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The EvoGrid: Simulating Pre-Biotic Emergent Complexity
From The EvoGrid: The Evolution Technology Grid
The EvoGrid: Simulating Pre-Biotic Emergent Complexity
Damer, B., Newman, P., Gordon, R., Barbalet, T.
Feb 16, 2009: Bruce Damer's presentation (download 33MB MP3) for transfer to PhD candidacy.
This research proposal rests on the hypothesis that it is possible to create a simulation, the “EvoGrid”, utilizing a large interconnected grid of computers which could plausibly model the pre-biotic chemical environment which was the precursor stage to evolution and life arising on Earth. The key innovation over previous efforts will be the use of a level testing function (Gordon, 2000) that searches for emergent complex self-organization within the discrete element system of the simulation, focusing computing resources and tuning parameters in order to permit the system to drive itself towards ever more complex emergent structures and processes. Research outcomes from the EvoGrid project may shed light on the origins of life on Earth and in the universe, and provide new tools for evolutionary biology, biochemistry, and complexity studies.
Figure 1 depicts this innovation as a tree in which multiple simulation instantiations form nodes that are either culled or selected for continuation based on observed phenomena. Branches do not represent generations but instead each is created based on testing for a new “level” of observed complexity. It is hoped that in this manner a pathway through the search space of possible emergent self-organized complexity could be opened enabling the simulation solution vector to progress far more rapidly than human visual operation and selection would permit.
Table 1 characterizes possible EvoGrid operational variants, including α – run with a fixed time period and observation but no level testing selection, or β – selected and iterated with a level testing function, or γ – permitting users to make their own observations and introduce experimental structures, and alter parameters. In addition, the simulation could be set up in mode 1 – modeled closely on biochemistry (creating a tool useful to efforts to create “wet” artificial life in the lab (Rasmussen et al., 2008)), or mode 2 – permitting the physics to vary creating hypothetical “toy” universes (for studies of possible emergent complex self-organization).
|α Alpha – autonomous, unselected||β Beta – autonomous, selected||γ Gamma – constructions and selection by user|
|¹Physics of part of known universe: a molecular fluid||α¹Analog: chemical solution left in initial conditions for fixed time||β¹Analog: chemical solutions selected and replicated by chemist||γ¹Analog: formulations modified by introducing new compounds|
|²Physics of arbitrary toy universes||α² Analog: “virtual” universe instantiated with parameters and run for fixed time||β² Analog: wide variety of universes initiated and selected to drive through search space||γ² Analog: “artificial life” environments user design and ongoing modification|
A key challenge to be met in formulating the EvoGrid will be creating and sustaining the conditions for a “ratcheting up”, of measurable complexity, also known as the problem of progress in evolution (Nitecki, 1988; reviewed for multi-cellular evolution in: Gordon, 2009). In the decades of artificial life research since Baricelli (1953) ran his numerical symbioorganisms on Von Neumann’s machine at the Institute for Advanced Study (Dyson, 1997), each system has saturated its resources at a fixed plateau of complex behavior (Ray, 1991). Over-simplified physics and insufficient computing resources may be the culprits behind this. The EvoGrid effort hopes to tackle these limitations by delivering both a β¹ variant for in support of biochemical origins of life efforts, and a β² variant for theoretical studies in emergent complexity, both having rich physics deployed in a large scale grid computing environment. If time and resources permit, interactive γ variants of both modes will be made available for researchers and the general public.
We have studied a number of mesoscale chemical simulators (Fellermann 2009) and are currently adapting the GROMACS (2009) open source molecular dynamics platform for the initial EvoGrid prototype. GROMACS supports analysis functions that can examine an area of the simulation space and these will be adapted to provide the level testing function and feedback loop. We propose that the level testing function would be tuned to look for ‘patterns in space and time’, but this remains a major definitional challenge (Martin & Gordon, 2001 and Barbieri, 2003).
We are hoping to observe entropy-reducing complexity surmounting two ore more plateaux, akin to the emergence of new localized physics. For example, if vesicles (closed membrane structures) were detected, then the EvoGrid computation could be optimized by turning on a higher level physics model simulating whole vesicles with attendant pressure differentials and cross-membrane transfusions. The invocation of multi-physics across increasing scales would computationally “follow” complexity as it ratchets through to higher levels. Two other points: random disruptions of simulation space may be necessary to emulate Earthly climatic or geological events, and all level testing must be carried out offline on regularly exported state snapshots, as to not bias the results of the live simulation runs. Lastly, it may be possible to deploy the simulation through UC Berkeley’s BOINC (2009) grid.
The research outcome will most certainly lie along a continuum, from relatively weak (the simulation moves along a vector in which net entropy is discernibly reduced) to very strong (the simulation is observed to generate forms and processes that are recognizably life-like).
In closing, we also believe that this work will lay the foundations which could later support the dual challenges posed by Gordon‘s (2008) origin of artificial life, and Dyson’s (1998) toy model to test his theory of the double origin of life.
Barbieri, M. (2003). The Organic Codes. An Introduction to Semantic Biology. Cambridge, Cambridge University Press.
Barricelli, N.A. (1953). Experiments in Bionumeric Evolution Executed by the Electronic Computer at Princeton, N. J.
BOINC on the web at: http://boinc.berkeley.edu/ (accessed 28 March 2009).
Darwin, C. (1872). Origin of Species by Means of Natural Selection or the Preservation of Favored Races in the Struggle for Life. New York, Modern Library, 6th.
Dyson, F. (1998) Origins of Life, Cambridge University Press.
Dyson, G. (1997) Darwin Among the Machines: The Evolution of Global Intelligence, Perseus Books.
Fellermann H. (2009) ‘Spatially resolved artificial chemistry’, In: A. Adamatzky and M. Komosinski (eds.), Artificial Life Models in Software II, Springer-Verlag.
Gordon, R. (2000). The emergence of emergence: a critique of "Design, observation, surprise!". Rivista di Biologia /Biology Forum 93(2), 349-356.
Gordon, R. (2008). Hoyle’s tornado origin of artificial life, a computer programming challenge. In: Divine Action and Natural Selection: Science, Faith and Evolution. Eds.: R. Gordon & J. Seckbach. Singapore , World Scientific: 354-367 http://www.biota.org/podcast/live.html#18
Gordon, R. (1999). The Hierarchical Genome and Differentiation Waves: Novel Unification of Development, Genetics and Evolution, Singapore & London: World Scientific & Imperial College Press. http://www.worldscibooks.com/books/lifesci/2755.html, 2 vols., 1836p.
GROMACS on the web at: http://www.gromacs.org/ (accessed 28 March 2009).
Martin, C.C. & R. Gordon (2001). The evolution of perception. Cybernetics & Systems 32(3-4), 393-409.
Nitecki, M.H., Ed. (1988). Evolutionary Progress? Chicago, University of Chicago Press.
Nowak M., Ohtsuki, H. (2008) ‘Prevolutionary dynamics and the origin of evolution’, P Natl Acad Sci USA, 105: 14924-14927.
Ray, T. (1991) ‘An approach to the synthesis of life’, in C. Langton, C. Taylor, J. Farmer, and S. Rasmussen (eds), Artificial Life II, Santa Fe Institute Studies in the Sciences of Complexity, 11:371-408, Redwood City CA: Addison-Wesley.
Rasmussen, S., Bedau, M., Chen, L., Deamer, D., Krakauer, D., Packard, N., Stadler, P. (2008) Protocells: Bridging Nonliving and Living Matter, Cambridge: MIT Press.