Here is some serious geekery for your Saturday entertainment. This is a fairly complex and computational model of how social communities emerge and survive over time through a process called "adaptive rewiring."

Here is a brief overview of how these coevolutionary networks might exist in different realms:

In a social network, communities might indicate factions, interest groups, or social divisions [1]; in biological networks, they encompass entities having the same biological function [5–7]; in the WorldWideWeb they may correspond to groups of pages dealing with the same or related topics [8]; in food webs they may identify compartments [9]; and a community in a metabolic or genetic network might be related to a specific functional task [10].

And for those of us who are not familiar with this field or its terminology, here is a glossary of network terminology, taken from Gross and Blasius (2008).

A brief network glossary.

Degree. The degree of a node is the number of nearest neighbours to which a node is connected. The mean degree of the network is the mean of the individual degrees of all nodes in the network.
Dynamics. Depending on the context, the term dynamics is used in the literature to refer to a temporal change of either the state or the topology of a network. In this paper, we use the term dynamics exclusively to describe a change in the state, while the term evolution is used to describe a change in the topology.
Evolution. Depending on the context the term evolution is used in the literature to refer to a temporal change of either the state or the topology of a network. In this paper, we use the term evolution exclusively to describe a change in the topology, while the term dynamics is used to describe a change in the state.
Frozen nodes. A node is said to be frozen if its state does not change over in the long-term behaviour of the network. In certain systems discussed here, the state of frozen nodes can change nevertheless on an even longer (topological) time scale.
Link. A link is a connection between two nodes in the networks. Links are also sometimes called edges or simply network connections.
Neighbours. Two nodes are said to be neighbours if they are connected by a link.
Node. The node is the principal unit of the network. A network consists of a number of nodes connected by links. Nodes are sometimes also called vertices.
Scale-free network. In scale-free networks, the distribution of node degrees follows a power law.
State of the network. Depending on the context, the state of a network is used either to describe the state of the network nodes or the state of the whole network including the nodes and the topology. In this review, we use the term state to refer exclusively to the collective state of the nodes. Thus, the state is a priori independent of the network topology.
Topology of the network. The topology of a network defines a specific pattern of connections between the network nodes.

And from the same authors (citation at the bottom of the post), here is a visual representation of how adaptive rewiring might look:

This is interesting stuff and it seems very relevant to emerging coevolutionary networks in P2P communities and other cooperative networks that emerging in the post-capitalist world.

Full Citation:
González-Avella, JC, Cosenza, MG, Herrera, JL, & Tucci, K. (2014, Jul 1). Emergence and persistence of communities in coevolutionary networks. arXiv:1407.0388v1

Emergence and persistence of communities in coevolutionary networks

J. C. González-Avella, M. G. Cosenza, J. L. Herrera, K. Tucci

ABSTRACT

We investigate the emergence and persistence of communities through a recently proposed mechanism of adaptive rewiring in coevolutionary networks. We characterize the topological structures arising in a coevolutionary network subject to an adaptive rewiring process and a node dynamics given by a simple voterlike rule. We find that, for some values of the parameters describing the adaptive rewiring process, a community structure emerges on a connected network. We show that the emergence of communities is associated to a decrease in the number of active links in the system, i.e. links that connect two nodes in different states. The lifetime of the community structure state scales exponentially with the size of the system. Additionally, we find that a small noise in the node dynamics can sustain a diversity of states and a community structure in time in a finite size system. Thus, large system size and/or local noise can explain the persistence of communities and diversity in many real systems.

I. INTRODUCTION

Many social, biological, and technological systems possess a characteristic network structure consisting of communities or modules, which are groups of nodes distinguished by having a high density of links between nodes of the same group and a comparatively low density of links between nodes of different groups [1–4]. Such a network structure is expected to play an important functional role in many systems. In a social network, communities might indicate factions, interest groups, or social divisions [1]; in biological networks, they encompass entities having the same biological function [5–7]; in the WorldWideWeb they may correspond to groups of pages dealing with the same or related topics [8]; in food webs they may identify compartments [9]; and a community in a metabolic or genetic network might be related to a specific functional task [10].

Since community structure constitutes a fundamental feature of many networks, the development of methods and techniques for the detection of communities represents one of the most active research areas in network science [2, 11–17]. In comparison, much less work has been done to address a fundamental question: how do communities arise in networks? [18].

Clearly, the emergence of characteristic topological structures, including communities, from a random or featureless network requires some dynamical process that modifies the properties of the links representing the interactions between nodes. We refer to such link dynamics as a rewiring process. Links can vary their strength, or they can appear and disappear as a consequence of a rewiring process. In our view, two classes of rewiring processes leading to the formation of structures in networks can be distinguished: (i) rewirings based on local connectivity properties regardless of the values of the state variables of the nodes, which we denote as topological rewirings; and (ii) rewirings that depend on the state variables of the nodes, where the link dynamics is coupled to the node state dynamics and which we call adaptive rewirings.

Topological rewiring processes have been employed to explain the origin of small-world and scale-free networks [19, 20]. These rewirings can lead to the appearance of community structures in networks with weighted links [21] or by preferential attachment driven by local clustering [22]. On the other hand, there is currently much interest in the study of networks that exhibit a coupling between topology and states, since many systems observed in nature can be described as dynamical networks of interacting nodes where the connections and the states of the nodes affect each other and evolve simultaneously [23–29]. These systems have been denoted as coevolutionary dynamical systems or adaptive networks and, according to our classification above, they are subject to adaptive rewiring processes. The collective behavior of coevolutionary systems is determined by the competition of the time scales of the node dynamics and the rewiring process. Most works that employ coevolutionary dynamics have focused on the characterization of the phenomenon of network fragmentation arising from this competition. Although community structures have been found in some coevolutionary systems [30–33], investigating the mechanisms for the formation of perdurable communities remains an open problem.

In this paper we investigate the emergence and the persistence of communities in networks induced by a process of adaptive rewiring. Our work is based on a recently proposed general framework for coevolutionary dynamics in networks [29]. We characterize the topological structures forming in a coevolutionary network having a simple node dynamics. We unveil a region of parameters where the formation of a supertransient modular structure on the network occurs. We study the stability of the community configuration under small perturbations of the node dynamics, as well as for different initial conditions of the system.

Reference:

Gross, T, and Blasius, B. (2008, Mar). Adaptive coevolutionary networks: A review. Journal of the Royal Society Interface; 5(20): 259-271. doi: 10.1098/​rsif.2007.1229

This is an old article (from 2011), but it offers an excellent overview of the progress that has been made in applying dynamic systems theory to cognitive development. The following is from a book chapter on Dynamic Systems Theories by Esther Thelen and Linda B. Smith:

Dynamic systems is a recent theoretical approach to the study of development. In its contemporary formulation, the theory grows directly from advances in understanding complex and nonlinear systems in physics and mathematics, but it also follows a long and rich tradition of systems thinking in biology and psychology. The term dynamic systems, in its most generic form, means systems of elements that change over time.

The authors then offer two themes that recur frequently in the history of developmental theory and in dynamic systems theory:

1. Development can only be understood as the multiple, mutual, and continuous interaction of all the levels of the developing system, from the molecular to the cultural.

2. Development can only be understood as nested processes that unfold over many timescales from milliseconds to years.

Thelen, E. & Smith, L.B. (2006). Dynamic Systems Theories. In Handbook of Child Psychology, Volume 1, Theoretical Models of Human Development, 6th Edition, William Damon (Editor), Richard M. Lerner (Volume editor), pp 258-312. 

For more general background, see the following articles:

• Complex Dynamical Systems Theory, by Professor Alicia Juarrero, author of Dynamics in action: Intentional behavior as a complex system. www.cognitive-edge.com
• Cognition as a dynamic system: Principles from embodiment, by Linda B. Smith; Developmental Review 25 (2005) 278–298.
• Development as a dynamic system, by Linda B. Smith and Esther Thelen; Trends in Cognitive Sciences, Vol.7 No.8, August 2003.

With that background, then, here is the feature article:

Full Citation:
Spencer, JP, Perone, S, and Buss, AT. (2011, Dec). Twenty years and going strong: A dynamic systems revolution in motor and cognitive development. Child Dev Perspect. 5(4): 260–266. doi:  10.1111/j.1750-8606.2011.00194.x

Twenty years and going strong: A dynamic systems revolution in motor and cognitive development

John P. Spencer, Sammy Perone, and Aaron T. Buss

Abstract

This article reviews the major contributions of dynamic systems theory in advancing thinking about development, the empirical insights the theory has generated, and the key challenges for the theory on the horizon. The first section discusses the emergence of dynamic systems theory in developmental science, the core concepts of the theory, and the resonance it has with other approaches that adopt a systems metatheory. The second section reviews the work of Esther Thelen and colleagues, who revolutionized how researchers think about the field of motor development. It also reviews recent extensions of this work to the domain of cognitive development. Here, the focus is on dynamic field theory, a formal, neurally grounded approach that has yielded novel insights into the embodied nature of cognition. The final section proposes that the key challenge on the horizon is to formally specify how interactions among multiple levels of analysis interact across multiple time scales to create developmental change.

_____

Twenty years is a long time for an individual scientist, but a relatively brief period for a scientific theory. This tension of time scales underlies our evaluation of dynamic systems theory (DST) and development below. In particular, we take the long view in our evaluation—to evaluate a new theoretical perspective in its infancy. From this vantage point, the differential success of individual variants of DST is normal; most critical is the evaluation en masse. In our view, DST has been extremely successful on the whole—in some cases, “revolutionary.” In the sections that follow, we explain our optimism, grounding our evaluation both in past accomplishments and in future prospects. Time will tell whether the word “revolution” reflects more than just our optimism.

What are the greatest contributions of the DST approach to development over the past 20 years?

Recent decades have seen a shift in thinking about development. Instead of characterizing what changes over development, there is a new emphasis on the how of developmental change (see Elman et al., 1997; Plumert & Spencer, 2007; Thelen & Smith, 1994). These explorations have revealed that simple notions of cause and effect are inadequate to explain development. Rather, change occurs within complex systems with many components that interact over multiple time scales, from the second-to-second unfolding of behavior to the longer time scales of learning, development, and evolution (see Christiansen & Kirby, 2003).

The introduction of DST into psychology has spurred this new way of thinking about change. Critically, DST did not emerge in isolation. Rather, it is one contributor to a broad shift in developmental science toward a systems metatheory (see Lerner, 2006) that encompasses a wide range of work from developmental systems theory (e.g., Gottlieb, 1991; Kuo, 1921; Lehrman, 1950), sociocultural and situated approaches (e.g., Baltes, 1987; Bronfrenbrenner & Ceci, 1994; Elder, 1998), ecological psychology (e.g., Adolph, 1997; Gibson & Pick, 2000; Turvey, 1990), and connectionism (e.g., Bates & Elman, 1993; Elman, 1990; Rumelhart & McClelland, 1986).

Within this family of work, confusion can arise in the distinction between two DSTs: dynamic systems theory and developmental systems theory (see Fox-Keller, 2005). These perspectives share many core principles; we can distinguish them by their histories and foci. Developmental systems theory was based on early work at the intersection of behavioral development, biology, and evolution by pioneers such as Lehrman and Kuo (see Ford & Lerner, 1992; Gottlieb, 1991; Griffiths & Gray, 1994; Kuo, 1921). This approach has focused on how development unfolds through an epigenetic process with cascading interactions across multiple levels of causation, from genes to environments (Johnston & Edwards, 2002). Dynamic systems theory, by contrast, developed from the mathematical analysis of complex physical systems (Gleick, 1998; Smith & Thelen, 2003). Consequently, this approach provides a way of mathematically specifying the concepts of systems metatheory while supporting the abstraction of these concepts into more cognitive domains (see, Spencer & Schöner, 2003). Thus, the aim of many dynamic systems approaches is to formally implement developmental processes to shed light on how behavior changes over time (Spencer et al., 2009; van Geert, 1991, 1998; van der Maas & Molenaar, 1992; van der Maas & Dolan, 2006; Warren, 2006). In this sense, dynamic systems theory and developmental systems theory share an emphasis on the step-by-step processes and multilevel interactions that shape development.

A key characteristic of systems metatheory that both approaches share is the rejection of classical dichotomies that have pervaded psychology for centuries: nature versus nurture, stability versus change, and so on (for discussion, see Spencer et al., 2009). In their place, systems metatheory takes the “organism in context” as its central unit of study, an inseparable unit in which it is impossible to isolate the behavioral and developmental state of the organism from external influences. Furthermore, behavior and development are emergent properties of system-wide interactions that can create something new from the many interacting components in the system (see Munakata & McClelland, 2003; Spencer & Perone, 2008; Thelen, 1992).

It is often helpful to consider historical change through the lens of contrast. According to Lerner (2006), systems metatheory has supplanted other influential metatheories, but which ones? To answer this, we conducted a survey of the fourth through sixth editions of the Handbook of Child Psychology: Theoretical Models of Human Development. These editions span more than 20 years in developmental psychology (from 1983 to 2006). Although this book is just one indication of how the field is changing, our survey revealed that four theoretical viewpoints have disappeared from the Handbook over time: nativism, cognitive and information processing, symbolic approaches, and Piaget’s theory. Of course, scholars still actively pursue all of these perspectives. It is notable, however, that they have something in common—an attempt to carve up behavior and development into parts (broad parts like nature versus nurture; specific parts like cognitive modules; or temporal partitions such as stages of processing or stages of development). Systems metatheory rejects this inherent partitioning.

Within the broad class of theories that make up systems metatheory, a central challenge is to examine what each perspective contributes. DST has had a particularly strong influence, bringing several critical concepts into mainstream developmental science. The first concept is that systems are self-organizing. Complex physical systems (such as the human child) comprise many interacting elements that span multiple levels from the molecular (for example, genes) to the neural to the behavioral to the social. Within the DS perspective, organization and structure come “for free” from the nonlinear and time-dependent interactions that emerge from this multilevel and high-dimensional mix (e.g., Prigogine & Nicolis, 1971). Thus, there is no need to build pattern into the system ahead of time because the system has an intrinsic tendency to create pattern. This gives physical systems a creative spark that we contend is central to the very notion of development—development is fundamentally about the emergence of something qualitatively new that was not there before.

Of course, the notion of qualitative change over development is not unique to DST (see, e.g., Gottlieb, 1991; Munakata & McClelland, 2003; Piaget, 1954; von Bertalannfy, 1950). But we contend that DST clarifies the distinction between quantitative and qualitative change (see Spencer & Perone, 2008; van Geert, 1998). According to DST, qualitative change occurs when there is a change in the layout of attractors, or special “habitual” states around which behavior coheres: when a new attractor appears, there is a qualitative change in the system. Although qualitative change can be special—it can reflect the emergence of something new that was not there before—it is not in opposition to quantitative change. Rather, quantitative changes in one aspect of the system can give rise to qualitatively new behaviors. This is one example where a classic dichotomy withers away in the face of a formal, systems viewpoint.

One of the historical challenges in defining qualitative and quantitative change is that changes occur over multiple time scales. For instance, a skilled infant can go from a crawling posture to a walking posture within a matter of seconds, but how is this “on-the-fly” transition related to the more gradual shift in the likelihood of crawling versus walking that unfolds across months in development (see Adolph, 1997)? In particular, it can be difficult to specify when the infant “has” walking, why walking comes and goes in different situations, and what drives this change over time. Again, DST has a unique perspective on these challenges. There is no competence/performance distinction in DST (see Thelen & Smith, 1994); rather, the emphasis is on how people assemble behavior in the moment in context. But because DST integrates processes over multiple time scales, it can explain why behavioral attractors—which form in real time—can emerge and become more likely over the longer times of learning and development (for discussion, see Spencer & Perone, 2008).

Another issue that researchers have directly examined using DST is the concept of “soft assembly.” According to this concept, behavior is always assembled from multiple interacting components that can be freely combined from moment to moment on the basis of the context, task, and developmental history of the organism. Esther Thelen talked about this as a form of improvisation in which components freely interact and assemble themselves in new, inventive ways (like musicians playing jazz). This gives behavior an intrinsic sense of exploration and flexibility, issues that Goldfield and colleagues (Goldfield, Kay, & Warren, 1993) have examined formally.

This characterization of behavior and development has led to an additional insight about the embodied nature of cognition. In particular, if behavior is softly assembled from many components in the moment, then the brain is not the “controller” of behavior. Rather, it is necessary to understand how the brain capitalizes on the dynamics of the body and how the body informs the brain in the construction of behavior. This has led to an emphasis on embodied cognitive dynamics (see Schöner, 2009; Spencer, Perone, & Johnson, 2009), that is, to a view of cognition in which brain and body are in continual dialogue from second to second.

A final strength of the DS approach is that it has generated a host of productive tools, including rich empirical programs (Samuelson & Horst, 2008; Smith, Thelen, Titzer, & McLin, 1999; Thelen & Ulrich, 1991; van der Maas & Dolan, 2006), formal modeling tools that can capture and quantify the emergence and construction of behavior over development (such as growth models, oscillator models, dynamic neural field models), and statistical tools that can describe the patterns of behavior observed over development (Lewis, Lamey, & Douglas, 1999; Molenaar, Boomsma, & Dolan, 1993; van der Maas & Dolan, 2006). These tools have enabled researchers to move beyond the characterization of what changes over development toward a deeper understanding of how these changes occur.

What is your critical evaluation of the progress of DS-inspired empirical research?

DST has led to a revolutionary change in how people think about motor development, and this type of revolutionary thinking is starting to take hold in cognitive development as well. We review the basis for this optimistic assessment below. Note that we focus on motor and cognitive development because these are our “home” domains. We will leave it to the other authors in this issue to evaluate other fields.

The dominant view of motor development for much of the 20th century was that the development of action occurred in a series of relatively fixed motor milestones. The emphasis was on normative development, the concept of motor programs that controlled action, and a sequence of milestones that was largely under genetic or biological control (for review, see Adolph & Berger, 2006). The landscape has shifted dramatically in the last 20 years, thanks in large part to the work of Esther Thelen (as well as other systems thinkers, most notably, Gibson, 1988; see Adolph & Berger, 2006). Today the field views motor development as emergent and exploratory with a new emphasis on individual development in context. Although this revolution in thinking was spurred by dynamic systems concepts, it was also driven forward by a wealth of empirical research.

For instance, Esther Thelen conducted a now-classic set of studies investigating the early disappearance of the stepping reflex. Thelen’s early work on stepping revealed that the coordination patterns that underlie stepping and kicking were strikingly similar. The puzzle was that newborn stepping disappeared within the first three months, whereas kicking continued and increased in frequency. To explain the disappearance of stepping, several researchers had proposed that maturing cortical centers inhibit the primitive stepping reflex or that stepping was phylogenetically programmed to disappear (e.g., Andre-Thomas & Autgaerden, 1966).

To probe the mystery of the disappearing steps, Thelen conducted a longitudinal study that focused on the detailed development of individual infants. Thelen, Fisher, and Ridley-Johnson (1984) found a clue in the fact that chubby babies and those who gained weight fastest were the first to stop stepping. This led to the hypothesis that it requires more strength for young infants to lift their legs when upright (in a stepping position) than when lying down (in a kicking position). To test this idea, Thelen and colleagues conducted two ingenious studies. In one, they placed small leg weights on two-month-old babies, similar in amount to the weight they would gain in the ensuing month. This significantly reduced stepping. In the other, they submerged older infants whose stepping had begun to wane in water up to chest levels. Robust stepping now reappeared. These data demonstrated that traditional explanations of neural maturation and innate capacities were insufficient to explain the emergence of new patterns and the flexibility of motor behavior.

Since this seminal work, Thelen and her colleagues have intensively examined the development of alternating leg movements (Thelen & Ulrich, 1991), the emergence of crawling (Adolph, Vereijken, & Denny, 1988), the emergence of walking (e.g., Adolph, 1997; Thelen & Ulrich, 1991), and the development of reaching (Corbetta, Thelen, & Johnson, 2000; Thelen, Corbetta & Spencer, 1996; Thelen et al., 1993). In all cases, these researchers have shown that new action patterns emerge in the moment from the self-organization of multiple components. The stepping studies elegantly illustrated this, showing how multiple factors cohere in a moment in time to create or hinder leg movements. And, further, these studies illustrate how changes in the components of the motor system over the longer time scale of development interact with real-time behavior.

In summary, DS concepts have led to a radical change in the conceptualization of motor development. But what about cognition? There have been a variety of DS approaches to cognitive development. For instance, researchers have used the concepts of DST to study early word learning (e.g., Samuelson, Schutte & Horst, 2008), language development (e.g., van Geert, 1991), the development of intelligence (e.g., Fischer & Bidell, 1998), and conceptual development and conservation behavior (e.g., van der Maas & Molenaar, 1992). A survey of these different approaches is beyond the scope of this article (see Spencer, Thomas & McClelland, 2009). We focus, instead, on one particular flavor of cognitive dynamics—dynamic field theory (DFT)—that emerged out of the motor approach that Thelen and colleagues pioneered (for discussion, see Spencer & Schöner, 2003).

The starting point for the DF approach was to consider several facts about neural systems. Neural systems are noisy, densely interconnected, and time-dependent; they pass continuous, graded, and metric information to one another; and they are continuously coupled via both short-range and long-range connections (Braitenberg & Schüz, 1991; Constantinidis & Steinmetz, 1996; Edelman, 1987; Rao, Rainer, & Miller, 1997). These neural facts raise deep theoretical challenges. How can a collection of neurons “represent” information amidst near-constant bombardment by other neural signals (Skarda & Freeman, 1987), and how do neurons, in concert with the body, generate stable, reliable behavior? To address these challenges, the DF framework emphasizes stable patterns of neural interaction at the level of population dynamics (see also Spivey, 2007). That is, rather than building networks that start from a set of spiking neurons, we have chosen to focus on the emergent product of the dynamics at the neural level—attractors at the level of the neural population.

The first steps toward a neurally grounded theory of cognitive development came from Thelen and Smith’s studies of the Piagetian A-not-B error (see Smith et al., 1999; Thelen, Schöner, Scheier, & Smith, 2001). This early work formalized a DFT of infant perseverative reaching, arguably the most comprehensive theory of infants’ performance in the Piagetian A-not-B task (Clearfield, Dineva, Smith, Diedrich, & Thelen, 2009; Smith et al., 1999; Spencer, Dineva, & Smith, 2009; Thelen et al., 2001). DFT has generated a host of novel behavioral predictions, and it explains how perseverative reaching arises as a function of (1) the infants’ history of prior reaches to A (Smith et al., 1999), (2) a bodily feel and visual perspective of reaching to A (Smith et al., 1999), (3) the distinctiveness of the targets and perceptual cues in the task space (Clearfield et al, 2009), (4) the delay between the cueing and reaching events (Diamond, 1985), (5) the number of targets in the task space, (6) the characteristics of the hidden object (and whether there is any hidden object whatsoever; see Smith et al., 1999), and (7) changes in infants’ reaching skill and working memory abilities over development (Clearfield, Diedrich, Smith, & Thelen, 2006; for related studies with older children, see Schutte, Spencer, & Schöner, 2003; Spencer, Smith, & Thelen, 2001).

More recently, we have extended the DF approach to a host of other topics in cognitive development. These topics include the processes that underlie habituation in infancy (Perone & Spencer, 2009; Schöner & Thelen, 2006), the control of autonomous robots and the development of exploratory motor behavior (Dineva, Faubel, Sandamirskaya, Spencer, & Schöner, 2008; Steinhage & Schöner, 1998), the development of visuospatial cognition (Simmering, Spencer, & Schutte, 2008), the processes that underlie visual working memory and the development of change detection abilities (Simmering, 2008), the processes that underlie early word learning behaviors (Samuelson et al., 2008), and the development of executive function (Buss & Spencer, 2008). This broad coverage of multiple aspects of development with the same theoretical framework underlies our optimism that the concepts of DST can have a revolutionary impact on cognitive development just as they had in motor development. Time will tell.

What are the challenges and necessary directions for the next 20 years?

A major accomplishment of DS approaches has been to move beyond the conceptual level to establish a tight link between formal theory and empirical research, leading to a greater understanding of the processes that underlie developmental change. Although there have been many successful applications of DS concepts, significant challenges remain. For instance, soft assembly makes it difficult to define the components of the “system” or subsystem under study. Similarly, the multiply determined nature of dynamic systems makes it difficult to identify “cause” because different factors can lead to different outcomes depending on the context and history of the individual.

In addition to these conceptual challenges, researchers in the next 20 years will have to build theories that formally connect processes across multiple levels of analysis. Figure 1 shows the nested, interacting systems that contribute to the organization of behavioral development from genetic to social levels. Each of these levels and the interactions among them are highly complex; thus, understanding how development happens as these levels interact over time will require formal theories that specify the nature of those interactions (for related ideas, see Gottlieb, 1991; Johnston & Edwards, 2002; Johnston & Lickliter, 2009). To date, multiple approaches have attempted to understand behavioral development at the different levels shown in Figure 1, but these efforts have not been tightly integrated across levels.

Figure 1
A central challenge on the horizon for dynamic systems theory is to formally integrate across reciprocally interacting levels from genetic to social and to integrate these levels across multiple time scales from in-the-moment interactions to learning ...

In addition to the challenge of formally connecting processes at multiple levels, it will be important to tackle a second challenge: integrating time scales. Within DST, nested, interacting systems come together to create developmental change as those systems interact through time. In particular, the multiple systems in Figure 1 produce a coherent behavioral system in the moment, and those in-the-moment behaviors have consequences that carry forward across the longer time scales of learning and development (see Smith & Thelen, 2003 for a discussion). Our research using DFT has effectively integrated real-time behavior with changes over learning (see, e.g., Lipinski, Spencer, & Samuelson, 2010; Schöner & Thelen, 2006; Thelen et al., 2001). Other approaches have examined these time scales as well (e.g., French, Mareschal, Mermillod, & Quinn, 2004; McMurray, Horst, Toscano, & Samuelson, 2009), but the longer time scales of development have been more elusive (but see Simmering et al., 2008; Schutte et al., 2003; Schutte & Spencer, 2009, for efforts in this direction).

One difficulty in this regard is that it is often hard to get a clear sense of developmental change empirically. Adolph, Robinson, Young, and Gill-Alvarez (2008), for example, showed how different views of developmental change are created simply by sampling rate of change. But developmental scientists face theoretical challenges in terms of integrating behavior over very long time scales. Spencer and Perone (2008) have taken one step toward addressing this issue by probing change in neural dynamics over relatively long time scales. In particular, they showed that the gradual accumulation of neural excitation in a simple, dynamic neural system created qualitative changes in the state in which the system operated. That is, as the system gradually accumulated a history, the system was biased to settle into new neural attractor states. We believe that it is possible to generalize from these concepts, and we are currently working to scale this demonstration up guided by a rich, longitudinal empirical data set (see Perone & Spencer, 2009).

Integrating dynamics across multiple systems and time scales is a daunting task. Even more challenging is to achieve this integration at the level of the individual child in context. But this is a critically important goal because it opens the door for examining atypical development. If we understand the complex dynamics through which systems interact over time at the level of individual children, we will be well positioned to create individual interventions that help steer the child toward positive developmental outcomes. That would indeed be revolutionary. Perhaps in the next 20 years we will realize this vision.

Acknowledgments

Preparation of this manuscript was supported by NIH RO1MH62480 awarded to John P. Spencer.

References available at the NHI/NCBI site.

Melanie Mitchell - How Can the Study of Complexity Transform Our Understanding of the World?

From Big Questions Online, Melanie Mitchell (Professor of Computer Science at Portland State University, and External Professor and Member of the Science Board at the Santa Fe Institute) offers a nice and very accessible overview of how the study of complex systems can help us better make sense of our world.

How Can the Study of Complexity Transform Our Understanding of the World?

By Melanie Mitchell
January 20, 2014

image: Stephen Hopkins

In 1894, the physicist and Nobel laureate Albert Michelson declared that science was almost finished; the human race was within a hair’s breadth of understanding everything:

It seems probable that most of the grand underlying principles have now been firmly established and that further advances are to be sought chiefly in the rigorous application of these principles to all the phenomena which come under our notice.

Bold and heady predictions like this often seem destined to topple, and, to be sure, the world of physics was soon shaken by the revolutions of relativity and quantum mechanics.

But as the 20th century unfolded, it turned out to be the phenomena closest to our own human scale— biology, social science, economics, politics, among others—that have most notably eluded explanation by any grand principles. The deeper we dig into the workings of ourselves and our society, the more unexpected complexity we find. Fittingly, it was in the 20th century that science began to bridge disciplinary boundaries in order to search for principles of complexity itself.

What is Complexity?

The “study of complexity” refers to the attempt to find common principles underlying the behavior of complex systems—systems in which large collections of components interact in nonlinear ways. Here, the term nonlinear implies that the system can’t be understood simply by understanding its individual components; nonlinear interactions cause the whole to be “more than the sum of its parts.”

Complex systems scientists try to understand how such collective sophistication can come about, whether it be in ant colonies, cells, brains, immune systems, social groups, or economic markets. People who study complexity are intrigued by the suggestive similarities among these disparate systems. All these systems exhibit self-organization: the system’s components organize themselves to act as a coherent whole without the benefit of any central or outside “controller”. Complex systems are able to encode and process information with a sophistication that is not available to the individual components. Complex systems evolve—they are continually changing in an open-ended way, and they learn and adapt over time. Such systems defy precise prediction, and resist the kind of equilibrium that would make them easier for scientists to understand.

Transforming Our Understanding

Of course all important scientific discoveries transform our understanding of nature, but I think that the study of complexity goes a step further: it not only helps us understand important phenomena, but changes our perspective on how to think about nature, and about science itself.

Here are a few examples of the surprising, perspective-changing discoveries of Complex Systems science. (If these don’t seem so surprising to you, it is because your perspective has already been changed by the sciences of complexity!)

Simple rules can yield complex, unpredictable behavior

Why can’t we seem to forecast the weather farther out than a week or so? Why is it so hard to project yearly variation in fishery populations? Why can’t we foresee stock market bubbles and crashes? In the past it was widely assumed that such phenomena are hard to predict because the underlying processes are highly complex, and that random factors must play a key role. However, Complex Systems science—especially the study of dynamics and chaos—have shown that complex behavior and unpredictability can arise in a system even if the underlying rules are extremely simple and completely deterministic. Often, the key to complexity is the iteration over time of simple, though nonlinear, interaction rules among the system’s components. It’s still not clear if unpredictability in the weather, stock market, and animal populations is caused by such iteration alone, but the study of chaos has shown that it’s possible.

More is Different

Above I reiterated the old saw, “the whole is more than the sum of its parts”. The physicist Phil Anderson coined a better aphorism: he noted that a key lesson of complexity is that “more is different”.

Ant colonies are a great example of this. As the ecologist Nigel Franks puts it, “The solitary army ant is behaviorally one of the least sophisticated animals imaginable...If 100 army ants are placed on a flat surface, they will walk around and around in never decreasing circles until they die of exhaustion.” Yet put half a million of them together and the group as a whole behaves as a hard-to-predict “superorganism” with sophisticated, and sometimes frightening, “collective intelligence”. More is different.

Similar stories can be told for neurons in the brain, cells in the immune system, creativity and social movements in cities, and agents in market economies. The study of complexity has shown that when a system’s components have the right kind of interactions, its global behavior—the system’s capacity to process information, to make decisions, to evolve and learn—can be powerfully different from that of its individual components.

Network Thinking

In the early 2000s, the complete human genome was sequenced. While the benefits to science were enormous, some of the predictions made by prominent scientists and others had a Michelsonian flavor (see first paragraph). President Clinton echoed the widely held view that the Human Genome Project would “revolutionize the diagnosis, prevention and treatment of most, if not all, human diseases.” Indeed, many scientists believed that a complete mapping of human genes would provide a nearly complete understanding of how genetics worked, which genes were responsible for which traits, and this would guide the way for revolutionary medical discoveries and targeted gene therapies.

Now, more than a decade later, these predicted medical revolutions have not yet materialized. But the Human Genome Project, and the huge progress in genetics research that followed, did uncover some unexpected results. First, human genes (DNA sequences that code for proteins) number around 21,000—much fewer than anyone thought, and about the same number as in mice, worms, and mustard plants. Second, these protein-coding genes make up only about 2% of our DNA. Two mysteries emerge: If we humans have comparatively so few genes, where does our complexity come from? And as for that 98% of non-gene DNA, which in the past was dismissively called "junk DNA", what is its function?

What geneticists have learned is that genetic elements in a cell, like ants in a colony, interact nonlinearly so as to create intricate information-processing networks. It is the networks, rather than the individual genes, that shape the organism. Moreover, and most surprising: the so-called “junk” DNA is key to forming these networks. As biologist John Mattick puts it, “The irony...is that what was dismissed as junk because it wasn’t understood will turn out to hold the secret of human complexity.”

Information-processing networks are emerging as a core organizing principle of biology. What used to be called “cellular signaling pathways” are now “cellular information processing networks.” New research on cancer treatments is focused not on individual genes but on disrupting the cellular information processing networks that many cancers exploit. Some types of bacteria are now known to communicate via “quorum sensing” networks in order to collectively attack a host; this discovery is also driving research into network-specific treatment of infections.

Over the last two decades an interdisciplinary science of networks has emerged, and has developed insights and research methods that apply to networks ranging from genetics to economics. Network thinking is the area of complex systems that has perhaps done the most to transform our understanding of the world.

Non-Normal is the New Normal

In 2009, Nobel Prize-winning economist Paul Krugman said, “Few economists saw our current crisis coming, but this predictive failure was the least of the field’s problems. More important was the profession’s blindness to the very possibility of catastrophic failures in a market economy.” At least part of this “blindness” was due to the reliance on risk models based on so-called normal distributions.

Figure 1: (a) A hypothetical normal distribution of the probability of financial gain or loss under trading. (b) A hypothetical long-tailed distribution, showing only the loss side. The “tail” of the distribution is the far right-hand side. The long-tailed distribution predicts a considerably higher probability of catastrophic loss than the normal distribution.

The term normal distribution refers to the familiar bell curve. Economists and finance professionals often use such distributions to model the probability of gains and risk of losses from investments. Figure 1(a) shows a hypothetical normal distribution of risk. I’ve marked a hypothetical “catastrophic loss” on the graph. You can see that, given this distribution of risk, the probability of such a loss would be very near zero. Less probable, maybe, than a lightning strike right where you’re standing. Something you don’t have to worry about. Unless the model is wrong.

The study of complexity has shown that in nonlinear, highly networked systems, a more accurate estimation of risk would be a so-called “long-tailed” distribution. Figure 1(b) shows a hypothetical long-tailed distribution of risk (here, only the “loss” side is shown). The longer non-zero “tail” (far right-hand side) of this distribution shows that the probability of a catastrophic loss is significantly higher than for a system obeying a normal distribution. If risk models in 2008 had employed long-tailed rather than normal distributions, the possibility of an “extreme event”—here, “catastrophic loss”—would have be judged more likely.

Because long-tailed distributions are now known to be signatures of complex networks, our growing understanding of such networks implies that risk models need to be rethought in many areas, ranging from disease epidemics to power grid failures; from financial crises to ecosystem collapses. The technologist Andreas Antonopoulos puts it succinctly: “The threat is complexity itself”.

Is Complexity a New Science?

“The new science of complexity” has become a catchphrase in some circles. Google reports nearly 87,000 hits on this phrase. But how “new” is the study of complexity? And to what extent is it actually a “science”?

The current scientific efforts centered around complexity have several antecedents. The Cybernetics movement of the 1940s and 50s, the General System Theory movement of the 1960s, and the more recent advent of Systems Biology, Systems Engineering, Systems Science, etc., all share goals with Complex Systems science: finding general principles that explain how system-level behavior emerges from interactions among lower-level components. The different movements capture different (though sometimes overlapping) communities and different foci of attention.

To my mind, Complexity refers not to a single science but rather to a community of scientists in different disciplines who share interdisciplinary interests, methodologies, and a mindset about how to address scientific problems. Just what this mindset consists of is hard to pin down. I would say it includes, first, the assumption that understanding complexity will require integrating concepts from dynamics, information, statistical physics, and evolution. And second, that computer modeling is an essential addition to traditional scientific theory and experimentation. As yet, Complexity is not a single unified science; rather, to paraphrase William James, it is still “the hope of a science”. I believe that this hope has great promise.

In our era of Big Data, what Complexity potentially offers is “Big Theory”—a scientific understanding of the complex processes that produce the data we are drowning in. If the field’s past contributions are any indication, Complexity’s sought-after big theory will even more profoundly transform our understanding of the world.

It’s something to look forward to. In the words of playwright Tom Stoppard: “It’s the best possible time to be alive, when almost everything you thought you knew is wrong.”

Discussion Questions

1. Can you identify any ways in which your own way of thinking has been changed by Complex Systems science?

2. The discussion above stated that when systems get too intricately networked, “the threat is complexity itself”. The network scientist Duncan Watts suggested that the notion “too big to fail” should be rethought as “too complex to exist.” Should we worry about the world becoming too complex? If so, what should we do about it?

3. To what extent do you think the ideas of complex systems are new? What would it take to create a unified science of complexity?

Resources and Further Reading:

http://complexityexplorer.org

• Anderson, P. W. More is different. Science, 177 (4047), 1972, 393-396.
• Bettencourt, L. M., Lobo, J., Helbing, D., Kühnert, C., & West, G. B. (2007). Growth, innovation, scaling, and the pace of life in cities. Proceedings of the National Academy of Sciences, 104(17), 7301-7306.
• Franks, N. R. Army ants: A collective intelligence. American Scientist, 77(2), 1989, 138-145.
• Hayden, E. C. Human genome at ten: Life is complicated. Nature, 464, 2010, 664-667.
• Krugman, P. How did economists get it so wrong? New York Times, September 2, 2009.
• Miller, J. H. and Page, S. E. Complex Adaptive Systems. Princeton University Press, 2007.
• Mitchell, M. Complexity: A Guided Tour. Oxford University Press, 2009
• Newman, M. E. J. Networks: An Introduction. Oxford University Press, 2009.
• Watts, D. Too complex to exist. Boston Globe, June 14, 2009.
• West, G. Big data needs a big theory to go with it. Scientific American, May 15, 2013.

Brendan is a lawyer, technology entrepreneur and author of The Simunye Hypothesis (Kindle only, $2.99), a restatement of holism that considers the emergence of universal consciousness based on new theories of particle behavior, genetic mutation, and social media.

Interesting and partial, at best.

Here is an additional short video in which Hughes talks about his philosophical model.


Enjoy!

The Emergence of Universal Consciousness: Brendan Hughes at TEDxPretoria

Published on Jan 21, 2014


It was Aristotle who first argued that the whole is something greater than the sum of its parts. More recently, quantum physicists have argued for the existence of a unified field in which all particles and forces exist. This short but provocative talk explores whether new theories of social media, particle behavior and genetic mutation support an understanding of the universe as a complex adaptive system with emergent consciousness.

A Neuroscientist's Radical Theory of How Networks Become Conscious (WIRED U.K.)

In the Japanese art of the rock garden, the artist must be aware  of the rocks' "ishigokoro" (‘heart,’ or ‘mind’)

Neuroscientist Christof Koch, chief scientific officer at the Allen Institute for Brain Science, has progressively become less hyper-rational in his understanding of consciousness and more Buddhist - and it's not clear yet if this is a good thing.

His newest pronouncement is his belief in panpsychism, defined below by Wikipedia:

In philosophy, panpsychism is the view that mind or soul (Greek: ψυχή) is a universal feature of all things, and the primordial feature from which all others are derived. The panpsychist sees him or herself as a mind in a world of minds.

Panpsychism is one of the oldest philosophical theories, and can be ascribed to philosophers like Thales, Plato, Spinoza, Leibniz and William James. Panpsychism can also be seen in eastern philosophies such as Vedanta and Mahayana Buddhism. During the 19th century, Panpsychism was the default theory in philosophy of mind, but it saw a decline during the latter half of the 20th century with the rise of logical positivism.[1] The recent interest in the hard problem of consciousness has once again made panpsychism a mainstream theory.

 Says Koch, "I argue that we live in a universe of space, time, mass, energy, and consciousness arising out of complex systems." This sounds like emergence to me, and less like panpsychism, which is the belief that mind/consciousness is inherent in the universe. I'm more likely to accept emergence as an explanation of consciousness that avoids issues of duality.

See the Stanford Encyclopedia of Philosophy entry on panpsychism for a better understanding of the arguments for and against, as well as its history in philosophy.

A neuroscientist's radical theory of how networks become conscious

15 November 13
by Brandon Keim


A map of neural circuits in the human brain - Human Connectome Project

It's a question that's perplexed philosophers for centuries and scientists for decades: where does consciousness come from? We know it exists, at least in ourselves. But how it arises from chemistry and electricity in our brains is an unsolved mystery.

Neuroscientist Christof Koch, chief scientific officer at the Allen Institute for Brain Science, thinks he might know the answer. According to Koch, consciousness arises within any sufficiently complex, information-processing system. All animals, from humans on down to earthworms, are conscious; even the internet could be. That's just the way the universe works.

"The electric charge of an electron doesn't arise out of more elemental properties. It simply has a charge," says Koch. "Likewise, I argue that we live in a universe of space, time, mass, energy, and consciousness arising out of complex systems."

What Koch proposes is a scientifically refined version of an ancient philosophical doctrine called panpsychism -- and, coming from someone else, it might sound more like spirituality than science. But Koch has devoted the last three decades to studying the neurological basis of consciousness. His work at the Allen Institute now puts him at the forefront of the BRAIN Initiative, the massive new effort to understand how brains work, which will begin next year.

Koch's insights have been detailed in dozens of scientific articles and a series of books, including last year's Consciousness: Confessions of a Romantic Reductionist. Wired talked to Koch about his understanding of this age-old question.

Wired: How did you come to believe in panpsychism?

Christof Koch: I grew up Roman Catholic, and also grew up with a dog. And what bothered me was the idea that, while humans had souls and could go to heaven, dogs were not supposed to have souls. Intuitively I felt that either humans and animals alike had souls, or none did. Then I encountered Buddhism, with its emphasis on the universal nature of the conscious mind. You find this idea in philosophy, too, espoused by Plato and Spinoza and Schopenhauer, that psyche -- consciousness -- is everywhere. I find that to be the most satisfying explanation for the universe, for three reasons: biological, metaphysical and computational.

Wired: What do you mean?

Koch: My consciousness is an undeniable fact. One can only infer facts about the universe, such as physics, indirectly, but the one thing I'm utterly certain of is that I'm conscious. I might be confused about the state of my consciousness, but I'm not confused about having it. Then, looking at the biology, all animals have complex physiology, not just humans. And at the level of a grain of brain matter, there's nothing exceptional about human brains.

Only experts can tell, under a microscope, whether a chunk of brain matter is mouse or monkey or human -- and animals have very complicated behaviours. Even honeybees recognise individual faces, communicate the quality and location of food sources via waggle dances, and navigate complex mazes with the aid of cues stored in their short-term memory. If you blow a scent into their hive, they return to where they've previously encountered the odor. That's associative memory. What is the simplest explanation for it? That consciousness extends to all these creatures, that it's an imminent property of highly organised pieces of matter, such as brains.

Wired: That's pretty fuzzy. How does consciousness arise? How can you quantify it?

Koch: There's a theory, called Integrated Information Theory, developed by Giulio Tononi at the University of Wisconsin, that assigns to any one brain, or any complex system, a number -- denoted by the Greek symbol of Φ -- that tells you how integrated a system is, how much more the system is than the union of its parts. Φ gives you an information-theoretical measure of consciousness. Any system with integrated information different from zero has consciousness. Any integration feels like something

It's not that any physical system has consciousness. A black hole, a heap of sand, a bunch of isolated neurons in a dish, they're not integrated. They have no consciousness. But complex systems do. And how much consciousness they have depends on how many connections they have and how they're wired up.

Wired: Ecosystems are interconnected. Can a forest be conscious?

Koch: In the case of the brain, it's the whole system that's conscious, not the individual nerve cells. For any one ecosystem, it's a question of how richly the individual components, such as the trees in a forest, are integrated within themselves as compared to causal interactions between trees.

The philosopher John Searle, in his review of Consciousness, asked, "Why isn't America conscious?" After all, there are 300 million Americans, interacting in very complicated ways. Why doesn't consciousness extend to all of America? It's because integrated information theory postulates that consciousness is a local maximum. You and me, for example: we're interacting right now, but vastly less than the cells in my brain interact with each other. While you and I are conscious as individuals, there's no conscious Übermind that unites us in a single entity. You and I are not collectively conscious. It's the same thing with ecosystems. In each case, it's a question of the degree and extent of causal interactions among all components making up the system.

Wired: The internet is integrated. Could it be conscious?

Koch: It's difficult to say right now. But consider this. The internet contains about 10 billion computers, with each computer itself having a couple of billion transistors in its CPU. So the internet has at least 10^19 transistors, compared to the roughly 1000 trillion (or quadrillion) synapses in the human brain. That's about 10,000 times more transistors than synapses. But is the internet more complex than the human brain? It depends on the degree of integration of the internet.
 
For instance, our brains are connected all the time. On the internet, computers are packet-switching. They're not connected permanently, but rapidly switch from one to another. But according to my version of panpsychism, it feels like something to be the internet -- and if the internet were down, it wouldn't feel like anything anymore. And that is, in principle, not different from the way I feel when I'm in a deep, dreamless sleep.

Wired: Internet aside, what does a human consciousness share with animal consciousness? Are certain features going to be the same?

Koch: It depends on the sensorium [the scope of our sensory perception -ed.] and the interconnections. For a mouse, this is easy to say. They have a cortex similar to ours, but not a well-developed prefrontal cortex. So it probably doesn't have self-consciousness, or understand symbols like we do, but it sees and hears things similarly.

In every case, you have to look at the underlying neural mechanisms that give rise to the sensory apparatus, and to how they're implemented. There's no universal answer.

Wired: Does a lack of self-consciousness mean an animal has no sense of itself?

Koch: Many mammals don't pass the mirror self-recognition test, including dogs. But I suspect dogs have an olfactory form of self-recognition. You notice that dogs smell other dog's poop a lot, but they don't smell their own so much. So they probably have some sense of their own smell, a primitive form of self-consciousness. Now, I have no evidence to suggest that a dog sits there and reflects upon itself; I don't think dogs have that level of complexity. But I think dogs can see, and smell, and hear sounds, and be happy and excited, just like children and some adults.

Self-consciousness is something that humans have excessively, and that other animals have much less of, though apes have it to some extent. We have a hugely developed prefrontal cortex. We can ponder.

Wired: How can a creature be happy without self-consciousness?

Koch: When I'm climbing a mountain or a wall, my inner voice is totally silent. Instead, I'm hyperaware of the world around me. I don't worry too much about a fight with my wife, or about a tax return. I can't afford to get lost in my inner self. I'll fall. Same thing if I'm traveling at high speed on a bike. It's not like I have no sense of self in that situation, but it's certainly reduced. And I can be very happy.

Wired: I've read that you don't kill insects if you can avoid it.

Koch: That's true. They're fellow travelers on the road, bookended by eternity on both sides.

Wired: How do you square what you believe about animal consciousness with how they're used in experiments?

Koch: There are two things to put in perspective. First, there are vastly more animals being eaten at McDonald's every day. The number of animals used in research pales in comparison to the number used for flesh. And we need basic brain research to understand the brain's mechanisms. My father died from Parkinson's. One of my daughters died from Sudden Infant Death Syndrome. To prevent these brain diseases, we need to understand the brain -- and that, I think, can be the only true justification for animal research. That in the long run, it leads to a reduction in suffering for all of us. But in the short term, you have to do it in a way that minimises their pain and discomfort, with an awareness that these animals are conscious creatures.

Wired: Getting back to the theory, is your version of panpsychism truly scientific rather than metaphysical? How can it be tested?

Koch: In principle, in all sorts of ways. One implication is that you can build two systems, each with the same input and output -- but one, because of its internal structure, has integrated information. One system would be conscious, and the other not. It's not the input-output behavior that makes a system conscious, but rather the internal wiring.

The theory also says you can have simple systems that are conscious, and complex systems that are not. The cerebellum should not give rise to consciousness because of the simplicity of its connections. Theoretically you could compute that, and see if that's the case, though we can't do that right now. There are millions of details we still don't know. Human brain imaging is too crude. It doesn't get you to the cellular level.

The more relevant question, to me as a scientist, is how can I disprove the theory today. That's more difficult. Tononi's group has built a device to perturb the brain and assess the extent to which severely brain-injured patients -- think of Terri Schiavo -- are truly unconscious, or whether they do feel pain and distress but are unable to communicate to their loved ones. And it may be possible that some other theories of consciousness would fit these facts.

Wired: I still can't shake the feeling that consciousness arising through integrated information is -- arbitrary, somehow. Like an assertion of faith.

Koch: If you think about any explanation of anything, how far back does it go? We're confronted with this in physics. Take quantum mechanics, which is the theory that provides the best description we have of the universe at microscopic scales. Quantum mechanics allows us to design MRI and other useful machines and instruments. But why should quantum mechanics hold in our universe? It seems arbitrary! Can we imagine a universe without it, a universe where Planck's constant has a different value? Ultimately, there's a point beyond which there's no further regress. We live in a universe where, for reasons we don't understand, quantum physics simply is the reigning explanation.

With consciousness, it's ultimately going to be like that. We live in a universe where organised bits of matter give rise to consciousness. And with that, we can ultimately derive all sorts of interesting things: the answer to when a fetus or a baby first becomes conscious, whether a brain-injured patient is conscious, pathologies of consciousness such as schizophrenia, or consciousness in animals. And most people will say, that's a good explanation.

If I can predict the universe, and predict things I see around me, and manipulate them with my explanation, that's what it means to explain. Same thing with consciousness. Why we should live in such a universe is a good question, but I don't see how that can be answered now.

Emergence Is Happening All Around Us, and Not Just in Nature

 

From the Santa Fe Institute, this is an excellent podcast on the science of emergence – "when simple stuff develops complex forms and complex behavior" – and all which occurs as an innovation with no prior model. There are a lot of people (myself included) who believe emergence is the best explanation for the rise of consciousness from what seems to be an inert lump of fatty tissue.



Via Wikipedia:

In philosophy, systems theory, science, and art, emergence is the way complex systems and patterns arise out of a multiplicity of relatively simple interactions. Emergence is central to the theories of integrative levels and of complex systems.

Biology can be viewed as an emergent property of the laws of chemistry which, in turn, can be viewed as an emergent property of particle physics. Similarly, psychology could be understood as an emergent property of neurobiological dynamics, and free-market theories understand economy as an emergent feature of psychology. 

A couple of books on the subject include Steven Johnson's Emergence: The Connected Lives of Ants, Brains, Cities, and Software (2002), John Holland's Emergence: From Chaos To Order (Helix Books) (1999), and Harold Morowitz's The Emergence of Everything: How the World Became Complex (2004).

Audio: Emergence Is Happening All Around Us, and Not Just in Nature

Oct. 17, 2013

On Big Picture Science, a podcast/radio program that airs on public radio stations nationwide, in a program on emergence and self organization, SFI Research Fellow Simon DeDeo explains how emergence abounds not only in nature, but also in human social systems.

"Emergence is ... something so common that we almost don't even notice it happening around us," DeDeo says. In social systems, for example, "we can look at the emergence of political parties. But what is a political party? In some sense, an individual, a supporter, plays somewhat the same role as a neuron does in the human brain."

Individuals within the party don't have a lot of power to shift its platforms, he says, "but the collective behavior of all of us in that party and the way we interact within that party somehow lead to a set of emergent rules that we call political science. That kind of separation -- the separation between, for example, what we would call in physics the microphysics of system and the macroscale behavior, the way in which those two things split apart, that's the basic story of emergence."

He goes on to explain why emergence in both kinds of systems is likely a process driven by evolutionary advantage.

Other program guests included new Nobel laureate and molecular and cell biologist Randy Schekman (UC Berkeley), neurobiologist Steve Potter (Georgia Institute of Technology), biological anthropologist Terence Deacon (UC Berkeley), and computer scientist Leslie Valiant (Harvard).

Listen to or download the podcast below.

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Emergence

Monday 14 October 2013
Big Picture Science

Listen right now:

Download file  

 Your brain is made up of cells. Each one does its own, cell thing. But remarkable behavior emerges when lots of them join up in the grey matter club. You are a conscious being – a single neuron isn’t.

Find out about the counter-intuitive process known as emergence – when simple stuff develops complex forms and complex behavior – and all without a blueprint.

Plus self-organization in the natural world, and how Darwinian evolution can be speeded up.

Guests:

• Randy Schekman – Professor of molecular and cell biology, University of California, Berkeley, 2013 Nobel Prize-winner
• Steve Potter – Neurobiologist, biomedical engineer, Georgia Institute of Technology
• Terence Deacon – Biological anthropologist, University of California, Berkeley
• Simon DeDeo – Research fellow at the Santa Fe Institute
• Leslie Valiant – Computer scientist, Harvard University, author of Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World