Abstract

This chapter investigates the evolutionary ecology of software focusing on the symbiotic relationship between software and innovation. An interplay between constraints, tinkering, and frequency-dependent selection drives the complex evolutionary trajectories of these socio-technological systems. Our approach integrates agent-based modeling and case studies, drawing on complex network analysis and evolutionary theory to explore how software evolves under the competing forces of novelty generation and imitation. By examining the evolution of programming languages and their impact on developer practices, we illustrate how technological artifacts co-evolve with and shape societal norms, cultural dynamics, and human interactions. This ecological perspective also informs our analysis of the emerging role of AI-driven development tools in software evolution. While large language models (LLMs) provide unprecedented access to information, their widespread adoption introduces new evolutionary pressures that may contribute to cultural stagnation, much like the decline of diversity in past software ecosystems. Understanding the evolutionary pressures introduced by AI-mediated software production is critical for anticipating broader patterns of cultural change, technological adaptation, and the future of software innovation.

Keywords: software networks, programming languages, evolution, scaling laws, tinkering, innovation

Bibliography

Ayala, F. J., and C. A. Campbell. 1974. “Frequency-Dependent Selection.” Annual Review of Ecology and Systematics 5 (1): 115–138. https://doi.org/10.1146/annurev.es.05.110174.000555.

Baldwin, C. Y., and K. B. Clark. 2000. Design Rules: The Power of Modularity. Vol. 1. Cambridge, MA: MIT Press.

Beach, F. A., D. O. Hebb, C. T. Morgan, and H. W. Nissen. 1960. The Neuropsychology of Lashley: Selected Papers of K. S. Lashley. New York: McGraw-Hill.

Betts, A., C. Gray, M. Zelek, R. C. MacLean, and K. C. King. 2018. “High Parasite Diversity Accelerates Host Adaptation and Diversification.” Science 360 (6391): 907–911. https://doi.org/10.1126/science.aam9974.

Brunswicker, S., and S. Mukherjee. 2023. “The Microstructure of Modularity in Design: A Design Motif View.” Industrial and Corporate Change 32 (1): 234–261. https://doi.org/10.1093/icc/dtac051.

Chaitin, G. J. 1966. “On the Length of Programs for Computing Finite Binary Sequences.” Journal of the ACM 13 (4): 547–569. https://doi.org/10.1145/321356.321363.

Challet, D., and S. Valverde. 2008. Fat Tails, Long Memory, Maturity and Ageing in Open-Source Software Projects. arXiv preprint 0802.3170. https://doi.org/10.48550/arXiv.0802.3170.

de Sousa, O. F., M. A. de Menezes, and T. J. P. Penna. 2009. “Analysis of the Package Dependency on Debian GNU/Linux.” Journal of Computational Interdisciplinary Sciences 1 (2): 127–133. https://doi.org/10.6062/jcis.2009.01.02.0015.

del Rio-Chanona, R. M., N. Laurentsyeva, and J. Wachs. 2024. “Large Language Models Reduce Public Knowledge Sharing on Online Q&A Platforms.” PNAS Nexus 3 (9): pgae400. https://doi.org/10.1093/pnasnexus/pgae400.

Dewett, T., and S. D. Williams. 2007. “Innovators and Imitators in Novelty-Intensive Markets: A Research Agenda.” Creativity and Innovation Management 16 (1): 80–92. https://doi.org/10.1111/j.1467-8691.2007.00421.x.

Duran-Nebreda, S., M. J. O’Brien, R. A. Bentley, and S. Valverde. 2022. “Dilution of Expertise in the Rise and Fall of Collective Innovation.” Humanities and Social Sciences Communications 9 (1): 1–10. https://doi.org/10.1057/s41599-022-01380-5.

Eick, S. G., T. L. Graves, A. F. Karr, J. S. Marron, and A. Mockus. 2001. “Does Code Decay? Assessing the Evidence from Change Management Data.” IEEE Transactions on Software Engineering 27 (1): 1–12. https://doi.org/10.1109/32.895984.

Eisler, Z., and J. Kertész. 2006. “Size Matters: Some Stylized Facts of the Stock Market Revisited.” The European Physical Journal B: Condensed Matter and Complex Systems 51: 145–154. https://doi.org/10.1140/epjb/e2006-00189-6.

Eldredge, N. 2015. Eternal Ephemera: Adaptation and the Origin of Species from the Nineteenth Century Through Punctuated Equilibria and Beyond. New York: Columbia University Press.

Gamma, E., R. Helm, R. Johnson, and J. Vlissides. 1995. Design Patterns: Elements of Reusable Object-Oriented Software. Boston: Pearson.

Gloor, P. A. 2006. Swarm Creativity: Competitive Advantage through Collaborative Innovation Networks. Oxford, UK: Oxford University Press.

Hickey, R. 2020. “A History of Clojure.” Proceedings of the ACM on Programming Languages 4 (HOPL): 1–46. https://doi.org/10.1145/3386321.

Knabe, J. F., C. L. Nehaniv, and M. J. Schilstra. 2008. “Do Motifs Reflect Evolved Function?—No Convergent Evolution of Genetic Regulatory Network Subgraph Topologies.” Biosystems 94 (1–2): 68–74. https://doi.org/10.1016/j.biosystems.2008.05.012.

Krapivsky, P. L., and S. Redner. 2005. “Network Growth by Copying.” Physical Review E 71 (3): 036118. https://doi.org/10.1103/PhysRevE.71.036118.

Lakos, J. 1996. Large-Scale C++ Software Design. Reading, MA: Addison-Wesley.

Lande, R. 1983. “The Response to Selection on Major and Minor Mutations Affecting a Metrical Trait.” Heredity 50 (1): 47–65. https://doi.org/10.1038/hdy.1983.6.

Langdon, W. B., and R. Poli. 1998. “Fitness Causes Bloat.” In Genetic Programming: First European Workshop, EuroGP ’98, edited by W. Banzhaf, R. Poli, M. Schoenauer, and T. C. Fogarty, vol. 1391 of Lecture Notes in Computer Science. Berlin: Springer.

Lehman, J., and K. O. Stanley. 2011. “Abandoning Objectives: Evolution through the Search for Novelty Alone.” Evolutionary Computation 19 (2): 189–223. https://doi.org/10.1162/EVCO_a_00025.

Li, Z., S. Lu, S. Myagmar, and Y. Zhou. 2006. “CP-Miner: Finding Copy-Paste and Related Bugs in Large-Scale Software Code.” IEEE Transactions on Software Engineering 32 (3): 176–192. https://doi.org/10.1109/TSE.2006.28.

Lynch, M. 2007. “The Frailty of Adaptive Hypotheses for the Origins of Organismal Complexity.” Proceedings of the National Academy of Sciences 104 (suppl. 1): 8597–8604. https://doi.org/10.1073/pnas.0702207104.

Mace, R., and C. J. Holden. 2005. “A Phylogenetic Approach to Cultural Evolution.” Trends in Ecology & Evolution 20 (3): 116–121. https://doi.org/10.1016/j.tree.2004.12.002.

Martínez-Mekler, G., R. A. Martínez, M. B. del Río, R. Mansilla, P. Miramontes, and G. Cocho. 2009. “Universality of Rank-Ordering Distributions in the Arts and Sciences.” PLoS One 4 (3): e4791. https://doi.org/10.1371/journal.pone.0004791.

Martocchio, J. J., and J. Webster. 1992. “Effects of Feedback and Cognitive Playfulness on Performance in Microcomputer Software Training.” Personnel Psychology 45 (3): 553–578. https://doi.org/10.1111/j.1744-6570.1992.tb00860.x.

McNerney, J., J. D. Farmer, S. Redner, and J. E. Trancik. 2011. “Role of Design Complexity in Technology Improvement.” Proceedings of the National Academy of Sciences 108 (22): 9008–9013. https://doi.org/10.1073/pnas.1017298108.

Milo, R., S. Shen-Orr, S. Itzkovitz, N. Kashtan, D. Chklovskii, and U. Alon. 2002. “Network Motifs: Simple Building Blocks of Complex Networks.” Science 298 (5594): 824–827. https://doi.org/10.1126/science.298.5594.824.

O’Brien, M. J., S. Valverde, S. Duran-Nebreda, B. Vidiella, and R. A. Bentley. 2024. “Punctuated Equilibrium at 50: Anything There for Evolutionary Anthropology? Yes; Definitely.” Evolutionary Anthropology: Issues, News, and Reviews 33 (1): e22009. https://doi.org/10.1002/evan.22009.

Richerson, P. J., and R. Boyd. 2008. Not by Genes Alone: How Culture Transformed Human Evolution. Chicago, IL: University of Chicago Press.

Schueller, W., J. Wachs, V. D. P. Servedio, S. Thurner, and V. Loreto. 2022. “Evolving Collaboration, Dependencies, and Use in the Rust Open Source Software Ecosystem.” Scientific Data 9 (1): 703. https://doi.org/10.1038/s41597-022-01819-z.

Shumailov, I., Z. Shumaylov, Y. Zhao, N. Papernot, R. Anderson, and Y. Gal. 2024. “AI Models Collapse when Trained on Recursively Generated Data.” Nature 631 (8022): 755–759. https://doi.org/10.1038/s41586-024-07566-y.

Smith, J. W., and C. W. Benkman. 2007. “A Coevolutionary Arms Race Causes Ecological Speciation in Crossbills.” The American Naturalist 169 (4): 455–465. https://doi.org/10.1086/511961.

Soergel, D. A. W. 2014. “Rampant Software Errors May Undermine Scientific Results.” F1000Research 3. https://doi.org/10.12688/f1000research.5930.2.

Solé, R. V., and S. Valverde. 2006. “Are Network Motifs the Spandrels of Cellular Complexity?” Trends in Ecology & Evolution 21 (8): 419–422. https://doi.org/10.1016/j.tree.2006.05.013.

Solé, R. V., S. Valverde, M. R. Casals, S. A. Kauffman, J. D. Farmer, and N. Eldredge. 2013. “The Evolutionary Ecology of Technological Innovations.” Complexity 18 (4): 15–27. https://doi.org/10.1002/cplx.21436.

Strotz, L. C., and B. S. Lieberman. 2023. “The End of the Line: Competitive Exclusion and the Extinction of Historical Entities.” Royal Society Open Science 10 (2): 221210. https://doi.org/10.1098/rsos.221210.

Turkle, S., and S. Papert. 1992. “Epistemological Pluralism and the Revaluation of the Concrete.” Journal of Mathematical Behavior 11 (1): 3–33. https://www.learntechlib.org/p/146817/.

Valverde, S. 2007. “Crossover from Endogenous to Exogenous Activity in Open-Source Software Development.” Europhysics Letters 77 (2): 20002. https://doi.org/10.1209/0295-5075/77/20002.

—. 2016. “Major Transitions in Information Technology.” Philosophical Transactions of the Royal Society B: Biological Sciences 371 (1701): 20150450. https://doi.org/10.1098/rstb.2015.0450.

—. 2017. “Breakdown of Modularity in Complex Networks.” Frontiers in Physiology 8: 497. https://doi.org/10.3389/fphys.2017.00497.

—. 2021. “The Long and Winding Road: Accidents and Tinkering in Software Standardization.” Metode Science Studies Journal 0 (11): 91–97. https://doi.org/10.7203/metode.11.16112.

Valverde, S., R. Ferrer-i-Cancho, and R. V. Solé. 2002. “Scale-Free Networks from Optimal Design.” Europhysics Letters 60 (4): 512. https://doi.org/10.1209/epl/i2002-00248-2.

Valverde, S., and R. V. Solé. 2005a. “Logarithmic Growth Dynamics in Software Networks.” Europhysics Letters 72 (5): 858. https://doi.org/10.1209/epl/i2005-10314-9.

—. 2005b. “Network Motifs in Computational Graphs: A Case Study in Software Architecture.” Physical Review E 72 (2): 026107. https://doi.org/10.1103/PhysRevE.72.026107.

—. 2007a. “Hierarchical Small-Worlds in Software Architecture.” In Dynamics of Continuous, Discrete & Impulsive Systems, Series B: Applications & Algorithms 14: 1–11. Special issue on Software Engineering and Complex Networks, edited by K. L. Teo and X. Liu.

—. 2007b. “Self-Organization Versus Hierarchy in Open-Source Social Networks.” Physical Review E 76 (4): 046118. https://doi.org/10.1103/PhysRevE.76.046118.

—. 2015a. “A Cultural Diffusion Model for the Rise and Fall of Programming Languages.” Human Biology 87 (3): 224–234. https://doi.org/10.13110/humanbiology.87.3.0224.

—. 2015b. “Punctuated Equilibrium in the Large-Scale Evolution of Programming Languages.” Journal of the Royal Society Interface 12 (107): 20150249. https://doi.org/10.1098/rsif.2015.0249.

Vidiella, B., S. Carrignon, R. A. Bentley, M. J. O’Brien, and S. Valverde. 2022. “A Cultural Evolutionary Theory that Explains Both Gradual and Punctuated Change.” Journal of the Royal Society Interface 19 (196): 20220570. https://doi.org/10.1098/rsif.2022.0570.

Wang, Y., Y. Pan, M. Yan, Z. Su, and T. H. Luan. 2023. “A Survey on ChatGPT: AI-Generated Contents, Challenges, and Solutions.” IEEE Open Journal of the Computer Society. https://doi.org/10.1109/OJCS.2023.3300321.

Webster, J., and J. J. Martocchio. 1993. “Turning Work into Play: Implications for Microcomputer Software Training.” Journal of Management 19 (1): 127–146. https://doi.org/https://doi.org/10.1177/014920639301900109.

Yang, J., H. Jin, R. Tang, X. Han, Q. Feng, H. Jiang, S. Zhong, B. Yin, and X. Hu. 2024. “Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond.” ACM Transactions on Knowledge Discovery from Data 18 (6): 1–32. https://doi.org/https://doi.org/10.1145/3649506.

Zenil, H., S. Hernández-Orozco, N. A. Kiani, F. Soler-Toscano, A. Rueda-Toicen, and J. Tegnér. 2018. “A Decomposition Method for Global Evaluation of Shannon Entropy and Local Estimations of Algorithmic Complexity.” Entropy 20 (8): 605. https://doi.org/10.3390/e20080605.

BACK TO The Economy as an Evolving Complex System IV