Skip to main content

Advertisement

SpringerLink
Log in

The theory of graceful extensibility: basic rules that govern adaptive systems

  • Published:
Environment Systems and Decisions Aims and scope Submit manuscript

Abstract

The paper introduces the theory of graceful extensibility which expresses fundamental characteristics of the adaptive universe that constrain the search for sustained adaptability. The theory explains the contrast between successful and unsuccessful cases of sustained adaptability for systems that serve human purposes. Sustained adaptability refers to the ability to continue to adapt to changing environments, stakeholders, demands, contexts, and constraints (in effect, to adapt how the system in question adapts). The key new concept at the heart of the theory is graceful extensibility. Graceful extensibility is the opposite of brittleness, where brittleness is a sudden collapse or failure when events push the system up to and beyond its boundaries for handling changing disturbances and variations. As the opposite of brittleness, graceful extensibility is the ability of a system to extend its capacity to adapt when surprise events challenge its boundaries. The theory is presented in the form of a set of 10 proto-theorems derived from just two assumptions—in the adaptive universe, resources are always finite and change continues. The theory contains three subsets of fundamentals: managing the risk of saturation, networks of adaptive units, and outmaneuvering constraints. The theory attempts to provide a formal base and common language that characterizes how complex systems sustain and fail to sustain adaptability as demands change.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Alderson DL, Doyle JC (2010) Contrasting views of complexity and their implications for network-centric infrastructures. IEEE SMC A 40:839–852

    Google Scholar 

  • Alderson DL, Brown GG, Carlyle M (2015) Operational models of infrastructure resilience. Risk Anal 35:4. https://doi.org/10.1111/risa.12333

    Article  Google Scholar 

  • Ashby (1956) An introduction to cybernetics. Chapman & Hall, London

    Book  Google Scholar 

  • Astrom KJ, Murray RM (2008) Feedback systems. Princeton University Press, Princeton

    Google Scholar 

  • Attneave F (1954) Some informational aspects of visual perception. Psychol Rev 61:183–193

    Article  CAS  Google Scholar 

  • Beaumont HJ, Gallie J, Kost C, Ferguson GC, Rainey PB (2009) Experimental evolution of bet hedging. Nature 462(5), 90–93. https://doi.org/10.1038/nature08504

    Article  CAS  Google Scholar 

  • Bialek W, de Ruyter van Steveninck, RR, Tishby N (2007) Efficient representation as a design principle for neural coding and computation. Quant Biol. arXiv:0712.4381 [q-bio.NC]

  • Bonner JT (1998) The origins of multicellularity. Integr Biol 1:27–36

    Article  Google Scholar 

  • Brenner N, Bialek W, de Ruyter van Steveninck, R (2000) Adaptive rescaling maximizes information transmission. Neuron 26:695–702

    Article  CAS  Google Scholar 

  • Bush SF, Hershey J, Vosburgh K (1999) Brittle system analysis. http://arxiv.org/pdf/cs/9904016.pdf downloaded 15 June 2006

  • Caporale LH, Doyle JC (2013) In Darwinian evolution, feedback from natural selection leads to biased mutations. Annals of the New York Academy of Science special issue on evolutionary dynamics and information hierarchies in biological systems. Ann Rep 1305:18–28

    CAS  Google Scholar 

  • Carlson JM, Doyle JC (2000) Highly optimized tolerance: robustness and design in complex systems. Phys Rev Lett 84(11):2529–2532

    Article  CAS  Google Scholar 

  • Chandra F, Buzi G, Doyle JC (2011) Glycolytic oscillations and limits on robust efficiency. Science 333:187–192

    Article  CAS  Google Scholar 

  • Chen Y-Z, Huang Z-G, Zhang H-F, Eisenberg D, Seager TP, Lai Y-C (2015) Extreme events in multilayer, interdependent complex networks and control. Sci Rep 5:17277. https://doi.org/10.1038/srep17277

    Article  CAS  Google Scholar 

  • Chuang S, Woods DD, Ting D, Cook RI, Hsu J-C (2018) Coping with Mass Casualties: Adaptations in a hospital without adequate capacity after the Formosa Fun Coast Dust Explosion. under review.

  • Conant RC, Ashby WR (1970) Every good regulator of a system must be a model of that system. Int J Syst Sci 1:89–97

    Article  Google Scholar 

  • Cook RI (2006) Being bumpable: consequences of resource saturation and near-saturation for cognitive demands on ICU practitioners. In: Woods DD, Hollnagel E (eds) Joint cognitive systems: patterns in cognitive systems engineering. Taylor & Francis/CRC Press, Boca Raton, pp 23–35

    Google Scholar 

  • Csete ME, Doyle JC (2002) Reverse engineering of biological complexity. Science 295:1664–1669

    Article  CAS  Google Scholar 

  • Dai L, Vorselen D, Korolev K, Gore J (2012) Generic indicators for loss of resilience before a tipping point leading to population collapse. Science 336(6085):1175–1177. https://doi.org/10.1126/science.1219805

    Article  CAS  Google Scholar 

  • Deary DS (2015) Sources of organizational resilience: Sustaining production and safety in a transportation firm. Unpublished dissertation, Ohio State University, Columbus OH

  • Deary DS, Walker KE, Woods DD (2013) Resilience in the Face of a Superstorm: A Transportation Firm Confronts Hurricane Sandy. In Proceedings of the human factors and ergonomics society, 57th Annual Meeting (pp. 329–333). Human factors and ergonomics society, Santa Monica, CA. https://doi.org/10.1177/1541931213571072

    Article  Google Scholar 

  • Dietz T, Ostrom E, Stern PC (2003) The struggle to govern the commons. Science 302(5652):1907

    Article  CAS  Google Scholar 

  • Doyle JC, Csete ME (2011) Architecture, constraints, and behavior. Proc Natl Acad Sci USA 2011 108(Suppl. 3):S15624–S15630

    Article  Google Scholar 

  • Doyle JC et al (2005) The “robust yet fragile” nature of the Internet. Proc Natl Acad Sci USA 102:14497–14502

    Article  CAS  Google Scholar 

  • Fairhall AL, Lewen GD, Bialek W, Van Steveninck RR (2001) Efficiency and ambiguity in an adaptive neural code. Nature 412:787–792

    Article  CAS  Google Scholar 

  • Fariadian AB, Annaswamy AM, Woods DD (2016) Towards a resilient control architecture: a demonstration of bumpless re-engagement following an anomaly in flight control. In: Choi J-K, Anctil A (eds) Sustainable conoscente network. Proceedings of the international symposium on sustainable systems and Technologies (ISSN 2329-9169)

  • Finkel M (2011) On flexibility: recovery from technological and doctrinal surprise on the battlefield. Stanford Security Studies, Palo Alto

    Google Scholar 

  • Hoffman RR, Woods DD (2011) Beyond Simon’s slice: five fundamental tradeoffs that bound the performance of macrocognitive work systems. IEEE Intell Syst 26(6):67–71

    Article  Google Scholar 

  • Hollnagel E (2009) The ETTO principle: efficiency-thoroughness trade-off: why things that go right sometimes go wrong. Ashgate, Farnham

    Google Scholar 

  • Hollnagel E, Woods DD, Leveson N (eds) (2006) Resilience engineering: concepts and precepts. Ashgate, Aldershot

    Google Scholar 

  • International Risk Governance Center (2016) Resource Guide on Resilience. Ecole Polytechnique Federale de Lausanne and International Risk Governance Center (IRGC). v29-07-2016, Lausanne. https://www.irgc.org/

  • Kirschner M, Gerhart J (2005) The plausibility of life. Yale University, New Haven

    Google Scholar 

  • Lansing JS, Kremer JN (1993) Emergent properties of Balinese water temples. Am Anthropol 95:97–114

    Article  Google Scholar 

  • Mendonça D, Wallace WA (2015) Factors underlying organizational resilience: the case of electric power restoration in New York City after 11 September 2001. Reliab Eng Syst Saf 141:83–91

    Article  Google Scholar 

  • Meyers LA, Bull JJ (2002) Fighting change with change: adaptive variation in an uncertain world. Trends Ecol Evol 17(12):551–557

    Article  Google Scholar 

  • Miller A, Xiao Y (2007) Multi-level strategies to achieve resilience for an organization operating at capacity: a case study at a trauma centre. Cognit Technol Work 9:51–66

    Article  Google Scholar 

  • Morison A, Murphy TB, Woods DD (2015) Human-robot interaction as extending human perception to new scales. In: Hoffman RR, Hancock PA, Scerbo M, Parasuraman R, Szalma JR (eds) Handbook of applied perception research, vol 2. Cambridge University Press, New York, p. 848–868

    Chapter  Google Scholar 

  • Narendra KS, Annaswamy AM (2005) Stable adaptive systems. Dover Publications, Mineola

    Google Scholar 

  • Nemeth CP, Nunnally M, O’Connor M, Brandwijk M, Kowalsky J, Cook RI (2007) Regularly irregular: how groups reconcile cross-cutting agendas and demand in healthcare. Cogni Technol Work 9:139–148

    Article  Google Scholar 

  • Ormerod P, Colbaugh R (2006) Cascades of failure and extinction in evolving complex systems. J Artif Soc Soc Simul. http://jasss.soc.surrey.ac.uk/9/4/9.html

  • Ostrom E (2003) Toward a behavioral theory linking trust, reciprocity, and reputation. In: Ostrom E, Walker J (eds) Trust and reciprocity: interdisciplinary lessons from experimental research. Russell Sage Foundation, New York

    Google Scholar 

  • Ostrom E (2012) Polycentric systems: multilevel governance involving a diversity of organizations. In: Brousseau E, Dedeurwaerdere T, Jouvet P-A, Willinger M (eds) Global environmental commons: analytical and political challenges in building governance mechanisms. Oxford University Press, Cambridge, pp 105–125

    Chapter  Google Scholar 

  • Park J, Seager TP, Rao PSC, Convertino M, Linkov I (2013) Integrating risk and resilience approaches to catastrophe management in engineering systems. Risk Anal 33(3):356–367. https://doi.org/10.1111/j.1539-6924.2012.01885.x

    Article  CAS  Google Scholar 

  • Patterson MD, Wears RL (2015) Resilience and precarious success. Reliab Eng Syst Saf 141:45–53

    Article  Google Scholar 

  • Patterson ES, Watts-Perotti JC, Woods DD (1999) Voice loops as coordination aids in space shuttle mission control. Comput Support Coop Work 8:353–371

    Article  CAS  Google Scholar 

  • Perry S, Wears R (2012) Underground adaptations: cases from health care. Cogn Technol Work 14:253–260. https://doi.org/10.1007/s10111-011-0207-2

    Article  Google Scholar 

  • Rieger CG (2010) Notional examples and benchmark aspects of a resilient control system. In: Proceedings of the IEEE, 3rd international symposium on resilient control systems (ISRCS); 2010. p. 64–71

  • Robbins J, Allspaw J, Krishnan K, Limoncelli T (2012) Resilience engineering: learning to embrace failure. Commun ACM, 55(11):40–47. https://doi.org/10.1145/2366316.2366331

    Article  Google Scholar 

  • Roth A (2008) What have we learned from market design? Hahn Lecture. Econ J 1818(March):285–310

    Article  Google Scholar 

  • Scheffer M, Bascompte J, Brock WA, Brovkin V, Carpenter SR, Dakos V et al (2009) Early-warning signals for critical transitions. Nature 461(7260):53–59

    Article  CAS  Google Scholar 

  • Seager TP, Clark SS, Eisenberg DA, Thomas JE, Hinrichs MM, Kofron R, Jensen CN, McBurnett LR, Snell M, Alderson DL (2017) Redesigning resilient infrastructure research. In: Linkov I, Palma Oliveira J (eds) Resilience and risk: methods and application in environment, cyber and social domains, Springer, Dordrecht

    Google Scholar 

  • Stephens RJ, Woods DD, Patterson ES (2015) Patient boarding in the emergency department as a symptom of complexity-induced risks. In: Wears RL, Hollnagel E, Braithwaite J (eds) Resilience in everyday clinical work. Ashgate, Farnham, pp 129–144

    Google Scholar 

  • Vespignani A (2010) Complex networks: the fragility of interdependency. Nature 464(7291):984–985

    Article  CAS  Google Scholar 

  • Wark B, Lundstrom BN, Fairhall A (2007) Sensory adaptation. Curr Opin Neurobiol 17:423–429

    Article  CAS  Google Scholar 

  • Wark B, Fairhall A, Rieke F (2009) Timescales of inference in visual adaptation. Neuron 61:750–761. https://doi.org/10.1016/j.neuron.2009.01.019

    Article  CAS  Google Scholar 

  • Watts-Perotti J, Woods DD (2009) Cooperative advocacy: a strategy for integrating diverse perspectives in anomaly response. Comput Support Coop Work 18:175–198

    Article  Google Scholar 

  • Wears RL, Woods DD (2007) Always adapting. Ann Emerg Med 50:517–519

    Article  Google Scholar 

  • Wears RL, Perry SJ, Anders S, Woods DD (2008) Resilience in the Emergency Department. In: Hollnagel E, Nemeth C, Dekker SWA (eds) Resilience engineering perspectives 1: remaining sensitive to the possibility of failure. Ashgate, Aldershot, pp 193–209

    Google Scholar 

  • Woods DD (2005) Creating foresight: lessons for resilience from Columbia. In: Starbuck WH, Farjoun M (eds) Organization at the limit: NASA and the Columbia disaster. Blackwell, Malden, pp 289–308

    Google Scholar 

  • Woods DD (2006) Essential characteristics of resilience. In: Hollnagel E, Woods D, Leveson. N (eds) Resilience engineering: concepts and precepts. Ashgate, Aldershot, pp 21–34

    Google Scholar 

  • Woods DD (2015) Four concepts for resilience and their implications for systems safety in the face of complexity. Reliab Eng Syst Saf 141:5–9. https://doi.org/10.1016/j.ress.2015.03.018

    Article  Google Scholar 

  • Woods DD (2016) The risks of autonomy: Doyle’s catch. J Cognit Eng Decis Mak 10(2):131–133

    Article  Google Scholar 

  • Woods DD (ed) (2017) STELLA report from the SNAFU catchers workshop on coping with complexity. SNAFU catchers consortium, October 4, 2017, downloaded from stella.report 10/05/2017

  • Woods DD, Branlat M (2010) Hollnagel’s test: being ‘in control’ of highly interdependent multi- layered networked systems. Cogn Technol Work 12:95–101

    Article  Google Scholar 

  • Woods DD, Branlat M (2011) Basic patterns in how adaptive systems fail. In: Hollnagel E, Pariès J, Woods DD, Wreathall J (eds) Resilience engineering in practice. Ashgate, Farnham, pp 127–144

    Google Scholar 

  • Woods DD, Hollnagel E (2006) Joint cognitive systems: patterns in cognitive systems engineering. CRC Press/Taylor & Francis Group, Boca Raton

    Book  Google Scholar 

  • Woods DD, Shattuck LG (2000) Distant supervision—local action given the potential for surprise. Cognit Technol Work 2:242–245

    Article  Google Scholar 

  • Woods DD, Wreathall J (2008) Stress–strain plot as a basis for assessing system resilience. In: Hollnagel E, Nemeth C, Dekker SWA (eds) Resilience engineering perspectives 1: remaining sensitive to the possibility of failure. Ashgate, Aldershot, pp 145–161

    Google Scholar 

  • Woods DD, Chan YJ, Wreathall J (2013) The stress–strain model of resilience operationalizes the four cornerstones of resilience engineering. In: Proceedings of the fifth international symposium on resilience engineering, resilience engineering association. Download from The Knowledge Bank. Columbus OH; http://hdl.handle.net/1811/60454 June 2013. p. 25–7

  • Zhou K, Doyle JC, Glover K (1996) Robust and optimal control. Prentice Hall, Englewood Cliffs

    Google Scholar 

Download references

Acknowledgements

The development of the theory has benefited from reactions, both positive and negative, from many people engaged in debates about complexity, safety, and resilience. I would like to thank the Resilience Engineering communities and all of those who have invited me to discuss and speak on the theory for stimulating inter-disciplinary lines of inquiry to make risky systems less brittle and more resilient. This research is supported in part by funding from National Science Foundation, Grant No. 1549815 and Department of Transportation DTRT13-G-UTC47.

Author information

Authors and Affiliations

  1. Department of Integrated Systems Engineering, The Ohio State University, 1971 Neil Ave, Columbus, OH, USA

    David D. Woods

Authors
  1. David D. Woods
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to David D. Woods.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Woods, D.D. The theory of graceful extensibility: basic rules that govern adaptive systems. Environ Syst Decis 38, 433–457 (2018). https://doi.org/10.1007/s10669-018-9708-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10669-018-9708-3

Keywords

Search

Navigation