Selected External References

This is a very incomplete page still under development. Send us suggestions in the contact page.

Listed here for convenience are publications of which we are aware that may be relevant to build on in developing the Gálapagos Project. This is for published papers and books, or arXiv entries, except under exceptional circumstances.

Table Of Contents

Economics

Development Economics

  • Paul M. Romer, Two Strategies for Economic Development: Using Ideas and Producing Ideas, The World Bank Economic Review, Volume 6, Issue suppl_1, (December 1992), Pages 63–91, https://doi.org/10.1093/wber/6.suppl_1.63

Complexity Economics

  • Arthur, W. B. Competing technologies, increasing returns, and lock-in by historical events. Econ. J. 99, 116–131 (1989).
  • Arthur, W. B. Algorithms and the Shift in Modern Science. Beijer Institute Discussion Paper 269 (Swedish Academy Sciences, 2020).
  • Arthur, W. B. The structure of invention. Res. Policy 36, 274–287 (2007).
  • Nature Series. From economics to physics
  • James McNerney, Charles Savoie, Francesco Caravelli, & J. Doyne Farmer. How production networks amplify economic growth (2018).
  • Hidalgo, C., Klinger, B., Barabási, A.L., & Hausmann, R. The Product Space Conditions the Development of Nations. Science, 317(5837), 482–487 (2007).
  • Hausmann, R., et al.,. The Atlas of Economic Complexity: Mapping Paths to Prosperity 2nd ed., Cambridge: MIT Press (2013).
  • Hidalgo, C. Why Information Grows: The Evolution of Order, from Atoms to Economies, New York: Basic Books (2015).
  • Arthur, W. B. The nature of technology. What it is and how it evolves. (Free Press, 2009).

Non-Equilibrium Economics

  • Devine, S. An Economy Viewed as a Far-from-Equilibrium System from the Perspective of Algorithmic Information Theory. (Entropy 2018), 20, 228.

Technological Progress

  • Nagy B, Farmer JD, Bui QM, Trancik JE. Statistical Basis for Predicting Technological Progress (2013).
  • Role of design complexity in technology improvement James McNerney, J. Doyne Farmer, Sidney Redner, Jessika E. Trancik. Proceedings of the National Academy of Sciences (May 2011), 108 (22) 9008-9013
  • Bloom, Nicholas, et al. “Are ideas getting harder to find?.” American Economic Review 110.4 (2020): 1104-44.
  • Cowen, Tyler, and Ben Southwood. “Is the rate of scientific progress slowing down?.” Available at SSRN 3822691 (2019).

Engineering

Inventions

  • Arthur, W. B. The nature of technology. What it is and how it evolves. (Free Press, 2009).
  • Arthur, W. B. & Polak, W. The evolution of technology within a simple computer model. Complexity 11, 23–31 (2006).
  • Ricard V. Solée, Sergi Valverde, Marti Rosas Casals, Stuart A. Kauffman, Doyne Farmer, & Niles Eldredge. The evolutionary ecology of technological innovations. (2013) Complexity, 18(4), 15–27.
  • Youn H, Strumsky D, Bettencourt LMA, Lobo J. Invention as a combinatorial process: evidence from US patents. J. R. Soc. Interface 12: 20150272 (2015).
  • Sood, V., Mathieu, M., Shreim, A., Grassberger, P., & Paczuski, M. Interacting Branching Process as a Simple Model of Innovation. Phys. Rev. Lett., 105, 178701 (2010).
  • Valverde, Sergi, and Ricard V. Solé. “Punctuated equilibrium in the large-scale evolution of programming languages.” Journal of The Royal Society Interface 12.107 (2015): 20150249.
  • Solé R., Amor D.R. and Valverde S. On singularities and black holes in combination-driven models of technological innovation networks. PLoS One, 11 (1) (2016), p. e0146180
  • Solé Ricard and Valverde Sergi. Evolving complexity: how tinkering shapes cells, software and ecological networks. Phil. Trans. R. Soc. (2020) B3752019032520190325 http://doi.org/10.1098/rstb.2019.0325 (Preprint: https://arxiv.org/pdf/1907.05528.pdf)
  • Duran-Nebreda, Salva, and Sergi Valverde. “Imitation-driven Cultural Collapse.” (2021). https://psyarxiv.com/dt6bx

Making Machines

Biology

Theoretical Biology

  • Krakauer, D., Bertschinger, N., Olbrich, E. et al. The information theory of individuality. Theory Biosci. 139, 209–223 (2020)
  • Maimon, Ron. “Computational Theory of Biological Function I.” arXiv preprint q-bio/0503028 (2005).
  • Akhlaghpour, Hessameddin. “An RNA-based theory of natural universal computation.” Journal of theoretical biology (2021): 110984.
  • Rosas FE, Mediano PAM, Jensen HJ, Seth AK, Barrett AB, Carhart-Harris RL, et al. (2020) Reconciling emergences: An information-theoretic approach to identify causal emergence in multivariate data. PLoS Comput Biol 16(12): e1008289. https://doi.org/10.1371/journal.pcbi.1008289
  • Mediano, Pedro AM, et al. “Greater than the parts: A review of the information decomposition approach to causal emergence.” arXiv preprint arXiv:2111.06518 (2021).

Origin of Life

  • Marshall, Stuart M., et al. “Identifying molecules as biosignatures with assembly theory and mass spectrometry.” Nature communications 12.1 (2021): 1-9.
  • Kempes, Christopher P., and David C. Krakauer. “The Multiple Paths to Multiple Life.” Journal of Molecular Evolution (2021): 1-12.
  • Smith, E., & Morowitz, H. J. The origin and nature of life on earth: the emergence of the fourth geosphere. Cambridge University Press (2016).
  • Pattee, Howard H. “The physical basis of coding and reliability in biological evolution.” The Origin of Life. Routledge, 2017. 67-93.

Artificial Life

  • Taylor, Tim, and Alan Dorin. Rise of the Self-Replicators. Springer, 2020.
  • Webster, Matt, and Grant Malcolm. “Hierarchical Components and Entity-based Modelling in Artificial Life.” ALIFE. 2008.
  • Webster, Matthew Paul. Formal models of reproduction: from computer viruses to artificial life. Diss. University of Liverpool, UK, 2008.
  • Randazzo, Ettore, Luca Versari, and Alexander Mordvintsev. “Recursively Fertile Self-replicating Neural Agents.” ALIFE 2021: The 2021 Conference on Artificial Life. MIT Press, 2021.
  • Sudhakaran, Shyam, et al. “Growing 3D Artefacts and Functional Machines with Neural Cellular Automata.” arXiv preprint arXiv:2103.08737 (2021).
  • Grbic, Djordje, et al. “EvoCraft: A New Challenge for Open-Endedness.” EvoApplications. 2021.
  • Chan, Bert Wang-Chak. Lenia and Expanded Universe. Artificial Life Conference Proceedings, (32), 221–229. arXiv:2005.03742 (2020).
  • David H. Ackley, & Trent R. Small (2014). Indefinitely Scalable Computing = Artificial Life Engineering. In Proceedings of The Fourteenth International Conference on the Synthesis and Simulation of Living Systems (ALIFE 14) 2014 (pp. 606–613). MIT Press.

Evolution

Cultural Evolution

  • Duarte, Miguel Feliciano da Cunha Oliveira. “Gamified Interfaces as a means to motivate exploration and comprehension of Musical Genres.” (2020).
  • Youngblood, Mason, Karim Baraghith, and Patrick E. Savage. “Phylogenetic reconstruction of the cultural evolution of electronic music via dynamic community detection (1975–1999).” Evolution and Human Behavior (2021).
  • Aguirre-Fernández, G., Barbieri, C., Graff, A. et al. Cultural macroevolution of musical instruments in South America. Humanit Soc Sci Commun 8, 208 (2021). https://doi.org/10.1057/s41599-021-00881-z
  • Mehr SA, Singh M, Knox D et al. Universality and diversity in human song. Science 366 (2019): https://doi.org/10.1126/science.aax0868
  • Gabora Liane and Steel Mike 2021. An evolutionary process without variation and selectionJ. R. Soc. Interface.182021033420210334 http://doi.org/10.1098/rsif.2021.0334
  • Simon Dedeo. Major Transitions in Political Order. In S.I. Walker, P.C.W. Davies, and G.F.R. Ellis, editors, From Matter to Life: Information and Causality. Cambridge University Press, Cambridge, United Kingdom, 2017. pp. 393-428 https://arxiv.org/abs/1512.03419
  • Chang, Kent K., and Simon DeDeo. “Divergence and the Complexity of Difference in Text and Culture.” Journal of Cultural Analytics 4.11 (2020): 1-36.
  • Miton, Helena, and Simon DeDeo. “The Cultural Transmission of Tacit Knowledge.” arXiv preprint arXiv:2201.03582 (2022).
  • Viteri, Scott, and Simon DeDeo. “Epistemic phase transitions in mathematical proofs.” Cognition 225 (2022): 105120.
  • DeDeo, Simon. “Conflict and computation on Wikipedia: A finite-state machine analysis of editor interactions.” Future Internet 8.3 (2016): 31.
  • Kelly, P., Winters, J., Miton, H., & Morin, O. (2020, April 25). The predictable evolution of letter shapes: An emergent script of West Africa recapitulates historical change in writing systems. https://doi.org/10.31235/osf.io/eg489
  • Cárdenas, Juan Pablo, et al. “The Structure of Online Information Behind Social Crises.” Frontiers in Physics 9 (2021): 650648.
  • Zannettou, Savvas, et al. “On the origins of memes by means of fringe web communities.” Proceedings of the Internet Measurement Conference 2018. 2018.

Biological Self-Replicators and Universal Constructors

  • Benenson, Yaakov, et al. “DNA molecule provides a computing machine with both data and fuel.” Proceedings of the National Academy of Sciences 100.5 (2003): 2191-2196.
  • Hutchison, Clyde A., et al. “Design and synthesis of a minimal bacterial genome.” Science 351.6280 (2016).
  • Bokov, Konstantin, and Sergey V. Steinberg. “A hierarchical model for evolution of 23S ribosomal RNA.” Nature 457.7232 (2009): 977-980.
  • Kriegman, S., Blackiston, D., Levin, M., Bongard, J. (2021) “Kinematic self-replication in reconfigurable organisms”, Proceedings of the National Academy of Sciences (PNAS), vol. 118, no. 49, e2112672118.

Energy Transitions

  • Maynard Smith, J. & Szathmáry, E. The Major Transitions in Evolution (WH Freeman, 1995).
  • Judson, O. The energy expansions of evolution. Nat Ecol Evol 1, 0138 (2017). https://doi.org/10.1038/s41559-017-0138

Biological Computation

Biological Complex Systems

  • Simon, H. A., and Howard H. Pattee. “Hierarchy theory: The challenge of complex systems.” Hierarchy Theory: The Challenge of Complex Systems 1 (1973).
  • Flack, J. (2017). Life’s Information Hierarchy. In S. Walker, P. Davies, & G. Ellis (Eds.), From Matter to Life: Information and Causality (pp. 283-302). Cambridge: Cambridge University Press. doi:10.1017/9781316584200.012

Autopoesis

  • Levin, Michael. “The computational boundary of a “self”: developmental bioelectricity drives multicellularity and scale-free cognition.” Frontiers in psychology 10 (2019): 2688.
  • Beer, Randall D. “Bittorio revisited: structural coupling in the Game of Life.” Adaptive Behavior 28.4 (2020): 197-212.
  • Beer, Randall D. “On the origin of gliders.” ALIFE 2018: The 2018 Conference on Artificial Life. MIT Press, 2018.
  • Beer, Randall D. “Characterizing autopoiesis in the game of life.” Artificial Life 21.1 (2015): 1-19.
  • Beer, Randall D. “The cognitive domain of a glider in the game of life.” Artificial life 20.2 (2014): 183-206.
  • Beer, Randall D. “Autopoiesis and cognition in the game of life.” Artificial Life 10.3 (2004): 309-326.

Computational Complexity

Entropy and Diversity

  • Fuentes, Miguel Angel. “Complexity and the emergence of physical properties.” Entropy 16.8 (2014): 4489-4496.
  • Posada, Jose Gallego, et al. “GAIT: A Geometric Approach to Information Theory.” International Conference on Artificial Intelligence and Statistics. PMLR, 2020.
  • Leinster, Tom, and Christina A. Cobbold. “Measuring diversity: the importance of species similarity.” Ecology 93.3 (2012): 477-489.

Chemistry

Chemical Computation

  • Flumini D., Weyland M.S., Schneider J.J., Fellermann H., Füchslin R.M. (2020) Towards Programmable Chemistries. Artificial Life and Evolutionary Computation. WIVACE 2019. Communications in Computer and Information Science, vol 1200. Springer, Cham. https://doi.org/10.1007/978-3-030-45016-8_15
  • Fontana, Walter, and Leo W. Buss. “The barrier of objects: From dynamical systems to bounded organizations.” (1996).
  • Regev, Aviv, and Ehud Shapiro. “The π-calculus as an abstraction for biomolecular systems.” Modelling in Molecular Biology. Springer, Berlin, Heidelberg, 2004. 219-266.
  • Buliga, Marius. “Chemical concrete machine.” arXiv preprint arXiv:1309.6914 (2013).
  • Angelone, Davide, et al. “Convergence of multiple synthetic paradigms in a universally programmable chemical synthesis machine.” Nature Chemistry 13.1 (2021): 63-69.
  • Mehr, S. Hessam M., et al. “A universal system for digitization and automatic execution of the chemical synthesis literature.” Science 370.6512 (2020): 101-108.
  • Parrilla-Gutierrez, Juan Manuel, et al. “A programmable chemical computer with memory and pattern recognition.” Nature communications 11.1 (2020): 1-8.
  • Goucher, J.P. Hex13, a GPU-based artificial chemistry simulator.https://gitlab.com/apgoucher/hex13

Chemical Replicators

  • Liu, Yu, and David JT Sumpter. “Mathematical modeling reveals spontaneous emergence of self-replication in chemical reaction systems.” Journal of Biological Chemistry 293.49 (2018): 18854-18863.
  • Miras, Haralampos N., et al. “Spontaneous formation of autocatalytic sets with self-replicating inorganic metal oxide clusters.” Proceedings of the National Academy of Sciences 117.20 (2020): 10699-10705.

Digital Chemistry

  • Emeral Cloud Lab.(2016-2021).https://www.emeraldcloudlab.com/
  • Gromski, Piotr S., Jarosław M. Granda, and Leroy Cronin. “Universal chemical synthesis and discovery with ‘the chemputer’.” Trends in Chemistry 2.1 (2020): 4-12.

Automatic Synthesis

  • Yadav, Maneesh K. “On the synthesis of machine learning and automated reasoning for an artificial synthetic organic chemist.” New Journal of Chemistry 41.4 (2017): 1411-1416.

Mathematics

Logic and Foundation of Mathematics

  • Kleene, Stephen Cole. “Introduction to metamathematics.” (1952).
  • Zalamea, Fernando. Synthetic philosophy of contemporary mathematics. MIT Press, 2012.
  • Stillwell, J. (2018). Reverse mathematics. Princeton University Press.

Sheaf Theory

  • Bredon, Glen E. Sheaf theory. Vol. 170. Springer Science & Business Media, 2012.
  • Michael Robinson. Sheaves are the canonical datastructure for sensor integration (2016).
  • Zalamea, Fernando. Modelos en haces para el pensamiento matemático. Monografía matemático-filosófica.(To Appear), 2020.
  • MacLane, Saunders, and Ieke Moerdijk. Sheaves in geometry and logic: A first introduction to topos theory. Springer Science & Business Media, 2012.
  • Baas, Nils A. “On the philosophy of higher structures.” International Journal of General Systems 48.5 (2019): 463-475.
  • Baas, Nils A. “On the mathematics of higher structures.” International Journal of General Systems 48.6 (2019): 603-624.
  • Caramello, Olivia. “The unification of mathematics via topos theory.” arXiv preprint arXiv:1006.3930 (2010).

Automatic Theorem Proving

  • Carneiro, Mario. “Metamath zero: The cartesian theorem prover.” arXiv preprint arXiv:1910.10703 (2019).
  • Jónathan Heras, & Ekaterina Komendantskaya. HoTT formalisation in Coq: Dependency Graphs & ML4PG (2014).
  • Buzzard, Kevin, Johan Commelin, and Patrick Massot. “Formalising perfectoid spaces.” Proceedings of the 9th ACM SIGPLAN International Conference on Certified Programs and Proofs. 2020. Project
  • Jeremy Avigad. (2021). The design of mathematical language. Available at: http://philsci-archive.pitt.edu/19508/

Fluid Computation

  • Terence Tao. Finite time blowup for an averaged three-dimensional Navier-Stokes equation. Journal of the American Mathematical Society, 29(3), 601–674 (2015).
  • Cardona, Robert, et al. “Constructing Turing complete Euler flows in dimension 3.” Proceedings of the National Academy of Sciences 118.19 (2021).
  • Prakash, Manu, and Neil Gershenfeld. “Microfluidic bubble logic.” Science 315.5813 (2007): 832-835.
  • Kawaguchi, Takako, Suzuki, Reiji, Arita, Takaya, & Chan, Bert (2021). Introducing asymptotics to the state-updating rule in Lenia. The 2021 Conference on Artificial Life.

Computer Science

Cellular Automata

  • Gardner, Martin. The Fantastic Combinations of Jhon Conway’s New Solitaire Game “Life.” Sc. Am. 223 (1970): 20-123.
  • Berlekamp, Elwyn R., John H. Conway, and Richard K. Guy. Winning ways for your mathematical plays, volume 4. AK Peters/CRC Press, 2004.
  • Wolfram, Stephen. A new kind of science. Vol. 5. Champaign, IL: Wolfram media, 2002.
  • Adamatzky, A. (ed.), Game of life cellular automata. Springer, 2010.
  • Johnston, N., Greene, D. Conway’s Game of Life Mathematics and Construction. Lulu.com (self-published), 2022. Avaiable at: https://conwaylife.com/book/

Automata Theory

  • Neumann, János, and Arthur W. Burks. Theory of self-reproducing automata. Vol. 1102024. Urbana: University of Illinois press, 1966.
  • Minsky, Marvin. “Computation: Finite and Infinite Machines Prentice Hall.” Inc., Engelwood Cliffs, NJ (1967).
  • Rhodes, John, Chrystopher L. Nehaniv, and Morris W. Hirsch. Applications of automata theory and algebra: via the mathematical theory of complexity to biology, physics, psychology, philosophy, and games. 2010.

Stochastic Computation

  • David H. Ackley, & Daniel C. Cannon (2011). Pursue Robust Indefinite Scalability. In Proc. HotOS XIII. USENIX Association.
  • Alaghi, Armin, and John P. Hayes. “Survey of stochastic computing.” ACM Transactions on Embedded computing systems (TECS) 12.2s (2013): 1-19.
  • Von Neumann, John. “Probabilistic logics and the synthesis of reliable organisms from unreliable components.” Automata Studies.(AM-34), Volume 34. Princeton University Press, 2016. 43-98.
  • David H. Ackley. The T2 Tile Project. https://t2tile.com/

Programable Matter

  • Gershenfeld, Neil. “Aligning the representation and reality of computation with asynchronous logic automata.” Computing 93.2-4 (2011): 91-102.
  • Cheung, Kenneth C., et al. “Programmable assembly with universally foldable strings (moteins).” IEEE Transactions on Robotics 27.4 (2011): 718-729.
  • Ghassaei, Amanda Paige. Rapid design and simulation of functional digital materials. Diss. Massachusetts Institute of Technology, 2016.
  • Langford, William Kai. Electronic digital materials. Diss. Massachusetts Institute of Technology, 2014.
  • Assembled Assemblers
  • Taylor, Tim, and Alan Dorin. Rise of the Self-Replicators. Springer, 2020.
  • Jensen, Johannes Høydahl. “Reservoir computing in-materio: Emergence and control in unstructured and structured materials.” (2021).
  • Horsman Clare, Stepney Susan, Wagner Rob C. and Kendon Viv 2014When does a physical system compute?Proc. R. Soc. A.4702014018220140182

Distributed Systems

  • L. Lamport, “Time, Clocks, and the Ordering of Events in a Distributed System”, Commun ACM 21, 558–65, (1978). doi:10.1145/359545.359563.
  • Lamport, Leslie, Robert Shostak, and Marshall Pease. The Byzantine Generals Problem Trans Prog. Lang. Syst. 4, 3 (July 1982), 382-401
  • Pease, Marshall, Robert Shostak, and Leslie Lamport. “Reaching agreement in the presence of faults.” Journal of the ACM (JACM) 27.2 (1980): 228-234.
  • Nakamoto, Satoshi. “Bitcoin: A peer-to-peer electronic cash system.” Decentralized Business Review (2008): 21260.
  • NKN, L. “NKN: a scalable self-evolving and self-incentivized decentralized network.” (2018). https://nkn.org/wp-content/uploads/2020/10/NKN_Whitepaper.pdf
  • Popov, Serguei, Hans Moog, Darcy Camargo, Angelo Capossele, Vassil Dimitrov, Alon Gal, Andrew Greve et al. “The coordicide.” Accessed Jan (2020): 1-30. https://files.iota.org/papers/20200120_Coordicide_WP.pdf
  • Grant Malcolm. Sheaves, Objects, and Distributed Systems. Electronic Notes in Theoretical Computer Science, 225, 3–19 (2009).
  • Goguen, J. A. Sheaf semantics for concurrent interacting objects. Mathematical Structures in Computer Science, 2(2), 159-191 (1992).

Algorithmic Complexity

  • Zenil, Hector, Narsis A. Kiani, and Jesper Tegnér. “Algorithmic information dynamics of persistent patterns and colliding particles in the game of life.” arXiv preprint arXiv:1802.07181 (2018).

Computational Complexity

  • Aaronson, Scott. “Why philosophers should care about computational complexity.” Computability: Turing, Gödel, Church, and Beyond 261 (2013): 327.
  • S. Goldwasser, S. Micali, and C. Rackoff. The knowledge complexity of interactive proof systems. SIAM J. Comput., 18(1):186208, 1989.

Machine Learning

  • François Chollet. On the Measure of Intelligence (2019).
  • Pascal Friederich, Mario Krenn, Isaac Tamblyn, & Alán Aspuru-Guzik. Scientific intuition inspired by machine learning-generated hypotheses. Machine Learning: Science and Technology, 2(2), 025027 (2021).
  • Mordvintsev, et al., “Thread: Differentiable Self-organizing Systems”, Distill, 2020.
  • Gasse, M., Chételat, D., Ferroni, N., Charlin, L., & Lodi, A. Exact Combinatorial Optimization with Graph Convolutional Neural Networks. In Advances in Neural Information Processing Systems 32 (2019).
  • Bengio, Yoshua, Andrea Lodi, and Antoine Prouvost. “Machine learning for combinatorial optimization: a methodological tour d’horizon.” European Journal of Operational Research. 2020.
  • Jacob M. Springer, & Garrett T. Kenyon. It’s Hard for Neural Networks To Learn the Game of Life (2020).
  • Hillis, W. Daniel; Christopher Sykes; George Dyson (October 2016). “Web of Stories – W Daniel Hillis – How to create an intelligence”. Web of Stories. Retrieved 07 September 2021.
  • IMO Grand Challenge
  • Tishby, N.; Zaslavsky, N. Deep learning, and the information bottleneck principle. In Proceedings of the 2015 IEEE Information Theory Workshop (ITW), Jerusalem, Israel, 26 April–1 May 2015; pp. 1–5.
  • Ha, David, and Yujin Tang. “Collective Intelligence for Deep Learning: A Survey of Recent Developments.” arXiv preprint arXiv:2111.14377 (2021).
  • Risi, S. The Future of Artificial Intelligence is Self-Organizing and Self-Assembling. https://sebastianrisi.com/self_assembling_ai/
  • Mahadevan, Sridhar. “Imagination machines: A new challenge for artificial intelligence.” Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 32. No. 1. 2018.

Compilers and Self-Replicating Programs

  • Thompson, Ken. “Reflections on trusting trust.” ACM Turing award lectures. 2007. 1983.
  • Aho, Alfred V., Ravi Sethi, and Jeffrey D. Ullman. “Compilers, principles, techniques.” Addison wesley 7.8 (1986): 9.

Formal Verification

  • Camilleri, Albert, Mike Gordon, and Tom Melham. Hardware verification using higher-order logic. No. UCAM-CL-TR-91. University of Cambridge, Computer Laboratory, 1986.
  • Aagaard, Mark D., et al. “A methodology for large-scale hardware verification.” International Conference on Formal Methods in Computer-Aided Design. Springer, Berlin, Heidelberg, 2000.

Physics

BioPhysics

  • Marshall, S. M., Moore, D., Murray, A. R., Walker, S. I., & Cronin, L. Quantifying the pathways to life using assembly spaces. (2019) arXiv preprint arXiv:1907.04649.
  • Marletto, Chiara. “Constructor theory of life.” Journal of The Royal Society Interface 12.104 (2015): 20141226.

Fundamental Physics

  • Deutsch, David. “Constructor theory.” Synthese 190.18 (2013): 4331-4359.
  • Deutsch, David, and Chiara Marletto. “Constructor theory of information.” Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 471.2174 (2015): 20140540.
  • Wolfram, Stephen. A Project to Find the Fundamental Theory of Physics. Wolfram Media, Incorporated, 2020.

Thermodynamics & Statistical Physics

  • Simon Dedeo. Introduction to Renormalization‐ online course. https://www.complexityexplorer.org/courses/67-introduction-to-renormalization Santa Fe Institute, 2017.
  • Flack Jessica C. Coarse-graining as a downward causation mechanism. Phil. Trans. R. Soc. (2017) A.3752016033820160338
  • Chiara Marletto. (2017). Constructor Theory of Thermodynamics. https://arxiv.org/abs/1608.02625
  • Chiara Marletto (2022). The information-theoretic foundation of thermodynamic work extraction. Journal of Physics Communications, 6(5), 055012.
  • Mandelbrot, Benoit. “On the derivation of statistical thermodynamics from purely phenomenological principles.” Journal of Mathematical Physics 5.2 (1964): 164-171.
  • L. Szilard, “Uber die ausdehnung der phenomenologischen thermodynamik auf der schwankungserscheinungen”, Z. Physik, vol. 32, pp. 753, 1925.
  • B. Mandelbrot, “An outline of a purely phenomenological theory of statistical thermodynamics–I: Canonical ensembles,” in IRE Transactions on Information Theory, vol. 2, no. 3, pp. 190-203, September 1956, doi: 10.1109/TIT.1956.1056804.
  • Uffink, Jos, and Janneke Van Lith. “Thermodynamic uncertainty relations.” Foundations of physics 29.5 (1999): 655-692.
  • Mandelbrot, Benoit. “The role of sufficiency and of estimation in thermodynamics.” The Annals of Mathematical Statistics (1962): 1021-1038.
  • Szilard, L. über die Entropieverminderung in einem thermodynamischen System bei Eingriffen intelligenter Wesen. Z. Physik 53, 840–856 (1929). https://doi.org/10.1007/BF01341281
  • Jaynes, Edwin T. “Gibbs vs Boltzmann entropies.” American Journal of Physics 33.5 (1965): 391-398.
  • Takahiro Sagawa (2013). Thermodynamics of Information Processing in Small Systems. Springer Japan.

Heat Engines

  • Einstein, A., L. Szilárd, “Accompanying notes and remarks for Pat. No. 1,781,541”. Mandeville Special Collections Library USC. Box 35, Folder 3, 1927; 52 pages. https://library.ucsd.edu/dc/object/bb8499907f
  • Einstein, A., L. Szilárd, “Refrigeration” (Appl: 16 December 1927; Priority: Germany, 16 December 1926) U.S. Patent 1,781,541, 11 November 1930.
  • Dannen, Gene. “The Einstein-Szilard Refrigerators.” Scientific American 276.1 (1997): 90-95.
  • Barbour, J. A History of Thermodynamics. 2020. http://platonia.com/A_History_of_Thermodynamics.pdf
  • Barbour, J. The janus point: a new theory of time. Random House, 2020.
  • Halpern, N. Y. Quantum Steampunk: The Physics of Yesterday’s Tomorrow. Johns Hopkins University Press 2022.

Nature of Time

  • Milburn, G. J. “The thermodynamics of clocks.” Contemporary Physics 61.2 (2020): 69-95.
  • Erker, Paul, et al. “Autonomous quantum clocks: does thermodynamics limit our ability to measure time?.” Physical Review X 7.3 (2017): 031022.
  • Schwarzhans, Emanuel, et al. “Autonomous temporal probability concentration: Clockworks and the second law of thermodynamics.” Physical Review X 11.1 (2021): 011046.

Computational Complexity

  • Mark Braverman, Jonathan Schneider, and Cristóbal Rojas. “Space-Bounded Church-Turing Thesis and Computational Tractability of Closed Systems.” Physical Review Letters. DOI: 10.1103/PhysRevLett.115.098701

Metatheories

MetaExplanation

  • Wojtowicz, Z., & DeDeo, S. From Probability to Consilience: How Explanatory Values Implement Bayesian Reasoning. Trends in Cognitive Sciences (2020).

Empirical MetaMathematics

Empirical MetaScience

  • An Zeng, Zhesi Shen, Jianlin Zhou, Jinshan Wu, Ying Fan, Yougui Wang, & H. Eugene Stanley. The science of science: From the perspective of complex systems. Physics Reports, 714-715, 1-73 (2017).
  • Anastasia Buyalskaya, Marcos Gallo, & Colin F. Camerer. The golden age of social science. Proceedings of the National Academy of Sciences, 118(5), e2002923118 (2021).
  • Santo Fortunato, et al. “Science of science”. Science 359. 6379 (2018).
  • Wang, D., Barabási, A.L. The Science of Science. Cambridge University Press; 1st edition (2021).

Music

Electronic Music

  • Xenakis, Iannis. Formalized music: thought and mathematics in composition. No. 6. Pendragon Press, 1992.

Sound Machines

  • Andrey Smirnov: Sound in Z: Experiments in Sound and Electronic Music in Early 20th Century Russia. Softcover, 2013, ISBN 978-3-86560-706-5, 281 pages, Koenig Books.

Philosophy

Phenomenology

  • Todes, Samuel. Body and world. MIT Press, 2001.
  • Lakoff, George, and Rafael Núñez. Where mathematics comes from. Vol. 6. New York: Basic Books, 2000.

Philosophy of Mathematics

  • Rota, Gian-Carlo. Indiscrete thoughts. Springer Science & Business Media, 2008.
  • Zalamea, Fernando. Synthetic philosophy of contemporary mathematics. MIT Press, 2012.
  • Corfield, David. Towards a philosophy of real mathematics. Cambridge University Press, 2003.
  • Klosinski, Leonard F. et al. Is Mathematics Inevitable? : a Miscellany. Washington, D.C: Mathematical Association of America, 2008. Print.
  • Marquiz,J., Abstract Mathematical Tools and Machines for Mathematics, Philosophia Mathematica, Volume 5, Issue 3, October 1997, Pages 250–272, https://doi.org/10.1093/philmat/5.3.250

Philosophy of Biology

  • Bergson, Henri. “Creative Evolution. Humanity’s Natural Creative Impulse.” (2012).
  • Pence, C. (2021). The Causal Structure of Natural Selection (Elements in the Philosophy of Biology). Cambridge: Cambridge University Press. doi:10.1017/9781108680691
  • Dupré, John. The Metaphysics of Biology. Cambridge University Press, 2021.