I’m intrigued by your framework (although way outside my wheelhouse, haha!). I was involved with finance and investment as a portfolio manager. Although I didn’t get deep into the theory, I was under the impression that the theory of complex adaptive systems dealt with the behavior of things like markets, where agents consciously adapt behavior based on feedback. What are your thoughts on CAS and how does it factor into your framework (if it does)?
The FDP's (Fundamental Design Principles) derives from systematic analysis of what enables complex adaptive systems to persist across radically different contexts—from subatomic particles to galactic superclusters, from single cells to multicellular organisms, from individuals to societies.
Each FDP addresses a specific thermodynamic requirement:
Symbiotic Purpose (SP) ensures the system generates net benefit rather than net extraction. In thermodynamic terms, this measures whether the system increases total order (negative entropy) or whether it dissipates energy without corresponding organization.
Adaptive Resilience (AR) measures whether the system can respond to perturbations without external intervention. This reflects the system’s information-processing capacity and feedback loop functionality.
Reciprocal Ethics (RE) evaluates whether exchanges within the system enable continued participation by all components. Extractive relationships deplete participants, eventually destroying the system.
Closed-Loop Materiality (CLM) measures entropy production efficiency. Systems that waste outputs rather than recycling them as inputs dissipate more energy per unit of function—they are thermodynamically profligate.
Distributed Agency (DA) reflects information-processing architecture. Centralized systems cannot process environmental information as rapidly or comprehensively as distributed systems, limiting adaptive capacity.
Contextual Harmony (CH) measures whether the system enhances or degrades its operational environment. Degradation eventually eliminates the context the system requires, creating self-undermining dynamics.
Emergent Transparency (ET) evaluates information availability within the system. Opacity prevents coordination and adaptation—components cannot adjust to conditions they cannot sense.
Intellectual Honesty (IH) measures whether the system acknowledges trade-offs and limitations. Denial of dysfunction prevents corrective response, leading to cascading failures.
Crucially, these principles apply universally.
An electron’s stability reflects high scores across all eight dimensions. A dysfunctional government agency’s collapse reflects low scores across the same eight dimensions.
The principles transcend scale and domain because they describe fundamental requirements for any system to persist through time. (Without them, life never begins!)
The most important theoretical insight is that the KOSMOS framework doesn’t merely provide one analytical lens among many but rather identifies the actual preconditions for evolutionary adaptation.
Systems that score low on FDPs cannot evolve because they lack the feedback mechanisms, distributed information-processing, reciprocal relationships, and transparent operations that enable learning and adaptation.
I’m intrigued by your framework (although way outside my wheelhouse, haha!). I was involved with finance and investment as a portfolio manager. Although I didn’t get deep into the theory, I was under the impression that the theory of complex adaptive systems dealt with the behavior of things like markets, where agents consciously adapt behavior based on feedback. What are your thoughts on CAS and how does it factor into your framework (if it does)?
Hi Don, and thanks for your reply.
The FDP's (Fundamental Design Principles) derives from systematic analysis of what enables complex adaptive systems to persist across radically different contexts—from subatomic particles to galactic superclusters, from single cells to multicellular organisms, from individuals to societies.
Each FDP addresses a specific thermodynamic requirement:
Symbiotic Purpose (SP) ensures the system generates net benefit rather than net extraction. In thermodynamic terms, this measures whether the system increases total order (negative entropy) or whether it dissipates energy without corresponding organization.
Adaptive Resilience (AR) measures whether the system can respond to perturbations without external intervention. This reflects the system’s information-processing capacity and feedback loop functionality.
Reciprocal Ethics (RE) evaluates whether exchanges within the system enable continued participation by all components. Extractive relationships deplete participants, eventually destroying the system.
Closed-Loop Materiality (CLM) measures entropy production efficiency. Systems that waste outputs rather than recycling them as inputs dissipate more energy per unit of function—they are thermodynamically profligate.
Distributed Agency (DA) reflects information-processing architecture. Centralized systems cannot process environmental information as rapidly or comprehensively as distributed systems, limiting adaptive capacity.
Contextual Harmony (CH) measures whether the system enhances or degrades its operational environment. Degradation eventually eliminates the context the system requires, creating self-undermining dynamics.
Emergent Transparency (ET) evaluates information availability within the system. Opacity prevents coordination and adaptation—components cannot adjust to conditions they cannot sense.
Intellectual Honesty (IH) measures whether the system acknowledges trade-offs and limitations. Denial of dysfunction prevents corrective response, leading to cascading failures.
Crucially, these principles apply universally.
An electron’s stability reflects high scores across all eight dimensions. A dysfunctional government agency’s collapse reflects low scores across the same eight dimensions.
The principles transcend scale and domain because they describe fundamental requirements for any system to persist through time. (Without them, life never begins!)
The most important theoretical insight is that the KOSMOS framework doesn’t merely provide one analytical lens among many but rather identifies the actual preconditions for evolutionary adaptation.
Systems that score low on FDPs cannot evolve because they lack the feedback mechanisms, distributed information-processing, reciprocal relationships, and transparent operations that enable learning and adaptation.