Simplexity Quest

Emergent Engineering Summarized


Many of today’s most pressing problems, like climate change or political polarization, stem from complex systems that were built or significantly modified by us. For example, the society we live in is a complex system that we have constructed. To solve these problems, we need to understand how to make complex systems change their behavior in specific ways.

✨ Emergent Engineering ✨ is the practice of applying principles from Complex Systems Science to the discipline of engineering and should come in handy when we try to engineer better systems or improve the existing ones.

In his 2019 article Emergent Engineering: Reframing the Grand Challenge for the 21st Century, David Krakauer gives a good introduction to the idea. He lists a set of classical engineering axioms and contrasts them with select properties of complex systems, showing how their applicability is limited. For each of the axioms, he then proposes an objective for Emergent Engineering to improve on the original axiom.

Here’s my summary:

Classical Engineering Axiom Conflicting Property of Complex Systems Objective of Emergent Engineering
Design according to well-understood scientific principles that hold for all components in isolation and in aggregate Few general design principles exist for adaptive components (cells, organisms, nations) in isolation or in the aggregate, where new unforeseen properties emerge Design toward better incentives: Seek to modify the reward or selective context in which semi-autonomous agents operate
Aim for fault-free components with very high levels of combined precision Components typically have high failure rates in all tasks and accomplish their objectives through statistical averaging and approximation across multiple scales and levels Accept significant component error rates and focus on mechanisms that can average and aggregate these effects to acceptable levels in the collective output
Minimize error and accept only the smallest system failure rates by eliminating uncertainty and reducing degrees of freedom of components Significant uncertainty and lack of information at both the component and aggregate level, and components have large and often poorly understood repertoires of behavior Design with an eye towards distributions of outcomes and not towards deltas (single optimal outcomes), pursuing average properties throughout
Design systems into linear ranges of operation where collective dynamics are predictable and controllable Most evolved complex systems operate in nonlinear and often near-critical regimes (close to thresholds and tipping points) Develop mechanisms for controlling nonlinear dynamics and predicting and influencing critical transitions
Reduce noise and adaptability of components to prevent unexpected emergent behaviors Adaptability of components is the rule, not the exception, and learning and adaptation are ongoing and irrepressible Harness adaptation to allow for continued exploration and exploitation rather than coercing systems into single states that require endless iterations of costly replacements, re-designs or re-orgs


📝 I’ve summarized the article here mostly for my own later reference. If you’re interested in Emergent Engineering please read Krakauer’s article itself. David C. Krakauer is the current president of the Santa Fe Institute, the world’s foremost research institute dedicated to Complex Systems.

📝 A few of the axioms above could also just as well stem from traditional management of organizations, and fail for the same given reasons. Adaptability of components is the rule, not the exception, and learning and adaptation are ongoing and irrepressible obviously applies to people.

📝 A great introduction to the broader topic of Complex Systems Science can be found at Complexity Explained.