Enablement – the Definitive Characteristic of Living Things

In my last posting A Proposal for an Odd Collaboration to Explore the Origin of Life with IT Professionals, I proposed that it might be worthwhile for researchers working on the origin of life or astrobiology to add some IT professionals to their research teams, and I also offered to become a junior member of such an odd collaboration. I am glad to report that since then I have had some very positive feedback on my proposal. Among others, I have had some very interesting email exchanges with Professor Stuart Kauffman, and also a very interesting phone conversation with him as well. In The Origin of Software the Origin of Life and A Brief History of Self-Replicating Information, we first showcased some of Stuart Kauffman’s many contributions to scientific thought, especially his concept of the apparent emergence of self-organized order in nonlinear systems far from thermodynamic equilibrium that he calls “order for free”, and also of his work with Boolean networks that provide a model for the autocatalytic networks of organic molecules that most likely kicked off the evolution of life on Earth. In this posting, I would like to explore another one of Stuart Kauffman’s very exciting new concepts called Enablement, by discussing some of the ideas in the paper and video presentation below, with the intent of proposing that Enablement may be the long sought for definitive characteristic of living things. Please see:

No entailing laws, but enablement in the evolution of the biosphere
Giuseppe Longo, Maël Montévil, Stuart Kauffman (Jan 11, 2012)

Stuart Kauffman also gave a very interesting talk at MIT on October 19, 2011, a few months before the above paper was published, covering the essentials of the paper, and which can be viewed at:

http://vimeo.com/30875984

Stuart Kauffman has been a long-time advocate of moving beyond reductionism in order to understand complex systems. In the above paper with Giuseppe Longo and Maël Montévil, the authors formalize a new concept that Kauffman calls Enablement. Imagine a pool table with all the balls racked up in the center. Next, we shoot a cue ball into the closely packed pool balls and observe what happens. In order to keep track of what we are observing, we establish some arbitrary state variables that seem to do a good job of keeping track of what is going on. In this example, we might choose the X and Y position of each ball and also its velocity components V_x and V_y relative to the pool table as arbitrary state variables to keep track of the system of pool balls. These state variables would probably do a better job of keeping track of what is going on than the colors of the pool balls, which most likely will not change much during the interactions of the pool balls. Physicists would say that the changing values of the state variables of X, Y, V_x and V_y with time constitute a phase space, and that we could calculate the trajectory of each ball in that phase space using a deterministic theory of motion, like Newton’s laws of motion or even quantum mechanics if we had to because the evolution of wavefunctions over time is deterministic. All we would need to do would be to find the initial conditions of the pool balls and the boundary conditions defined by the size and shape of the pool table, and then simply integrate the differential equations that define the deterministic theory of motion that we use for the calculation, using the initial and boundary conditions to define the unique analytic solution for the problem at hand. Now according to Laplace, given the initial conditions of the cue ball’s original position and velocity, plus a deterministic theory of motion like Newton’s laws, and the boundary conditions defined by the size and shape of the pool table, we could exactly predict how the system of balls would evolve over time because we could predict the trajectory of each ball in the phase space we defined. Kauffman calls this capability of predicting trajectories in phase space “entailment” because we can “entail” the future course of each pool ball in the system of pool balls. Laplace was so impressed by Newtonian thought that he famously proclaimed that entailment could be extended to the entire Universe. Given Newtonian mechanics and knowing the positions of all the atoms in the Universe and their motions in 3D space at one particular time, would allow one to predict all future events in the Universe. However, with the rise of deterministic chaos theory in the last century, we realized that Laplace was not entirely correct in his analysis because nearly all systems in the Universe are nonlinear, and we also discovered that nonlinear systems are very sensitive to initial conditions, so although we could theoretically predict the trajectory of each pool ball in phase space, in practice we cannot do so because we cannot determine the initial conditions of the system with infinite precision and accuracy. See Software Chaos for more details on deterministic chaos.

The concept of Enablement goes one step further. Kauffman contends that what makes complex systems like the biosphere different from others is that the biosphere can change the boundary conditions of its phase space as it proceeds along a particular trajectory in phase space by means of taking advantage of Darwinian preadaptations. Personally, I prefer to use Stephen Gould’s term of exaptation, rather than Darwin’s preadaptation, because it removes the vague inference of a teleological intent found in the term preadaptation. Kauffman points out that the first difficulty we would have in extending our pool table analysis to the biosphere would be in choosing the proper state variables to be used to keep track of the biosphere because we do not know in advance where the biosphere may be heading on its own. For example, suppose in our pool table example we only used unnumbered pool balls, and relied upon their colors to keep track of their individual positions and movements. If we had then placed a green filter over our pool table lights, suddenly, the green number 6 ball on the green felt would have disappeared in the green light, and would have become invisible, like a neutrino back in the 1920s. In this case, color would indeed have become an important state variable to keep track of, but as Wolfgang Pauli did in 1930 with neutrinos, we could still deduce the existence of the green number 6 ball by means of the conservation of energy, momentum, and angular momentum of the other balls. Now from a purely Darwinian perspective, the seemingly proper state variables to use for the biosphere should be measures of utility that provide a survival advantage to the possessor, but specifically defining such state variables in advance would be very difficult indeed. Instead, let us just imagine an infinite and unbounded phase space of utility, consisting of all possible uses of all possible things. For Kauffman, the concept of Enablement means that as organisms evolve along a particular trajectory in utility phase space, they may develop a function for one purpose that can be easily exapted for another purpose, like the trajectory of lungfish in the deep past that led to the development of swim bladders by exapting the functions of their primitive lungs into the function of a primitive swim bladder, and consequently, led to the development of swim bladders that could be used for neutral buoyancy locomotion. Paleontologists currently believe that swim bladders evolved when lungfish, scurrying about from pond to pond, took in water into their primitive lungs and survived. Suddenly, their primitive lungs took on a new function, that of neutral buoyancy locomotion, that had nothing to do with the original purpose of lungs to take in oxygen, but which could be put to good use for low-energy locomotion, and provided a survival advantage to its possessors and to their progeny. With the concept of Enablement, Kauffman maintains that the potential phase space of utility of the biosphere at any given time is essentially infinite and unbounded because with each new added function the biosphere comes up with, a new “AdjacentPossible” in its available portion of the infinite and unbounded utility phase space also becomes available. For example, in Kauffman’s MIT presentation he uses the analogy of the near infinite uses of a screwdriver. The screwdriver is also one of my own favorite analogies for explaining the Darwinian concept of preadaptation or exaptation. In Self-Replicating Information and When Toasters Fly I used a similar analogy for screwdrivers:

What happens is that organisms develop a primitive function for one purpose, through small incremental changes, and then discover, through serendipity, that this new function can also be used for something completely different. This new use will then further evolve via inheritance, innovation and natural selection. For example, we have all upon occasion used a screwdriver as a wood chisel in a pinch. Sure the screwdriver was meant to turn screws, but it does a much better job at chipping out wood than your fingernails, so in a pinch it will do quite nicely. Now just imagine Darwin’s processes of inheritance, innovation and natural selection at work selecting for screwdrivers with broader and sharper blades and a butt more suitable for the blows from a hammer, and soon you will find yourself with a good wood chisel. At some distant point in the future, screwdrivers might even disappear for the want of screws, leaving all to wonder how the superbly adapted wood chisels came to be.

As an IT professional, you probably do this all the time. How often do you write code from scratch? I know that I never do. I simply find the closest piece of existing code that I have on hand and then turn the screwdriver into a wood chisel through small incremental changes to the code, by testing each small change to see how closely my screwdriver has evolved towards being a wood chisel. And I think that most of us also code using this Darwinian process of inheritance, innovation and natural selection too. I am a rather lazy programmer, so many times rather than thinking through a new chunk of code during the iterative process of coding and testing, I will simply make an “educated guess” at the new code to be introduced. After 35 years of coding, you begin to code by “ear”. Many times, I can fall upon the correct code after a few shots of directed random change, and that sure beats racking your brain over new code. Surprisingly, sometimes I even come up with “better” code through this Darwinian process than if I sat down and carefully thought it all through. This has probably been going on since 1945 when Konrad Zuse wrote the first “Guten Tag Welt!” program in Plankalkuel – just speculating here on the origin of the compulsion for all programmers, new to a computer language, to write the obligatory “Hello World!” program as their first effort. So the basic idea of grabbing some old code or architectural design elements from a couple of older Applications and slowly modifying them through an iterative process of inheritance, innovation and natural selection into a new Application is no stranger to IT. As Simon Conway Morris commented in Life’s Solution (2003) "How much of a complex organism, say a humanoid, has evolved at a much earlier stage, especially in terms of molecular architecture? In other words, how much of us is inherent in a single-celled eukaryote, or even a bacterium? Conversely, we are patently more than microbes, so how many genuinely evolutionary novelties can we identify that make us what we are? It has long been recognized that evolution is a past master at co-option and jury-rigging: redeploying existing structures and cobbling them together in sometimes quite surprising ways. Indeed, in many ways that is evolution”. When I first read these words, I accidentally misread the quote as "Indeed, in many ways that is IT”.

In his MIT presentation, Kauffman goes on to describe how the opportunistic and serendipitous use of preadaptations or the exploitation of what he calls the AdjacentPossible in utility phase space changes the very boundary conditions of the utility phase space available to the biosphere at any given time. And once an evolutionary trajectory has been deflected by such an enabling event, like the development of swim bladders, it opens a whole new ecological niche within the biosphere in a manner of "radical emergence" because now parasites can take up residency in the swim bladders that never before existed. It is as if the size and shape of our pool table were constantly changing due to the trajectories of the pool balls themselves. The key insight here is that the biosphere has changed the boundary conditions of its utility phase space over time by means of exapting already existing functions into new and unpredictable uses. Certainly, the crossing over of "dead" molecules to "live" molecules in the deep past must have been one of those dramatic Enabling events of radical emergence. To Kauffman, such diverted paths through utility phase space “enable” the course of evolution of the biosphere through utility phase space, rather than “entail” the course of evolution through utility phase space in a Newtonian sense. Because we cannot predefine the ever-changing boundary conditions of the utility phase space, or even the state variables to use to create a utility phase space in the first place, the old concepts of integrating differential equations subject to initial conditions and boundary conditions that has served science so well in the past cannot be used to predict the evolution of complex interacting things like the biosphere. Kauffman then goes on to explain that the concept of Enablement also extends to the econosphere and to the evolution of technology over time as well. Kauffman explains to the students at MIT that it is much easier to invent new technologies today than it was 50,000 years ago because the utility phase space of technology has exploded over the past 50,000 years, and consequently has a much larger AdjacentPossible than it did in the past. And with each new innovation that one of the MIT graduates might come up with in the future, the utility phase space of technology will again increase in size in new and unexpected ways, with the technologies they invent being used in unanticipated manners.

We have certainly seen Enablement in action in the evolution of software over the past 70 years, and also in the evolution of the memes as well. After all, both the econosphere and the technosphere are meme-complexes, as are all of the other cultural artifacts and activities of mankind. See SoftwareBiology, When Toasters Fly and How to Use Softwarephysics to Revive Memetics in Academia for more details on how Enablement has combined with the Darwinian mechanisms of inheritance, innovation and natural selection to shape the evolution of the genes, memes, and software.

With this background in hand, I would now like to propose that the concept of Enablement may be the defining characteristic of “living things”. I believe this leads to a unique and unambiguous definition of life for the very first time:

Life – A form of self-replicating information with the ability to change the boundary conditions of its utility phase space in unpredictable ways by means of exaptation.

For many years I have been advocating the idea that there are now three Replicators on the planet in a Dawkinsian sense – genes, memes, and software, but I have always struggled with what makes these Replicators different from other forms of replicating information, like a quartz crystal forming in a melt. I am now confident that the defining characteristic of “life” is the ability of self-replicating information to change the boundary conditions of its utility phase space in new and unpredictable ways by means of exapting current functions into new uses that change the size and shape of its utility phase space. So I would like to add this defining characteristic to my previous seven:

Self-Replicating Information – Information that persists through time by making copies of itself or by enlisting the support of other things to ensure that copies of itself are made.

The Characteristics of Self-Replicating Information
All forms of self-replicating information have some common characteristics.

1. All self-replicating information evolves over time through the Darwinian processes of inheritance, innovation and natural selection, which endows self-replicating information with one telling characteristic – the ability to survive in a Universe dominated by the second law of thermodynamics and nonlinearity.

2. All self-replicating information begins spontaneously as a parasitic mutation that obtains energy, information and sometimes matter from a host.

3. With time, the parasitic self-replicating information takes on a symbiotic relationship with its host.

4. Eventually, the self-replicating information becomes one with its host through the symbiotic integration of the host and the self-replicating information.

5. Ultimately, the self-replicating information replaces its host as the dominant form of self-replicating information.

6. Most hosts are also forms of self-replicating information.

7. All self-replicating information has to be a little bit nasty in order to survive.

8. The defining characteristic of self-replicating information is the ability of self-replicating information to change the boundary conditions of its utility phase space in new and unpredictable ways by means of exapting current functions into new uses that change the size and shape of its particular utility phase space.

As human beings, we have a tendency to overdo the labeling of things, as if the very act of us naming something gives it some semblance of reality, and I think this has been one of our problems in defining what life is in an unambiguous way. For the purposes of softwarephysics, I have always maintained that organic life, memes, and software are all forms of a higher, more encompassing whole, that I call Self-Replicating Information. Richard Dawkins and Susan Blackmore call these entities Replicators, and indeed Susan Blackmore has also proposed that there are now three Replicators on the planet. For Susan Blackmore, technical memes or “temes” are the third type of Replicator, as she outlined in her TED presentation at:

Memes and "temes"
https://www.ted.com/talks/susan_blackmore_on_memes_and_temes

Similarly, I have deemed software as the third Replicator because of the impact that the rise of software has had on the planet over the past 70 years, and because there may soon come a day when software will break free and begin to write itself. But in all cases, I think that the defining characteristic of the Replicators is their ability to change the boundary conditions of their utility phase space in new and unpredictable ways by means of exaptation, and that is why the genes, memes and software all seem to have a life of their own, independent of human intention.

Comments are welcome at scj333@sbcglobal.net

To see all posts on softwarephysics in reverse order go to:
https://softwarephysics.blogspot.com/

Regards,
Steve Johnston