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Likewise, in the ninetieth century, the concept of energy was nebulous.
In that situation James Prescott Joule performed a famous experiment in which he compared mechanical energy and heat and so he could lay down a unit for the measurement of heat, thus initiating the science of thermodynamics. I would in this context like to mention that I have suggested a measurement of the relative values of complexity Ekstig a , Anyhow, in the present analysis the results of these measurements are not used, nor are they necessary prerequisites.
I merely base my analysis in the present paper on qualitative reasoning although the conclusions are quite compatible with my previous measurements. The contributors grapple with the notion of increasing complexity in the cosmological and biological spheres, and are frustrated by the lack of a definition of complexity generally agreed on. Do we really need to wait for a precise definition to think about complexity and its limits?
If natural selection has forced complexity to increase, as many authors seem to maintain, how then to explain the fact that the oldest organisms of today, i. Or, as Lamarck asked already when the very idea of evolution was quite new, if the active power of nature compels life to mount steadily up the chain of being, how can we still see the complete hierarchy today? Similarly one may ask how a general trend of increasing complexity can be compatible with the observation that most species show only marginal changes during most of their evolutionary history.
The biological world is bewilderingly complex. But what, really, is complexity? These attempts testify the difficulties in defining complexity in terms of more fundamental concepts. I therefore suggest that we at present have to be content with a description by means of common and easily understood concepts. As a very simple first attempt of an answer, I would like to use a formulation by McShea and Brandon , saying that complexity means the number of parts or the amount of differentiation among parts within an individual. Features of living organisms such as the locomotive organs, internal organs like the heart and the kidneys, the sense organs, the ability to react on external stimuli, the social behavior, the understanding of symbols, the use of language, and the intelligence are examples of features displaying complexity according to the suggested meaning.
The natural variation of such capabilities in a population is what natural selection is acting on and it is reasonable to think that an enhanced capability of such features in many cases give the organism a reproductive advantage, thus implying that increased complexity is driven by natural selection. Maybe an example may clarify the reasoning.
Let us look at the evolution of the heart. In the primitive fish the chambers are arranged sequentially, later developing into an S-shape form in ray-finned fish. In reptiles such as crocodiles, this form was during several intermediate steps developed into a true four-chamber heart, making possible the separation of the de-oxygenated blood for the lungs and the oxygenated blood for to the rest of the body. This means an increase of the number of parts and of the amount of differentiation among these parts of the heart, in other words an increasing level of complexity. The changes are driven by their reproductive advantage, in this case even making possible the invasion of a new habitat, the transition to terrestrial life.
The contention of complexity as driven by natural selection is, I think, appropriate even in the many situations where the environmental contingencies offer no advantage of a change because, in such cases, natural selection is nonetheless active in eliminating unfavorable changes, thus keeping the organism alive at an unchanged level of complexity.
This feature illustrates the fact that organisms occurring first are the most primitive and that the more complex have appeared later. View at Google Scholar E. A way for the second law to increase complexity. View at Google Scholar H. To this end, we provide a sampling of topics within behavioral ecology where we think complexity theory may be illuminating. This is a self-organized structure.
I will come back to this issue. My aim of the present paper is to investigate the evolutionary process in a broad sense and its coupling to the concept of complexity. Especially, I will show that the analysis of complexity in its connection to natural selection solves the enigma posed by Lamarck as discussed above. Finally, I extend the analysis to the human species, thus uniting the evolution of humanity with the rest of animal evolution. With his classical book What is Life?
The thermodynamic point of view is recently taken up with the idea of natural selection as forcing genomes to behave like a Maxwellian Demon, thus causing genomic complexity to increase Adami et al. Still more provocatively, Wolpert p. Might it somehow be, as he asks, that the second law, which increases disorder in a closed system, not only allows for open subsystems to increase their order, but also actually drives them to do so?
Some authors include cultural evolution in their analyses. It should also be mentioned that there are scientists rejecting the notion of increasing complexity. Chimp and human, lizard and fungus, we have all evolved over some three billion years by the process known as natural selection. In all his writing Richard Dawkins avoids talking about complexity. However, he explicitly denies increasing complexity to be driven by natural selection.
In this metaphor, a drunkard is staggering along the sidewalk at random. But on one side there is the wall of the bar making a limit that causes him to stray away from the wall. Gould means that in an analogous way, living organisms are drifting toward higher complexity at random, because there is a limit of minimal complexity. Therefore, Gould maintains, there is no need of a drive by natural selection. Applied to my model, this limit of minimal complexity is the time axis in the diagram of Fig.
This implies, in the evolutionary process, that a species of high complexity occasionally could revert into a previous form of lower complexity. Such regresses are exceptional, at least in great steps, and are seen to some extent mainly amongst parasites. As Conway Morris p. A principal diagram of complexity versus time. Rationales for its construction and interpretation are discussed in the text. Another obstruction for a species to return to a previous level of lower complexity is understood in terms of genomic information content.
In addition, it seems that in many cases when a species is confronted with severe environmental difficulties, it goes extinct instead of changing to a lower level of complexity. Likewise, it seems to me improbable that a random diffusion process eventually should end up in the steady state situation that is observed in millions of species in millions of years.
McShea is together with Robert Brandon McShea and Brandon claiming that there is an even more fundamental biological law than natural selection: However, the authors claim that complexity may be increasing even without the action of natural selection.
Their rationales and experimental tests in support of this contention are summarized by Zimmer Similarly, Kauffman attempts to explain complexity to emerge even without natural selection to help it along. As we may conclude from the above extracts of the rich literature on complexity, a majority of scientists in the field maintain that evolution at large displays a trend towards increasing complexity.
Wilson makes these notions more explicit by observing that such a growth is observed to periodically rise upwards although in intermediate periods slow to a virtual halt. Furthermore, he points out that species emerge quickly and fully formed after rapid bursts of evolution, then persist almost unchanged for millions of years. Wilson bases his statements on initiated observations, and therefore I take his words as a basic empirical support for my theses.
I have previously developed a similar diagram as a result of an analysis of the evolution of individual developmental courses Ekstig b. In the present approach, I give a complementary analysis of the diagram in terms of natural selection. As a starting point, we may imagine a depiction of the evolutionary course of increasing and accelerating complexity by means of a curve, not unlike an exponential function in the diagram shown as the stepwise curve. But there is a problem with such a general notion of growing complexity because, as is generally observed, far from all species follow such a trend.
In fact, as stated by Wilson, a majority of species of all levels of complexity is by and large stable over time. Still more noticeable is that the oldest organisms of today, although being exposed to environmental contingencies and natural selection for the longest time, nonetheless are amongst the most primitive. These observations are, as we can see, consonant with the conundrum Lamarck expressed in the citations above. The intuitive idea of a generally growing complexity as driven by natural selection must therefore be revised. These horizontal lines, representing the complexity of stagnant species, have their starting points connected to the stepwise curve.
This feature illustrates the fact that organisms occurring first are the most primitive and that the more complex have appeared later. It also illustrates the thought that life on earth originated mainly once, because, as soon as the first forms were established, we may imagine that subsequent appearing forms were outcompeted. In evolution, as it is said, there are no hopeful monsters.
The stepwise curve represents the upper border of complexity and at the same time the common descent of all species, a central consequence of the present conception of the evolutionary process. The diagram illustrates that major transitions occur at the steps in the curve representing the upper border of complexity.
After such an elevation, the new species normally stabilizes at the new niche in the complexity space, thus forming a new horizontal line above those previously formed. This procedure is then repeated over and over again, leading to a cumulative elevation of complexity. Thus, when fish evolved into amphibians and reptiles, far from all fish species followed that enterprise.
Most of them remained in the sea and in the diagram of Fig. Such a stepwise increase of complexity is by McShea analyzed in terms of levels of nestedness. In this conjecture, McShea maintains that stasis predominates with rare large steps upward at the maximum, a principle that, as we can see, agrees with the present analysis.
Evolution and the Emergent Self is an eloquent and evocative new synthesis that explores how The Rise of Complexity and Behavioral Versatility in Nature. Evolution and the Emergent Self: The Rise of Complexity and Behavioral Versatility in Nature [Raymond Neubauer] on www.farmersmarketmusic.com *FREE* shipping on .
Furthermore, extinctions are not indicated, although it is easy to depict such occurrences by bringing corresponding lines to their ends at the point of time of extinction. Another feature of the evolutionary process not displayed in the diagram is that there is a tremendous number of species deviating from the few lines shown in the diagram with their speciation occurring even after the separation of the highest borderline. Such deviations may be directed upwards as well as downwards but in each such occurrence, I think the changes in complexity are small in comparison to its increases to the highest borderline.
This process has given rise to the rich diversity of life. After this descriptive introduction of the diagram of Fig. I maintain that the natural variation of the functional capability of many features by natural selection is driven to successively higher levels of complexity. However, there is a problem with such a general notion of increasing complexity inasmuch as most species, as I already have indicated, after their emergence persist almost unchanged. The challenge is therefore to explain the significant increases of complexity in some lineages and the undeniable success of the great majority of species that do not change much over long times.
Since life makes use of the elements commonly seeded into space by burning and expiring stars, it is reasonable to speculate that the evolution of life and intelligence that happened on our planet may be found across the universe. Hardcover , pages. Published December 6th by Columbia University Press. To see what your friends thought of this book, please sign up. To ask other readers questions about Evolution and the Emergent Self , please sign up. Be the first to ask a question about Evolution and the Emergent Self.
Lists with This Book. This book is not yet featured on Listopia. Oct 03, Gregg Sapp rated it liked it. To Neubauer biology, U. In order for a species to adapt to a changing environment or variable conditions, it must command a great deal of information within itself — in its DNA, genes and neurons, but also in its culture and its society. As the author shows, this is true not only for homo sapiens, but also in other big-brained species, like chimpanzees, crows, dolphins and elephants. If life evolves everywhere in the direction of increased complexity, then, he concludes, it is entirely possible that intelligent life must occur wherever in the universe conditions are conducive to its emergence.
This fascinating, big-picture discussion takes several huge leaps, but remains consistent in its basic assessment of how evolution works. This book will likely be controversial among academics, but find receptive niches among non-scientist readers. Aug 10, Rossdavidh rated it liked it Shelves: As "Big Idea" books go, this is pretty Big. Neubauer, who holds dual degrees in English literature and zoology, is thinking about as big as one can, with this book. It is essentially his aim, here, to knit together the evolution of the universe since the Big Bang, the evolution of life on Earth, the evolution of complex multicellurity, the emergence of high-level thought processes among us and a few other species, and the emergence of culture among humans.
They are all, he seems to be saying, ex As "Big Idea" books go, this is pretty Big. They are all, he seems to be saying, examples of the same process. Hold on to your hat. To reassure us, perhaps, that he isn't just blowing smoke here, he dives deep into a number of these topics. You get to see the molecular structure of ADP, comparison of African and Asian elephant trunks, the shape of hook tools made by New Caledonian crows, the brain-to-body mass ratio of a variety of primates on one graph, what a neuron in the brain looks like, and much more.
I like books with lots of pictures, the same as I did when I was five years old, and I salute Neubauer for his willingness to help me along with some visual aids. The centerpiece of this extensive collection of illustrations, is a graph of time vs. You see the Milky Way, Sun, Earth, plants, animals, and human society all mapped onto the same trend, and presented as similar examples of the same ongoing process, happening everywhere in the universe and throughout time.
Thinking of yourself as an example of the same kind of phenomena as the Earth and western civilization, is something you would normally expect of raving philosophical nonsense, but Neubauer brings enough data to the story he is telling that it never seems like nonsense. I found particularly intriguing the time spent on corvids ravens, crows, etc. We learn, in some detail, about how each of these species has rich social lives, a sense of self, the ability to manipulate their environment with purpose-made tools, and so on. He believes that, if we humans had not invented culture, there were several other kinds of life including, but not only, other primates that were headed in the same direction.
The implication, of course, is that if culture was more or less inevitable or at least, not a freak accident on Earth, then it is likely happening in a lot of other places in the universe as well. Although this might stabilize dyadic relationships, it is not immediately obvious whether the cumulative interactions among many group members relying on such a process would stabilize and generate the observed linear hierarchies.
In fact, recent models have shown that this process can converge to a stable hierarchy Beacham ; Chase and Seitz In these models, the final hierarchies are more likely to be linear when relationships are first established sequentially within subgroups, and the functions relating combatant self-ranking to the probability of winning a contest are nonlinear. Put more generally, the linearity of hierarchies is here seen as an emergent property, an artifact if you will, of complex nonlinear interactions between group members. Given the early concerns and clues, why has it taken so long for this interpretation of hierarchy linearity to appear?
We think the major impediment was the obsession with reductionist science that arose in the s and is only now beginning to yield to alternative approaches. The basic assumption of reductionism is that complicated systems can be understood by characterizing the properties and behaviors of their component parts and then adding these up to generate the whole.
Look at any issue of Behavioral Ecology or Animal Behaviour and you will find many successful applications of this approach: Mating systems, parent—offspring conflicts, contest behavior, cooperation, and communication are commonly broken down into dyadic interactions and the properties of any larger ensemble predicted by adding up the dyadic behaviors. Emergent properties, by definition, do not fit into this paradigm.
But then, how often does the reductionist strategy fail? Surely, we have done pretty well with the reductionist approach for decades. Are emergent properties common enough to worry about? In this essay, we argue that it is time for behavioral ecologists to move beyond our exclusive focus on dyads and examine how much we might be missing by not treating complexity head on. Many other fields such as physics, neurobiology, computer science, and economics have already made this transition for a lucid and nonmathematical survey, see Strogatz Thanks to May and his students, theoretical ecology made this leap long ago.
For some reason, it has been slow to appear in behavioral ecology. When attending recent ISBE and ABS meetings, we have been surprised at how few colleagues invoke or even seem aware of complex systems theory. There are indeed exceptions, which we note below, but even here the researchers seem so focused on their own specific topic that they often do not make any effort to tie their results into the general predictions of complexity theory. We do not claim to be experts in this approach. But we have learned enough recently to realize that, were we starting our careers now, this is a key approach in which we would want to invest.
We think complexity is one of the major remaining frontiers in our field. This essay seeks to provide some basic background on the topic and suggest just a few of the behavioral ecological topics where the application of complexity approaches may prove enlightening. One problem with any evolving field of research is that workers adopting different entry points to that field coin their own names for equivalent or at least overlapping processes. This is certainly true for complexity theory. Below, we provide an initial definition of complexity and then summarize several different entry points to complexity that are likely to be relevant to behavioral ecology.
A number of authors have tried to provide broad definitions of complexity. For our purposes, we shall adopt a version of one proposed by Mitchell Given that a system is an ensemble of interacting entities, then a complex system is one that exhibits at least some properties that cannot be explained as the linear sum superposition of properties of the component elements. The exceptional properties are said to be emergent. This definition sets us up to introduce our first entry point, nonlinear dynamic systems.
Although behavioral ecologists often focus on stable equilibria e.
The temporal changes are called the dynamics of the system. Typically, one identifies key variables that describe the current state of the system and then derives equations that predict how these variables will change in the next time interval. The dimension of the system depends on how many key variables are invoked. The equations can be deterministic no chance involved or stochastic in which new values for the key variables are drawn at random from some distribution. The right sides of these equations include extrinsic parameters that are currently fixed in value. They usually also include the values of the key variables in the current state.
Given some starting values for the key variables, one can successively apply the dynamic equation again and again to plot the trajectory of the system over time. The trajectory may be quite different depending on the initial starting point and the values of the included parameters. See Strogatz for a detailed introduction to dynamic systems analysis.
In a linear system, none of the variables on the right side of the dynamic equation have any exponents other than 1, there are no products or ratios of key variables, and none of the key variables is present as the argument of a trigonometric, exponential, or similar function. It is easy to predict the trajectory of a linear system as each key variable changes independently; the next state of the system is just the linear sum of the next states of each key variable.
If a parameter is varied, the system responds proportionally. Reductionism assumes linear systems: Here, you break a system down into its components, see how each component changes over time, and add these changes up to predict the overall state of the system at each successive time point. There are no emergent outcomes in a linear system. This does not mean that linear systems are boring: Linear system trajectories can progress to an equilibrium where further change stops, spiral off into infinity, or exhibit oscillations at some fixed frequency set by the parameters and initial conditions.
However, each of these trajectories is entirely predictable given the equations and the values of the extrinsic parameters and initial variable values. A nonlinear system is one in which one or more of the conditions required for linear systems is violated. Like linear systems, nonlinear dynamics can move a system to a stable equilibrium point or spiral off into infinity. However, variation in parameter values may not result in proportional variation in the system but, instead, trigger major qualitative changes into totally different states.
Such shifts in state are called bifurcations. Nonlinear systems can exhibit oscillations, but unlike the harmonic oscillations of linear systems, where the frequency is set by the external parameters and initial conditions, the oscillations of nonlinear systems are limit cycles whose frequencies depend on the system itself.
If the dimension of the system is sufficiently large, changing the parameters can cause the system to go into deterministic or stochastic chaos. A good example of a nonlinear system is the set of vibrating membranes that create signal sounds in vertebrates Wilden et al. Consider a terrestrial mammal in which airflow through the larynx acts as a parameter affecting the paired vocal chords on each side of the flow cavity. At very low flows, the folds remain at an immobile equilibrium.
At a critical but still moderate flow, the thinner parts of each fold begin limit cycle oscillations, sweeping out a repeated 2-dimensional trajectory. Given the moderate flow and their proximity, the 2-folds act as coupled oscillators and lock into the same frequency. This is the normal vocalization mode, and because it is periodic but invariably nonsinusoidal, the resulting sound appears on a spectrogram as a harmonic series.
At a somewhat higher flow, the 2-folds continue to oscillate but the coupling between them breaks down and they may adopt slightly different frequencies. This is called biphonation. At even higher flows, the entire complex of vocal folds on each side begins to oscillate, but given the larger masses and the shift to 3-dimensional trajectories, at a lower frequency.
This would be seen on a spectrogram as a sudden shift from harmonics to subharmonics spaced some fraction, often half, of that seen in the prior series. Finally, at high enough flows, the system lapses into chaos and the spectrogram shows a wide band of noise. One can see several of these modes in 3 successive calls by a wild parrot in Figure 1.
Nonlinear behaviors in bird vocal organ. First call on left shows typical harmonic series of stable limit cycle vibrations in syrinx. Middle call shows appearance of subharmonics in last third of call arrow points to relevant section. Final call lapses into chaos in last two-thirds. Frequency scale vertical axis: Although the versatility of vertebrate sound-producing organs is itself interesting to behavioral ecologists studying communication, the broader message here is that any nonlinear system may be capable of such sudden bifurcations.
And nonlinear systems must be common in behavioral ecology. Where bifurcations are possible, they will by definition lead to emergent states, and the corresponding nonlinear systems will fit our definition of complex systems. The take-home message is that knowing something about nonlinear system dynamics will help us look for possible complexity in behavioral ecology. There has been considerable recent interest in the role of network processes in behavioral ecology see McGregor ; Croft et al. Nearly any interacting ensemble of animals can be modeled as a network including primate troops, males on a lek, nesting colonies of seabirds and pinnipeds, communication systems, etc.
When are animal social or communication networks complex systems? Given our prior definitions, it should be clear that a network might be either a linear or a nonlinear system: It will depend on the nature of the interactive links between network members. If these relationships are essentially linear, then we would not expect to see emergent properties and the trajectories followed by these networks should be predictable by knowing the relevant equations, starting points, and ambient parameters.
However, there are many reasons to believe that the complicated ways that group or network members can affect each other and then be affected in turn by the resulting feedback will generate nonlinear linkages between individuals see Strogatz quote above.
The dominance hierarchies we outlined earlier are a clear case in point. When the network links are largely nonlinear, then we should not be surprised to see the network act like a complex system showing bifurcations and emergent properties like synchronization or other qualitative changes in state. Such emergent behaviors are well known in other kinds of networks such as ecological webs, neurobiological systems, the Internet, and various physical systems Grossberg ; Goldberger et al.