Adaptive Stream Processing Architecture Inspired by Human Cognitive Constraints and Bounded Rationality

Abstract

This paper proposes an adaptive stream processing architecture inspired by human cognitive constraints and bounded rationality to address information overload in high-throughput environments. Conventional stream processing architectures typically enforce uniform, full-context parsing, which leads to computational saturation, token explosions, and brittle exception-handling under extreme workloads.

Rather than treating cognitive limitations as operational deficiencies, the proposed framework formalizes them as evolved resource-allocation strategies. The architecture introduces five functionally distinct processing modes:

1. Keyword Scanning (semantic compression)

2. Defensive Suppression (stability preservation)

3. Gestalt Peripheral Monitoring (macroscopic situation awareness)

4. Internal Loop Prioritization (internal resource reallocation)

5. Exploratory Sampling (search space diversity preservation via hash, random, or heuristic methods)

Additionally, the framework integrates macro-level clustering with minority signal preservation to ensure simultaneous data compression and anomaly retention. By converting system saturation from a terminal exception into a controlled, adaptive mode transition, this methodology provides an engineering foundation for building resilient, context-aware information systems operating under persistent overload.

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BibTeX RIS

BibTeX

@misc{student2026adaptive,
  title = {{Adaptive Stream Processing Architecture Inspired by Human Cognitive Constraints and Bounded Rationality}},
  author = {By STUDENT},
  howpublished = {Zenodo},
  year = {2026},
  month = oct,
  doi = {10.5281/zenodo.23641360},
  url = {https://zenodo.org/records/23641360},
  note = {CC BY 4.0},
}

RIS

TY  - UNPB
TI  - Adaptive Stream Processing Architecture Inspired by Human Cognitive Constraints and Bounded Rationality
AU  - By STUDENT
PY  - 2026
DA  - 2026/10/25
PB  - Zenodo
DO  - 10.5281/zenodo.23641360
UR  - https://zenodo.org/records/23641360
AB  - This paper proposes an adaptive stream processing architecture inspired by human cognitive constraints and bounded rationality to address information overload in high-throughput environments. Conventional stream processing architectures typically enforce uniform, full-context parsing, which leads to computational saturation, token explosions, and brittle exception-handling under extreme workloads.  Rather than treating cognitive limitations as operational deficiencies, the proposed framework formalizes them as evolved resource-allocation strategies. The architecture introduces five functionally distinct processing modes:  1. Keyword Scanning (semantic compression)  2. Defensive Suppression (stability preservation)  3. Gestalt Peripheral Monitoring (macroscopic situation awareness)  4. Internal Loop Prioritization (internal resource reallocation)  5. Exploratory Sampling (search space diversity preservation via hash, random, or heuristic methods)  Additionally, the framework integrates macro-level clustering with minority signal preservation to ensure simultaneous data compression and anomaly retention. By converting system saturation from a terminal exception into a controlled, adaptive mode transition, this methodology provides an engineering foundation for building resilient, context-aware information systems operating under persistent overload.
ER  -

Abstract

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