- Open Access
Inventive processes in nature: from information origin in chemical evolution to technological exhaustion
© The Author(s). 2017
- Received: 4 December 2016
- Accepted: 24 July 2017
- Published: 15 August 2017
It has been ten years since the 2006 work of Abel and Trevors wherein the cybernetic path of life’s origin was proposed as an alternative to the widely held views of such origin being self-ordering and self-organisation. Cybernetic adaptation is now recognised as a cornerstone of biological and technological evolution and as well as of artificial intelligence (AI) and cognition. It is expected that chemical evolution, preceding biological evolution, will have a cybernetic explanation as well. Among all evolutions, only AI evolutionary computation and cognition are accessible via the scientific method. For biological and technological evolutions, we only have the example of one, while for chemical evolution we have no template at all. The aim of this essay is to look for commonalities in all evolutions and attempt to fill in the missing pieces of the chemical and technological evolutions with knowledge that can be obtained by observing evolutions with a complete record. Types of information – quantum, chemical and functional – are defined, and their roles explained. It is proposed that the temporal survivability of information should be considered as a factor of general evolutionary fitness for all evolutionary adaptations. This study further suggests that because all experimentation spaces are finite they may become exhausted due to convergence towards optimal configurations. Such exhaustion of important experimental areas might reflect the observed decay of technological innovation and economic growth.
- Chemical evolution
- Biological evolution
- Evolutionary cybernetics
Invention is a search result that endures over a space of combinatorial possibilities, while innovation involves the replication and diffusion of inventions. Inventors have explored and tested potential ideas that might become inventions, which were subsequently retested on the market as replicated products and services (Kell and Lurie-Luke 2014). Scientists have long observed that technological innovation closely resembles biological evolution (Nelson and Winter 1982); moreover, ideas based on evolution have been introduced into computational artificial intelligence (AI) (Forrest 1993; Koza et al. 1999). AI, technological development, and evolution can all be considered creative processes within the universal Darwinism proposed by R. Dawkins (1983). These processes all generate, process, and test information for fitness (Bedau et al. 2000). When tested, this information faces oblivion. If identified as unfit, this information will eventually be forgotten and erased. Surviving information presents a new addition to the combinatorial space for further improvements, where experimentation provides for information reuse.
Currently, it is not well understood how these informational processes work in nature (Bedau et al. 2000). In particular, biology has not been able to adequately explain early evolution (Yockey 2005), where the period of prebiotic, chemical evolution is of particular interest. It is a prolific view that chaotic dissipative processes with observable criticality or known self-assembly or self-organising processes might have somehow played a part in chemical evolution. Abel and Trevors (Abel 2006) brought this view into question by pointing out the simple fact that entropy-generating dissipative processes destroy information and leave none behind after the driving force is removed. This is in stark contrast to processes of life where information is built up and even left behind after the driving force is removed. Abel and Trevors postulate that the origin of life must be cybernetic. The aim of this study is to provide sought cybernetic view of the processes of life through the description of inventive processes in nature.
Innovation milestones throughout the evolution of the Earth
Physical information representation
DNA, neural, technological
Molecular noisy search
Molecular noisy search
Molecular noisy search, high frequency mutations
Low frequency mutations
Neural experimentation through neural noisy search, numerical mutation operators in AI
Information addition (polymerisation), horizontal chemical information exchange
Old information addition (polymerisation)
+ horizontal gene transfer
Old information addition (polymerisation)
+ horizontal gene transfer + sexual information crossover
Thought experimentation, numerical crossover in AI
Invention diffusion method
Diffusion in liquid solvent
Parallel chemistry, solvent diffusion
Replication, solvent diffusion
Replication by heredity, solvent diffusion
industrially manufactured copies
Dominant exergy source
Radiation (UV) + planetary dynamics
UV + some metabolic
UV + metabolic
UV + visible + metabolic
Metabolic (+ nuclear, solar…)
Pillar 1: Information origin
Three levels of information: quantum, chemical, and functional
Origin and loss of chemical information
If an imaginary world without an energy source remained untouched at constant temperature T, eventually all of the local disequilibrium would disappear and all metastable states would empty, reflecting spontaneous thermal decay. This world would reach a state of maximum entropy, i.e., Boltzmann’s ‘heat death’. Here, we consider a thought experiment using such a ‘dead’ world with a composition capable of covalent and ionic chemical bonding. Before the ultraviolet band (UV) pulse irradiates the imaginary world, there is uncertainty concerning which molecules would form and remain preserved. This uncertainty represents the missing information prior to the experiment. After the UV pulse irradiates the planet, the remaining photochemically synthesised metastable molecules (Ranjan and Sasselov 2016) remove this uncertainty by holding experimental outcome information. These newly synthesised, metastable molecules decay unevenly, depending on the molecular structure and local environmental conditions. These molecular decay times vary vastly among different molecules. For example, metastable organics might have aqueous solution half-lives that range from minutes to billions of years (Lazcano and Miller 1996). If these molecules were left to decay indefinitely, then the imaginary planet would slowly return to its original ‘heat death’ state. Should the UV source be reignited prior to the return of the planet to the dead state, more complicated molecules would form based on those preserved from the previous experiment. This chemical information build-up, without intent or purpose, reflects natural experimentation, where natural selection facilitates the reuse of molecules that survive environmental stress.
Thermal molecular jiggling (spontaneous erase), photon absorption (photolysis), or bindings with other molecules can erase the information in a metastable chemical (Fig. 1). This latter process is common in metabolic reactions where the partial loss of previously stored chemical energy occurs. In all of these cases, chemical (or physical) potential energy dissipates into low-level thermal quantum information, which eventually diffuses out of the world and into the colder universe as heat through an increase in entropy. The irradiation of the quantum ‘ash’ of the ‘dead’ information into the universe makes such information irrecoverably lost to the world.
Chemical information storage is proportional to the temperature of the local environment. A substantial increase in temperature would result in the massive decomposition of stored information. Increasing the temperature of our fictional paper message would induce decomposition. This is true for all biological, organic material and any other material used in industrial manufacturing processes. In contrast, decreased temperature increases the survivability of chemical and functional information and enables the storage of more functional information in the same chemicals.
Origin and loss of functional information
In his brilliant analysis of Maxwell’s demon, L. Szilárd (Szilárd 1929) proposed that the possession of functional information might have thermodynamic consequences. It was shown by (Brillouin 1953; Bennett 1982) and (Toyabe et al. 2010) that chemically (or physically) storing functional information bit value requires at least k B T ln(2) of thermodynamic work, where k B is Boltzmann’s constant and T is the environmental temperature. This thermodynamic work represents the energy needed to instil information into matter (Fig. 1). Thus, it takes a jolt of energy of at least k B T ln(2) to store one functional bit of information chemically or physically into a metastable state, where ‘physically’ refers to information storage by means of metastable physical mechanisms such as those in electronic computers.
Functional information written over a chemical information base has been recognised in information theories, texts, digital computers, genomes, and other arenas. Functional information emerges as a result of a noisy search on the level of chemical evolution, as will be explained in the section of this study dealing with the emergence of functionality. When functional information emerges, it can be refined by higher-level experimentation such as conducted by sentient beings or on the level of biological functional information by Darwinian evolution. The erasure or ‘death’ of such functional information, the cornerstone of Darwinian natural selection, is viewed in terms of the Landauer principle (Landauer 1961; Bérut et al. 2012), where any loss of functional information results in the conversion of some free energy into heat and in a corresponding increase in thermodynamic entropy.
Because any functional message can be written in more bits than the minimum necessary, a functional bit of information can be chemically encoded in multiples of k B T ln(2) to achieve better message survivability and reading resolution. In the metaphorical message on paper, increasing chemical information bits would mean the use of thicker, more rugged paper and thicker writing ink. When considering the messaging efficiency, multiples of k B T ln(2), required for encoding functional information, are as close as possible to a value of one. Example of such tightly optimised functional encodings include digital data communication devices.
The thermodynamic-entropy-like mathematical tool called Shannon entropy (Shannon 1948) should not be confused with thermodynamic entropy, which reflects the chemical or physical configuration at one level lower. Indeed, the relation between the two entropies becomes less clear in a limiting case, where an information bit of encoded functional information approaches the k B T ln(2) limit. The two entropies probably converge in such a limiting case.
For instance, the Escherichia coli (E. coli) bacteria is thought to contain 0.6 MB of specific genetic functional information (Blattner et al. 1997), while the entire E. coli bacterium contains up to 20 GB of discoverable chemical information calculated after (Morowitz 1955). Both forms of information are readable to us. Genetic information can be measured in terms of Shannon entropy, and this information represents white paper written instructions for the functioning of E. coli.
As the information of quantum systems is not originated and cannot be destroyed, the natural selection mechanisms must be classical. The proposition of information ‘death’ is essential for natural experimentation and fitness selection. The chemically metastable exergy stored in the world can be viewed as an experimental information repository available to nature.
Exergy as chemical information potential
Engineers, and recently economists (Warr and Ayres 2012), have recognised the advantageous treatment of energy conversion into thermodynamic work through exergy. Chemical exergy, introduced by (Rant 1956), is defined as the maximum work obtained when the considered system is brought into a stable reaction with reference substances present in the environment at its reference pressure and temperature. The beauty of using exergy theory lies in the fact that the chemically stored exergy in the world also represents stored chemical information potential (Fig. 1). If we return to the thought experiment with the imaginary world irradiated with UV light, we can conclude that photochemically-generated metastable molecules represent increases in free energy, exergy, and chemical information. All these notions are equivalent. As UV light can be converted into thermodynamic work, this energy can also be considered as an exergy source. The UV light of the stars in the universe carries low-thermodynamic-entropy quantum information to worlds, where this information is partially converted into stored chemical information or chemical exergy. Thus, stars are exergy sources for feeding chemical exergy to worlds (Chen 2005).
Pillar 2: Exploration mechanisms
A noisy search through cybernetic exploration and exploitation
In biological theory, functional information is sometimes regarded in terms of noisy information channels, where certain specific, genetic information is communicated relative to processes in the organism (Yockey 2005). Newer approaches, however, depart from this view, as genetic information is somewhat less specific than originally thought (Longo et al. 2012). The exploration ‘decision’ processes at an intracellular level depend on noise for input (Balazsi et al. 2011). (Tyagi 2010) elegantly explained this notion using E. coli. Assuming that genetic information is specific, the test bacteria under the same conditions should be configured in the same way. However, this did not occur in his experiment, and the tested E. coli assumed varying, noisy, configurations.
In our earlier thought experiment on the UV-illuminated world, we skipped the factual exploratory ‘choices’ of nature. Experiences in the field of AI (Koza et al. 1999; Traulsena et al. 2009) and biological evolution (Balazsi et al. 2011; Tyagi 2010) have shown us that the initial exploration and exploration beyond the known range requires distortion or mutation of the information. A noisy search is conducted in nature with chemical and functional information. This noise makes biological information less specific and thus ensures the necessary variation. Noise sources comprise thermal molecular jiggling, radiation noise for chemical experimentation, or visual and audible noise (Mehta et al. 2012) input that sentient beings receive, or any other form of natural noise capable of influencing an evolutionary search.
Exploration and exploitation are two distinct search mechanisms in evolutionary optimisation algorithms. Now, do not let the word ‘algorithm’ sway you into thinking that these processes are somehow isolated to something done with computers. Quite the opposite. These mechanisms were first identified by evolutionary biologists soon after the genetic mechanisms of evolution were discovered. Even before these mechanisms morphed into digital computers in the 1990s, from the 1950s to 1980s economists increasingly realised that technological development resembles that of biological evolution and the terms “exploration and exploitation” were first proposed in the economics of innovation in technology (March 1991).
Let us return to the 1950s. Geneticists realised that information contained in genes was now and then modified by mutations which caused the appearance of new modifications of life. Usually, a new variation of simple life has a low chance of survival and the chance that this new entity has a fitness improvement over the previously known inventions of nature is very small indeed. Mutations generate changes that can reach any combination from the combinatorial space. This makes the probability that a mutation search point ends up near an existing solution remote. Therefore, a mutation-based exploratory search is also referred to as a far search. This far search does not need any previous knowledge. Exploration thus boldly goes into the unknown. If the new solution has at least sufficient fitness, its information will remain preserved. If not, it will be deleted.
It took until the 1993 work of Hilario and Gogarten (Hilario and Gogarten 1993) before the horizontal gene transfer of complex life became understood. Genetic information crossover in sexual reproduction was of course known beforehand, but was poorly understood. Its function in protecting the genetic material was quickly recognised; its function in the execution of an exploitation evolutionary search became clearer after Hilario and Gogarten’s work was combined with the experiences of the in-silico computational experimentation with evolutionary search algorithms that became widespread in the 1990s. Nature invented numerous ways to exchange these complete segments of prior knowledge. The best known is sexual reproduction, where the genetic material of relatives is transferred. In this way, the offspring would have a high chance of survival and changes to the phenotype would be small, incremental, and seemingly continuous. This is an exploitation near search. Exploitation is capable of a limited far search as horizontal gene transfer can be achieved in nonsexual ways that overcome the safeguards of sexuality, but these events are rare. Nevertheless, even this exploitation far search is limited to the knowledge range previously identified by exploration. It cannot extend beyond it.
Why is an exploitation search so much more advantageous than an exploratory far search?
A mutation search might yield an improved solution in one in a million, for example. An exploitation search through information crossover might yield an improvement in just one in ten trials (Doerr et al. 2008). And nearly all subjects survive. The advantage increase might not be as radical as would be achieved in a far search, but to beat competitors it is sufficient if you are 5% more fit. There is no need to be 300% more fit to win the local struggle for evolutionary fitness.
And indeed, genetic mutation rates in biological evolution first decreased when evolution moved from RNA to DNA and later again when sexual crossover was introduced (Drake et al. 1998). Changes to mutation rates were several orders of magnitude per transition. This inevitably means that exploratory far searches were pushed aside and replaced by more productive near searches (Szabó et al. 2002), which gave the fundamentally discontinuous biological evolution an appearance of gradual change.
What about cognition, technology, and AI?
All of them have been identified as employing exploration and exploitation principles when dealing with information and optimisation/adaptation. Human inventive thinking (Campbell 1960) had been associated with an evolution-like process even before C. Darwin published his famous book on evolution. As early as 1855, A. Bain (Bain 1855) discussed trial-and-error thinking as part of the human intellect. Cognition in humans obeys the principles described above. When a child is born, it possesses little prior knowledge. A newborn child starts a noisy search. He or she makes random-like experimental movements, which the child evaluates and thus slowly builds knowledge in his or her neural network. Later in life, we also store highly abstract knowledge that originates in experimentation. Prior knowledge is again used thorough exploitation. More on the cybernetics of cognitive thought experimentation can be found in (Donoso et al. 2014).
I have saved examples regarding AI for last
Such use is usually referred to as evolutionary computation, or even better, evolutionary algorithms (EA) (Črepinšek et al. 2013). New inventions in technology can be identified in this way (Koza et al. 1999). In general, EA is used in various applications where a previously unknown solution to a problem must be located within a predefined exploration space. Nowadays, the use of EA is moving to various network optimisation jobs and most notably to the intelligence behind self-driving cars, where they must mimic the driver’s improvisation and decision-making capacity.
Increased complexity through the information addition and the emergence of functionality
The increasing complexity of competing elements increases experimental complexity (Gutowski 2005). Competitors must add similar properties, thereby also increasing complexity. Once a high level of complexity is attained, complexity adaptation must be considered; a decrease in complexity may become advantageous. For example, jet engine complexity decreased over the years to improve efficiency and reliability (Koff 2004). Polymerisation is a mechanism of chemical information addition (Andrieux and Gaspard 2008) and experimental complexity build-up (Joyce 2002; Walker et al. 2012) in chemical evolution. In the case of evolutionary algorithms in the field of AI, numerical genome elongations serve as a mechanism for increased member complexity (Decraene et al. 2011).
The authors of in silico noisy search experiments (Walker et al. 2012) have demonstrated the emergence of functionality through such information addition. In general, functionality emerges as a consequence of a noisy search where unfit (those with short decomposition times) polymerisation attempts perish and selected configurations with functions that aid survivability, such as resistance to UV radiation or resistance to thermal or hydrolytic decomposition, remain. Such informational polymers are expected to have configurations that are non-random and can thus be associated with a configurational component that is recognised as biological functional information, as explained by (Yockey 2005) in Chapter 6.
These information addition processes take advantage of horizontal information exchange, which when genes become available is considered to be horizontal gene transfer (Hilario and Gogarten 1993; Woese 2002; Arnoldt et al. 2015). There is the limitation that added and exchanged information, likely resulting from the pool of prior exploration or pre-assembled composite information, must exist beforehand.
In the case of human inventions, ‘outside-the-box thinking’ is associated with the addition of a noisy search component. Adding a pre-existing component from a remote technical field still involves an inventive step (Moir 2012), whereas adding a known component from the same field of arts is not considered inventive but, rather, a form of non-inventive horizontal information exchange in technology.
The Miller-Urey experiment revisited
The Miller-Urey (Miller and Urey 1959) (MU) experiment showed that amino acids can form from certain plausible early Earth prebiotic components and a sparking exergy source. However, the greater importance of the MU experiment might lie elsewhere. The MU experiment generated a small-scale natural experimentation model involving both exploration search and chemical information addition through polymerisation. Chemical information addition generated high-molecular-weight tholins at the bottom of the experimental flask. These tholins represent the point of experimental exhaustion. Prolonged experimentation did not influence the outcome, as heavy molecules excluded themselves from further experiments by accumulating away from the exergy source. In this case, reduced diffusion of chemical information through over-polymerisation exhausted the experimentation process. Unrestricted polymerisation is a problem requiring further consideration, as overcoming this obstacle on early Earth might have been a decisive pathway for further chemical evolution into individual entities rather than a giant polymerised rubbery blob, which could have formed instead. For evolution to produce several cooperating or competing agents, there must be a naturally present condition that limits polymer overgrowth (Walker et al. 2012). The MU experiment showed another profound property of nature: nature can conduct inventive experimentation, a form of natural selection, without heredity and/or self-replication. Experiments are conducted in parallel with the direct production of experimental chemicals without the need to replicate these materials from a template. In the MU experiment, a sparking source induced gaseous components to excite, ionise, and subsequently decay. Excited and ionised components primarily relaxed and recombined, but while in an excited state, some components bond to other neighbouring molecules. The end-results only showed chemical products with decay times long enough to survive until a human observer evaluated them.
An experiment to test for the presence of bioinformation (i.e., functional information) in tholins can be devised. An MU-like experiment could be devised to prevent the deposition of the resulting tholins, and by varying the molecular selection pressures (hydrolysis, thermolysis, photolysis) the structural correlations of the resulting tholins to applied pressures should become apparent.
The generalised invention process
Consider an invention process that comprises an external exergy source and an experimental space with configurable particles, which can receive work to reconfigure their free energy state. The invention process in the experimental space consists of the experimental set-up and the experimentation. The experimentation involves the exposure of information to the selection process. This selection process represents natural selection, where entropy-generating processes selectively erase stored information.
Primary process – the primary exergy efficiency of chemical information generation
The primary process is the equivalent of an initial information injection into an optimisation machine. It is a ‘far from equilibrium’ process, where an exergy source fills metastable chemical states. Examples of such processes are the photochemical formation of organics, biomass formation, and the injection of initial random numbers into an evolutionary computation machine. Primary exergy efficiency is introduced. Primary exergy efficiency is a ratio between the exergy stored in metastable chemical/physical structures and the exergy delivered into a system.
The closed thermodynamic system experiences B′ ≤ 0, reflecting the 2nd law of thermodynamics. However, an open system might experience periods of B′ > 0 through exergy inflow and corresponding macroscopic exergy efficiency ε = <e>, where <> denotes the average over the ensemble.
There is an experimental space with the innovation process running when invention process and experimental information diffusion occur. Experimental information diffusion is the ability of a system to exchange the surviving information potential B among experiments.
The functional information innovation process
Functional innovation in natural Darwinian experimentation and technological developments through human invention operates on the ‘paper surface’ generated by the underlying chemical information. Functional innovation is thus limited by the world’s chemical information generation. On the large scale, the functional development of a world can be measured through the generation of chemical information and its exergy efficiency. The efficiency indicates the quality or developmental stage of the functional information. The direct quantitative description of worldwide functional information-generating innovation is impossible, reflecting the complexity and chaotic nature of experimentation, but efficiency measurements might be an elegant method for obtaining insight into the developmental stage of natural experimentation.
Subspaces and exhaustion
The abstract division of an experimental space into sub-spaces with different efficiencies and productivities is a matter of convenience. Civilisation might have the static experimental fields (sub-spaces) of metallurgy, agronomy, mining, robotics, and others. Experimental (sub-)spaces in nature might also be dynamic, where the experimental environment changes over time. All experimental spaces and sub-spaces are finite with finite maximum configurable or discoverable (when previously configured) information. Thus, experimentation can reach an exhaustive point at which the results diminish to small refinements. If the experimental space is at least quasi-static, the experimental process would eventually uncover the entire experimental space and store information concerning accessible yet unrefined inventions. Exhaustion can be viewed simply as a convergence of the optimisation algorithm. The closer to the optimum the solution is, the slower the convergence of the evolutionary search will be.
Biological innovation and exobiology
There has been much success since 1800 in the identification of the physical pathways that played a role during the evolution of the Earth (Yockey 2005). The experiments of (Miller and Urey 1959), for example, demonstrated how short polymerised organics could form from a sparking exergy source. Other exergy sources include geothermal activity (Miller and Urey 1959), various radiations (Miller and Urey 1959; Chyba and Sagan 1992), hypersonic entry shocks (Chyba and Sagan 1992), or any other forms of exergy that do not originate from previously stored exergy. Chemically stored exergy should be viewed as stored information from prior experimentation. The exergy flow from photon radiation is typically assumed to be the dominant source of chemical productivity (Chyba and Sagan 1992). Other exergy contributions such as geothermal, atmospheric electric discharge (Miller and Urey 1959; Chyba and Sagan 1992), and background ionising radiation are typically small (Miller and Urey 1959; Chyba and Sagan 1992). In addition, one cannot neglect the potential information exchange between celestial structures within a cosmic system through various impact ejections (Gladman et al. 2005) and dust particles (Chyba and Sagan 1992). Dynamic changes to the experimental space reflecting intermittent cosmic events, stellar radiation variations (Chyba and Sagan 1992), and the influence of life itself might result in the loss of information. Any small amount of preserved information finds the changed environment as a beneficial new experimental space that adds new potential solutions to the preserved solutions.
Natural experimentation sub-spaces on other worlds and the Earth’s silicate melt sub-space
The atmosphere of Saturn’s moon Titan, for example, has two distinct, experimental spaces separated by two different UV bands (Tamburelli et al. 2014), where intense natural experimentation can be expected. In contrast, celestial bodies with liquid water under an ice crust, e.g. Europa and Enceladus, are places where only moderate experimentation is expected to have occurred, reflecting the low geothermal exergy availability (Chyba and Sagan 1992). If mere geothermal disequilibrium under a solid crust were sufficient, then we would have observed signs of evolved natural experimentation beneath the Earth’s crust. There, observed from the standpoint of chemical evolution, polymerised glassy volcanic minerals, such as obsidian, are the most evolved metastable structures (Zotov 2003). Silicate melts convecting under the Earth’s crust at varying pressures receive geothermal exergy to decrease configurational entropy through polymerisation (Lee et al. 2003; Lee et al. 2004). At elevated temperatures, high kinetic energy enables the exploration of states of higher potential energy. At sufficiently high pressure, and even at somewhat lower temperatures, the shape of potential wells changes in a way that gives rise to new configurations (Mysen and Richet 2005), derived from horizontal chemical information exchange in silicate melts where large oxide structures diffuse and exchange their locations within the polymerised silicate matrix (Wang et al. 2014). The region of polymerising silicate melts can be viewed as Earth’s separate genesis in the prebiotic, chemical evolution stage.
Saturn’s moon Titan does have a patchy liquid surface and a sufficient solar exergy flux (McKay 2014). It has two atmospheric, liquid, and possibly other networked experimental spaces. The identification of any build-up of substantial accumulated experimental information in our solar system (other than on the Earth) is likely going to occur on Titan. C. McKay and H. Smith (McKay and Smith 2005) proposed how metabolic processes on Titan could be detected through atmospheric composition. Subsequently, Cassini/Huygens provided evidence, but not conclusive evidence, of the H2 vanishing towards the ground (Strobel 2010; Niemann et al. 2010). Nevertheless, these findings are as close as we have come to understanding Earth-independent genesis of life.
Microscopic productivity limits
The chemical information experimental set-up is a micro-process where the laws of statistical mechanics come into play. The actual conversion processes require time to complete. (Jarzynski 1997) and (Crooks 1999) explained that only infinitely slow conversion can be thermodynamically reversible. The economy of life, however, dictates that these processes should be fast in order to prevent entropy-increasing processes from damaging the stored information. Conversion becomes less efficient with the increasing speed of the process. Thus, productivity involves fundamental inefficiency. The higher the productivity, the more exergy is required for the same amount of information modification and storage, hence decreasing the efficiency. Higher productivity increases exergy consumption (England 2013). This limitation concerns biological entities, electronic computation, and manufacturing processes. A person who runs will notice that doubling one’s speed does not simply double the ‘effort’. The same happens whenever doubling the speed of an industrial robot or overclocking a microprocessor to double frequency. Increasing productivity without fundamentally improving the apparatus disproportionately increases exergy consumption. This property might seem worrisome as economic growth most often occurs through productivity gains, depending on increasingly more difficult fundamental improvements to the apparatus through innovation. (England 2013) indirectly showed that invention prefers the replication of an apparatus with a shorter lifespan produced at a higher rate, consistent with observations in technological development (Prakash et al. 2016).
The invention of metabolism and fossil exergy efficiency
where B′ is the information accumulation rate and B’ con is the rate of consumption of pre-existing chemical information, such as biomass by collective organisms and fossil fuel by a society. B’ eras is the information erase rate, which in biological systems represents a loss of information, reflecting corrosive and imperfectly efficient metabolic processes. Eqs. 4 and 5 are now general qualitative equations of natural experimentation, representing biomass generation analogous to the world’s gross domestic product, and the two efficiencies, ε F and ε, describe the rough quality of the information. Note that eqs. 4 and 5 do not separate chemical and functional information.
Metabolic life burns stored information for purposes beyond invention and replication. A new ‘fossil’ efficiency, ε F , for burning or metabolising stored information B has been introduced. Biological metabolism and the use of fossil fuels are evolutionarily equivalent innovations. They are both opportunistic uses of ‘free’ exergy ‘lying around’ and previously stored in the primary process.
Earth exergy efficiencies
The economic development of modern civilisation experienced step-wise discoveries of new experimental fields. These discoveries increased gross domestic product (GDP) through the broad use of direct exergy E’, available fossil exergy B, and increases in efficiencies. Currently, the Earth’s biosphere successfully converts incident solar exergy radiation of 119,600 TW to 2.9 TW (Chen 2005) of stored chemical exergy εE’, with an approximate primary efficiency ε of 0.00003. Economic growth requires a constant increase in the information generation rate B’ gen , which we could consider to be a proxy of GDP. (Ayres et al. 2003) showed that much of this growth reflects improvements in fossil exergy efficiency ε F and the subsequent rebound (the Khazzoom–Brookes postulate) effect to broaden fossil exergy use. These data (Laitner 2013) and the data from previous studies (Ayres et al. 2003) demonstrated that U.S. fossil exergy efficiency increased almost linearly from 0.025 in 1900 to 0.139 in 2010 (Ayres and Warr did not strictly consider exergy efficiency as in Eq. 4). The two efficiencies are for the time the only measures of the quality of the accumulated functional information.
Fossil exergy efficiency, ε F , represents the efficiency of the utilisation of previously stored chemical exergy. This metabolic-like process exists on the shoulders of the primary process, which stores this chemical exergy with primary efficiency ε. This relation holds for the whole biosphere encompassing both the living and the technological. In the context of this essay, any consumption of exergy is considered experimentation. Biological and industrial replications create experiments where information is either gained, lost, preserved, or combined into a new experiment where more exergy is consumed.
As can be expected, technological evolution relies on far and near searches. Until 1900, searches were predominantly exploratory far searches, as discovery-level inventions were accumulated. Examples of discovery-level inventions include fire, electric power, wire and wireless messaging, fossil fuel power, pharmaceuticals, and many others. By approximately the year 1900, enough of these far-search points had been accumulated that it became clear that the refinement thereof through systematic research could be more advantageous. Surely it was evident that improvements to the automobile made more sense than exploring for an entirely alternate mode of transportation. Improvement of existing ideas seems to be the way to go. This is how exploitation slowly replaced exploration in technology.
We, humans, evaluate technological inventions through our own evolutionary fitness. An invention whose utilisation cannot enhance the user’s fitness will be rejected and deleted from use. This is how biological natural selection finds its way into technology.
“Endogenous growth” is an idea in economics that claims that the best growth results can be achieved through investment into targeted R&D. The downside of this paradigm is that it calls for a reduction in far-search exploration, which happened, as evidenced in Youn’s analysis of patents (Youn et al. 2015). A far search is deemed too risky and uneconomical. No one seems to have noticed that such overuse of exploitation necessarily results in ever smaller improvements in consecutive inventions.
Innovation exhaustion and economic slowdown
(Gordon 2012) and (Buchanan 2015) documented general innovation contraction in the U.S. Specifically, for silicon chip technology (Esmaeilzadehy et al. 2011; Austin 2015) it is still technologically possible to further decrease microprocessor resolution, nevertheless, the efficiency boundaries in multiprocessor die arrangements have been reached and the cost jumps resulting from the introduction of even smaller chip resolutions yield a positive return with ever more difficulty (Austin 2015). Even if the number of transistors on a chip is doubled, such efforts will no longer result in equivalent performance gains (Austin 2015). A similar situation can be observed with conventional drugs. Previous studies (Scannell et al. 2012) reported an increase in efforts towards pharmaceutical discovery in accordance with ‘Eroom’s law’ (Moore’s law in reverse). These observations were correct, but the analysis seems to be wrong. However, these authors did notice a ‘low-hanging fruit’ problem, suggesting that easy things have already been discovered. Nevertheless, the authors ascribe the innovation slowdown to other factors. This and other slowdowns in nearly all engineering fields reflect ‘the low-hanging fruit’ problem or, more precisely, the problem of the exhaustion of the experimental space. The most obvious example is the near exhaustion of thermodynamic-conversion efficiency improvements in combustion engines in the late 1960s (Ayres et al. 2003).
How modern communication technology contributes to the slowdown of economic growth
Economic growth in GDP per capita can only be due to increased energy intensity (capital investment in more of the same) or through genuine innovative efficiency improvements in technology (efficiency improvement to the apparatus). Since the year 2000, there has been token productivity gain in the OECD member states (Baldi and Harms 2015).
There can be no hope for AI-driven technological singularity. Any further enhancements in combinatorial capacity, which is what AI does, would merely accelerate convergence and exhaustion.
Naturally occurring metastable chemical and physical states can serve as testable information carriers. In the presence of a substantial thermodynamic disequilibrium, information is constantly generated and tested, representing a new view of chemical reaction models where reactants are observed in terms of survivability. Pre-biotic chemical evolution does not require replication with explicit heredity but, rather only needs information addition, diffusion, and an exergy source. A form of informational ‘heredity’ in terms of an exploitation search is required, however.
The information potential, B, is introduced to aid in the understandability of chemical evolution informational processes. Because any functional information is superimposed over chemical information, it represents the maximum functional information in a world. The physics-based necessary condition for evolution can be defined as the requirement for an experimental space with the innovation process running. However, this is not a sufficient condition in itself. The sufficient condition is only fulfilled when innovation-generated information exceeds the amount of information lost to deleterious processes.
Evolutionary optimisations (i.e., inventive processes) have three recognisable phases. Initially, this is a random, noisy exploratory search. Once exploration yields sufficient stored information (i.e., knowledge) the evolutionary process transits to an exploitation search, which can be recognised by a lower number of information ‘mutations’ and increased combinatorial inventions. An exploitation search greatly speeds up convergence towards optimal configurations. Improved communication between agents further speeds up convergence towards the inevitable exhaustion of the experimental space.
For economic development and technology, the statistical mechanics-based productivity limits suggest that the productivity of any process cannot be increased without a decrease in energy efficiency if no change is made to the apparatus. Changes to the apparatus in technology are becoming incrementally less significant due to exhaustion.
Human inventors also evaluate their own fitness gains through their inventions. If the societal (taxes) or organisational situation in a corporation, for example, is such that others would benefit from a tentative innovation more than the inventor, then he or she will strive to move elsewhere or to withhold worthy ideas.
Current innovative management strategies are oriented towards systematic approaches based on perceived needs, which frequently result in pursuing matters of already exhausted experimental spaces with ever-diminishing returns. The exhaustion of broader experimental fields can only be overcome through the identification of new broad fields via basic sciences being pushed into the risky unknown through exploration searches.
Exploration searches can be clearly distinguished from exploitation by not having definable, measurable goals. The measurability of the expected results should be taken as a strong indication that the prospective activity is exploitative rather than explorative.
Although our options as regards the metabolic uses of fossil fuels seem exhausted, Earth’s primary exergy efficiency is merely 0.00003, which must be taken as indicating the direction of future exploration.
The author would like to thank Miroslav Halilovič, Boris Štok, and Yury Shimansky for their valuable comments, and especially thank Bob Skiles for his endorsement and detailed review of this work.
AK is among world’s leading industrial researchers and inventors. Through his career in technology, he contributed numerous technologies with worldwide impact. As with all inventors, his interests span beyond engineering and technology. He has a background in evolutionary computation, polymers, photochemistry, physics, thermodynamics, information theories, evolutionary biology and economics.
The authors declare that they have no Competing interest.
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- Abel DL, Trevors JT (2006) Self-organization vs. self-ordering events in life-origin models. Physics of Life Reviews. doi:10.1016/j.plrev.2006.07.00 Google Scholar
- An S et al (2014) Experimental test of the quantum Jarzynski equality with a trapped-ion system. Nat Phys 11:193–199View ArticleGoogle Scholar
- Andrieux D, Gaspard P (2008) Nonequilibrium generation of information in copolymerization processes. Proc Natl Acad Sci U S A 105:9516–9521View ArticleGoogle Scholar
- Arnoldt H, Strogatz SH, Timme M (2015) Toward the Darwinian transition: switching between distributed and speciated states in a simple model of early life. Phys Rev E 92(052909):1–9Google Scholar
- Austin T (2015) Bridging The Moore’s Law Performance Gap with Innovation Scaling. Keynote talk. In: Proceedings of the 6th ACM/SPEC International Conference on Performance Engineering, pp 1–1Google Scholar
- Ayres RU, Ayres LW, Warr B (2003) Exergy, power and work in the US economy, 1900–1998. Energy 28:219–273View ArticleGoogle Scholar
- Bain A (1855) The Senses and the Intellect. Parker, Son, LondonView ArticleGoogle Scholar
- Balazsi G, Van Oudenaarden A, Collins JJ (2011) Cellular decision making and biological noise: from microbes to mammals. Cell 144:910–925View ArticleGoogle Scholar
- Baldi G, Harms P (2015) Productivity Growth, Investment, and Secular Stagnation (No. 83). DIW Berlin, German Institute for Economic Research. http://www.diw.de/en/diw_01.c.519231.en/press/diw_roundup/productivity_growth_investment_and_secular_stagnation.html Accessed 5 June 2017
- Bedau MA et al (2000) Open problems in artificial life. Artif Life 6:363–376View ArticleGoogle Scholar
- Bennett CH (1982) The thermodynamics of computation—a review. Int J Theor Phys 21:905–940View ArticleGoogle Scholar
- Bérut A, Arakelyan A, Petrosyan A, Ciliberto S, Dillenschneider R, Lutz E (2012) Experimental verification of Landauer’s principle linking information and thermodynamics. Nature 483:187–189View ArticleGoogle Scholar
- Blattner FR et al (1997) The complete genome sequence of Escherichia coli K-12. Science 277:1453–1462View ArticleGoogle Scholar
- Brillouin L (1953) The negentropy principle of information. J Appl Phys 24:1152–1163View ArticleGoogle Scholar
- Buchanan M (2015) Innovation slowdown. Nat Phys 11:2View ArticleGoogle Scholar
- Campbell DT (1960) Blind variation and selective retention in creative thought as in other knowledge processes. Psychol Rev 67:380–400View ArticleGoogle Scholar
- Chen GQ (2005) Exergy consumption of the Earth. Ecol Model 184:363–380View ArticleGoogle Scholar
- Chyba CF, Sagan C (1992) Endogenous production, exogenous delivery and impact-shock synthesis of organic molecules: an inventory for the origins of life. Nature 355:125–132View ArticleGoogle Scholar
- Črepinšek M, Liu S-H, Mernik M (2013) Exploration and exploitation in evolutionary algorithms: a survey. ACM Comput Surv 45:268–308Google Scholar
- Crooks GE (1999) Entropy production fluctuation theorem and the nonequilibrium work relation for free energy differences. Phys Rev E 60:2721View ArticleGoogle Scholar
- Dawkins R (1983) Universal Darwinism. In: Bendall DS (ed) Evolution from molecules to man. Cambridge University Press, New York, pp 403–425Google Scholar
- Decraene J, Chandramohan M, Zeng F, Low MYH, Cai W (2011) Evolving Agent-Based Model Structures Using Variable-Length Genomes. In: Proceedings of the Fourth International workshop on Optimisation in Multi-Agent Systems, pp 68–85Google Scholar
- Doerr B, Happ E, Klein C (2008) Crossover Can Provably Be Useful in Evolutionary Computation. In: GECCO ‘08 Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, pp 539–546View ArticleGoogle Scholar
- Donoso et al (2014) Foundations of human reasoning in the prefrontal cortex. Science 344:1481–1486. doi:10.1126/science.1252254 View ArticleGoogle Scholar
- Drake JW, Charlesworth B, Charlesworth D, Crow JF (1998) Rates of spontaneous mutation. Genetics 148:1667–1686Google Scholar
- Ellis AD, Suibhne NM, Saad D, Payne DN (2016) Communication networks beyond the capacity crunch. Phil Trans R Soc A 374:20150191. doi:10.1098/rsta.2015.0191 View ArticleGoogle Scholar
- England JL (2013) Statistical physics of self-replication. J Chem Phys 139:121923View ArticleGoogle Scholar
- Esmaeilzadehy H, Blem E, St. Amant R, Sankaralingam K, Burger D (2011) Dark Silicon and The End of Multicore Scaling. In: Proceedings of the 38th annual international symposium on computer architecture, ISCA'11, pp 365–376Google Scholar
- Forrest S (1993) Genetic algorithms: principles of natural selection applied to computation. Science 261:872–878View ArticleGoogle Scholar
- Gladman B, Dones L, Levinson HF, Burns JA (2005) Impact seeding and reseeding in the inner solar system. Astrobiology 5:483–496View ArticleGoogle Scholar
- Godfrey-Smith P, Sterelny K (2016) Biological Information: The Stanford Encyclopedia of Philosophy (Summer 2016 Edition), Edward N. Zalta (ed.), https://plato.stanford.edu/archives/sum2016/entries/information-biological/ Accessed 31 May 2017
- Gordon RJ (2012) Is U.S. Economic Growth Over? Faltering Innovation Confronts the Six Headwinds. In: NBER working paper series, Working Paper 18315Google Scholar
- Griffiths PE, Gray RD (1994) Developmental systems and evolutionary explanation. J Phil 91:277–304View ArticleGoogle Scholar
- Gutowski MW (2005) Biology, Physics, Small Worlds and Genetic Algorithms. Leading Edge Computer Science Research, Nova Science Publishers, Inc. p 165–218Google Scholar
- Hilario E, Gogarten JP (1993) Horizontal transfer of ATPase genes--the tree of life becomes a net of life. Bio Systems 31:111–119View ArticleGoogle Scholar
- Jablonka E (2002) Information: its interpretation, its inheritance, and its sharing. Philos Sci 69:578–605View ArticleGoogle Scholar
- Jarzynski C (1997) Nonequilibrium equality for free energy differences. Phys Rev Letters 78:2690–2693View ArticleGoogle Scholar
- Jarzynski C (2011) Equalities and inequalities: Irreversibility and the second law of thermodynamics at the nanoscale. Annu Rev Condens Matter Phys 2:329–335View ArticleGoogle Scholar
- Joyce GF (2002) Molecular evolution: Booting up life. Nature 420(6913):278–279View ArticleGoogle Scholar
- Kell DB, Lurie-Luke E (2014) The virtue of innovation: innovation through the lenses of biological evolution. J R Soc Interface 12:20141183View ArticleGoogle Scholar
- Koff BL (2004) Gas Turbine Technology Evolution: A Designers Perspective. J Propuls Power 20:577–595View ArticleGoogle Scholar
- Koza JR, Bennett FH III, Stiffelman O (1999) Genetic Programming as a Darwinian Invention Machine. In: Proceedings of the Second European Workshop on Genetic Programming. Springer-Verlag, London, pp 93–108Google Scholar
- Kremer M (1993) Population Growth and Technological Change: One Million B.C. to 1990. Q J Econ 108:681–716. doi:10.2307/2118405 View ArticleGoogle Scholar
- Laitner JA (2013) Linking Energy Efficiency to Economic Productivity: Recommendations for Improving the Robustness of the U.S. Economy. In: Report Nr. E13F, American Council for Energy-Efficient EconomyGoogle Scholar
- Landauer R (1961) Irreversibility and heat generation in the computing process. IBM J Res Dev 5:183–191View ArticleGoogle Scholar
- Landauer R (1996) The physical nature of information. Phys Lett A 217:188–193View ArticleGoogle Scholar
- Lazcano A, Miller SL (1996) The origin and early evolution of life: prebiotic chemistry, the pre-RNA world, and time. Cell 85:793–798View ArticleGoogle Scholar
- Lee SK, Fei Y, Cody GD, Mysen BO (2003) Order and disorder in sodium silicate glasses and melts at 10 GPa. Geophys Res Lett 30:1845Google Scholar
- Lee SK, Cody GD, Fei Y, Mysen BO (2004) Nature of polymerization and properties of silicate melts and glasses at high pressure. Geochim Cosmochim Acta 68:4189–4200View ArticleGoogle Scholar
- Longo G, Miquel P-A, Sonnenschein C, Soto AM (2012) Is information a proper observable for biological organization? Prog Biophys Mol Biol 109:108–114View ArticleGoogle Scholar
- March JG (1991) Exploration and Exploitation in Organizational Learning. Organ Sci 2:71–87View ArticleGoogle Scholar
- McKay CP (2014) Requirements and limits for life in the context of exoplanets. Proc Natl Acad Sci U S A 111:12628–12633View ArticleGoogle Scholar
- McKay CP, Smith HD (2005) Possibilities for methanogenic life in liquid methane on the surface of Titan. Icarus 178:274–276View ArticleGoogle Scholar
- Mehta R, Zhu R, Cheema A (2012) Is noise always bad? Exploring the effects of ambient noise on creative cognition. J Cons Res 39:784–799View ArticleGoogle Scholar
- Miller SL, Urey HC (1959) Organic compound synthesis on the primitive Earth Science 130:245–251Google Scholar
- Moir HVJ (2012) Empirical evidence on the inventive step. Eur Intellect Prop Rev 35:246–252Google Scholar
- Morowitz HJ (1955) Some order-disorder considerations in living systems. Bull Math Biophys 17:81–86View ArticleGoogle Scholar
- Mysen BO, Richet P (2005) Silicate Glasses and Melts: Properties and Structure. Elsevier, AmsterdamGoogle Scholar
- Nelson RR, Winter SG (1982) An Evolutionary Theory of Economic Change. Harvard University Press, CambridgeGoogle Scholar
- Niemann HB et al (2010) Composition of Titan's lower atmosphere and simple surface volatiles as measured by the Cassini-Huygens probe gas chromatograph mass spectrometer experiment. J Geophys Res 115:1–22View ArticleGoogle Scholar
- OECD Triadic patent families 1984–2014 (2017). https://data.oecd.org/rd/triadic-patent-families.htm Accessed 2 June 2017
- Prakash S, Dehoust G, Gsell M, Schleicher T, Stamminger R (2016) Einfluss der Nutzungsdauer von Produkten auf ihre Umweltwirkung: Schaffung einer Informationsgrundlage und Entwicklung von Strategien gegen Obsoleszenz. Dessau-Roßlau, UmweltbundesamtGoogle Scholar
- Ranjan S, Sasselov DD (2016) Influence of the UV environment on the synthesis of prebiotic molecules. Astrobiology 16:68–88View ArticleGoogle Scholar
- Rant Z (1956) Exergie, Ein neues Wort für “technische Arbeitsfähigkeit”. Forsch Geb Ingenieurwes 22:36–37Google Scholar
- Ruelle D (2017) The origin of life seen from the point of view of non-equilibrium statistical mechanics. arXiv preprint arXiv:1701.08388Google Scholar
- Samal JR, Pati AK, Kumar A (2011) Experimental test of the quantum no-hiding theorem. Phys Rev Lett 106:080401View ArticleGoogle Scholar
- Scannell J, Blanckley A, Boldon H, Warrington B (2012) Diagnosing the decline in pharmaceutical R&D efficiency. Nat Rev Drug Discov 11:191–200View ArticleGoogle Scholar
- Shannon CE (1948) A mathematical theory of communication. Bell Syst Tech J 27(379–423):623–656View ArticleGoogle Scholar
- Stoker CR et al (1990) Microbial metabolism of tholin. Icarus 85:241–256View ArticleGoogle Scholar
- Strobel DF (2010) Molecular hydrogen in Titan’s atmosphere: implications of the measured tropospheric and thermospheric mole fractions. Icarus 208:878–888View ArticleGoogle Scholar
- Strumsky D, Lobo J (2015) Identifying the sources of technological novelty in the process of invention. Res Policy 44:1445–1461View ArticleGoogle Scholar
- Szabó P, Scheuring I, Czárán T, Szathmáry E (2002) In silico simulations reveal that replicators with limited dispersal evolve towards higher efficiency and fidelity. Nature 420(6913):340–343View ArticleGoogle Scholar
- Szilárd L (1929) On the decrease of entropy in a thermodynamic system by the intervention of intelligent beings. Z Phys 53:840–856View ArticleGoogle Scholar
- Tamburelli IC, Gudipati MS, Lignell A, Jacovi R, Piétri N (2014) Spectroscopic studies of non-volatile residue formed by photochemistry of solid C4N2: a model of condensed aerosol formation on Titan. Icarus 234:81–90View ArticleGoogle Scholar
- Timeline of historic inventions (2017). https://en.wikipedia.org/wiki/Timeline_of_historic_inventions Accessed 2 June 2017
- Total number of patents in force worldwide tops 6 million (2008). Science|Business Publishing Ltd. 2011 http://sciencebusiness.net/news/70341/Total-number-of-patents-in-force-worldwide-tops-6-million Accessed 2 June 2017
- Toyabe S, Sagawa T, Ueda M, Muneyuki E, Sano M (2010) Experimental demonstration of information-to-energy conversion and validation of the generalized Jarzynski equality. Nat Phys 6:988–992View ArticleGoogle Scholar
- Traulsena A, Hauert C, De Silva H, Nowak MA, Sigmund K (2009) Exploration dynamics in evolutionary games. Proc Natl Acad Sci U S A 106:709–712View ArticleGoogle Scholar
- Tyagi S (2010) E. coli, what a noisy bug. Science 329:518–519View ArticleGoogle Scholar
- Walker SI, Grover MA, Hud NV (2012) Universal sequence replication, reversible polymerization and early functional biopolymers: a model for the initiation of prebiotic sequence evolution. PLoS One 7:1–12Google Scholar
- Wang Y et al (2014) Atomistic insight into viscosity and density of silicate melts under pressure. Nat Commun 5:3241Google Scholar
- Warr B, Ayres RU (2012) Useful work and information as drivers of economic growth. Ecol Econ 73:93–102View ArticleGoogle Scholar
- Woese CR (2002) On the evolution of cells. Proc Natl Acad Sci U S A 99:8742–8747View ArticleGoogle Scholar
- Wright L (1973) Functions. Phil Review 82:139–168View ArticleGoogle Scholar
- Yockey HP (2005) Information Theory, Evolution, and the Origin of Life. Cambridge university press, New YorkView ArticleGoogle Scholar
- Youn H, Strumsky D, Bettencourt LMA, Lobo J (2015) Invention as a combinatorial process: evidence from U.S. patents. J R Soc Interface 12:20150272Google Scholar
- Zeilinger A (2005) The message of the quantum. Nature 438:743View ArticleGoogle Scholar
- Zotov N (2003) Structure of natural volcanic glasses: diffraction versus spectroscopic perspective. J Non-Cryst Solids 323:1–6View ArticleGoogle Scholar