These results suggested that crickets used recent contest success as a rule of thumb to estimate their relative fighting ability. In the absence of better information, this was not a bad option. But it could lead to mistakes: Should 1 of 2 equally competent fighters slip during a confrontation and then lose the contest, the loser would reset its self-rank lower and the winner would adjust its self-rank higher. 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.
Introduction
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.
Complexity and behavioral ecology | Behavioral Ecology | Oxford Academic
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. Power laws, in which 1 variable is a function of some other variable the argument raised to some exponent, are common in nature. Power laws are self-similar also called scale free: A plot of functional results versus various values of the argument will have the same shape regardless of the scale of values used. Fractals are sets of objects numbers, points, lines, etc. The absolute value of the exponent is the dimension of the fractal.
One can estimate the fractal dimension by plotting the logarithm of the function output against the log of the argument values; the absolute value of the slope should equal the fractal dimension. The set of points defining a straight line on a plane has a dimension, both classical and fractal, of 1. The set of points filling a bounded area on that plane will have a classical and fractal dimension of 2. But a squiggling line in the plane that meets fractal criteria will have a fractal dimension between 1.
The power function defining a fractal set can be either deterministic or stochastic; if the latter, the log—log plot will show a scattering of points, but we should still be able to discern a fixed exponent from a regression slope. What do fractals have to do with complexity? It turns out that the dynamics of complex systems often lead to fractal sets.
The more structured the fractal set, the lower the fractal dimension; more random sets have higher fractal dimensions. The efficiency of functional activities is thought to be optimal for intermediate fractal dimensions. For example, the geometric distributions of blood vessels and the pulmonary tree have an intermediate fractal dimension.
Biochemical systems with fractal distributions of connections are thought to be more robust to breakdowns than other designs Gallos et al. Foragers searching for sparse and randomly distributed prey can optimize search by drawing successive step lengths from a stochastic fractal distribution with a dimension of about 2.
Evolution and the Emergent Self: The Rise of Complexity and Behavioral Versatility in Nature
Even complex systems that are experiencing chaos will trace out nonrepeating trajectories that cumulatively obey a fractal rule Strogatz For example, the relative proportions of waves of different amplitude in the waveform of a larynx oscillating chaotically will follow a fractal distribution rule. An enormous amount of theoretical effort has focused on the appearance of fractal structures in complex networks see summary in Chapter 15 of Bradbury and Vehrencamp Most biological and human e.
However, the log—log plots are not always linear, implying that something else is going on. In many cases, for example, metabolic networks, the network is organized into hierarchical clusters and modules. Because this same modular structure recurs at various scales, these networks are structured fractals low dimension. These tend to have higher fractal dimensions. Current theory and data suggest that the more structured networks sacrifice speed and extent to which local effects are propagated throughout the network but gain robustness and resilience against functional breakdowns by isolating key hubs.
The less structured systems are less functionally resilient, but communication propagates more quickly and effectively Song et al. The combination of fractal and network tools thus provides some very interesting insights to the function and robustness of various network designs. In short, the pattern is an emergent property of the system, rather than a property imposed on the system by an external ordering influence.
By this and our prior definitions, self-organizing systems are complex, and models of their dynamics that predict their behavior well e. Fractals clearly fit the definition of self-organized systems: A fixed power law rule results in highly complex patterns regardless of scale. Many social networks of animals and people invoke very simple and local rules but generate complex global patterns and are thus self-organized. Self-organizing systems with few degrees of freedom e. The classical physics example is a trickle of dry sand grains onto a flat surface Creutz Over time, a peaked mound of sand builds up on its own: This is a self-organized structure.
When the slope of the pile walls is steep enough, the system becomes self-organized critical. The notion of self-organized criticality has also been applied to earthquakes, forest fires, disease epidemics, stock market crashes, and species diversity collapses in ecological communities.
It should be obvious that these various phenomena all show high degrees of overlap. 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.
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- In The Time of Twelve.
- Complexity, Natural Selection and the Evolution of Life and Humans.
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. This was already a conclusion I was ready to agree with, but Neubauer brings new evidence to my attention, and even when he deals with subjects I already had read about, he does so in ways that gave fresh perspective. He does, it should be said, overshoot his available evidence late in the book, venturing a bit into spiritual or philosophical matters for which there isn't much data available to keep us grounded in reality.
But this is a minor flaw in an otherwise excellent book. Jan 02, Rick rated it really liked it. The author proposes that findings in chemistry, biochemistry, molecular biology, neurology and animal behavior point toward development of complexity in structure and in behavior as a natural, even inevitable, consequence of life's need to attain and maintain homeostatic systems keeping the organism stable and functioning in a constantly changing, sometimes threatening environment.
Calling upon an impressive breadth of information and using a writing style that is both informative and engaging Neubauer's book is accessible to both the scientist and non-scientist. I actually found it inspiring in many instances, simply because of the wonderful confluences of thought that recurred throughout the book. The part I didn't like was at the end, where Neubauer tries to make a case for the compatibility of scientific evidence for the natural-ness and seeming inevitability of the development of self-organizing and intelligent systems in this universe and religious beliefs that ascribe this tendency to supernatural powers.
The argument was lame Anselm's ontological argument was a masterpiece by comparison and, in my opinion, totally unnecessary. Why couldn't he have simply laid out the observations as the truly marvelous and beautiful things they are in and of themselves, without need of a contrived religious explanation for what we do not yet understand?
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Mar 19, Baal Of rated it liked it Shelves: I almost gave this 4 stars, until the author ended on a sour note with his namby-pamby injection of religion into the discussion that sounds like it was mostly cribbed from Gould's NOMA essay. He also failed to present the other side of the fine-tuning argument, focusing only on the puddle noticing how well it fits in the pothole.
Bryan Atkins rated it it was amazing Jul 11, David Coon rated it it was amazing May 07, Samantha rated it really liked it Jun 12, Daniel rated it really liked it Mar 17,