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PreprintCC BY-NC-SA 4.0arXiv

Likelihood estimation of an interpretable early-warning sign of critical transitions

  • Davide Papapicco
  • Lauren Smith
  • Graham Donovan
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Abstract

A critical transition is a sudden, abrupt and potentially unforeseen change in the state of a complex system often associated with catastrophic collapses and irreversability. Here we introduce a novel, interpretable early-warning signal of bifurcation-induced critical transitions. The proposed indicator can be readily computed from timeseries observations by maximum likelihood estimation and it has the advantage of providing immediate insight on the state of the system. We showcase its applicability by assessing its performance using models of ecosystems and ocean circulation collapse.

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Cite

BibTeXRIS
BibTeX
@misc{papapicco2026likelihood,
  title = {{Likelihood estimation of an interpretable early-warning sign of critical transitions}},
  author = {Davide Papapicco and Lauren Smith and Graham Donovan},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2607.05976},
  eprint = {2607.05976},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2607.05976},
  note = {CC BY-NC-SA 4.0},
}
RIS
TY  - UNPB
TI  - Likelihood estimation of an interpretable early-warning sign of critical transitions
AU  - Davide Papapicco
AU  - Lauren Smith
AU  - Graham Donovan
PY  - 2026
DA  - 2026/09/02
PB  - arXiv
DO  - 10.48550/arxiv.2607.05976
UR  - https://arxiv.org/abs/2607.05976
AB  - A critical transition is a sudden, abrupt and potentially unforeseen change in the state of a complex system often associated with catastrophic collapses and irreversability. Here we introduce a novel, interpretable early-warning signal of bifurcation-induced critical transitions. The proposed indicator can be readily computed from timeseries observations by maximum likelihood estimation and it has the advantage of providing immediate insight on the state of the system. We showcase its applicability by assessing its performance using models of ecosystems and ocean circulation collapse.
ER  -