The new business opportunities and prospects emerging in Europe within the Common Market and other Western and European countries also raise important ethical challenges. This work comprises a collection of ethical insights to enhance the conduct of business in an evolving Europe.

What is it that evolves in cultural evolution? This is a question easily posed but not so easily answered. According to common interpretations of cultural evolutionary theory, it is not strictly agents that change over time or proliferate during cultural transmission, but their socially transmitted behavior, what they communicate or acquire via social learning – in short: their interactions. This means that we have to put these cultural interactions into an evolutionary setting and show how they evolve within cultural populations, (...) i.e. within social networks. But the social networks themselves also evolve, which brings us to a multi-level approach of cultural evolution, implementing both, individual and group selection. In this paper I will assume that the microlevel is given by a description of cultural agents, their behavior and decisions, whereas the macrolevel describes the dynamics on population structure and in particular population boundaries in social networks (since we are not really able to identify something analogous to ‘species’ in cultural evolution). In this paper, I am going to offer a specific mathematical model, that makes use of game theory for representing the cultural microlevel and graph theory for the cultural macrolevel. It has to be shown, how both can formally be linked in a synthetic attempt. (shrink)

Most animals have significant behavioral expertise built in without having to explicitly learn it all from scratch. This expertise is a product of evolution of the organism; it can be viewed as a very long term form of learning which provides a structured system within which individuals might learn more specialized skills or abilities. This paper suggests one possible mechanism for analagous robot evolution by describing a carefully designed series of networks, each one being a strict augmentation of the (...) previous one, which control a six legged walking machine capable of walking over rough terrain and following a person passively sensed in the infrared spectrum. As the completely decentralized networks are augmented. the robot’s performance and behavior repetoire demonstrably improve. The rationale for such demonstrations is that they may provide a hint as to the requirements for automatically building massive networks to carry out complex sensory-motor tasks. The experiments with an actual robot ensure that an essence of reality is maintained and that no critical disabling problems have been ignored. (shrink)

Hierarchies occur widely in evolving self‐organizing ecological, biological, technological, and social networks, but detecting and comparing hierarchies is difficult. Here we present a metric and technique to quantitatively assess the extent to which self‐organizing directed networks exhibit a flow hierarchy. Flow hierarchy is a commonly observed but theoretically overlooked form of hierarchy in networks. We show that the ecological, neurobiological, economic, and information processing networks are generally more hierarchical than their comparable random networks. We (...) further discovered that hierarchy degree has increased over the course of the evolution of Linux kernels. Taken together, our results suggest that hierarchy is a central organizing feature of real‐world evolving networks, and the measurement of hierarchy opens the way to understand the structural regimes and evolutionary patterns of self‐organizing networks. Our measurement technique makes it possible to objectively compare hierarchies of different networks and of different evolutionary stages of a single network, and compare evolving patterns of different networks. It can be applied to various complex systems, which can be represented as directed networks. (shrink)

The so-called Baldwin Effect generally says how learning, as a form of ontogenetic adaptation, can influence the process of phylogenetic adaptation, or evolution. This idea has also been taken into computation in which evolution and learning are used as computational metaphors, including evolving neural networks. This paper presents a technique called evolving self-taught neural networks – neural networks that can teach themselves without external supervision or reward. The self-taught neural network is intrinsically motivated. Moreover, the (...) self-taught neural network is the product of the interplay between evolution and learning. We simulate a multi-agent system in which neural networks are used to control autonomous agents. These agents have to forage for resources and compete for their own survival. Experimental results show that the interaction between evolution and the ability to teach oneself in self-taught neural networks outperform evolution and self-teaching alone. More specifically, the emergence of an intelligent foraging strategy is also demonstrated through that interaction. Indications for future work on evolving neural networks are also presented. (shrink)

Human contact networks constitute a multitude of individuals and pairwise contacts among them. However, the dynamic nature, which generates the evolution of human contact networks, of contact patterns is unknown yet. Here, we analyse three empirical datasets and identify two crucial mechanisms of the evolution of temporal human contact networks, i.e., the activity state transition laws for an individual to be socially active and the contact establishment mechanism that active individuals adopt. We consider both of the two (...) mechanisms to propose a temporal network model, the so-called memory-driven model, of human contact networks. Then, we study the susceptible-infected spreading processes on empirical human contact networks as well as four corresponding temporal network models and compare the full prevalence time of SI processes with various infection rates on the networks. The full prevalence time of SI processes in the MD model is the same as that in real-world human contact networks. Moreover, we find that the individual activity state transition promotes the spreading process, while the contact establishment of active individuals suppresses the prevalence. Besides, we observe that individuals who establish social ties with a small exploring rate are still able to induce an endemic which prevails in the networks. The study offers new insights to predict and control the diffusion processes on networks. (shrink)

In this paper I discuss one of the key issuesin the philosophy of neuroscience:neurosemantics. The project of neurosemanticsinvolves explaining what it means for states ofneurons and neural systems to haverepresentational contents. Neurosemantics thusinvolves issues of common concern between thephilosophy of neuroscience and philosophy ofmind. I discuss a problem that arises foraccounts of representational content that Icall ``the economy problem'': the problem ofshowing that a candidate theory of mentalrepresentation can bear the work requiredwithin in the causal economy of a mind and (...) anorganism. My approach in the current paper isto explore this and other key themes inneurosemantics through the use of computermodels of neural networks embodied and evolvedin virtual organisms. The models allow for thelaying bare of the causal economies of entireyet simple artificial organisms so that therelations between the neural bases of, forinstance, representation in perception andmemory can be regarded in the context of anentire organism. On the basis of thesesimulations, I argue for an account ofneurosemantics adequate for the solution of theeconomy problem. (shrink)

Network analysis views complex systems as networks with well-defined structural properties that account for their complexity. These characteristics, which include scale-free behavior, small worlds and communities, are not to be found in networks such as random graphs and lattices that do not correspond to complex systems. They provide therefore a robust ground for claiming the existence of “complex networks” as a non-trivial subset of networks. The theory of complex networks has thus been successful in making (...) systematically explicit relevant marks of complexity in the form of structural properties, and this success is at the root of its current popularity. Much less systematic has been, on the other hand, the definition of the properties of the building components of complex networks. The obvious assumption is that these components must be nodes and links. Generally, however, the internal structure of nodes is not taken into account, and links are serendipitously identified by the perspective with which one looks at the network to be analyzed. For instance, if the nodes are Web pages that contain information about scientific papers, one point of view will match the relevant links with hyperlinks to similar Web pages, and another with citations of other articles. We intend to contribute here a systematic approach to the identification of the components of a complex network that is based on information theory. The approach hinges on some recent results arising from the convergence between the theory of complex networks and probabilistic techniques for content mining. At its core there is the idea that nodes in a complex network correspond to basic information units from which links are extracted via methods of machine learning. Hence the links themselves are viewed as emergent properties, similarly to the broader structural properties mentioned above. Indeed, beside rounding up the theory, this approach based on learning has clear practical benefits, in that it makes networks emerge from arbitrary information domains. We provide examples and applications in a variety of contexts, starting from an information-theoretic reconstruction of the well-known distinction between “strong links” and “weak links” and then delving into specific applications such as business process management and analysis of policy making. (shrink)

We propose a model of evolving protection against systemic risk related to recovery. Using the failure potential in network-agent dynamics, we present a process-based simulation that provides insights into alternative interventions and their mechanical uniqueness. The fundamental operating principle of this model is that computation allows greater emphasis on optimizing the recovery within the general regularity of random network dynamics. The rules and processes that are used here could be regarded as useful techniques in systemic risk measurement relative to (...) numerical failure reduction analyses. (shrink)

For at least the past several decades, medicine has been embroiled in a crisis concerning the nature of its professionalism. The fundamental questions that drive this ongoing crisis are primarily three. First, what is the nature of medical professionalism? Second, who are medical professionals? Third, what does medicine or these professionals profess or promise? In this paper, the professionalism crisis vis-à-vis these questions is examined and analyzed chiefly in terms of both Francis Peabody’s and Edmund Pellegrino’s writings. Based on their (...) writings, I introduce a conceptual framework for professionalism to address the crisis. In addition, I contend that to address the professionalism crisis adequately, medicine’s position within an evolving health care network must also be considered. To that end, I first discuss the genesis of the crisis in terms of the Flexner Report and especially Peabody’s response to it. Next, I explore how the crisis intensified during the twentieth century, particularly in terms of medicine’s ultimate scientification and eventual commercialization, and how Pellegrino reacted to this. I then propose a health care professionalism cycle and a care-competence cycle to provide a conceptual framework for addressing the crisis. I conclude that medicine’s position is no longer as the center of health care but rather as another node within a wider evolving health care network. And the resolution of medicine’s professionalism crisis depends on medicine’s positioning and defining itself in terms of the professionalism for each of the other professions within the health care network. (shrink)

The technology of undersea communications, from stranded-wire telegraph cables in the 1850s to fiber-optic cables at the end of the twentieth century, and its social, political, and economic impact. By the end of the twentieth century, fiber-optic technology had made possible a worldwide communications system of breathtaking speed and capacity. This amazing network is the latest evolution of communications technologies that began with undersea telegraph cables in the 1850s and continued with coaxial telephone cables a hundred years later. Communications under (...) the Seas traces the development of these technologies and assesses their social, economic, and political effects. If we cannot predict the ultimate consequences of today's wired world--its impact on economic markets, free expression, and war and peace--or the outcome of the conflict between wired and wireless technology, we can examine how similar issues have been dealt with in the past. The expert contributors to this volume do just that, discussing technical developments in undersea cables, management of the cables by private and public interests, and the impact on military and political activities. Chapters cover such topics as the daring group of nineteenth-century entrepreneurs who wove a network of copper wires around the world ; the opening of the telegraphic network to general public use; the government- and industry-forced merger of wireless and cable companies in Britain; and the impact of the cable network on diplomacy during the two world wars. (shrink)

With the fast development of web 2.0, information generation and propagation among online users become deeply interweaved. How to effectively and immediately discover the new emerging topic and further how to uncover its evolution law are still wide open and urgently needed by both research and practical fields. This paper proposed a novel early emerging topic detection and its evolution law identification framework based on dynamic community detection method on time-evolving and scalable heterogeneous social networks. The framework is (...) composed of three major steps. Firstly, a time-evolving and scalable complex network denoted as KeyGraph is built up by deeply analyzing the text features of all kinds of data crawled from heterogeneous online social network platforms; secondly, a novel dynamic community detection method is proposed by which the new emerging topic is detected on the modeled time-evolving and scalable KeyGraph network; thirdly, a unified directional topic propagation network modeled by a great number of short texts including microblogs and news titles is set up, and the topic evolution law of the previously detected early emerging topic is identified by fully utilizing local network variations and modularity optimization of the “time-evolving” and directional topic propagation network. Our method is proved to yield preferable results on both a huge amount of computer-generated test data and a great amount of real online network data crawled from mainstream heterogeneous social networks. (shrink)

This paper is part of a larger project to determine how to build agent-based cognitive models capable of initial associative intelligence. Our method here is to take McClelland’s 1981 “Jets and Sharks” dataset and rebuild it using a nonlinear dynamic system with an eye toward determining which parameters are necessary to govern the interactivity of agents in a multi-agent cognitive system. A few number of parameters are suggested concerning diffusion and infusion values, which are basically elementary forms of information entropy, (...) and multi-dimensional overlap from properties to objects and then from objects back to the properties that define them. While no agent-based model is presented, the success of the dynamic systems that are presented here suggest strong starting points for further research in building cognitive complex adaptive systems. (shrink)

Some philosophers suggest that the development of scientificknowledge is a kind of Darwinian process. The process of discovery,however, is one problematic element of this analogy. I compare HerbertSimon's attempt to simulate scientific discovery in a computer programto recent connectionist models that were not designed for that purpose,but which provide useful cases to help evaluate this aspect of theanalogy. In contrast to the classic A.I. approach Simon used, ``neuralnetworks'' contain no explicit protocols, but are generic learningsystems built on the model of (...) the interconnections of neurons in thebrain. I describe two cases that take the connectionist approach a stepfurther by using genetic algorithms, a form of evolutionary computationthat explicitly models Darwinian mechanisms. These cases show thatDarwinian mechanisms can make novel discoveries of complex, previouslyunknown patterns. With some caveats, they lend support to evolutionaryepistemology. (shrink)

A network of gene regulation organized in a hierarchical and combinatorial manner is crucially involved in the development of the neural network, and has to be considered one of the main substrates of genetic change in its evolution. Though qualitative features may emerge by way of the accumulation of rather unspecific quantitative changes, it is reasonable to assume that at least in some cases specific combinations of regulatory parts of the genome initiated new directions of evolution, leading to novel capabilities (...) of the brain. These notions are applied, in this paper, to the evolution of the capability of cognition-based human empa­thy. It is suggested that it has evolved as a secondary effect of the evolution of strategic thought. Development of strategies depends on abstract representations of one’s own pos­sible future states in one’s own brain to allow assessment of their emotional desirability, but also on the representation and emotional evaluation of possible states of others, allowing anticipation of their behaviour. This is best achieved if representations of others are con­nected to one’s own emotional centres in a manner similar to self-representations. For this reason, the evolution of the human brain is assumed to have established representations with such linkages. No group selection is involved, because the quality of strategic thought affects the fitness of the individual. A secondary effect of this linkage is that both the actual states and the future perspectives of others elicit vicarious emotions, which may contribute to the motivations of altruistic behaviour. (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)

We consider how an epistemic network might self-assemble from the ritualization of the individual decisions of simple heterogeneous agents. In such evolved social networks, inquirers may be significantly more successful than they could be investigating nature on their own. The evolved network may also dramatically lower the epistemic risk faced by even the most talented inquirers. We consider networks that self-assemble in the context of both perfect and imperfect communication and compare the behaviour of inquirers in each. This (...) provides a step in bringing together two new and developing research programs, the theory of self-assembling games and the theory of network epistemology. (shrink)

We explicate representational content by addressing how representations that ex- plain intelligent behavior might be acquired through processes of Darwinian evo- lution. We present the results of computer simulations of evolved neural network controllers and discuss the similarity of the simulations to real-world examples of neural network control of animal behavior. We argue that focusing on the simplest cases of evolved intelligent behavior, in both simulated and real organisms, reveals that evolved representations must carry information about the creature’s environ- ments (...) and further can do so only if their neural states are appropriately isomor- phic to environmental states. Further, these informational and isomorphism rela- tions are what are tracked by content attributions in folk-psychological and cognitive scientific explanations of these intelligent behaviors. (shrink)

We need to clarify at least four aspects of selfhood if we are to reach a better understanding of consciousness in general, and of its alternate states. First, how did we develop our self-centred psychophysiology? Second, can the four familiar lobes of the brain alone serve, if only as preliminary landmarks of convenience, to help understand the functions of our many self-referent networks? Third, what could cause one's former sense of self to vanish from the mental field during an (...) extraordinary state of consciousness? Fourth, when a person's physical and psychic self do drop off briefly, how has conscious experience then been transformed? In particular, what happens to that subject's personal sense of time? Our many-sided self arose in widely distributed brain networks. Since infancy, these self-oriented circuits have been over-conditioned by limbic biases. Selfhood then seems to have evolved along lines suggesting at least in shorthand the operations of a kind of ‘I-Me-Mine’ complex. But what happens when this egocentric triad briefly dissolves? Novel states of consciousness emerge. Two personally-observed states are discussed: insight-wisdom ; internal absorption. How do these two states differ phenomenologically? The physiological processes briefly suggested here emphasize shifts in deeper systems, and pivotal roles for thalamo-cortical interactions in the front and back of the brain. (shrink)

Network neuroscience provides a systems approach to the study of the brain and enables the examination of interactions measured at different temporal and spatial scales. We review current methods to quantify the structure of brain networks and compare that structure across different clinical cohorts, cognitive states, and subjects. We further introduce the emerging mathematical concept of multilayer networks and describe the advantages of this approach to model changing brain dynamics over time. We conclude by offering several concrete examples (...) of how multilayer network approaches to neuroimaging data provide novel insights into brain structure and evolving function. (shrink)

Genes are gained and lost over the course of evolution. A recent study found that over 1,800 new genes have appeared during primate evolution and that an unexpectedly high proportion of these genes are expressed in the human brain. But what are the molecular functions of newly evolved genes and what is their impact on an organism's fitness? The acquisition of new genes may provide a rich source of genetic diversity that fuels evolutionary innovation. Although gene manipulation experiments are not (...) feasible in humans, studies in model organisms, such as Drosophila melanogaster, have shown that new genes can quickly become integrated into genetic networks and become essential for survival or fertility. Future studies of new genes, especially chimeric genes, and their functions will help determine the role of genetic novelty in the adaptation and diversification of species. (shrink)

There is wide agreement that community engagement is important for many research types and settings, often including interaction with ‘representatives’ of communities. There is relatively little published experience of community engagement in international research settings, with available information focusing on Community Advisory Boards or Groups (CAB/CAGs), or variants of these, where CAB/G members often advise researchers on behalf of the communities they represent. In this paper we describe a network of community members (‘KEMRI Community Representatives’, or ‘KCRs’) linked to a (...) large multi-disciplinary research programme on the Kenyan Coast. Unlike many CAB/Gs, the intention with the KCR network has evolved to be for members to represent the geographical areas in which a diverse range of health studies are conducted through being typical of those communities. We draw on routine reports, self-administered questionnaires and interviews to: 1) document how typical KCR members are of the local communities in terms of basic characteristics, and 2) explore KCR's perceptions of their roles, and of the benefits and challenges of undertaking these roles. We conclude that this evolving network is a potentially valuable way of strengthening interactions between a research institution and a local geographic community, through contributing to meeting intrinsic ethical values such as showing respect, and instrumental values such as improving consent processes. However, there are numerous challenges involved. Other ways of interacting with members of local communities, including community leaders, and the most vulnerable groups least likely to be vocal in representative groups, have always been, and remain, essential. (shrink)

MicroRNAs (miRNAs) constitute an abundant family of 22‐nucleotide RNAs that base‐pair to target mRNAs and typically inhibit their expression. To assess the global impact of animal miRNAs on gene regulation, the expression of predicted targets and their cognate miRNAs was extensively analyzed in mammals and Drosophila.1,2 In general, targets are co‐expressed at relatively low or undetectable levels in the same tissues as the miRNAs predicted to regulate them. Additionally, genes that are highly co‐expressed with miRNAs usually lack target sites. The (...) authors conclude that many animal genes are under evolutionary pressure to maintain or avoid complementary sites to miRNAs.1,2 Thus, the miRNA pathway broadly contributes to the complex gene regulatory networks that shape animal tissue development and identity. BioEssays 28: 449–452, 2006. © 2006 Wiley Periodicals, Inc. (shrink)

Finding suitable analysis techniques for networks generated from social processes is a difficult task when the population changes over time. Traditional social network analysis measures may not work in such circumstances. It is argued that agent-based social networks should not be constrained by a priori assumptions about the evolved network and/or the analysis techniques. In most agent-based social simulation models, the number of agents remains fixed throughout the simulation; this paper considers the case when this does not hold. (...) Thus the aim of this paper is to demonstrate how the network signatures change when the agents’ population depends upon endogenous social processes. We argue for a much wider attention from the social simulation community in addressing this open research problem. (shrink)

In this paper, we adopt a physiological perspective in order to produce an intelligible overview of biological transmission in all its diversity. This allows us to put forward the analysis of transmission mechanisms, with the aim of complementing the usual focus on transmitted factors. We underline the importance of the structural, dynamical, and functional features of transmission mechanisms throughout organisms’ life cycles in order to answer to the question of what is passed on across generations, how and why. On this (...) basis, we propose a vision of biological transmission as networks of heterogeneous physiological mechanisms, not restricted to transmission mechanisms stricto sensu. They prove to be themselves suited candidates for evolutionary explanations. They are processes both necessary for evolution to happen and resulting themselves from evolution. This leads us to call for a strategy of endogenization to account for transmission, and more specifically inheritance, as evolved and evolving physiological mechanisms. (shrink)

This article focuses on the micro-level phenomena related to emergent ways of organizing. It explores how new ways of organizing might be enabled or inhibited through the networking activities and knowledge flows that organizational members engage in within a multinational business organization after the set-up of an innovative Internet business unit. The article considers innovation and networking as social practices mediated in this particular case study through knowledge-sharing activities. This perspective on innovation, networking, and knowledge leads to a conceptualization of (...) organizations that stresses their inherent complexity and their interactive and co-evolving nature with their environments. (shrink)

Through a systematic foregrounding of temporality as a framework of analysis, the dynamics of neo-liberal globalization and the revolution in ICTs constitute a new epistemological context. From this perspective the world as an economic, social, cultural, and political postmodernity becomes apparent. The article argues that liberal democracy was created and evolved in a specific context too. It was one formed through the interactions of Enlightenment thought and capitalist action - both of which were suffused by the temporality of the clock. (...) For nearly three-hundred years we have taken for granted its meter of the clock as the measure of temporal reality, and have similarly accepted that liberal democracy functions on the same temporal basis. This has blinded us to a "social acceleration" that has developed through the convergence of neo-liberal economics and ICTs to the point where liberal democracy and its clock time functioning simply cannot synchronize with the times of the network society. Classical liberal democracy has been rendered ineffectual as a means of democratic action, and neo-liberal globalization offers nothing to replace it. (shrink)

This article explores the emergence of standards and regulation associated with the provision of Closed Circuit Television (CCTV) surveillance systems in the United Kingdom. It argues that despite the intrusive and controlling nature of CCTV technology there is limited formal intervention in the form of legislation, governing its introduction and use. Instead government has sought to influence the regulation of the technology indirectly through its ability to shape and govern policy networks in the policy arena. In doing so, it (...) is argued here, that government remains the dominant actor in the policy process. (shrink)

Hyperlink cinema is an emergent film genre that seeks to push the boundaries of the medium in order to mirror contemporary life in the globalized community. Films in the genre thus create an interacting network across space and time in such a way as to suggest that people’s lives can intersect on scales that would not have been possible without modern technologies of travel and communication. This allows us to test the hypothesis that new kinds of media might permit us (...) to break through the natural cognitive constraints that limit the number and quality of social relationships we can manage in the conventional face-to-face world. We used network analysis to test this hypothesis with data from 12 hyperlink films, using 10 motion pictures from a more conventional film genre as a control. We found few differences between hyperlink cinema films and the control genre, and few differences between hyperlink cinema films and either the real world or classical drama (e.g., Shakespeare’s plays). Conversation group size seems to be especially resilient to alteration. It seems that, despite many efficiency advantages, modern media are unable to circumvent the constraints imposed by our evolved psychology. (shrink)

This paper presents the architecture and initial feasibility results of a proto-type communication network that utilizes genetic programming to evolve services and protocols as part of network operation. The network evolves responses to environmental conditions in a manner that could not be preprogrammed within legacy network nodes a priori. A priori in this case means before network operation has begun. Genetic material is exchanged, loaded, and run dynamically within an active network. The transfer and execution of code in support of (...) the evolution of network protocols and services would not be possible without the active network environment. Rapid generation of network service code occurs via a genetic programming paradigm. Complexity and Algorithmic Information Theory play a key role in understanding and guiding code evolution within the network. (shrink)

We are today agents of a peculiar reality, the global network or the system for automated information production. Our condition in the global network is that of agents of the real, since we all contribute to the coproduction of this ever-evolving process. Nevertheless, I will argue, this reality is but the effect of the adoption of a notion of instrumental pragmatic rationality which denies the existence of any other possible reality as the actualization of different determinations of Reason. While (...) following Deleuze s notion of deterritorialization, I ll show that the real philosophical choice does not concern the introduction of new moves that create differentiations within a universal game, but that it is concerned with the possible abolition of the agency which is instantiated within the collective network. (shrink)

PurposeTrust is a social factor at the foundations of human action. The pervasiveness of trust explains why it has been studied by a large variety of disciplines, and its complexity justifies the difficulties in reaching a shared understanding and definition. As for all the social factors, trust is continuously evolving as a result of the changes in social, economic and technological conditions. The internet and many other Information and Communication Technologies (ICT) solutions have changed organizational and social life. Such (...) mutated scenarios challenged what researchers know about trust, thus several studies tried to investigate the difference between online and traditional (physical) environments. The purpose of this paper is to solve this multi‐dimensional puzzle by presenting a conceptual framework that will take into consideration the complexity of ICT mediated‐trust.Design/methodology/approachThe extant literature still lacks a homogeneous framework and presents a large amount of different perspectives, each one addressing a very specific issue. By using the methodology suggested by Short and Cropanzano the paper proposes a conceptual model for understanding the dynamics of trust in online settings. In doing so the authors' adopted the Actor Network Theory conceptual frame for disentangling the ambiguous role of technology in its relation with trust.FindingsThis paper provides an ultimate conceptual model on trust in virtual settings.Research limitations/implicationsThe proposed model will help systematize the extant contributions and also identify the gaps that can be addressed by future researches.Originality/valueThe model permits the understanding of the dynamics of trust in online settings. (shrink)

Reaction network is a promising framework for representing complex systems of diverse and even interdisciplinary types. In this approach, complex systems appear as self-maintaining structures emerging from a multitude of interactions, similar to proposed scenarios for the origin of life out of autocatalytic networks. The formalism of chemical organization theory mathematically specifies under which conditions a reaction network is stable enough to be observed as a whole complex system. Such conditions specify the notion of organization, crucial in COT. In (...) this paper, we show that the structure and operation of organizations can be advanced towards a formal framework of resilience in complex systems. That is, we show that there exist three fundamental types of change defined for reaction networks, and that these perturbations not only provide a general representation of perturbations in the context of resilience but also pave the ground to formalize different forms of resilient responses. In particular, we show that decomposing the network’s operational structure into dynamically decoupled modules allows to formalize what is the impact of a perturbation and to what extent any potential compensation to that perturbation will be successful. We illustrate our approach with a toy model of a farm that operates in a sustainable way producing milk, eggs, and/or grains from other resources. With the help of simulations, we analyze the different types of perturbations and responses that the farm can undergo and how that affects its sustainable operation. (shrink)

In contrast to the traditional relational semiotics, biosemiotics decisively deviates towards dynamical aspects of signs at the evolutionary and developmental time scales. The analysis of sign dynamics requires constructivism to explain how new components such as subagents, sensors, effectors, and interpretation networks are produced by developing and evolving organisms. Semiotic networks that include signs, tools, and subagents are multilevel, and this feature supports the plasticity, robustness, and evolvability of organisms. The origin of life is described here as (...) the emergence of simple self-constructing semiotic networks that progressively increased the diversity of their components and relations. Primitive organisms have no capacity to classify and track objects; thus, we need to admit the existence of proto-signs that directly regulate activities of agents without being associated with objects. However, object recognition and handling became possible in eukaryotic species with the development of extensive rewritable epigenetic memory as well as sensorial and effector capacities. Semiotic networks are based on sequential and recursive construction, where each step produces components that are needed for the following steps of construction. Construction is not limited to repair and reproduction of what already exists or is unambiguously encoded, it also includes production of new components and behaviors via learning and evolution. A special case is the emergence of new levels of organization known as metasystem transition. Multilevel semiotic networks reshape the phenotype of organisms by combining a mosaic of features developed via learning and evolution of cooperating and/or conflicting subagents. (shrink)

A formalism is introduced to represent the connective organization of an evolving neuronal network and the effects of environment on this organization by stabilization or degeneration of labile synapses associated with functioning. Learning, or the acquisition of an associative property, is related to a characteristic variability of the connective organization: the interaction of the environment with the genetic program is printed as a particular pattern of such organization through neuronal functioning. An application of the theory to the development of (...) the neuromuscular junction is proposed and the basic selective aspect of learning emphasized. (shrink)

I criticize Herbert Simon 's argument for the claim that complex natural systems must constitute decomposable, mereological or functional hierarchies. The argument depends on certain assumptions about the requirements for the successful evolution of complex systems, most importantly, the existence of stable, intermediate stages in evolution. Simon offers an abstract model of any process that succeeds in meeting these requirements. This model necessarily involves construction through a decomposable hierarchy, and thus suggests that any complex, natural, i.e., evolved, system is constituted (...) by a decomposable hierarchy. I argue that Stuart Kauffman's recent models of genetic regulatory networks succeed in specifying processes that could meet Simon 's requirements for evolvability without requiring construction through a decomposable hierarchy. Since Kauffman's models are at least as plausible as Simon 's model, Simon 's argument that complex natural systems must constitute decomposable, mereological or functional hierarchies does not succeed. (shrink)

Cognitive science has evolved since early disputes between radical empiricism and radical nativism. The authors are reacting to the revival of radical empiricism spurred by recent successes in deep neural network (NN) models. We agree that language-like mental representations (language-of-thoughts [LoTs]) are part of the best game in town, but they cannot be understood independent of the other players.