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Sprint projectSep 11, 2026Sivas, Türkiye

The Somatic-Heuristic Decision Loop (SHDL)

Beyzanur Bulut

Submitted to AI Incident Response Sprint. Sprint projects are early-stage work by participants, not Apart Research publications.

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Report: The Somatic-Heuristic Decision Loop (SHDL)

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The Somatic-Heuristic Decision Loop (SHDL) provides a biophysically plausible architecture for high-velocity AI incident response in dynamic, high-uncertainty environments. Traditional artificial intelligence paradigms rely heavily on deterministic computational processing, creating critical latency bottlenecks during emergency states. Drawing from Antonio Damasio’s Somatic Marker Hypothesis and Daniel Kahneman’s Dual-System Theory, SHDL resolves the trade-off between execution velocity and cognitive bias propagation.

By integrating subconscious heuristic shortcuts (System 1) with an Anterior Cingulate Cortex (ACC)-mediated conflict resolution mechanism, the framework enables real-time threat evaluation while dynamically shifting to System 2 reasoning during high-conflict anomaly states. Ultimately, the model demonstrates how combining biophysically plausible heuristics with dynamic safety monitoring provides a scalable, real-time decision-making architecture for critical AI incident response scenarios.

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How much would this matter for AI safety if it worked? How innovative is it? For scores of 4-5: is this actually new to the field, or replicating recent work?

Scoring guide
  1. 1Negligible. No clear problem addressed, or no meaningful novelty.
  2. 2Limited. Addresses a real problem but with a generic or well-trodden approach. Incremental at best.
  3. 3Moderate. Clear problem with a reasonable approach; some novelty in framing or method beyond routine application of existing tools.
  4. 4Significant. Important problem with an original approach, or identifies a neglected problem area. A valuable contribution others could build on.
  5. 5Exceptional. Tackles a critical AI safety problem with a genuinely novel approach, or opens a new research direction. Clear theory of change. You'd be excited to share this with researchers in the area.

How sound are methodology, implementation, and findings?

Scoring guide
  1. 1Seriously flawed. Methodology broken, results uninterpretable, or implementation doesn't work.
  2. 2Weak. Approach has significant gaps: missing validation, flawed experimental design, or incomplete implementation.
  3. 3Competent. Technically solid given the short duration. Methodology makes sense, results are interpretable, limitations acknowledged, work builds toward clear conclusions.
  4. 4Strong. Thorough methodology with convincing validation. Results clearly support conclusions. Immediately useful for future work.
  5. 5Exceptional. Ambitious scope executed rigorously. Surprising findings, novel methods, or unusually robust validation.

How clearly are work, findings, and impact potential communicated?

Scoring guide
  1. 1Incomprehensible. Cannot determine what the project is actually claiming or doing.
  2. 2Hard to follow. Key information buried, missing, or diluted by excessive length. Significant effort to extract main points.
  3. 3Clear enough. Can understand the problem, approach, and results without undue effort. Core content clearly present: problem, method, findings, limitations.
  4. 4Well presented. Easy to follow, well-structured, appropriate level of detail. Target audience would get it quickly.
  5. 5Exceptionally clear. A pleasure to read. Complex ideas made accessible. Could serve as a model for how to present this type of work.
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Cite this project

@misc{bulut2026somaticheuristic,
  title = {{The Somatic-Heuristic Decision Loop (SHDL)}},
  author = {Beyzanur Bulut},
  year = {2026},
  month = sep,
  note = {Submitted to AI Incident Response Sprint, an Apart Research Sprint},
  howpublished = {\url{https://apartresearch.com/sprints/projects/the-somaticheuristic-decision-loop-shdl-mm79}},
  url = {https://apartresearch.com/sprints/projects/the-somaticheuristic-decision-loop-shdl-mm79}
}

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