A Path of Total System Breakdown

Both system characteristics and environmental parameters can influence the transition from social norms to a total system breakdown. Behavioral patterns evolve naturally over time, and changes within a system often remain invisible until critical points are reached. The figure below highlights the gap between a stable social standard and a state of complete breakdown. Biological and Non-Biological Systems develop progressively along a trajectory of expected performance standards. When operations adhere to consistent parameters, the system exhibits stability and harmonic balance, known as Mode 1 (M1).

In Biological Systems, unknown external entities can disrupt this balance. System elements may override established decision-making processes, deviating from the standard evolutionary path into Mode 2 (M2). In this mode, invisible forces alter processes and disrupt the average balance (Mode 3 or M3).

This disruption in M3 can lead to countless side effects. Three potential outcomes of system adjustments are depicted in figures M4-0, M4-1, and M4-2. Mode 4-0 involves increased parameter complexity across multiple channels, while presumptive side effects (M4-1 and M4-2) are often disregarded in the system’s default settings. Even a single side effect can lead to the development of complex, interconnected structures, known as Complexity Mapping Patterns (Mode 5 or M5).

These patterns of complexity hinder system progress and trigger pathophysiological mechanisms that impair performance. A severe system crash can occur, disabling restore functions (Mode 6 or M6). The  system becomes vulnerable without functional recovery, and even critical data storage may be lost. The system may become trapped in a cycle resembling obsessive-compulsive behavior, where outputs are suppressed, but inputs remain largely unaffected.

A Non-Biological System may begin collecting override values across all layers via process inputs (Mode 7 or M7). However, it struggles to integrate these values fully. In contrast, a Biological System may enter a suspended state (Mode 8 or M8) and cannot reboot or restore itself fully. The system remains stuck, and rational optimization efforts are unlikely to produce a recovery key, rendering the system incapable of returning to a stable state.

 

Observation:

Optimization always reverts the current mode setting to the previous one. For instance, optimizing in Mode 7 shifts the system status back to Mode 6. However, rational suboptimization struggles to change the system status from Mode 8 to Mode 6. System developers must adopt a heightened awareness of system status, particularly when the system is positioned between discrepancy modes and total breakdown.

 

Observation:

Rational optimization involves a logical testing approach that avoids involving other subcomponents and their set properties’ factors beyond the life-cycle process.