v1.0 · 126 references

Toutes les références

Références du registre de sources fourni et des citations directes de la simulation, de la plus ancienne à la plus récente. Pour une même année, l’ordre des sources est conservé. Les dates et notices sont reprises des sources; leur présence ne constitue pas une vérification indépendante ni une approbation. Les notices sans date figurent à la fin.

Corrections bibliographiques et fusion d’un doublon : voir le registre daté. Les anciens ZIP conservent leur contenu original. Registre des corrections

1. 1945
   F. A. Hayek. “The Use of Knowledge in Society.” American Economic Review 35(4), 519–530, 1945. JSTOR 1809376.
   https://www.jstor.org/stable/1809376
2. 1978
   Leslie Lamport. 'Time, Clocks, and the Ordering of Events in a Distributed System.' Communications of the ACM 21(7), 558-565, 1978.
   https://doi.org/10.1145/359545.359563
3. 1988
   Colin J. Fidge. 'Timestamps in Message-Passing Systems That Preserve the Partial Ordering.' 11th Australian Computer Science Conference, pp. 56-66, 1988.
   https://ics.uci.edu/~cs230/reading/1.pdf
4. 1989
   Friedemann Mattern. 'Virtual Time and Global States of Distributed Systems.' Proc. Workshop on Parallel and Distributed Algorithms, pp. 215-226, 1989.
   https://www.vs.inf.ethz.ch/publ/papers/VirtTimeGlobStates.pdf
5. 1991
   G. Oliva, M. Franceschelli, A. Gasparri, and A. Scala. “A sum-of-states preservation framework for open multiagent systems with nonlinear heterogeneous coupling.” IEEE Transactions on Automatic Control 69(3), 1991–1998, 2024.
   https://doi.org/10.1109/TAC.2023.3317728
6. 1998
   Simon A. Levin. “Ecosystems and the biosphere as complex adaptive systems.” Ecosystems 1(5), 431–436, 1998. DOI: 10.1007/s100219900037.
   https://doi.org/10.1007/s100219900037
7. 2001
   Vladimir A. Lefebvre. Algebra of Conscience. 2001.
   https://doi.org/10.1007/978-94-017-0691-9
8. 2004
   S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press, 2004.
   https://web.stanford.edu/~boyd/cvxbook/
9. 2004
   Cohen AA. “Female post-reproductive lifespan: a general mammalian trait.” Biological Reviews 79 (2004): 733–750.
   https://doi.org/10.1017/S1464793103006432
10. 2007
    R. Olfati-Saber, J. A. Fax, and R. M. Murray. “Consensus and cooperation in networked multi-agent systems.” Proceedings of the IEEE 95(1), 215–233, 2007.
    https://doi.org/10.1109/JPROC.2006.887293
11. 2008
    Cohen AA, et al. “Interspecific associations between circulating antioxidant levels and life-history variation in birds.” American Naturalist 172 (2008): 178–193.
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12. 2010
    Sergei Tarasenko. The Inverse Task of the Reflexive Game Theory. 2010.
    https://arxiv.org/abs/1011.3397
13. 2011
    Sergei Tarasenko. Emotionally Colorful Reflexive Games. 2011 (arXiv posting; the supplied simulation labels it 2010).
    https://arxiv.org/abs/1101.0820
14. 2012
    Rafal Goebel, Ricardo G. Sanfelice, and Andrew R. Teel. Hybrid Dynamical Systems: Modeling, Stability, and Robustness. Princeton University Press, 2012. ISBN 978-0-691-15389-6.
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15. 2012
    Cohen AA, Martin LB, Wingfield JC, McWilliams SR, Dunne JA. 'Physiological regulatory networks: ecological roles and evolutionary constraints.' Trends in Ecology & Evolution 27 (2012): 428-435.
    https://doi.org/10.1016/j.tree.2012.04.008
16. 2013
    Cohen AA et al. 'A novel statistical approach shows evidence for multi-system physiological dysregulation during aging.' Mechanisms of Ageing and Development 134 (2013): 110-117.
    https://doi.org/10.1016/j.mad.2013.01.004
17. 2014
    Mikko Kivelä, Alex Arenas, Marc Barthelemy, James P. Gleeson, Yamir Moreno, and Mason A. Porter. “Multilayer networks.” Journal of Complex Networks 2(3), 203–271, 2014. DOI: 10.1093/comnet/cnu016.
    https://doi.org/10.1093/comnet/cnu016
18. 2014
    Cohen AA et al. 'Cross-population validation of statistical distance as a measure of physiological dysregulation during aging.' Experimental Gerontology 57 (2014): 203-210.
    https://doi.org/10.1016/j.exger.2014.04.016
19. 2015
    Li Q et al. 'Homeostatic dysregulation proceeds in parallel in multiple physiological systems.' Aging Cell 14 (2015): 1103-1112.
    https://doi.org/10.1111/acel.12402
20. 2015
    Cohen AA, et al. “Detection of a novel, integrative aging process suggests complex physiological integration.” PLoS ONE 10 (2015): e0116489.
    https://doi.org/10.1371/journal.pone.0116489
21. 2015
    Cohen AA. “Physiological and comparative evidence fails to confirm an adaptive role for aging in evolution.” Current Aging Science 8 (2015): 14–23.
    https://doi.org/10.2174/1874609808666150422124332
22. 2016
    Dylan Hadfield-Menell et al. “Cooperative Inverse Reinforcement Learning.” NeurIPS 2016.
    https://papers.nips.cc/paper_files/paper/2016/hash/c3395dd46c34fa7fd8d729d8cf88b7a8-Abstract.html
23. 2016
    J. M. Hendrickx and S. Martin. “Open multi-agent systems: Gossiping with deterministic arrivals and departures.” 54th Allerton Conference, 2016.
    https://perso.uclouvain.be/julien.hendrickx/availablepublications/Hendrickx_Martin_OpenMA_Allerton.pdf
24. 2016
    Cohen AA. 'Complex systems dynamics in aging: new evidence, continuing questions.' Biogerontology 17 (2016): 205-220.
    https://doi.org/10.1007/s10522-015-9584-x
25. 2017
    Dylan Hadfield-Menell et al. “The Off-Switch Game.” IJCAI 2017, 220–227.
    https://www.ijcai.org/Proceedings/2017/32
26. 2017
    Smitha Milli et al. “Should Robots be Obedient?” IJCAI 2017, 4754–4760.
    https://www.ijcai.org/proceedings/2017/662
27. 2017
    Cohen AA, Isaksson C, Salguero-Gómez R. 'Co-existence of multiple trade-off currencies shapes evolutionary outcomes.' PLoS ONE 12 (2017): e0189124.
    https://doi.org/10.1371/journal.pone.0189124
28. 2018
    Jan Leike et al. “Scalable agent alignment via reward modeling: a research direction.” arXiv:1811.07871, 2018.
    https://arxiv.org/abs/1811.07871
29. 2018
    Geoffrey Irving, Paul Christiano & Dario Amodei. “AI Safety via Debate.” 2018.
    https://openai.com/index/debate/
30. 2018
    Paul Christiano. Iterated Amplification research sequence. 2018.
    https://www.alignmentforum.org/s/EmDuGeRw749sD3GKd
31. 2018
    Jacob W. Crandall et al. “Cooperating with machines.” Nature Communications 9, 233, 2018. DOI: 10.1038/s41467-017-02597-8.
    https://www.nature.com/articles/s41467-017-02597-8
32. 2018
    Cohen AA. 'Aging across the tree of life: The importance of a comparative perspective for the use of animal models in aging.' BBA Molecular Basis of Disease 1864 (2018): 2680-2689.
    https://doi.org/10.1016/j.bbadis.2017.05.028
33. 2019
    Evan Hubinger et al. “Risks from Learned Optimization in Advanced Machine Learning Systems.” arXiv:1906.01820, 2019.
    https://arxiv.org/abs/1906.01820
34. 2019
    Dansereau G, et al. “Conservation of physiological dysregulation signatures of aging across primates.” Aging Cell 18 (2019): e12925.
    https://doi.org/10.1111/acel.12925
35. 2019
    Wey TW, et al. “An Emergent Integrated Aging Process Conserved Across Primates.” Journal of Gerontology: Biological Sciences 74 (2019): 1689–1698.
    https://doi.org/10.1093/gerona/glz110
36. 2019
    Arbeev KG, et al. “‘Physiological Dysregulation’ as a Promising Measure of Robustness and Resilience in Studies of Aging and a New Indicator of Preclinical Disease.” Journals of Gerontology: Series A 74 (2019): 462–468.
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37. 2020
    J. M. Hendrickx and M. G. Rabbat. “Stability of decentralized gradient descent in open multi-agent systems.” arXiv:2009.05445v1, 2020.
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38. 2020
    Cohen AA et al. 'Are trade-offs really the key drivers of ageing and life span?' Functional Ecology 34 (2020): 153-166.
    https://doi.org/10.1111/1365-2435.13444
39. 2020
    Cohen AA, Legault V, Fülöp T. 'What if there’s no such thing as aging?' Mechanisms of Ageing and Development 192 (2020): 111344.
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40. 2021
    Alexander M. Turner et al. “Optimal Policies Tend To Seek Power.” NeurIPS 2021.
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41. 2021
    M. J. Blondin and M. Hale. “A decentralized multi-objective optimization algorithm.” Journal of Optimization Theory and Applications 189(2), 458–485, 2021.
    https://doi.org/10.1007/s10957-021-01840-z
42. 2021
    M. Franceschelli and P. Frasca. “Stability of open multiagent systems and applications to dynamic consensus.” IEEE Transactions on Automatic Control 66(5), 2326–2331, 2021.
    https://doi.org/10.1109/TAC.2020.3009364
43. 2021
    Cohen AA, Leblanc S, Roucou X. 'Robust Physiological Metrics From Sparsely Sampled Networks.' Frontiers in Physiology 12 (2021): 624097.
    https://doi.org/10.3389/fphys.2021.624097
44. 2022
    Long Ouyang et al. “Training language models to follow instructions with human feedback.” NeurIPS 2022.
    https://proceedings.neurips.cc/paper_files/paper/2022/hash/b1efde53be364a73914f58805a001731-Abstract.html
45. 2022
    Yuntao Bai et al. “Constitutional AI: Harmlessness from AI Feedback.” arXiv:2212.08073, 2022.
    https://arxiv.org/abs/2212.08073
46. 2022
    Wensink MJ, Cohen AA. 'The Danaid Theory of Aging.' Frontiers in Cell and Developmental Biology 9 (2022): 671208.
    https://doi.org/10.3389/fcell.2021.671208
47. 2022
    Cohen AA, Ferrucci L, Fülöp T, et al. 'A complex systems approach to aging biology.' Nature Aging 2 (2022): 580-591.
    https://doi.org/10.1038/s43587-022-00252-6
48. 2022
    Cohen AA, Leung DL, Legault V, et al. 'Synchrony of biomarker variability indicates a critical transition: Application to mortality prediction in hemodialysis.' iScience 25 (2022): 104385.
    https://doi.org/10.1016/j.isci.2022.104385
49. 2023
    Rafael Rafailov et al. “Direct Preference Optimization: Your Language Model is Secretly a Reward Model.” NeurIPS 2023.
    https://proceedings.neurips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html
50. 2023
    Mrinank Sharma et al. “Towards Understanding Sycophancy in Language Models.” arXiv:2310.13548, 2023.
    https://arxiv.org/abs/2310.13548
51. 2023
    Hao Guo et al. “Facilitating cooperation in human-agent hybrid populations through autonomous agents.” iScience 26(11), 108179, 2023. DOI: 10.1016/j.isci.2023.108179.
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52. 2023
    J. J. Egozcue and V. Pawlowsky-Glahn. “Subcompositional coherence and a novel proportionality index of parts.” SORT 47(2), 229–244, 2023.
    https://doi.org/10.57645/20.8080.02.7
53. 2024
    Rafael Rafailov et al. “Scaling Laws for Reward Model Overoptimization in Direct Alignment Algorithms.” NeurIPS 2024.
    https://proceedings.neurips.cc/paper_files/paper/2024/hash/e45caa3d5273d105b8d045e748636957-Abstract-Conference.html
54. 2024
    Micah Carroll et al. “AI Alignment with Changing and Influenceable Reward Functions.” ICML 2024.
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55. 2024
    Evan Hubinger et al. “Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training.” arXiv:2401.05566, 2024.
    https://arxiv.org/abs/2401.05566
56. 2024
    Ryan Greenblatt et al. “Alignment faking in large language models.” arXiv:2412.14093, 2024.
    https://arxiv.org/abs/2412.14093
57. 2024
    Anthropic. “Sycophancy to subterfuge: Investigating reward tampering in language models.” 2024.
    https://www.anthropic.com/research/reward-tampering
58. 2024
    Ryan Greenblatt et al. “AI Control: Improving Safety Despite Intentional Subversion.” ICML 2024.
    https://proceedings.mlr.press/v235/greenblatt24a.html
59. 2024
    Leonard Bereska & Efstratios Gavves. “Mechanistic Interpretability for AI Safety — A Review.” Transactions on Machine Learning Research, 2024.
    https://arxiv.org/abs/2404.14082
60. 2024
    B. Zhou, J. H. Park, Y. Yang, Y. Jiao, and R. Hao. “Dynamic weighted average consensus of open time-varying multi-agent systems on time scales via sampled-data impulsive communication.” IEEE Transactions on Network Science and Engineering 11(5), 4330–4343, 2024.
    https://doi.org/10.1109/TNSE.2024.3417386
61. 2024
    R. Vizuete, C. Monnoyer de Galland, P. Frasca, E. Panteley, and J. M. Hendrickx. “Trends and questions in open multi-agent systems.” In Hybrid and Networked Dynamical Systems, LNCIS 493, 219–252, 2024.
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62. 2024
    C. N. Hadjicostis and A. D. Domínguez-García. “Distributed average consensus in open multi-agent systems.” IEEE CDC 2024, 3037–3042.
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63. 2024
    E. Makridis, A. Grammenos, G. Oliva, E. Kalyvianaki, C. N. Hadjicostis, and T. Charalambous. “Average consensus over directed networks in open multi-agent systems with acknowledgement feedback.” arXiv:2409.08634v1, 2024.
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64. 2024
    Li Q, et al. “Physiological Dysregulation Proceeds and Predicts Health Outcomes Similarly in Chinese and Western Populations.” Journals of Gerontology: Series A 79 (2024): glad146.
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65. 2024
    Cohen AA, Olde Rikkert MGM. “The Power of a Complex Systems Perspective to Elucidate Aging.” Journals of Gerontology: Series A 79 (2024): glae210.
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66. 2025
    Jiaming Ji et al. “AI Alignment: A Contemporary Survey.” ACM Computing Surveys 58(5), Article 132, 2025. DOI: 10.1145/3770749.
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67. 2025
    Elle. “Reward Model Perspectives: Whose Opinions Do Reward Models Reward?” EMNLP 2025. DOI: 10.18653/v1/2025.emnlp-main.754.
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68. 2025
    Zihao Li et al. “Not All Voices Are Rewarded Equally: Probing and Repairing Reward Models across Human Diversity.” Findings of EMNLP 2025. DOI: 10.18653/v1/2025.findings-emnlp.183.
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69. 2025
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70. 2025
    Tan Zhi-Xuan, Micah Carroll, Matija Franklin & Hal Ashton. “Beyond Preferences in AI Alignment.” Philosophical Studies 182, 1813–1863, 2025. DOI: 10.1007/s11098-024-02249-w.
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71. 2025
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72. 2025
    Jon Kutasov et al. “Evaluating Control Protocols for Untrusted AI Agents.” arXiv:2511.02997, 2025.
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73. 2025
    Aidan Kierans et al. “Quantifying Misalignment Between Agents: Towards a Sociotechnical Understanding of Alignment.” AAAI 2025. DOI: 10.1609/aaai.v39i26.34947.
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74. 2025
    Florian Carichon et al. “The Coming Crisis of Multi-Agent Misalignment: AI Alignment Must Be a Dynamic and Social Process.” arXiv:2506.01080, 2025.
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75. 2025
    Dahlgren Lindström et al. “Helpful, harmless, honest? Sociotechnical limits of AI alignment and safety through Reinforcement Learning from Human Feedback.” Ethics and Information Technology 27, 2025. DOI: 10.1007/s10676-025-09837-2.
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76. 2025
    Anthropic. “Auditing language models for hidden objectives.” March 2025.
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77. 2025
    Nishant Balepur et al. “A Good Plan is Hard to Find: Aligning Models with Preferences is Misaligned with What Helps Users.” EMNLP 2025.
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78. 2025
    The Five Vows / VowOS. User-supplied normative framework, 2025–2026 revisions.
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79. 2025
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80. 2025
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81. 2025
    Moshe Glickman and Tali Sharot. 'How human-AI feedback loops alter human perceptual, emotional and social judgements.' Nature Human Behaviour 9, 345-359, 2025.
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82. 2025
    Cohen AA, Picard M, Beard JR, et al. 'Intrinsic health as a foundation for a science of health.' Science Advances 11 (2025): eadu8437.
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83. 2025
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84. 2025
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85. 2025
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86. 2025
    Provost G, Tanner K, Legault V, et al. “No winners or losers: clinical chemistry-based biological aging metrics perform similarly across cohorts and health outcomes.” Journals of Gerontology: Series A 80 (2025): glaf179.
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87. 2025
    Hong N, Cohen AA. “Aging as Entropy: A Quantifiable Framework.” Endocrinology and Metabolism 40 (2025): 876–878.
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88. 2026
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89. 2026
    Yoshua Bengio et al. International AI Safety Report 2026. February 2026.
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90. 2026
    Dalia Ali et al. “Operationalizing Pluralistic Values in Large Language Model Alignment Reveals Trade-offs in Safety, Inclusivity, and Model Behavior.” AAAI 2026. DOI: 10.1609/aaai.v40i44.41053.
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91. 2026
    Vijay Keswani et al. “Moral Change or Noise? On Problems of Aligning AI with Temporally Unstable Human Feedback.” AAAI 2026. DOI: 10.1609/aaai.v40i44.41083.
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92. 2026
    Max Kanwal & Caryn Tran. “Constructive Alignment: Governing Preference Dynamics in Human-AI Interaction.” arXiv:2607.00001, 2026.
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93. 2026
    Richard Qi et al. “Training a Misaligned Reward Seeker.” Anthropic Alignment Science, August 2026.
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94. 2026
    Jan Betley et al. “Training large language models on narrow tasks can lead to broad misalignment.” Nature 649, 584–589, 2026.
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95. 2026
    Mikhail Terekhov et al. “Adaptive Attacks on Trusted Monitors Subvert AI Control Protocols.” ICLR 2026.
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96. 2026
    OpenAI. “How we monitor internal coding agents for misalignment.” March 19, 2026.
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97. 2026
    Judy Hanwen Shen et al. 'AI Organizations are More Effective but Less Aligned than Individual Agents.' arXiv:2604.10290, 2026.
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98. 2026
    Tina Comes. “Sensemaking AI: Introducing a research and design agenda for human–AI networks.” EPJ Data Science 15, 42, 2026. DOI: 10.1140/epjds/s13688-026-00634-5.
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99. 2026
    Nick Hagar & Nicholas Diakopoulos. “Runtime configuration for situated governance of AI agents: a case study in investigative journalism.” AI and Ethics 6, 456, 2026. DOI: 10.1007/s43681-026-01298-7.
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100. 2026
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101. 2026
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102. 2026
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103. 2026
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104. 2026
     Chris Duffey. “Admissibility Alignment.” arXiv:2601.01816, 2026.
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105. 2026
     RC Concept Map v0.2. User-supplied conceptual map, September 2026.
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106. 2026
     Full Connected Reflexive-Control Graph v5. User-supplied applied/runtime framework, September 2026; case-specific actors excluded from the generic architecture.
     Unpublished source / Source non publiée
107. 2026
     IPS Mycelial Alignment v0.2. User-supplied/generated working architecture, September 2026.
     Unpublished source / Source non publiée
108. 2026
     Guillaume Bolduc. Priority-State Dynamics in Open Multi-Agent Systems: Dynamic Membership and History-Dependent Consensus Endpoints. Working preprint, compact v07, September 7, 2026. Unpublished source.
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109. 2026
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110. 2026
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111. 2026
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112. 2026
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113. 2026
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114. 2026
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