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  Modélisation et simulation décosystèmes: Des modèles déterministes aux simulations à
  événements discrets

   Patrick Coquillard and David R. C. Hill
   Paris: Masson, Recherche en Écologie
   1997
   Paper: ISBN 2-225-85363-0

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   Reviewed by
   [3]David Servat
   Laboratoire d'Informatique, Université Paris 6, 4 Place Jussieu, 75252 Paris,
   CEDEX 05, France.
   Cover of book

   Patrick Coquillard and David Hill provide us with an overview of a wide range of
   techniques in the field of ecosystem modelling and simulation: from classical
   approaches such as Markovian analysis, to discrete event simulation techniques
   like Monte-Carlo simulation and object-oriented modelling. Their book is intended
   as an advanced textbook and will therefore prove useful not only to graduate or
   PhD students seeking current information on the field of modelling, but also to
   researchers from many other disciplines wishing to get an upto date and fully
   referenced synthesis of modelling and simulation techniques in this field.

   Because ecosystem modelling issues are very topical at present and increasingly
   provoke interest from diverse disciplines within the scientific community, there
   is considerable interest and use to be had from a book which attempts to settle
   some definitions and clarify concepts, while taking stock of techniques currently
   in use.

   The book consists of three independent parts: part one provides us with the
   fundamentals, part two with a synthesis of classic models and part three presents
   some simulation techniques that prove more efficient than the classical ones in
   attempting to integrate spatial effects with ecosystem simulations. The
   organisation of the book is, to my mind, very much suited to a multi-disciplinary
   approach. Indeed the first part of the book gives the basic definitions which
   provide the minimum of shared knowledge which is a necessary starting point for
   any interdisciplinary communication.

   The authors synthesise pre-existing literature and their own experience to
   address fundamental questions about the meaning of key concepts like "system" and
   "model". They also examine the methodological choices which underpin the
   modelling process. To the three characteristics that Popper finds in all models
   (a model must share some resemblance with the real system, a model must be a
   simplification of the real system, a model is an idealisation of the real
   system), the authors add that if a model is to reproduce some of the behaviour of
   the real system as well as possible, it will do so in accordance with the
   objectives that the model designer has established.. Such fundamental issues are
   sometimes not given as much importance as they ought to be and it is valuable to
   recall them from time to time. The most important task for the apprentice
   modeller to perform, according to this view, is to identify the aim of the model.
   Simulation involves having a model evolve over time, so that we may understand
   the behaviour of the real system and apprehend some of its dynamic
   characteristics [4](Hill 1993). With respect to particular objectives and the
   knowledge and data available for the real system, several methodological choices
   condition all model construction processes:
     * The abstraction level or level of study: Should cells, micro-organisms,
       individuals, groups or populations be the fundamental objects of study?
       Several levels might be involved simultaneously, for example even when
       describing the global behaviour of a population, data from the individual
       level will certainly be needed.
     * The level of detail: As a model is bound to be a simplification of the real
       system, which of the factors affecting the system might legitimately be
       neglected? A discussion of the trade-off between an increase in model
       complexity and the gains in terms of validation and knowledge gives the
       reader hints on how to evaluate the level of detail in his own model.
     * Time granularity: In this respect, the model should be congruent with respect
       to the real system. The time slice to choose is that of the most frequent
       events occurring in the real system among those retained for modelling
       purposes.
     * Modelling methods: Analytic methods, stochastic models or modelling by means
       of simulations?

   The authors give us a thorough overview of modelling techniques, describing the
   advantages and drawbacks for each technique with particular reference to their
   ability to cope with spatial effects in simulated phenomena. This property is
   becoming more and more important when designing models for wide ecosystems in
   which phenomena are deeply dependent on space and time resolution and
   representation. In the light of many examples of experiments, mainly in the field
   of environmental ecosystem modelling, we now aware that classic models, based on
   differential equations or Markovian analysis are rather reluctant to take into
   account multiple scale phenomena and space-time interactions among individuals.
   If they do so, it is at a high cost in terms of computation and loss of their
   main benefits such as their simplicity. See for example [5]Lippe et al., (1985)
   for derived Markovian analysis, or Kimura's stepping stone model, Skellman's
   diffusion model [6](Skellman 1951) and other models described in [7]Renshaw
   (1995).

   As far as the integration of spatial effects is concerned, the authors favour
   discrete event simulation techniques, among which they focus on Monte Carlo
   simulations, cellular automata and individual based simulations (agents and
   object oriented modelling). The latter techniques provide efficient means for
   producing equivalence in the forms of software components and the analysis
   entities that domain experts are used to manipulating, yet at the cost of an
   urgent need to work with technical experts from computer science and distributed
   artificial intelligence.

   The expressive power of these techniques goes far beyond that of classical models
   which merely describe global parameters and fail to represent interacting
   phenomena. The trade-off however is that these techniques are hard to use as far
   as the validation and exploitation of the model are concerned.

   The authors present much discussion on the particular field which I would like to
   focus on from now on. As a PhD student working on multi-agent simulations of
   physical processes, I am much interested in their topical coverage of this
   approach.

   One key point in multi-agent simulations (and in all object-oriented modelling as
   well) is the use of randomness through pseudo-random number generators. These
   enable us to simulate the parallel nature of natural phenomena by executing
   processes in random order and to compensate for our lack of knowledge about the
   actual causes of some events by representing them as having a probability of
   occurrence. However, it is easy to forget once we make use of randomness
   routinely that it conditions all the outcomes of our experiments, and as such,
   deserves much concern from the perspective of efficiency. Coquillard and Hill are
   very well aware of this and intend to draw our attention to it. They provide a
   thorough review of classic pseudo-random number generation techniques using
   congruential methods, loop-back registers and generator shuffling. Furthermore,
   they present a whole battery of quality tests for these generators, along with
   thorough references to the literature: [8]Knuth (1981), [9]Fishman (1978),
   [10]Marsaglia et al. (1990) and [11]Ripley (1990). A whole chapter is dedicated
   to this issue and focuses on the spatial cover and pseudo-period of different
   generators. In conclusion, the authors point out that for current computer
   architectures, 32 bits do not provide a good implementation of congruential
   generators and a passage to 64 or even 128 bits would be necessary. As a
   consequence, they recommend the shuffling of generators based on loop-back
   registers. Such detailed discussions, often absent from books on simulation, are
   of great interest for those of us wanting to implement models on specific
   computer architectures.

   The authors discuss both the verification of the simulation model, that is,
   according to [12]SCS (1979), "substantiation that a computerised model represents
   a conceptual model within specified limits of accuracy", and its validation,
   "substantiation that a computerized model within its domain of applicability
   possesses a satisfactory range of accuracy consistent with the intended
   application of the model". Both these steps refer to experimental frameworks,
   defining the observed data model inputs: initialisation of input parameters,
   input time constraints, definition of model outputs and stopping conditions of
   the simulation. The verification step consists mainly of a software engineering
   validation process for the simulation program: a battery of tests from component
   tests to the integration test, well known by programmers. The authors then
   present some techniques for validation, such as validation by confrontation:
   domain experts are asked to evaluate the accuracy of the simulation results with
   respect to their own experience in the field, validation through replication,
   functional validation both structural, that is determining if the model structure
   is consistent with respect to the underlying reality, and in extreme conditions,
   which involves investigating the effects of extreme values of parameters or tests
   with partial submodels.

   Graphical validation and animation enable us to represent the transient dynamics
   of the model and thus prove useful in terms of pedagogy and debugging, but do not
   provide full validation. This is because graphical animation is obtained through
   slower processing than the model is actually capable of. Thus events which are
   unexpected, with respect to the animation, may occur when processing at full
   speed. Despite this, graphical presentation is a good way of making the user feel
   comfortable with the model, as they may see some graphical equivalent of what
   they believe to occur in reality. Ultimately statistical validation through the
   use of confidence intervals and spectral analysis will be necessary when
   assessing the validity of the model in predictive terms. In this field the
   calibration of the model according to real results (data sampling) does not
   provide any validation. In order that it should, it is necessary to be able to
   calibrate the model on the basis of several real experiments, for example at
   different sites.

   The final chapter of the book provides the reader with a complete review of
   several applications of the techniques discussed above, mainly from the field of
   ecology: modelling the expansion of caulerpa algae in the south east of France,
   modelling forest growth taking into account spatial effects and simulating the
   dynamics of calluna vulgaris heathlands. All these examples are quite interesting
   though slightly difficult to appreciate fully when one is not aware of the detail
   in the particular fields.

   When I read this book, I was eager to learn more about time management within
   discrete event simulations. I must admit I was a bit disappointed by the authors'
   coverage of that particular topic. Whereas they provide detailed advice for
   dealing with spatial effects, they merely present a few alternative approaches to
   time scheduling (either event driven or clock driven) and process
   synchronisation. Of course such a huge topic could easily fill a whole book in
   its own right.

   To conclude, I should say that this book is quite interesting and succeeds in
   giving the reader a respectable level of background on a wide range of simulation
   techniques. It is just as important to be aware of the techniques one does not
   use, so as to be able to justify one's choices of modelling methods and the book
   is obviously useful in this respect. Moreover, the authors provide sufficient
   references to facilitate further study of any particular topic.

   At the beginning of the book, the authors draw attention to the view of current
   ecosystem modelling presented by [13]Jorgensen (1994). He asserts that most
   existing models provide us with informative and precise answers to very narrow
   questions, and suggests, in order to try and cope with the extreme complexity of
   ecosystems, that we should work towards models that would give partial and
   incomplete answers to much wider questions. To my mind the book by Coquillard and
   Hill is in keeping with this approach and therein lie both its strengths and
   weaknesses as a textbook.

  * References

   FISHMAN G. S. 1978. Principles of Discrete Event Simulation, John Wiley and Sons,
   New York.

   HILL D. R. C. 1993. Analyse orientée: Objet et modélisation par simulation,
   Addison-Wesley, Reading, Ma.

   JORGENSEN S. E. 1994. Fundamentals of Ecological Modelling, Elsevier, Amsterdam.

   KNUTH D. E. 1981. The Art of Computer Programming: Volume 2, Semi-numerical
   Algorithms, second edition, Addison-Wesley.

   LIPPE E., J. T. De Smit and D. C. Glenn-Lewin. 1985. Markov models and
   succession: A test from a heathland in the Netherlands. Journal of Ecology,
   73:775-791.

   MARSAGLIA G., A. Zaman and W. Tsang 1990. Toward a universal random number
   generator. Statistics and Probability Letters, 9:35-39.

   RENSHAW E. 1995. Modelling biological populations in space and time, Cambridge
   University Press, Cambridge.

   RIPLEY B. D. 1990. Thoughts on pseudo-random number generators. Journal of
   Computational and Applied Mathematics, 31:153-163.

   TECHNICAL COMMITTEE ON MODEL CREDIBILITY, SOCIETY FOR COMPUTER SIMULATION. 1979.
   Terminology for Model Credibility. Simulation, 32: 103-107.

   SKELLMAN J. G. 1951. Random dispersal in theoretical populations. Biometrica,
   38:196-218.

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References

   1. https://www.jasss.org/admin/copyright.html
   2. https://www.jasss.org/JASSS.html
   3. mailto:David.Servat@lip6.fr
   4. https://www.jasss.org/1/2/review2.html#hill1993
   5. https://www.jasss.org/1/2/review2.html#lippe1985
   6. https://www.jasss.org/1/2/review2.html#skellman1951
   7. https://www.jasss.org/1/2/review2.html#renshaw1995
   8. https://www.jasss.org/1/2/review2.html#knuth1981
   9. https://www.jasss.org/1/2/review2.html#fishman1978
  10. https://www.jasss.org/1/2/review2.html#marsaglia1990
  11. https://www.jasss.org/1/2/review2.html#ripley1990
  12. https://www.jasss.org/1/2/review2.html#scs1979
  13. https://www.jasss.org/1/2/review2.html#jorgensen1994
  14. https://www.jasss.org/1/2/contents.html
  15. https://www.jasss.org/admin/copyright.html