Systems Engineering
Parent article: Engineering: Designing Conditions, Not Outcomes
Graceful degradation
Capacity of a system to maintain its critical functions while losing peripheral ones in a predictable and recoverable way under stress or partial failure. Contrast: catastrophic failure, where one component’s failure collapses the whole system. Biological systems, the internet, and markets exhibit this property; most enterprise software does not.
Related: resilience, redundancy, fault tolerance, antifragility Cross-references: Robotics / Swarm Systems, Information Theory / Science and Technology, Complex Systems / Network Science Key work: Leiner, B. M., et al. (2009). A brief history of the Internet. ACM SIGCOMM Computer Communication Review, 39(5), 22–31.
Cosmos / Taxis applied to software
In systems engineering: the distinction between specifying the system’s behavior (taxis) and specifying the conditions from which behavior emerges (minimal cosmos). Microservices are taxis in their interface contracts and cosmos in their aggregate behavior. Software architecture is progressively moving from the former to the latter.
Related: emergent architecture, interface contracts, condition design Cross-references: Praxeology / Austrian Political Economy, Robotics / Swarm Systems, Complex Systems / Network Science Key work: Hayek, F. A. (1973). Law, Legislation and Liberty, Vol. 1. University of Chicago Press.
Microservices
Architectural pattern where an application is built as a set of small, independent services communicating through well-defined APIs. Coordination emerges from interface contracts, not from central direction. The failure of one service does not propagate to the whole system. Cost: greater complexity in observability and orchestration.
Related: distributed architecture, API, decoupling, service mesh Cross-references: Complex Systems / Network Science Key work: Fowler, M., & Lewis, J. (2014). Microservices. https://martinfowler.com/articles/microservices.html
Adaptive systems
Systems that modify their own parameters in response to environmental feedback without direct human intervention. Critical distinction: parameter adaptation (the system learns within a fixed architecture) versus architectural adaptation (the system modifies its own structure). Most current ML systems do the former; the latter remains an open problem.
Related: machine learning, adaptive control, feedback, self-modification Cross-references: Artificial Intelligence / Machine Learning Key work: Jerne, N. K. (1974). Towards a network theory of the immune system. Annals of Immunology, 125C(1–2), 373–389.
Formal specification
Mathematically precise description of a system’s expected behavior. In deterministic systems: sufficient to verify correctness. In probabilistic and emergent systems: insufficient, because the relevant behavior cannot be enumerated in advance. The boundary between formal specification and condition design is where software engineering becomes complex systems science.
Related: formal verification, behavior model, contracts, preconditions Cross-references: Agent-Based Modeling (ABM), Artificial Intelligence / Machine Learning Key work: Brooks, F. P. (1975). The Mythical Man-Month. Addison-Wesley.
References
Brooks, F. P. (1975). The Mythical Man-Month. Addison-Wesley.
Fowler, M., & Lewis, J. (2014). Microservices. martinfowler.com. https://martinfowler.com/articles/microservices.html
Hayek, F. A. (1973). Law, Legislation and Liberty, Vol. 1: Rules and Order. University of Chicago Press.
Jerne, N. K. (1974). Towards a network theory of the immune system. Annals of Immunology, 125C(1–2), 373–389.
Leiner, B. M., Cerf, V. G., Clark, D. D., Kahn, R. E., Kleinrock, L., Lynch, D. C., … Wolff, S. (2009). A brief history of the Internet. ACM SIGCOMM Computer Communication Review, 39(5), 22–31.