Competence Criteria for Articulating Global Variables

The articulation of global variables within a system platform is not merely a technical task; it is a strategic and philosophical responsibility. Global variables shape the behavior, boundaries, and adaptive capacity of the entire system. Therefore, system designers entrusted with this role must meet a comprehensive set of competence criteria:

 

1-Knowledge of Universal Variables

 

Designers must understand overarching principles, such as equilibrium, entropy, feedback loops, scalability, and adaptability, that transcend individual systems. These universal variables influence how systems evolve, interact, and stabilize across contexts.

 

2-Deep Understanding of System Resources

A system’s resources, whether human, technological, informational, or environmental, form the substrate upon which global variables operate. Designers must grasp both the quantitative limits and qualitative dynamics of these resources. Humanity must be a vital priority in the design of the system platform.

 

3-Proficiency in System Development

 

Technical competence in architecture, modeling, integration, and optimization is essential. Designers should be able to build flexible frameworks that allow global variables to be adjusted without destabilizing the entire structure.

 

4-Comprehensive Knowledge of System Operations

 

 Beyond development, designers must understand how the system behaves in real-time. Operational insight enables anticipating cascading effects when global variables are modified.

 

5-Awareness of Internal and External Environments

 

Systems do not function in isolation. Designers must account for internal dynamics (organizational culture, structural hierarchies, embedded routines) and external pressures (economic forces, regulatory frameworks, social expectations, environmental constraints).

 

6-Understanding of Fundamental Activities and Routines

 

Recurring processes sustain every system. Designers must comprehend these baseline routines to ensure that global variables align with the system’s core functions rather than disrupt them.

 

Observation 1: The Challenge of Comprehensive Competence

 

Even highly skilled system designers may find it difficult to fully satisfy all these criteria simultaneously. Complexity, uncertainty, and the presence of invisible entities, latent variables, hidden biases, and emergent behaviors can limit the predictability of global variables.

For this reason, an ideal system platform should not rely solely on individual competence. Instead, it should be structurally capable of:

 

1-Encapsulating invisible entities within measurable system resources.

2-Detecting anomalies through feedback mechanisms.

3-Conveying subtle disturbances across subsystems without distortion.

4-Processing uncertainty through adaptive algorithms.

 

In essence, the platform itself must possess reflexive intelligence, an embedded capacity to self-correct, learn, and reveal hidden dynamics that human designers may overlook.

 

Observation 2: The Optical Society and System Stability

 

The concept of an optical society may be interpreted as a transparent, observable, and feedback-rich social system, one where information flows clearly and accountability is visible. Historically, societies that have institutionalized transparency and collective oversight have demonstrated stronger stability patterns. For example, the democratic framework of the European Union emphasizes regulatory transparency, but this transparency can sometimes be limited, potentially affecting shared governance structures. At the same time, the long-term institutional continuity of countries like Sweden reflects robust social trust and systemic visibility. However, the long-term institutional parameters need to be sustained and promoted in the social framework.

 

In such environments:

 

1-Information asymmetry is reduced.

2-Hidden distortions are more rapidly identified.

3-Resource distribution tends toward equilibrium.

4-Life-history patterns, education, employment, and social mobility become more predictable and optimized.

 

An optical society thus promotes systemic stability by minimizing opacity. When inhabitants (system resources) can clearly observe and interpret systemic signals, they align their behaviors with long-term equilibrium rather than short-term distortions.

 

Integrated Perspective

The articulation of global variables requires not only technical competence but also structural transparency. A resilient system platform must integrate:

 

1-Competent designers,

2-Adaptive infrastructure focuses on resilience, using innovative technology, real-time monitoring, and flexible designs to prevent premature obsolescence and ensure long-term sustainability.

3-An optical social environment that reduces invisibility.

 

When these elements converge, global variables can be calibrated to promote sustainable performance, equitable outcomes, and stable life-history trajectories within the broader system ecosystem.

Invisible Entities Penetrating a System Platform

Invisible entities, subtle patterns of influence, embedded assumptions, algorithmic biases, and non-transparent signals can infiltrate a system platform through multiple pathways. Within the Conscious Component, thoughts, visions, and creative impulses serve as adaptive mechanisms that respond to perceived environmental noise. When the system detects instability, ambiguity, or distortion, consciousness generates interpretative frameworks and strategic narratives intended to restore coherence. However, these same cognitive outputs can unintentionally introduce new variables into the system structure.

Influential decision-makers operating beyond visible global competition often transmit strategic algorithmic codes into the system platform. These codes do not enter the structure randomly; they are embedded through the global variables that govern system logic, priorities, and performance criteria. Once inserted, such codes recalibrate optimization processes, reshape evaluation metrics, and subtly redefine what the system interprets as efficiency, growth, or success.

External actors, such as lobbyists, intermediaries, or opportunistic agents, may further manipulate these global variables. By adjusting regulatory parameters, incentive structures, or informational inputs, they introduce modified datasets that contain invisible entities within the environmental domain. These entities may take the form of concealed dependencies, distorted feedback loops, or asymmetrical information flows. Because they are integrated at foundational levels, they remain undetected within routine operational diagnostics.

Over time, invisible entities become encapsulated within system resources, capital allocation models, technological infrastructures, human networks, and communication channels. They also embed themselves within output frameworks, influencing product quality, policy outcomes, cultural narratives, and institutional trust. What appears to be an organic system evolution may, in reality, be the cumulative effect of concealed algorithmic modifications.

Thus, system platforms require meta-observational mechanisms capable of detecting non-transparent alterations in global variables. Without reflective auditing and cross-layer verification, invisible entities can propagate across modules, amplifying complexity and reducing systemic resilience.

In this framework (Figure 3), invisible entities are not merely anomalies; they represent dynamic, often adaptive forces that can either destabilize or transform a system depending on how consciously and transparently they are identified and integrated.

 

                                                                                 

                                                                    

                                                

The Progress of Research on the Path of Life

This research program has evolved through several conceptual phases, each deepening the analytical framework for understanding decision-making processes within complex systems.

The initial phase (2007) concentrated on the presence of invisible entities within Non-Biological System platforms. These entities were conceptualized as latent structural variables, suboptimal algorithmic codes embedded beyond explicitly defined global variables. The central argument was that system instability, inefficiency, and unintended consequences often originate not from visible design flaws, but from concealed algorithmic misalignments operating beneath formal system architecture. This stage emphasized detection, measurement, and optimization of hidden structural deficiencies within complex organizational and technological systems.

In the subsequent phase, the research shifted toward observational analysis of fuzzy decision-making models among Systems Owners. Here, the focus moved from structural defects to behavioral dynamics. The study examined how ambiguity, bounded rationality, incomplete information, and environmental turbulence contribute to wicked decisions, choices whose consequences are nonlinear, unpredictable, and often detrimental to both the system platform and vulnerable environmental contexts. This phase highlighted the interaction between decision-makers and system environments, demonstrating how external pressures, economic constraints, and competitive forces distort rational evaluation and amplify uncertainty.

The subsequent conceptual development proposed a deeper inquiry into the sources and functional mechanisms underlying recurring decision-making patterns. Rather than analyzing decisions solely at the behavioral or structural level, this stage sought to investigate the internal architecture that generates such patterns. It marked a transition from surface-level system analysis to an integrative model encompassing Conscious and Subconscious Components.

This progression opened a theoretical threshold into a new research domain: the interaction between cognitive processes, algorithmic instinct cycles, and non-physical dimensions of experience. Within this framework, the Conscious Component is conceptualized as a creative, adaptive module capable of generating decision maps, structured representations of possible actions, risks, and anticipated outcomes. In parallel, the Subconscious Component is described as an embedded algorithmic system composed of modules and submodules that contain preconfigured code shaped by evolutionary, social, and experiential inputs.

 

Case studies within this research illustrate how decision maps are co-produced by:

 

1-The creative synthesis and logical modeling of the Conscious Component.

2-The structural characteristics and instinctive algorithmic codes are embedded within the Subconscious Component.

 

The interaction between these components generates observable decision trajectories. Importantly, these evolutionary paths are not solely rational constructs; they are competitive environmental pressures. Within a competitive world framework, scarcity dynamics, status hierarchies, economic constraints, and survival imperatives exert measurable influence on the logical data processed by the Conscious Component. Over time, system environments reshape and recalibrate internal algorithmic codes, reinforcing specific instinctive patterns while suppressing others. Thus, decision-making becomes an emergent phenomenon arising from:

 

1-External environmental forces.

2-Internal algorithmic instinct cycles.

3-Cognitive modeling within the Conscious Component.

4-Latent structural variables embedded within the system platform.

 

The structural map of instincts beyond the Subconscious Component, referenced in the following figure, represents an attempt to formalize this multilayered interaction. It provides a conceptual framework for analyzing how competitive and cooperative drives, algorithmic predispositions, and environmental signals converge to influence observable system behavior.

 

In summary, the progress of this research reflects a movement from structural system analysis to behavioral observation, and ultimately toward an integrative model of consciousness, subconscious algorithmic architecture, and environmental interaction. This evolving framework aims to provide a transdisciplinary foundation for understanding decision-making across Biological and Non-Biological Systems and environmental aftermaths.

                                                                             

                                                                                

Algorithmic Mechanisms beyond Decision-Making