Abstract
Dawkins often talks about how energy flows governed by thermodynamic laws drive evolution. The Dawkinsian concept of complexity is closely associated with the concept of entropy. Entropy is not disorder; it is energy dispersal. The maximum entropy production principle provides a deeper thermodynamic explanation for the growth of cosmic complexity. It is a principle of path selection. Driven by thermodynamic forces, evolution does design work. Dawkins often states that evolution designs organisms. He naturalizes the organic design arguments. He wonders about the values being optimized by evolution. He argues that evolution is not utilitarian; it does not aim to maximize happiness. His discussions of the axiological aspects of evolution rely on Stoic and Platonic ideas. Evolution maximizes the virtues that emerge through competitive struggle. It maximizes the arete that appears in the agon. Arete is beautiful and good. The Dawkinsian picture of our universe closely resembles a modernized Stoicism. But the Stoic picture depends on a deeper Platonic picture. For the Platonists, the universe strives to maximize vision; it strives to maximize reflexivity. Maximizing reflexivity is a core idea that organizes many Dawkinsian ideas into a coherent whole.
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Notes
- 1.
ADC 84–5; AT 397.
- 2.
- 3.
- 4.
GSE 413–16.
- 5.
Boltzmann defined entropy as S = k log W, where W is the macro-multiplicity of the state S. His constant k can be set to 1. Macro-multiplicity (GSE 416).
- 6.
The complexity of some type is the logarithm of its arbitrary multiplicity divided by its stable multiplicity (Sect. 1.2 in Chapter 2 ). This is just the logarithm of its micro-multiplicity divided by its macro-multiplicity. But the logarithm of x divided by y is the logarithm of x minus the logarithm of y. So the complexity of any type is the logarithm of its micro-multiplicity minus the logarithm of its macro-multiplicity.
- 7.
Silk (2001: 113).
- 8.
- 9.
GSE 414–16.
- 10.
BW 94; GSE 412–6; ADC 84–5.
- 11.
Improbability pump (GSE 416). Blessed (UR 5).
- 12.
Murdoch (1992: chs. 18 and 19).
- 13.
Swenson (2006: 318).
- 14.
MEPP (Martyushev and Seleznev 2006).
- 15.
Swenson (2009: 334).
- 16.
Rescher states that “in the virtual competition for existence among alternatives it is the comparatively best that is bound to prevail” (2010: 33–34). The MEPP says the path that produces entropy the fastest will usually prevail.
- 17.
Steinhart (2018) surveys evidence for the MEPP.
- 18.
The MEPP has been confirmed in many examples of biochemical and biological self-organization. The MEPP accurately predicts the evolution of beta-lactamase enzymes (Dobovisek et al. 2011) and the evolution of the enzyme ATP synthase (Dewar et al. 2006). Proton pumps in photosynthesis operate produce entropy at rates very close to the maximum (Juretic and Zupanovic 2003). It correctly predicts bacterial metabolism (Unrean and Srienc 2012). Replicator systems evolve towards states in which entropy production is maximized (Martin and Horvath 2013). A model based on MEPP does well at predicting evolutionary trends (Skene 2015).
- 19.
GSE 236.
- 20.
- 21.
AT 699.
- 22.
Dewar (2006).
- 23.
Steinhart (2018).
- 24.
Schneider and Kay (1994).
- 25.
- 26.
BW 94; ADC 84–5; GSE 413–6; AT 397; SITS 337.
- 27.
BW ch. 3; CMI ch. 6; ADC ch. 2.2; AT 676.
- 28.
ADC 100–2, 210.
- 29.
- 30.
The slow-growth law states that “deep objects cannot be quickly produced from shallow ones by any deterministic process, nor with much probability by a probabilistic process, but can be produced slowly” (Bennett 1988: 1).
- 31.
Machta (2011: 037111–037116).
- 32.
One proxy for the logical depth of some thing is the time it takes to decompress a compressed description of it (Zenil and Delahaye 2010). Another proxy is the free energy rate density of Chaisson (2006). This is the amount of free energy passing through one gram of matter of the thing in one second.
- 33.
For the great chain, see Lovejoy (1936). For the Stoic great chain, see Cicero (On the Nature of the Gods, II.33–5).
- 34.
ADC 208; GSE 155–9.
- 35.
Anselm (Monologion, ch. 4).
- 36.
- 37.
Dennett (1995: 511–513).
- 38.
Values of organisms (CMI ch. 3; ADC ch. 5.4; AT 681–9). Adaptive fitness (ADC 208).
- 39.
Intrinsic value is complexity (Steinhart 2014: secs. 72–74).
- 40.
Carl Sagan (1980), Cosmos, Episode 1, “The Shores of the Cosmic Ocean.”
- 41.
Genes model past environments (UR ch. 10). Brains model current environments (UR ch. 11). Brains model the universe (UR 312).
- 42.
- 43.
Steinhart (2012).
- 44.
- 45.
Peirce (1965) presents an evolutionary cosmology (6.33). He says the universe began in a state of chaos (1.409, 6.214, 6.215, 6.33, 8.317). Through the self-negation of its nothingness, this chaos starts to self-organize (6.217–20). Through continued self-relation, self-reinforcing regularities emerge (1.409, 6.490, 8.317). Time and space emerge (1.411–16, 6.214, 8.318). Laws of nature emerge (1.412, 6.13, 7.513–15). The flow of cosmic energy can produce a branching tree of universes (1.412). The streams of cosmic activity converge to an infinite omega point (1.409, 6.33, 8.317). The process of cosmic evolution is driven by the imperative to maximize reflexivity.
- 46.
Cirkovic (2003).
- 47.
SITS 272.
- 48.
Creative volition (CMI 16–17). Imagination (CMI 19). Foresight (BW 5).
- 49.
Designoid versus design (CMI ch. 1). Illusion of design (CMI 7, 25). Cumulative finding (CMI 28). Knives (CMI 11). Design is not finding (CMI 28).
- 50.
Designoid organs (ADC 225–6; GD 24, 139, 143, 168, 188; GSE 21, 334, 371; AT 633). Like pots of pitcher plants (CMI 12) and Venus fly traps (CMI 14). Designoid non-human artifacts (CMI 6–18). Designoid ecosystems (ADC 225–6).
- 51.
BW 21; CMI 7, 25.
- 52.
- 53.
- 54.
- 55.
Johnson-Laird (2005).
- 56.
Temkin and Eldredge (2007).
- 57.
GSE 407.
- 58.
Darwinism in our brains (UR 8). Design is cumulative finding (AT 688).
- 59.
Evolution designs things (EP 59–71). Things designed by evolution include: ancestral bodies (SG 26, 261); alarm calls of birds (SG 170); feathered wings (EP 68); worker ants (EP 128); eyes (EP 261; ROE 78); bodies of fish (ROE 93). Electronic searches of his texts will reveal dozens of other examples.
- 60.
Evolution designs things (BCD 323). Brains run Darwinian design algorithms (IA 104; 2015: 15). Airplanes (AT 688).
- 61.
- 62.
Dennett (1995: 69; his italics).
- 63.
- 64.
Kelly (2010).
- 65.
BW 4–6; GD ch. 4; etc.
- 66.
GD 96–99.
- 67.
Rejecting step three (GD 145) leads to a spectacular error (GD 169).
- 68.
EP 449; SITS 120–1; etc.
- 69.
BW 316–7.
- 70.
- 71.
Dawkins rejects the Lovelockian Gaia (EP 357–61; CMI 268; UR 222–4; SITS 153). Perhaps a more scientific pagan might devise a better Gaia (EP 360; ADC 173).
- 72.
Nietzsche (The Will to Power, sec. 796).
- 73.
Divine Engineer (ROE 104–5) is not utilitarian (ROE 103–4, 131–2; GSE 390–5).
- 74.
Maximize genetic replication (ROE 131; GSE 392). Maximizing dramatic beauty (SG 78; ROE 119–20; UR 219–20). Thrill of the chase (GSE 384; GD 161). Neither cruel nor kind (ROE 95–6, 131; ADC 8–9). Deeper than its utility (UR 5–6, 21–4).
- 75.
Dawkins proximately endorses utilitarianism (e.g. ADC ch. 1.3; SITS 301–8). But his ultimate axiology assumes Stoic and Platonic values deeper than utility.
- 76.
UR xi, 151; TL 73–4; SSSF.
- 77.
Nietzsche, The Will to Power, sec. 1052.
- 78.
Maximize arete (ADC ch. 5.4; CMI ch. 3; ROE ch. 4). Mutation (CMI 80–5). Survival of the stable (SG 12). Pushing towards improvement (CMI 85; ADC ch. 5.4). Wolves with elite genes (CMI 86). Eyes improve (CMI 163). Natural selection improves organisms (BW 305, his italics; AT 681–9). Good at what they do (CMI 90).
- 79.
Arms races (EP ch. 4; BW ch. 7; ADC ch. 5.4; AT 683–9; etc.). Teeth and toxins (AT 685). Evolution of evolvability (ADC ch. 5.4; EE). Adaptive complexes (ADC 206).
- 80.
SG xiv; GD ch. 6; GSE 62; AT 458–62, etc.
- 81.
SG 201, 331; ADC 9–11; GD 246; SITS 39–40.
- 82.
SG ch. 12; GD ch. 6.
- 83.
Misunderstands entropy (ADC 84–5; AT 397). Objects to vital forces (ROE 18; ADC chs. 1.6, 3.3; SITS 4, 213).
- 84.
- 85.
Steinhart (2018).
- 86.
Kelly (2010: 63).
- 87.
Memes (SG ch. 11); designoid (CMI ch. 1); theorum (GSE ch. 1).
- 88.
Many entropic forces acting on molecules and molecular assemblies have strengths of a few kT per nanometer, thus producing forces in the piconewton range (Marenduzzo et al. 2006). So axiotropy acts with similar strengths. It changes the microstates of systems. But changes in microstates scale up to become changes in macrostates.
- 89.
Anscombe says that machinery “should or ought to be oiled, in that running without oil is bad for it, or it runs badly without oil” (1958: 6). Universes are machines that can run well or badly. They ought to have axiotropy, in that they run badly without it.
- 90.
Amor fati (ADC 12–3; GD 20, 403–5; AK 188). Serenity (GSE 401). Austere poetry (EP 258). Indifference is more beautiful (UR xi, 41–2, 118). Valuable spectacle (UR 2–6; GD 404–20). Vision makes life worth living (UR x, 313). Scientific truth is too beautiful (1995c; see UR 114–21). Aesthetic argument (FH 99).
- 91.
Plotinus (Enneads, 2.3.18, 3.2.15–18, 3.6.2).
- 92.
Nietzsche (The Birth of Tragedy, sec. 5).
- 93.
Astrology is ugly (1995c; UR 118). Crystals (ADC 46). Religions impoverish us (AT 700). True reverence (AT 700). Vision is a blessing (UR 5).
- 94.
Plato (Republic, 508b–520a).
- 95.
Plato (Theaetetus, 176a5–b2).
- 96.
Plotinus (Enneads, 3.8).
- 97.
UR 312.
- 98.
Just as organisms run entelechies, so universes run entelechies. By this analogy, much of Foot’s (2001: chs. 2 and 3) theory of natural goods and duties transfers to universes.
- 99.
If some thing x has a duty or imperative to F, then x ought to F; this is a deontic de re property. It is axiomatic that ought implies can. So the deontic de re implies the modal de re property that x possibly Fs. If x possibly Fs, then there exists some possible y such that y is a version of x and y does F. Since the deontic property entails the modal property, and the modal points beyond x, the deontic points beyond x.
- 100.
EP 384; GD 173–6, 185; GSE 426; MR 165; AT 2–4; SITS 272.
- 101.
GD 51, chs. 7–9.
- 102.
References
Anderson, J. (2002). The Airplane: A History of Its Technology. Reston, VA: American Institute of Aeronautics and Astronautics.
Anscombe, G. E. M. (1958). Modern moral philosophy. Philosophy, 33(124), 1–19.
Basalla, G. (1988). The Evolution of Technology. New York: Cambridge University Press.
Benitez, E. (1995). The good or the demiurge: Causation and the unity of good in Plato. Apeiron, 28, 113–139.
Bennett, C. (1988). Logical depth and physical complexity. In R. Herken, The Universal Turing Machine: A Half-Century Survey (pp. 227–257). New York: Oxford University Press.
Bennett, C. (1990). How to define complexity in physics, and why. In W. Zurek (Ed.), Complexity, Entropy, and the Physics of Information (pp. 137–148). Reading, MA: Addison-Wesley.
Brey, X. (2008). Technological design as an evolutionary process. In P. Vermaas, P. Kroes, A. Light, & S. Moore (Eds.), Philosophy and Design (pp. 61–76). New York: Springer.
Bruton, E. (1979). The History of Clocks and Watches. New York: Rizzoli.
Catling, D. (2013). Astrobiology: A Very Short Introduction. New York: Oxford.
Chaisson, E. (2006). The Epic of Evolution: The Seven Ages of our Cosmos. New York: Columbia University Press.
Chapman, E., Childers, D., & Vallino, J. (2016). How the second law of thermodynamics has informed ecosystem ecology through its history. BioSciences, 66, 27–39.
Cirkovic, M. (2003). Physical eschatology. American Journal of Physics, 71(2), 122–133.
Dennett, D. (1995). Darwin’s Dangerous Idea: Evolution and the Meanings of Life. New York: Simon & Schuster.
Dennett, D. (2004). Could there be a Darwinian account of human creativity? In A. Moya & E. Font (Eds.), Evolution: From Molecules to Ecosystems (pp. 273–279). New York: Oxford University Press.
Derex, M., et al. (2019). Causal understanding is not necessary for the improvement of culturally evolving technology. Nature Human Behavior. Online at www.nature.com/articles/s41562-019-0567-9. Accessed 29 April 2019.
Dewar, R. (2006). Maximum entropy production and non-equilibrium statistical mechanics. In A. Kleidon & R. Lorenz (Eds.), Non-Equilibrium Thermodynamics and the Production of Entropy (pp. 41–55). New York: Springer.
Dewar, R., Juretic, D., & Zupanovic, P. (2006). The functional design of the rotary enzyme ATP synthase is consistent with maximum entropy production. Chemical Physics Letters, 430, 177–182.
Dil, E., & Yumak, T. (2018). Emergent entropic nature of fundamental interactions. Online at arxiv.org/abs/1702.04635. Accessed 19 April 2019.
Dobovisek, A., et al. (2011). Enzyme kinematics and the maximum entropy production principle. Biophysical Chemistry, 154, 49–55.
Dyson, F. (1985). Infinite in All Directions. New York: HarperCollins.
Dyson, G. (1997). Darwin Among the Machines: The Evolution of Global Intelligence. Reading, MA: Perseus Books.
Dyson, G. (2012). Turing’s Cathedral: The Origins of the Digital Universe. New York: Vintage Press.
Ebbing, D., & Gammon, S. (2017). General Chemistry (Eleventh ed.). Boston, MA: Cengage Learning.
England, J. (2013). Statistical physics of self-replication. Journal of Chemical Physics, 139(121923), 1–8.
England, J. (2014). A new physics theory of life (interview with N. Wolchover). Quanta Magazine. Online at www.quantamagazine.org/a-new-thermodynamics-theory-of-the-origin-of-life-20140122/.
England, J. (2015). Dissipative adaptation in driven self-assembly. Nature Nanotechnology, 10, 919–923.
Enoch, J. (1998). The enigma of early lens use. Technology and Culture, 39(2), 273–291.
Essinger, J. (2004). Jacquard’s Web: How a Hand-Loom Led to the Birth of the Information Age. New York: Oxford University Press.
Foot, P. (2001). Natural Goodness. New York: Oxford University Press.
Gattringer, C., & Lang, C. (2009). Quantum Chromodynamics on the Lattice. New York: Springer.
Greene, B. (2005). The Fabric of the Cosmos. New York: Vintage.
Johnson-Laird, P. (2005). Flying bicycles: How the Wright brothers invented the airplane. Mind and Society, 4, 27–48.
Juretic, D., & Zupanovic, P. (2003). Photosynthetic models with maximum entropy production in irreversible charge transfer steps. Computational Biology and Chemistry, 27, 541–553.
Kelly, K. (2010). What Technology Wants. New York: Viking.
Kotz, J., Treichel, P., & Townsend, J. (2009). Chemistry & Chemical Reactivity (Vol. 2). Belmont, CA: Thompson.
Kouvaris, K., et al. (2017). How evolution learns to generalise. PLoS Computational Biology, 13(4), e1005358.
Kurzweil, R. (2005). The Singularity Is Near: When Humans Transcend Biology. New York: Viking.
Leibniz, G. W. (1697). On the ultimate origination of the universe. In P. Schrecker & A. Schrecker (Eds.) (1988) Leibniz: Monadology and Other Essays (pp. 84–94). New York: Macmillan Publishing.
Lovejoy, A. (1936). The Great Chain of Being. Cambridge, MA: Harvard University Press.
Machta, J. (2011). Natural complexity, computational complexity, and depth. Chaos, 21, 0371111–0371118.
Marenduzzo, D., Finn, K., & Cook, P. (2006). The depletion attraction: An underappreciated force driving cellular organization. Journal of Cell Biology, 175(5), 681–686.
Martin, O., & Horvath, J. (2013). Biological evolution of replicator systems: Towards a quantitative approach. Origins of Life and Evolution of Biospheres, 43, 151–160.
Martyushev, L. (2013). Entropy and entropy production: Old misconceptions and new breakthroughs. Entropy, 15, 1152–1170.
Martyushev, L., & Seleznev, V. (2006). Maximum entropy production principle in physics, chemistry, and biology. Physics Reports, 426, 1–45.
Mataxis, T. (1962, September). Change of life, computer style. Army, 13(2), 61–67.
Murdoch, I. (1992). Metaphysics as a Guide to Morals. London: Chatto & Windus.
Partner, M., Kashtan, N., & Alon, U. (2008). Facilitated variation: How evolution learns from past environments to generalize to new environments. PLoS Computational Biology, 4(11), e1000206.
Peirce, C. S. (1965). Collected papers of Charles Sanders Peirce. In C. Hartshorne & P. Weiss (Eds.), Cambridge. MA: Harvard University Press.
Penrose, R. (1979). Singularities and time-asymmetry. In S. Hawking & W. Israel (Eds.), General Relativity: An Einstein Centenary Survey (pp. 581–638). New York: Cambridge University Press.
Rescher, N. (1979). Leibniz: An Introduction to His Philosophy. Totowa, NJ: Rowman & Littlefield.
Rescher, N. (2010). Axiogenesis: An Essay in Metaphysical Optimalism. New York: Lexington Books.
Rubin, M. (1986). Spectacles: Past, present, and future. Survey of Opthamalogy, 30(5), 321–327.
Rutherford, D. (1995). Leibniz and the Rational Order of Nature. New York: Cambridge University Press.
Schneider, E., & Kay, J. (1994). Life as a manifestation of the second law of thermodynamics. Mathematical and Computer Modelling, 19(6–8), 25–48.
Silk, J. (2001). The Big Bang (3rd ed.). New York: Henry Holt & Co.
Simonton, D. (2010). Creative thought as blind-variation and selective retention. Physics of Life Reviews, 7, 156–179.
Simonton, D. (2015). Thomas Edison’s creative career. Psychology of Aesthetics, Creativity, and the Arts, 9(1), 2–14.
Skene, K. (2015). Life’s a gas: A thermodynamic theory of biological evolution. Entropy, 17, 5522–5548.
Springel, V., et al. (2005, June 2). Simulations of the formation, evolution and clustering of galaxies and quasars. Nature, 435, 629–636.
Steinhart, E. (2012). Royce’s model of the Absolute. Transactions of the Charles S. Peirce Society, 48(3), 356–384.
Steinhart, E. (2014). Your Digital Afterlives: Computational Theories of Life After Death. New York: Palgrave Macmillan.
Steinhart, E. (2018). Spirit. Sophia, 56(4), 557–571.
Swenson, R. (2006). Spontaneous order, autocatakinetic closure, and the development of space-time. Annals of the New York Academy of Sciences, 901, 311–319.
Swenson, R. (2009). The fourth law of thermodynamics or the law of maximum entropy production (LMEP). Chemistry, 18(1), 333–339.
Temkin, I., & Eldredge, N. (2007). Phylogenetics and material cultural evolution. Current Anthropology, 48(1), 146–153.
Tipler, F. (1995). The Physics of Immortality: Modern Cosmology, God and the Resurrection of the Dead. New York: Anchor Books.
Tzafestas, S. (2018). Energy, Information, Feedback, Adaptation, and Self-Organization. New York: Springer.
Unrean, P., & Srienc, F. (2012). Predicting the adaptive evolution of Thermoanaerobacterium saccharolyticum. Journal of Biotechnology, 158, 259–266.
Verlinde, E. (2016). Emergent gravity and the dark universe. SciPost Physics, 2(3.016), 1–41. https://doi.org/10.21468/scipostphys.2.3.016.
Vogelsberger, M., et al. (2014). Introducing the Illustris Project: Simulating the coevolution of dark and visible matter in the universe. Monthly Notices of the Royal Astronomical Society, 444(2), 1518–1547.
Wald, R. (2006). The arrow of time and the initial conditions of the universe. Studies in History and Philosophy of Modern Physics, 37, 394–398.
Watson, R., & Szathmary, E. (2016). How can evolution learn? Trends in Ecology & Evolution, 31(2), 147–157.
Watson, R., et al. (2016). Evolutionary connectionism. Evolutionary Biology, 43, 553–581.
Wright, P. (1970). Entropy and disorder. Contemporary Physics, 11(6), 581–588.
Yen, J., et al. (2014). Thermodynamic extremization principles and their relevance to ecology. Austral Ecology, 39, 619–632.
Zenil, H., & Delahaye, J.-P. (2010). On the algorithmic nature of the world. In G. Dodig-Crnkovic & M. Burgin (Eds.), Information and Computation. Singapore: World Scientific.
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Steinhart, E. (2020). Reflexivity. In: Believing in Dawkins. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-43052-8_3
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