of large numbers of genes, and the involvement of complex networks of gene regulation. This implies a new emphasis on integrative, systems theoretical approaches. It may be called 'holistic' if the term is used without (...) irrational overtones, in the general sense of directing attention to integrated features of organs and organisms. In the history of biology, seemingly conflicting reductionist and holistic notions have alternated, with bottom-up as well as top-down approaches eventually contributing to the solutions of basic problems. By now, there is no doubt that biological features and phenomena are rooted in physico-chemical processes of the molecules involved; and yet, integrated systems aspects are becoming more and more relevant in developmental biology, brain and behavioural science, and socio-biology. -/- . (shrink)

A new layer of complexity, constituted of networks of information token recurrence, has been identified in socio-technical systems such as the Wikipedia online community and the Zooniverse citizen science platform. The identification of this complexity reveals that our current understanding of the actual structure of those systems, and consequently the structure of the entire World Wide Web, is incomplete, which raises novel questions for data science research but also from the perspective of social epistemology. Here we establish the principled (...) foundations and practical advantages of analyzing information diffusion within and across Web systems with Transcendental Information Cascades, and outline resulting directions for future study in the area of socio-technical systems. We also suggest that Transcendental Information Cascades may be applicable to any kind of time-evolving system that can be observed using digital technologies, and that the structures found in such systems comprise properties common to all naturally occurring complex systems. (shrink)

In this essay we collect and put together a number of ideas relevant to the under- standing of the phenomenon of creativity, confining our considerations mostly to the domain of cognitive psychology while we will, on a few occasions, hint at neuropsy- chological underpinnings as well. In this, we will mostly focus on creativity in science, since creativity in other domains of human endeavor have common links with scientific creativity while differing in numerous other specific respects. We begin by briefly (...) introducing a few basic notions relating to cognition, among which the notion of ‘concepts’ is of basic relevance. The myriads of concepts lodged in our mind constitute a ‘conceptual space’ of an enormously complex structure, where con- cepts are correlated by beliefs that are themselves made up of concepts and are as- sociated with emotions. The conceptual space, moreover, is perpetually in a state of dynamic evolution that is once again of a complex nature. A major component of the dynamic evolution is made up of incessant acts of inference, where an inference occurs essentially by means of a succession of correlations among concepts set up with beliefs and heuristics, the latter being beliefs of a special kind, namely, ones relatively free of emotional associations and possessed of a relatively greater degree of justification. Beliefs, along with heuristics, have been described as the ‘mind’s software’, and con- stitute important cognitive components of the self-linked psychological resources of an individual. The self is the psychological engine driving all our mental and physical activity, and is in a state of ceaseless dynamics resulting from one’s most intimate ex- periences of the world accumulating in the course of one’s journey through life. Many of our psychological resources are of a dual character, having both a self-linked and a shared character, the latter being held in common with larger groups of people and imbibed from cultural inputs. We focus on the privately held self-linked beliefs of an individual, since these are presumably of central relevance in making possible inductive inferences – ones in which there arises a fundamental need of adopting a choice or making a decision. Beliefs, decisions, and inferences, all have the common link to the self of an individual and, in this, are fundamentally analogous to free will, where all of these have an aspect of non-determinism inherent in them. Creativity involves a major restructuring of the conceptual space where a sustained inferential process eventually links remote conceptual domains, thereby opening up the possibility of a large number of new correlations between remote concepts by a cascading process. Since the process of inductive inference depends crucially on de- cisions at critical junctures of the inferential chain, it becomes necessary to examine the basic mechanism underlying the making of decisions. In the framework that we attempt to build up for the understanding of scientific creativity, this role of decision making in the inferential process assumes central relevance. With this background in place, we briefly sketch the affect theory of decisions. Affect is an innate system of response to perceptual inputs received either from the exter- nal world or from the internal physiological and psychological environment whereby a positive or negative valence gets associated with a perceptual input. Almost every sit- uation faced by an individual, even one experienced tacitly, i.e., without overt aware-ness, elicits an affective response from him, carrying a positive or negative valence that underlies all sorts of decision making, including ones carried out unconsciously in inferential processes. Referring to the process of inferential exploration of the conceptual space that gener- ates the possibility of correlations being established between remote conceptual do- mains, such exploration is guided and steered at every stage by the affect system, analogous to the way a complex computer program proceeds through junctures where the program ascertains whether specified conditions are met with by way of generating appropriate numerical values – for instance, the program takes different routes, depending on whether some particular numerical value turns out to be positive or negative. The valence generated by the affect system in the process of adoption of a choice plays a similar role which therefore is of crucial relevance in inferential processes, especially in the exploration of the conceptual space where remote domains need to be linked up – the affect system produces a response along a single value dimension, resembling a number with a sign and a magnitude. While the affect system plays a guiding role in the exploration of the conceptual space, the process of exploration itself consists of the establishment of correlations between concepts by means of beliefs and heuristics, the self-linked ones among the latter having a special role in making possible the inferential journey along alternative routes whenever the shared rules of inference become inadequate. A successful access to a remote conceptual domain, necessary for the creative solution of a standing problem or anomaly – one that could not be solved within the limited domain hitherto accessed – requires a phase of relatively slow cumulative search and then, at some stage, a rapid cascading process when a solution is in sight. Representing the conceptual space in the form of a complex network, the overall process can be likened to one of self-organized criticality commonly observed in the dynamical evolution of complex systems. In order that inferential access to remote domains may actually be possible, it is necessary that restrictions on the exploration process – necessary for setting the context in ordinary instances of inductive inference – be relaxed and a relatively free exploration in a larger conceptual terrain be made possible. This is achieved by the mind going into the default mode, where external constraints – ones imposed by shared beliefs and modes of exploration – are made inoperative. While explaining all these various aspects of the creative process, we underline the supremely important role that analogy plays in it. Broadly speaking, analogy is in the nature of a heuristic, establishing correlations between concepts. However, analo- gies are very special in that these are particularly effective in establishing correlations among remote concepts, since analogy works without regard to the contiguity of the concepts in the conceptual space. In establishing links between concepts, analogies have the power to light up entire terrains in the conceptual space when a rapid cas- cading of fresh correlations becomes possible. The creative process occurs within the mind of a single individual or of a few closely collaborating individuals, but is then continued by an entire epistemic community, eventually resulting in a conceptual revolution. Such conceptual revolutions make pos- sible the radical revision of scientific theories whereby the scope of an extant theory is broadened and a new theoretical framework makes its appearance. The emerging theory is characterized by a certain degree of incommensurability when compared with the earlier one – a feature that may appear strange at first sight. But incommen- surability does not mean incompatibility and the apparently contrary features of the relation between the successive theories may be traced to the multi-layered structureof the conceptual space where concepts are correlated not by means of single links but by multiple ones, thereby generating multiple layers of correlation, among which some are retained and some created afresh in a conceptual restructuring. We conclude with the observation that creativity occurs on all scales. Analogous to correlations being set up across domains in the conceptual space and new domains being generated, processes with similar features can occur within the confines of a domain where a new layer of inferential links may be generated, connecting up sub- domains. In this context, insight can be looked upon as an instance of creativity within the confines of a domain of a relatively limited extent. (shrink)

The social media platform Twitter platform has played a crucial role in the Black Lives Matter (BLM) movement. The immediate, flexible nature of tweets plays a crucial role both in spreading information about the movement’s aims and in organizing individual protests. Twitter has also played an important role in the right-wing reaction to BLM, providing a means to reframe and recontextualize activists’ claims in a more sinister light. The ability to bring about social change depends on the balance of these (...) two forces, and in particular which side can capture and maintain sustained attention. The present study examines 2 years worth of tweets about BLM (about 118 million in total). Timeseries analysis reveals that activists are better at mobilizing rapid attention, whereas right-wing accounts show a pattern of moderate but more sustained activity driven by reaction to political opponents. Topic modeling reveals differences in how different political groups talk about BLM. Most notably, the murder of George Floyd appears to have solidified a right-wing counter-framing of protests as arising from dangerous “terrorist” actors. The study thus sheds light on the complex network and rhetorical effects that drive the struggle for online attention to the BLM movement. (shrink)

[A revised and updated version of an earlier article 'Understanding Creativity: Affect Decision and Inference' (unpublished), posted at PhilArchive in 2021.] -/- This book looks at the creative process in the human mind. -/- Creativity involves a major restructuring of the conceptual space where a sustained inferential process eventually links remote conceptual domains, thereby opening up the possibility of a large number of new correlations between remote concepts by a cascading process. Since the process of inductive inference depends crucially on (...) decisions at critical junctures of the inferential chain, it becomes necessary to examine the basic mechanism underlying the making of decisions. In the framework that we attempt to build up for the understanding of scientific creativity, this role of decision making in the inferential process assumes central relevance. -/- Referring to the process of inferential exploration of the conceptual space that generates the possibility of correlations being established between remote conceptual domains, such exploration is guided and steered at every stage by the affect system, -/- While the affect system plays a guiding role in the exploration of the conceptual space, the process of exploration itself consists of the establishment of correlations between concepts by means of beliefs and heuristics, the self-linked ones among the latter having a special role in making possible the inferential journey along alternative routes whenever the shared rules of inference become inadequate. -/- Representing the conceptual space in the form of a complex network, the overall process can be likened to one of self-organised criticality commonly observed in the dynamical evolution of complex systems. An instance of self-organised criticality is found in the avalanche set up in a slowly growing sand-pile. (shrink)

In this paper, we present the results of the construction and validation of a new psychometric tool for measuring beliefs about free will and related concepts: The Free Will Inventory (FWI). In its final form, FWI is a 29-item instrument with two parts. Part 1 consists of three 5-item subscales designed to measure strength of belief in free will, determinism, and dualism. Part 2 consists of a series of fourteen statements designed to further explore the complex network of people’s (...) associated beliefs and attitudes about free will, determinism, choice, the soul, predictability, responsibility, and punishment. Having presented the construction and validation of FWI, we discuss several ways that it could be used in future research, highlight some as yet unanswered questions that are ripe for interdisciplinary investigation, and encourage researchers to join us in our efforts to answer these questions. (shrink)

We are quickly passing through the historical moment when people work in front of a single computer, dominated by a small CRT and focused on tasks involving only local information. Networked computers are becoming ubiquitous and are playing increasingly significant roles in our lives and in the basic infrastructure of science, business, and social interaction. For human-computer interaction o advance in the new millennium we need to better understand the emerging dynamic of interaction in which the focus task is no (...) longer confined to the desktop but reaches into a complex networked world of information and computer-mediated interactions. We think the theory of distributed cognition has a special role to play in understanding interactions between people and technologies, for its focus has always been on whole environments: what we really do in them and how we coordinate our activity in them. Distributed cognition provides a radical reorientation of how to think about designing and supporting human-computer interaction. As a theory it is specifically tailored to understanding interactions among people and technologies. In this article we propose distributed cognition as a new foundation for human-computer interaction, sketch an integrated research framework, and use selections from our earlier work to suggest how this framework can provide new opportunities in the design of digital work materials. (shrink)