Let us formulate our understanding of white-box model. The main problem of such modelling is a creation of adequate axioms.

The deterministic cellular automata modelling may be considered as a simple universal inference automaton of mechanistic insight into studied phenomena as it is based on a detailed visualisation of dynamics of all interrelated processes of a complex system under study.Ī white-box cellular automata modelling is the most direct way to gain a mechanistic insight into a complex system under study. The presented here method can be used in different areas of modelling of competition of agents of any nature. The lattice size, initial cell-states pattern of the whole cellular-automaton field (initial pattern), the type of a neighbourhood and the rules of transitions between states of elementary cell are the key parameters in the cellular-automata modelling. Correct formulation of the basic interpretations requires a deep and holistic objective knowledge of simulated phenomena. An important feature of this modelling approach is the need for formulation of adequate axioms, based on properties of modelling objects. The advantages of deterministic cellular automata models are in clarity of their cause-effect relationships, the absence of rounding errors (accuracy), in the simplicity of setting boundary conditions, solvability and holistic relationship of local and global characteristics of models, a detailed mapping of spatial-temporal events. A particular advantage of the individual-based cellular automata models is that they are able to reflect holistic spatial-temporal mechanisms of dynamic interrelations among all levels of population ecosystem - between individuals and their mini habitats and between their mini habitats and the whole ecosystem (macro habitat). Logical cellular automata are most effective for individual-based modelling in terms of local dependencies. Cellular automata are the most effective mathematical tool for creating discrete models of complex systems. The discrete nature of the real world is naturally to model using discrete mathematics. Keywords: population dynamics white–box modelling ecological physics resource competition conservation biology, cellular automat biodiversity IntroductionĪdvantages of the cellular automata modelling. We believe that such a transparent mathematical modelling from the first principles is very promising not only for the ecology, but also for modelling of any complex dynamic systems. The system of basic axioms of the general theory of ecological physics performs the role of the cellular automata rules. Here we demonstrate that logical cellular automata allow to create transparent models of ecosystems. Such a theory should have a system of basic axioms. We guess that a general theory of ecological physics should include as an abstract physical description of an ecosystem and a tool for its transparent (“white box”) modelling. Due to the inseparable mutuality of organism and environment, ecological physics should combine biophysics (physics of organisms) and environmental physics. The observed loss of biodiversity calls to create a general theory of ecological physics for better understanding of the events.

Automaton antonym