Academia.eduAcademia.edu

Outline

Title
Abstract
Introduction
Tuna Project Overview
Conclusions
References

Engineering emergence

Profile image of Fiona PolackFiona Polack

2006

Abstract

Abstract We explore various definitions and characteristics of emergence, how we might recognise and measure emergence, and how we might engineer emergent systems. We discuss the TUNA (Theory Underpinning Nanotech Assemblers) project, which is investigating emergent engineering in the context of molecular nanotechnology, and use the TUNA case study to explore an architecture suitable for emergent complex systems

Related papers

Self-organization, emergence and the architecture of complexity
Francis Heylighen

Proceedings of the 1st European conference on …, 1989

Emergence is a classical concept in systems theory, where it denotes the principle that the global properties defining higher order systems or “wholes” (eg boundaries, organization, control, ...) can in general not be reduced to the properties of the lower order subsystems or ...

View PDFchevron_right
<title>Complex systems engineering from nano- to macro-scale</title>
Geoff James

Complex Systems, 2005

The design of complex systems to achieve desired outcomes-complex systems engineering-is achievable in numerous natural systems and in some systems of human construction. This paper concerns multi-agent complex systems that comprise a large number of autonomous, interacting elements. Emergence presents a rich variety of behaviours for the designer to use; however, the unpredictability of emergence is a barrier to conventional engineering methodology. By probing examples of engineered systems and looking for common features, a design methodology may be sought.

View PDFchevron_right
3.5.0 Can We Engineer the Emergent Behavior of A System‐of‐Systems?
Azad Madni

INCOSE International Symposium, 2009

Emergent behaviors exist in biological systems, physical systems and human performance. It is an inherited nature of a System‐of‐Systems (SoS). SoS displays a global complexity that cannot be adequately managed by hierarchical structures and central control; therefore, traditional systems engineering and management approaches are necessary but insufficient for a SoS.Little is currently known about constructing an interoperable network of systems and the incorporation of emergent behaviors. The purpose of this panel is to explore the possibilities of developing an architecture model including the emergent behavior.The challenge is how to understand the initiation mechanisms of the emergent behaviors for a particular system architecture model so that the resident beneficial or harmful emergent behaviors can be enhanced or mitigated with selected changes in the model. Is model‐based the only feasible approach to develop the architecture model with emergent behavior? If this is the answ...

View PDFchevron_right
The concept of emergence in complexity science: Finding coherence between theory and practice
Laura Franklin-Hall

Proceedings of the Complex …, 2002

The concept of emergence plays a central role in the science(s) of complexity. Yet while its importance seems substantial, its clarity is not. Some researchers provide only vague definitions of emergence, or define it by example rather than by providing necessary and sufficient ...

View PDFchevron_right
Towards Engineering Biosystems with Emergent Collective Functions
Jan-Ulrich Kreft

Many complex behaviours in biological systems emerge from large populations of interacting molecules or cells, generating functions that go beyond the capabilities of the individual parts. Such collective phenomena are of great interest to bioengineers due to their robustness and scalability. However, engineering emergent collective functions is difficult because they arise as a consequence of complex multi-level feedback, which often spans multiple length-scales. Here, we present a perspective on how some of these challenges could be overcome by using multi-agent modelling as a design framework within synthetic biology. Using case studies covering the construction of synthetic ecologies to biological computation and synthetic cellularity, we show how multi-agent modelling can capture the core features of complex multi-scale systems and provide novel insights into the underlying mechanisms which guide emergent functionalities across scales. The ability to unravel design rules underp...

View PDFchevron_right
7.6.0 How to Engineer the Emergent Behavior of a System of Systems
Azad Madni

INCOSE International Symposium, 2008

Emergent behaviors exist in biological systems, physical systems and human performance. Little is currently known about constructing an interoperable network of systems and the incorporation of known emergent behaviors. The purpose of this panel is to explore the methods and principles in developing an architecture including the emergent behavior.SoS displays a global complexity that cannot be adequately managed by hierarchical structures and central control; therefore, traditional systems engineering and management approaches are inadequate and insufficient for a SoS.The challenge is how to understand the initiation mechanisms of the emergent behaviors for a particular system architecture model so that the resident beneficial or harmful emergent behaviors can be enhanced or mitigated with selected changes in the current system architectural model. Is model‐based the only feasible approach to develop the architecture model with emergent behavior? If this is the answer, what kind of ...

View PDFchevron_right
Emergent Engineering: A Radical Paradigm Shift
Mihaela Ulieru

Int. J. Autonomous and Adaptive Communications …, 2009

View PDFchevron_right
Modeling the peak of emergence in systems: Design and katachi
Alessio Erioli

Progress in biophysics and molecular biology, 2017

It is difficult to model emergence in biological systems using reductionist paradigms. A requirement for computational modeling is that individual entities can be recorded parametrically and related logically, but their transformation into systems cannot be captured this way. The problem stems from an inability to formally represent system-level interactions and implicit influences, such as context, shifts in causal agency or scale, and self-reference. This hampers biological systems modeling and its computational counterpart, indicating a need for new fundamental abstraction frameworks that include system-level characteristics. We develop an approach that formally captures these characteristics, focusing on the way they come together to enable transformation at the &#39;peak&#39; of an emergent process. An example from virology is presented, in which two seemingly antagonistic systems - the herpes cold sore virus and its host - are capable of altering their basic biological objecti...

View PDFchevron_right
Toward Engineering Biosystems With Emergent Collective Functions
Vittorio Bartoli

Frontiers in Bioengineering and Biotechnology, 2020

Many complex behaviors in biological systems emerge from large populations of interacting molecules or cells, generating functions that go beyond the capabilities of the individual parts. Such collective phenomena are of great interest to bioengineers due to their robustness and scalability. However, engineering emergent collective functions is difficult because they arise as a consequence of complex multi-level feedback, which often spans many length-scales. Here, we present a perspective on how some of these challenges could be overcome by using multi-agent modeling as a design framework within synthetic biology. Using case studies covering the construction of synthetic ecologies to biological computation and synthetic cellularity, we show how multi-agent modeling can capture the core features of complex multi-scale systems and provide novel insights into the underlying mechanisms which guide emergent functionalities across scales. The ability to unravel design rules underpinning these behaviors offers a means to take synthetic biology beyond single molecules or cells and toward the creation of systems with functions that can only emerge from collectives at multiple scales.

View PDFchevron_right
The Engineering of Emergence in Complex Adaptive Systems
Anet Potgieter

Agent-oriented software engineering is a new software engineering paradigm that is ideally suited to the analysis and design of complex systems. The main focus of these methodologies is to engineer a complex system in such a way that the correct emergent behavior results. In a complex adaptive system, however, emergent behavior cannot be predicted during analysis and design as it evolves only after implementation. By restricting emergent behavior as is done in most agent-oriented software engineering approaches, a complex system cannot be fully adaptive as well. We propose the BaBe methodology that will enable a complex system to be adaptive as the system can learn from its environment during run-time and modify its behavior in order to adapt to changes in its environment. This methodology adds a run-time emergence model consisting of distributed Bayesian Behavior Networks to the agent-oriented software engineering lifecycle. These networks are initialized by the human software engi...

View PDFchevron_right

References (52)

  1. R. Abbott. Open at the top; open at the bottom; and contin- ually (but slowly) evolving. In IEEE International Confer- ence on System of Systems Engineering, Los Angeles, USA, Apr. 2005.
  2. R. Abbott. Emergence explained: getting epiphenomena to do real work. Feb. 2006. arXiv:cs/0602045.
  3. R. H. Abraham. Dynamics and self-organization.
  4. In F. E. Yates, A. Garfinkel, D. Walter, and G. Yates, editors, Self- organizing Systems: The Emergence of Order, pages 599- 613. Plenum, 1987.
  5. R. Alexander, M. Hall-May, and T. Kelly. Characterisation of systems of systems failures. In 22nd Int. Systems Safety Conference, Aug. 2004.
  6. P. W. Anderson. More is different. Science, 177(4047):393- 396, 1972.
  7. Aristotle. Metaphysics, volume book H (VIII). 350 BC. Translation from W. D. Ross, Aristotle's metaphysics, 2 vols, Oxford University Press, 1924.
  8. N. A. Baas and C. Emmeche. On emergence and explana- tion.
  9. Intellectica, 25(2):67-83, 1997. Also published as SFI Working Paper 97-02-008.
  10. R. Banach, C. Jeske, S. Fraser, R. Cross, M. Poppleton, S. Stepney, and S. King. Approaching the formal design and development of complex systems: The retrenchment posi- tion. In Workshop on Software and Complex Systems, 9th IEEE International Conference on Engineering of Complex Computer Systems, Florence, Italy, 2004.
  11. R. Banach and M. Poppleton. Retrenchment: An engineer- ing variation on refinement. In D. Bert, editor, 2nd Interna- tional B Conference, Montpellier, France, April 1998, vol- ume 1393 of LNCS, pages 129-147. Springer, 1998.
  12. M. A. Bedau. Weak emergence. In J. Tomberlin, edi- tor, Philosophical Perspectives 11: Mind, Causation, and World, pages 375-399. Blackwell, 1997.
  13. R. D. Beer. A dynamical systems perspective on agent- environment interaction. Artificial Intelligence, 72:173-215, 1995.
  14. E. R. Berlekamp, J. H. Conway, and R. K. Guy. Winning Ways for Your Mathematical Plays Volume 2: games in par- ticular. Academic Press, 1982.
  15. M. H. Bickhard and D. T. Campbell. Emergence. In P. B. Andersen, C. Emmeche, N. O. Finnemann, and P. V. Christiansen, editors, Downward Causation, pages 322- 348. Aarhus University Press, 2000.
  16. A. Burns, I. J. Hayes, G. Baxter, and C. J. Fidge. Mod- elling temporal behaviour in complex socio-technical sys- tems. Technical Report YCS-2005-390, Department of Computer Science, University of York, 2005.
  17. D. T. Campbell. 'Downward Causation' in hierarchically or- ganised biological systems. In F. J. Ayala and T. Dobzhan- sky, editors, Studies in the Philosophy of Biology: reduction and related problems, chapter 11, pages 179-186. Macmil- lan, 1974.
  18. R. J. Campbell and M. H. Bickhard. Physical- ism, emergence and downward causation. 2001. www.lehigh.edu/˜mhb0/physicalemergence.pdf.
  19. A. Clark. Being There: putting brain, body and world to- gether again. Oxford University Press, 1997.
  20. J. P. Crutchfield. The calculi of emergence: Computation, dynamics, and induction. In Physica D special issue on the Proceedings of the Oji International Seminar, 1994.
  21. G. Despotou and T. Kelly. Extending the safety case con- cept to address dependability. In 22nd Int. Systems Safety Conference, Aug. 2004.
  22. K. E. Drexler. Engines of Creation. Anchor Books, 1986.
  23. K. E. Drexler. Nanosystems: Molecular Machinery, Manu- facturing, and Computation. Wiley, 1992.
  24. C. Emmeche, S. Køppe, and F. Stjernfelt. Explaining emer- gence: towards an ontology of levels. Journal for General Philosophy of Science, 28:83-119, 1997.
  25. J. Faith. Why gliders don't exist: Anti-reductionism and emergence. In C. Adami, R. Belew, H. Kitano, and C. Tay- lor, editors, Artificial Life VI: Proceedings of the Sixth Inter- national Conference on Artificial Life, pages 389-392, 1998.
  26. R. P. Feynman. There's plenty of room at the bottom. In A. Hey, editor, Feynman and Computation: exploring the limits of computers, pages 63-76. Perseus Books, 1999.
  27. R. A. Freitas Jr. Some limits to global ecophagy by biovorous nanoreplicators, with public policy recommenda- tions. Foresight Institute, Apr. 2000.
  28. M. Gardner. Mathematical games: The fantastic combina- tions of John Conway's new solitaire game "Life". Scientific American, 223(4):120-123, October 1970.
  29. J. Goldstein. Emergence as a construct: History and issues. Emergence, 1(1):49-72, 1999.
  30. J. Goldstein. Emergence: A construct amid a thicket of con- ceptual snares. Emergence, 2(1):5-22, 2000.
  31. J. H. Holland. Emergence: from chaos to order. Oxford University Press, 1998.
  32. W. Hordijk, J. P. Crutchfield, and M. Mitchell. Mechanisms of emergent computation in cellular automata. In A. E. Eiben, T. Bäck, M. Schoenauer, and H.-P. Schwefel, edi- tors, Parallel Problem Solving from Nature V, pages 613- 622. Springer, 1998.
  33. S. A. Kauffman. The Origins of Order: self-organization and selection in evolution. Oxford University Press, 1993.
  34. D. V. Newman. Emergence and strange attractors. Philoso- phy of Science, 63(2):245-261, 1996.
  35. T. O'Conner. Emergent properties. Americal Philosophical Quarterly, 31:91-104, 1994.
  36. F. Polack and S. Stepney. Emergent properties do not refine. In REFINE 2005 Workshop, Guildford, UK, April 2005, vol- ume 137 of ENTCS, pages 163-181. Elsevier, 2005.
  37. F. Polack, S. Stepney, H. Turner, P. Welch, and F. Barnes. An architecture for modelling emergence in CA-like sys- tems.
  38. In M. S. Capcarrere, A. A. Freitas, P. J. Bentley, C. G. Johnson, and J. Timmis, editors, Advances in Artificial Life: ECAL 2005, Canterbury, UK, September 2005, volume 3630 of LNAI, pages 433-442. Springer, 2005.
  39. P. Prusinkiewicz and A. Lindenmayer. The Algorithmic Beauty of Plants. Springer, 1990.
  40. P. Rendell. Turing Universality of the Game of Life. In A. Adamatzky, editor, Collision-Based Computing. Springer, 2002.
  41. C. W. Reynolds. Flocks, herds, and schools: A distributed behavioral model. Computer Graphics (SIGGRAPH '87
  42. Conference Proceedings), 21(4):25-34, 1987.
  43. E. M. A. Ronald, M. Sipper, and M. S. Capcarrère. Testing for emergence in artificial life. In D. Floreano, N. J.-D., and F. Mondada, editors, Advances in Artificial Life: 5th European Conference, volume 1674 of LNCS, pages 13-20. Springer, 1999.
  44. S. Schneider, A. Cavalcanti, H. Treharne, and J. Woodcock. Bloody CSP: a layered behavioural model of platelets. In ICECCS 2006. IEEE, 2006. (these proceedings).
  45. S. Stepney, D. Cooper, and J. Woodcock. More powerful Z data refinement: pushing the state of the art in industrial refinement. In J. P. Bowen, A. Fett, and M. G. Hinchey, editors, The Z Formal Specification Notation, 11th Inter- national Conference of Z Users, Berlin, Germany, Septem- ber 1998, volume 1493 of LNCS, pages 284-307. Springer, 1998.
  46. S. Stepney, D. Cooper, and J. Woodcock. An electronic purse: Specification, refinement, and proof. Technical monograph PRG-126, Oxford University Computing Lab- oratory, July 2000.
  47. H. Turner and S. Stepney. Rule migration: Exploring a de- sign framework for modelling emergence in CA-like sys- tems. In ECAL Workshop on Unconventional Computing, 2005.
  48. H. Turner, S. Stepney, and F. Polack. A simulation environ- ment for emergent properties (extended abstract). In ECCS 2005: European Conference on Complex Systems, Paris, France, 2005.
  49. H. Turner, S. Stepney, and F. Polack. Rule migration: A design framework for emergence. International Journal of Unconventional Computing, 2(4), 2006.
  50. R. Weaver, G. Despotou, T. Kelly, and J. McDermid. Com- bining software evidence -arguments and assurance. In REBSE05, St Louis, USA. ACM, 2005.
  51. P. Welch and F. Barnes. Mobile Barriers for occam-pi: Se- mantics, Implementation and Application. In J. Broenink, H. Roebbers, J. Sunter, P. Welch, and D. Wood, editors, Communicating Process Architectures 2005, volume 63 of Concurrent Systems Engineering Series, pages 289-316. IOS Press, Sept. 2005.
  52. P. H. Welch, F. R. M. Barnes, and F. A. C. Polack. Commu- nicating complex systems. In ICECCS 2006. IEEE, 2006. (these proceedings).

Related papers

Engineering Systems Which Generate Emergent Functionalities
Marie-Pierre Gleizes

2007

Complexity of near future and even nowadays applications is exponentially increasing. In order to tackle the design of such complex systems, being able to engineer self-organising systems is a promising approach. This way, the whole system will autonomously changes its behaviour as its parts locally reorganise themselves, always providing an adapted function. This paper proposes to focus on engineering such systems generating emergent functionalities. We will first define two important concepts to take into account in such a context: Emergence and Self-Organisation. Building on these two concepts, we will highlight three main challenges researchers have to cope with: (i) how to control the system at the macro level by only focusing on the design of agents at the micro level, (ii) what kind of tools, models and guides are needed to develop such systems in order to help designers and (iii) how validation of such systems can be achieved? Each of these three challenges will be explained and positioned in regard to the main existing approaches. Our solutions combining emergence and self-organisation will be expounded for each challenge.

View PDFchevron_right
Programming the Emergence in Morphogenetically Architected Complex Systems
René Doursat, Jean Petitot, Franck Varenne

Acta Biotheoretica, 63 (3), 2015, p. 295-308, 2015

Large sets of elements interacting locally and producing specific architectures reliably form a category that transcends the usual dividing line between biological and engineered systems. We propose to call them morphogenetically architected complex systems (MACS). While taking the emergence of properties seriously, the notion of MACS enables at the same time the design (or “meta-design”) of operational means that allow controlling and even, paradoxically, programming this emergence. To demonstrate our claim, we first show that among all the self-organized systems studied in the field of Artificial Life, the specificity of MACS essentially lies in the close relation between their emergent properties and functional properties. Second, we argue that to be a MACS a system does not need to display more than weak emergent properties. Third, since the notion of weak emergence is based on the possibility of simulation, whether computational or mechanistic via machines, we see MACS as good candidates to help design artificial self-architected systems (such as robotic swarms) but also harness and redesign living ones (such as synthetic bacterial films).

View PDFchevron_right
Emergence and Systems Engineering
Russ Abbott

cs.calstatela.edu

View PDFchevron_right
From modularity to emergence: a primer on the design and science of complex systems
Chih-Chun Chen

Electrical networks, flocking birds, transportation hubs, weather patterns, commercial organisations, swarming robots... Increasingly, many of the systems that we want to engineer or understand are said to be ‘complex’. These systems are often considered to be intractable because of their unpredictability, non-linearity, interconnectivity, heterarchy and ‘emergence’. Such attributes are often framed as a problem, but can also be exploited to encourage systems to efficiently exhibit intelligent, robust, self-organising behaviours. But what does it mean to describe systems as complex? How do these complex systems differ from the more easily understood ‘modular’ systems that we are familiar with? What are the underlying similarities between different systems, whether modular or complex? Answering these questions is a first step in approaching the design and science of complexity. However, to do so, it is necessary to look beyond the specifics of any particular system or field of study. We need to consider the fundamental nature of systems, looking for a common way to view ostensibly different phenomena. This primer introduces a domain-neutral framework and diagrammatic scheme for characterising the ways in which systems are modular or complex. Rather than seeing modularity and complexity as inherent attributes of systems, we instead see them as ways in which those systems are characterised by those who are interested in them. The framework is not tied to any established mode of representation (e.g. networks, equations, formal modelling languages) nor to any domain-specific terminology (e.g. ‘vertex’, ‘eigenvector’, ‘entropy’). Instead, it consists of basic system constructs and three fundamental attributes of modular system architecture, namely structural encapsulation, function-structure mapping and interfacing. These constructs and attributes encourage more precise descriptions of different aspects of complexity (e.g. emergence, self-organisation, heterarchy). This allows researchers and practitioners from different disciplines to share methods, theories and findings related to the design and study of different systems, even when those systems appear superficially dissimilar.

View PDFchevron_right
Emergent complex systems
jerome ravetz

Futures, 1994

Complex systems are becoming the focus of important innovative research and application in many areas, reflecting the progressive displacement of classical physics and the emergence of a new and creative role for mathematics. This article makes a distinction between ordinary and emergent complexity and argues that a full analysis requires dialectical thinking. In so doing the authors aim to provide a philosophical foundation for post-normal science. The exploratory analysis developed here is complementary to those conducted with a more formal, mathematical approach, and begins to articulate what lies on the other side of that somewhat indistinct divide, the conceptual space called emergent complexity.

View PDFchevron_right
580 California St., Suite 400
San Francisco, CA, 94104
© 2025 Academia. All rights reserved