Multi-process algorithms are designed to handle multiple computation
threads simultaneously, acting as reflective frameworks for testing hypotheses
and decision-making. They can reduce operational costs, maximize profits, and
provide a competitive edge for algorithm designers.
These algorithms feature parameters and configurations
based on the system's activities and profit-oriented goals. This study limits
algorithm features and explores two model-based approaches that employ pattern
recognition techniques. Multi-process algorithms can be analyzed from two
perspectives: parallel and consecutive, each focusing on structural patterns,
scenarios, and transactional processes within a system.
In the parallel approach, the algorithm simultaneously modifies
the properties of individual processes and entities across internal and
external system environments using Global Variables. This method can be broken
down into two distinct phases. The algorithm establishes modified parameter
properties within a consolidation process roadmap in the first phase. In the
second phase, this roadmap aligns with the target's instance threads, ensuring
the parameters are appropriately aligned using unique keys to tackle the
targeted property. The outcome of this planning often leads to hidden profits,
as illustrated in the subsequent section (Fig 1).
In contrast, the consecutive approach modifies the
properties of processes and entities sequentially across internal and external
system environments. Each modified parameter targets a specific property, which
can involve tackling multiple targets. When multiple targets are present, the
algorithm requires an additional operation known as the "Entanglement
Cycle" before achieving its desired outcome (Fig 2).
Both approaches, whether parallel or consecutive, enable
the system to maintain the integrity of its operations while unlocking hidden
benefits through complex, strategic tasks. However, one possible drawback of
multi-process algorithms is the potential for competitors to intercept
transparent algorithm parameters. By observing these parameters, an opponent
may reverse-engineer the algorithm's logic, gaining insight into its processes
and goals. Competitors could monitor modified parameters within internal and
external environments, identifying the underlying objectives of the algorithm's
design.
Observation:
Multi-process algorithm parameters have the potential to generate
Invisible Entities within the system platform. These entities operate behind
the scenes without direct visibility while influencing various processes and
outcomes. By interacting with system components and Global Variables, Invisible
Entities play a critical role in executing hidden tasks, optimizing
performance, and driving profits without being easily detected. Their ability
to remain concealed allows the system to perform complex operations while
minimizing the risk of external interference or competitive observation.
Observation:
The stimulus-response approach method lets opponents observe and
assess the entire process reliably. By applying specific stimuli and analyzing
the corresponding responses, opponents can evaluate the algorithm's behavior,
gaining insights into its structure and functionality. This method allows for a
thorough examination of the process, increasing the reliability of observation
and potentially exposing vital operational details.
