Resetting optimized competitive first-passage outcomes in non-Markovian systems
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
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2026
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| author | Pal, Suvam Das, Rahul Pal, Arnab |
| author_facet | Pal, Suvam Das, Rahul Pal, Arnab |
| contents | We investigate the role of stochastic resetting in non-Markovian systems, where memory effects arise due to slow relaxation, rugged energy landscapes, disordered environments, and molecular crowding. Using the celebrated continuous-time random walk (CTRW) framework, we analyze first-passage processes with multiple competing outcomes and examine how resetting can selectively enhance desired events. We characterize the efficiency of resetting through conditional mean first-passage times (MFPTs) and demonstrate that its impact is highly sensitive to the underlying waiting-time statistics. Furthermore, we derive an inequality that quantifies how resetting controls fluctuations in conditional first-passage times (FPTs), revealing regimes where variability is significantly suppressed. Our results provide a systematic understanding of how long-term memory influences competitive first-passage outcomes and establish resetting as a powerful control mechanism beyond the conventional Markovian setting. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_01986 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Resetting optimized competitive first-passage outcomes in non-Markovian systems Pal, Suvam Das, Rahul Pal, Arnab Statistical Mechanics Soft Condensed Matter Probability Chemical Physics We investigate the role of stochastic resetting in non-Markovian systems, where memory effects arise due to slow relaxation, rugged energy landscapes, disordered environments, and molecular crowding. Using the celebrated continuous-time random walk (CTRW) framework, we analyze first-passage processes with multiple competing outcomes and examine how resetting can selectively enhance desired events. We characterize the efficiency of resetting through conditional mean first-passage times (MFPTs) and demonstrate that its impact is highly sensitive to the underlying waiting-time statistics. Furthermore, we derive an inequality that quantifies how resetting controls fluctuations in conditional first-passage times (FPTs), revealing regimes where variability is significantly suppressed. Our results provide a systematic understanding of how long-term memory influences competitive first-passage outcomes and establish resetting as a powerful control mechanism beyond the conventional Markovian setting. |
| title | Resetting optimized competitive first-passage outcomes in non-Markovian systems |
| topic | Statistical Mechanics Soft Condensed Matter Probability Chemical Physics |
| url | https://arxiv.org/abs/2604.01986 |