Human instincts can be modified through the Optimal
Superego Adjuster, which operates within social contexts. However, global
variables of Non-Biological Systems in unsupervised domains in social
environments can alter instinctual properties and reshape the Superego
structure. These changes align with general systems theory, adapting to
external environmental shifts. Biological Systems respond to changes by
internally adjusting for survival mechanism codes in the Instinct Component.
Global variables of Non-Biological Systems influence friendly
and unfriendly algorithmic codes within the Subconscious Component through
environmental contexts, shaping behavioral tendencies to highlight the
Competitive World domain, where ethical and unethical influences play a crucial
role in shaping instincts and decision-making models.
Observations:
1-Unique Instinct
Patterns
Each individual has a distinct psychological fingerprint based on their
specific pattern of activated and inactivated instincts. No two people share
the same instinctual composition in algorithmic codes, making every person
inherently unique.
2-Ethical and
Unethical Influences
Certain entities operating beyond sustainable competitive advantage in system
platforms have introduced unethical algorithmic models in social contexts.
These models can activate unfriendly instincts. To foster positive behavioral
outcomes, global variables of Non-Biological Systems should be structured to
reinforce admirable instincts while suppressing detrimental ones.
3-Unseen
Instinctual Forces
Scientific advancements could help identify instances of algorithmic codes of
hidden instinctual forces within Biological and then improve Non-Biological Systems.
Understanding how to activate or deactivate these instincts through external
environmental factors may unlock breakthroughs in behavioral science and how
humans can make optimal decisions in the real world.
4-Genetic
Influence on Instincts
Some instincts operate through inherited genetic algorithms, whether activated
or dormant. Deciphering these genetic mechanisms could enhance decision-making
models to address complex challenges in adaptive behaviors and design and optimize
system operations for running requirements with realistic expectations in
Non-Biological Systems.
