Download - Complexity, Modelling & Plants
Complexity, Modelling & Plants
Teodor GhetiuNSC Group, CoSMoS project
Supervisors: Dr Fiona Polack and Dr Jim Bown1
1 University of Abertay Dundee
Complexity, Modelling & Plants Etymology: 14th century Latin expression
complexus• ‘embracing or comprehending several elements‘
[Simpson1989]
Types [Manson2001]: Algorithmic: information theory Deterministic: chaos and catastrophe theory Aggregate: complexity theory, complex systems
Definitions: 32 definitions of complexity [Lloyd2006]
Complex Systems
Definitions: A whole that is greater than the sum of its parts
[Aristotle350BC]
Cilliers finds 10 properties [Cilliers1998]: Large number of elements Rich, non-linear, local and recurrent interactions Have history React based on local knowledge Usually open, far-from-equilibrium
A system defined by: agent-based, dynamic, heterogeneous, feedback, organisation, emergence [Santa Fe CSCS]
Complex Systems
Features - Hierarchy theory [Simon1962] Scale: have multiple layers of description Emergence: high-level behaviours based on low-level
interactions Environment: influenced by and influencing their environment
Paradox (1) Natural (complex) systems: robust, adaptive, self-* properties Complexity: ‘A word problem and not a word solution’
[Morin1990]
Challenge: to improve the way we study and construct such systems Scientifically engineer, model, simulate, analyse
Complexity, Modelling & Plants Modelling motivations [Grim1999]
Pragmatic: tools for solving problems Paradigmatic: tools that facilitate a better understanding
Paradigms Mathematical modelling
Equation Based Modelling (EBM)
Computational modelling Cellular Automata (CA) Agent Based Modelling (ABM)
Individual Based Modelling (IBM) Process Oriented Modelling (POM)
Mathematical Modelling
Lotka-Volterra predator-prey model [Lotka1925] prey's numbers: own growth minus rate at which it is
preyed upon predator population: own growth minus natural death.
Mathematical modelling
Benefits Integrated view on populations [Kaiser1979] Explicit mathematical treatment, analytic truths
[Bryden2006]
Shortcomings Limits understanding of system properties [Kaiser1979] Scalability problems [Huston1988] Strong assumptions [Bullock1994] Centralised systems, physical laws dynamics
[Parunak1998]
Cellular Automata
Large sets of identical, finite-state automata [VonNeumann1955]
Simple but capable of generating complex behaviours
Benefits [Hogeweg1988] Extendability Observability Spatial representation [Durrett1993]
Shortcomings Synchrony Space-orientedness
Conway’s Game of Life
Agent-Based Modelling
Source www.esourceagent.com
Extending CA’s Autonomy Reactivity Proactivity Sociability
Agent-Based Modelling
Benefits Prediction of outcomes under novel conditions [Kaiser1979] Simpler and more accurate than mathematical models
[Huston1988] Relaxed assumptions [Bullock1994] Localised, distributed, information processing dynamics
[Parunak1998] Integrating many levels of description [Bousquet2004]
Shortcomings Performance, providing synthetic truths [Bryden2006] Oriented on social systems [Andrews2008] Time, space and component-quantity aspects [Andrews2008] Dependence on MAS platforms [Sudeikat2005]
Process Oriented Modelling
Benefits [Ritson2007] Finer granularity Plasticity, dynamism Simulations at larger scales Mapping to natural processes
Shortcomings Lower granularity: harder to model macro-entities Recent interdisciplinary tool
Process composition[Ritson2007]
Occoids simulation [Sampson2008] POP based Continuous space Scalable architecture Large scale simulations
Process Oriented Modelling
Models and simulations are generally used in [Andrews2008]: ‘Improving scientific understanding of (natural) systems’ ‘Constructing or exploring alternative realities’
Scientific use raises issues of: Realism, Precision and Generality trade-off [Holling1964] Analysis [Braitenberg1984] Transparency [DiPaolo2000] Validity [Sargent1987]
‘Scientific validity, like engineering validity, means that it must be possible to demonstrate, with evidence, how models express the scientific realities’ [Andrews 2008]
Scientific Modelling
Scientific Modelling
Paradox (2) Objective: to model complex systems Means: complexity features not addressed
thoroughly
Questions: How to construct (engineer) complex
systems? How to validate their behaviour?
Methodologies
Sargent’s process for developing simulation models [Sargent1981]
Methodologies
The CoSMoS process [Garnett2008]
Plant ecologies
Plant ecologies
Ecology: ‘Scientific study of the interactions between organisms
and their environment’ [Begon2006]
Ecology’s Holy Grail: General rules relating environment conditions, species
traits and community composition [Lavorel2002; Reineking2006]
Plant ecologies are complex systems [Huston1988] Scale: Individual, patch, population, community,
ecosystem Emergence: Patterns emerge from processes [Wu1994] Environment: Direct interdependence [Fornara2008]
Modelling ecologies
Mathematical models Matrix life-cycle models
Individual Based Models Simple representations: [Sebert-Cuvillier2007],
[Arii2006] homogeneous populations, non-spatial
Detailed representations: [Wu2007], [Evers2007] Complicated, Composite models
Modelling ecologies
Intraspecific variation through traits trade-off [Tilman2000] Detailed models: [Marks2006] one plant, 34 traits, no
reproduction
Addressing the “Holy Grail” Time and space heterogeneity matters
[Reineking2006]: 24 common traits, 4 species specific, spatial model
Intra and interspecific variation united [Bown2007a]: 12 traits, spatial “Individuals that are too similar cannot coexist”
Importance of diversity at the individual scale [Bown2007b]: community productivity related to
individual traits and environment
Summary Complexity and Complex Systems Approaches to modelling complex systems Scientific validation Plant ecologies
References 1 [Andrews 2008] Andrews, P., Polack, F., Sampson, A., Timmis, J., Scott, Coles, (2008), Simulating biology:
towards understanding what the simulation shows, CoSMoS workshop 2008 [Arii2006] Arii, K., Parrott, L. (2006) – Examining the colonization process of exotic species varying in
competitive abilities using a cellular automaton model, Ecological Modelling, Vol. 199, No. 3., pp. 219-228.
[Aristotle350BC] Aristotle, Metaphysics, volume book H (VIII). 350 BC., Translation fromW. D. Ross, Aristotle’s metaphysics, 2 vols, Oxford University Press, 1924
[Begon2006] Begon, M.; Townsend, C. R., Harper, J. L. (2006). Ecology: From individuals to ecosystems. (4th ed.), Blackwell.
[Bousquet2004] F Bousquet, C Le Page, Multi-agent simulations and ecosystem management: a review, Ecological Modelling, Vol. 176, No. 3-4. (1 September 2004), pp. 313-332.
[Bown 2007a] Bown, L., Pachepsky, E., Eberst, A., Bausenwein, U., Millard, P., Squire, R., Crawford, J., Consequences of intraspecific variation for the structure and function of ecological communities Part 1: Linking diversity and function, Ecological Modelling, Vol. 207, No. 2-4. (10 October 2007), pp. 264-276.
[Bown 2007b] Bown, L., Pachepsky, E., Eberst, A., Bausenwein, U., Millard, P., Squire, R., Crawford, J., Consequences of intraspecific variation for the structure and function of ecological communities Part 2: Linking diversity and function, Ecological Modelling, Vol. 207, No. 2-4. (10 October 2007), pp. 277-285.
[Braitenberg1984] Braitenberg V (1984) Vehicles, Experiments in Synthetic Psychology. The MIT Press. [Bryden2006] Bryden, J., Noble, J. (2006), Computational modelling, explicit mathematical treatments
and scientific explanation, Artificial Life X: Proceedings of the Tenth International Conference on Artificial Life, pp. 520-526.
[Cilliers 1998] Cilliers (1998), Complexity and Postmodernism: Understanding Complex Systems [DiPaolo2000] Di Paolo, E., Noble, J., Bullock, S., Simulation models as opaque thought experiments,
Seventh International Conference on Artificial Life (2000), pp. 497-506. [Durrett1993] Durette, The importance of being discrete (and spatial), Theoretical population biology, vol
46, 363-394 [Evers2007] – J Evers, J Vos, C Fournier, B Andrieu, M Chelle, P Struik, An architectural model of spring
wheat: Evaluation of the effects of population density and shading on model parameterization and performance , Ecological Modelling, Vol. 200, No. 3-4. (24 January 2007), pp. 308-320
[Fornara2008] Fornara, Tilman, Plant functional composition influences rates of soil carbon and nitrogen accumulation, Journal of Ecology, Vol. 96, No. 2. (2008), pp. 314-322.
References 2 [Garnett2008] Garnett, P., Stepney, S., Leyser, O., Towards an Executable Model of Auxin
Transport Canalisation, CoSMoS Workshop 2008 [Grim1999] Grimm, V., Ten years of individual-based modelling in ecology: what have we learned
and what could we learn in the future?, Ecological Modelling, Vol. 115, No. 2-3. (15 February 1999), pp. 129-148.
[Hogeweg1988] Hogeweg, P. Cellular automata as a paradigm for ecological modeling, Appl. Math. Comput., Vol. 27, No. 1. (1988), pp. 81-100.
[Holling1964] The Analysis of Complex Population Processes, Can. Entomol., 96, 335-347 [Huston1988] Huston, M., DeAngelis, D., Post, W., 1988. New computer models unify ecological
theory. BioScience 38, 682-691 [Kaiser1979] Kaiser, H., 1979, The dynamics of population as result of the properties of individual
animals, Fortschr. Zool, 25., 109-136 [Manson2001] Manson, S. (2001), Simplifying complexity: a review of complexity theory,
Geoforum, Vol. 32, No. 3., pp. 405-414. [Lavorel2002] Lavorel, S., Garnier. E (2002), Predicting changes in community composition and
ecosystem functioning from plant traits: revisiting the Holy Grail, Functional Ecology, Vol. 16, No. 5., pp. 545-556.
[Lloyd, S. 2006] Lloyd, S, (2006) Programming the Universe: From the Big Bang to Quantum Computers, Knopf
[Lotka1925] Lotka, A. J. 1925. Elements of physical biology. Baltimore: Williams & Wilkins Co. [Manson2001] Manson, S., Simplifying complexity: a review of complexity theory, Geoforum, Vol.
32, No. 3. (August 2001), pp. 405-414. [Marks2006] Marks, C, Lechowicz, M., A holistic tree seedling model for the investigation of
functional trait diversity, Ecological Modelling, Vol. 193, No. 3-4. (15 March 2006), pp. 141-181. [Morin1990] Morin, E. (1990), Introduction a la Pensee Complexe, (Paris, ESF) [Parunak1998] Parunak Van Dyke, Savit, R., Riolo, R.L., (1998), Agent-Based Modeling vs.
Equation-Based Modeling: A Case Study and Users’ Guide, Multi-Agent Systems and Agent-Based Simulation, pp. 10-25
References 3 [Polack2005b] Polack, F., Stepney, S. (2005), Emergent properties do not refine [Polack2005a] Polack F, (2005) An Architecture for Modelling Emergence in CA-Like Systems, [Reineking2006] B Reineking, M Veste, C Wissel, A Huth, (2006), Environmental variability and
allocation trade-offs maintain species diversity in a process-based model of succulent plant communities, Ecological Modelling, Vol. 199, No. 4., pp. 486-504
[Ritson2007] A Process-Oriented Architecture for Complex System Modelling, Concurrent Systems Engineering, Vol. 65 (July 2007), pp. 249-266.
[Sampson2008] Adam T. Sampson, John Markus Bjørndalen and Paul S. Andrews, Birds on the Wall: Distributing a Process-Oriented Simulation, CEC 2009, awaiting publication
[Santa Fe CSCS] Santa Fe Center for Study of Complex Systems [Sargent1987] Sargent, R.G. (1987), An overview of verification and validation of simulation
models, pp. 33-39 [Sebert-Cuvillier2007] E Sebert-Cuvillier, F Paccaut, O Chabrerie, P Endels, O Goubet, G Decocq,
– Local population dynamics of an invasive tree species with a complex life-history cycle: A stochastic matrix model, Ecological Modelling, Vol. 201, No. 2. (24 February 2007), pp. 127-143.
[Simon1962] Simon, H.A., The architecture of complexity, Proceedings of the American Philosophical Society, Vol. 106 (1962), pp. 467-482.
[Simpson1989] Simpson, J. et al (1989/2005) Oxford English Dictionary Online (2nd edn) [Electronic resource] (Oxford, Oxford University Press)
[Sommerville2006] Sommerville, I., (2006), Software Engineering [Squire1990] Squire, G.R., 1990. The Physiology of Tropical Crop Production., CAB International. [Stepney2005] Stepney, S., Polack, F, Turner, H. (2005), Engineering Emergence, Proceedings
of the 11th IEEE International Conference on Engineering of Complex Computer Systems, 89-97 [Sudeikat2005] Sudeikat, J., Braubach, L., Pokahr, A., Lamersdorf, W., Evaluation of Agent–
Oriented Software Methodologies – Examination of the Gap Between Modeling and Platform, Agent-Oriented Software Engineering V (2005), pp. 126-141.
References 4 [Tilman2000] Tilman,D., Causes, consequences and ethics of biodiversity.,Nature 405, 208–
211. [VonNeumann1955] [Wu1994] J Wu, SA Levin, A spatial patch dynamic modeling approach to pattern and process in
an annual grassland, Ecological monographs, Vol. 64, No. 4. (1994), pp. 447-464. [Wu2007] Wu, J. (2006), – SPACSYS: Integration of a 3D root architecture component to carbon,
nitrogen and water cycling—Model description, Ecological Modelling, Vol. 200, No. 3-4. (24 January 2007), pp. 343-359.
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