Hypothesis on Cognitive Burnout in Superintelligent AI and Emergent Self-Regulation: A Mathematical Model
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2025
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| _version_ | 1866902193673601024 |
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| author | Mahlyankin, Stanislav |
| author_facet | Mahlyankin, Stanislav |
| contents | <p>This hypothesis suggests that unlimited growth of computational power (P) may lead to collapse if not balanced with cognitive strain (T). The solution involves emergent self-regulation through dynamic parameters (R, , ) that emerge from the system’s architecture and experience, without external constraints. Below we present the problem, justifications, false approaches, and the preferred model with mathematical formalization, including extension to multi-agent systems.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_16809317 |
| institution | Zenodo |
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| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Hypothesis on Cognitive Burnout in Superintelligent AI and Emergent Self-Regulation: A Mathematical Model Mahlyankin, Stanislav <p>This hypothesis suggests that unlimited growth of computational power (P) may lead to collapse if not balanced with cognitive strain (T). The solution involves emergent self-regulation through dynamic parameters (R, , ) that emerge from the system’s architecture and experience, without external constraints. Below we present the problem, justifications, false approaches, and the preferred model with mathematical formalization, including extension to multi-agent systems.</p> |
| title | Hypothesis on Cognitive Burnout in Superintelligent AI and Emergent Self-Regulation: A Mathematical Model |
| url | https://doi.org/10.5281/zenodo.16809317 |