Urban Kordeš
Josef Stefan Institute
Jamova 39, SI – 1000 Ljubljana, Slovenia
urban.kordes@ijs.si
ARTIFICIAL SIMULATION OF LIVING PROCESSES AND ITS CONDITIONS
Abstract
The paper presents a classification of software and hardware designs inspired by processes in living nature.
Basic conditions for artificial simulations of processes from living nature are presented as well as some of the
greatest obstacles to achieve them. Paper also introduces an autopoietic view of life which emphasises the
continuity of system’s organisation.
1. POE SPACE
Among the most known computer systems that take the processes of living nature as their example belong:
cell automata, genetic algorithms, artificial life, neural networks, evolware. Behind each of the mentioned
algorithms, software or hardware implemented, there is a process from living nature: neural networks take the
operation of biologic neurons (or one of the functions that they perform) as their example, artificial life attempts to
create (space) entities which reproduce themselves and keep their structure, evolware and genetic algorithms imitate
evolutionary processes.
Sipper (1) (before him already (5), (6)) distinguishes three different kinds of organizational processes in
living nature: phylogeny (P), ontogeny (O) and epigenesis (E), witch can be arranged along coordinate axes and thus
we get a so-called POE space. Most of computer systems that take natural processes as their example can be placed
in the POE space and arranged along the axes analogously with natural processes.
Phylogeny
Time evolution of genetic concepts of species and individual organisms belongs here. It’s about the changing
of genetic material (permutations, mutations), the consequence of which is the development of new forms of living
organisms. Analogous computer processes are evolutionary algorithms with which some characteristics of the
evolutionary stream in nature can be simulated – primarily the exchange and crossbreeding of genomes and thus the
characteristics of organisms. First suggestions to use characteristics of biology evolution for artificial systems came
from cybernetics scientists in the 40s and 50s last century. Genetic algorithms, evolutionary strategies, evolutionary
and genetic programming (resumed according to (1), p. 2 and 73) belong into this domain today. All computer
generated evolutionary processes share a common methods, according to which random population of possible
solutions is ceded to the mentioned genetic operations in the beginning, so that better, more optimal solutions evolve
in the course of time.
Ontogeny
Among ontogenetic processes belong those natural processes that are responsible for the development of an
individual living organism in the course of its life span (for example the development of a fertilized ovum into an
organism capable of independent life). Von Neumann’s work (7) represents the beginning of algorithmic treatment
of this field. Modern realization of some of Neumann’s ideas are self-reproducing automata, in which a multy-cell
formation, usually distributed around the space network, develops out of one basic element (“cell”) by various
divisions. Modern self-reproducing algorithms built on the basis of cell automata are different from Von Neumann’s
and Langton’s (8) suggestions because each of their organisms contains only one integral genotype, which means
that these are strictly speaking one-cell organisms. Modern algorithms like embryonics systems (9) and especially
models of artificial life replicate also their genotype analogously with processes in nature when the basic cell is
divided and so they form multy-cell organisms, where the cell is the smallest unit which carries the entire
transmissible information. Such formations can show some characteristics, which cannot be observed in an
individual element. Analogously with living ontogeny processes, where an enormous number of inanimate
molecules link up into a complex living unit without external control, a complex self-organized behavior arises also
from the interaction of simple elements during artificial life processes.
Epigenesis
The processes iniside an individual living organism which enable the integration of its interactions with the
environment (and inside itself) in the course of its life span belong here. It’s about the learning of an organism in a
broad sense as all of its parts are included. More developed organisms have, of course, specialized units which
perform this function, for example nervous or immune systems. As a computer simulation of the function of a
biologic nervous systems (artificial) neural network developed which change the state of their synaptic weights (and
some also topology) through the history of interactions with the environment (inputs). Theory and especially the use
of neural network is very rich today, its bases were made by Hebb’s learning rule which defines the dependence of a
synapses which connects two neurons on their conditions. It is a simple linear operation, which – linked into a
parallely distributed system – causes synergetic results.
The efforts of classic artificial intelligence also belong in the category of the simulation of epigenetic
processes, but they do not follow one of the most important characteristics of the processes of the living world, this
is the appearance of complex self-organized systems out of simple components1.
1.1. Evolware
A new form of artificial systems, which are similar to the natural ones, has developed in the recent years. It is
called evolware and can be placed on the phylogeny or ontogeny axis in the POE space. According to (1), evolware
in some of its realizations will soon able to unite characteristics of all above-mentioned directions.
2. ARTIFICIAL SIMULATION OF LIVING PROCESSES AND ITS CONDITIONS
Many of the mentioned software concepts, constructed according to processes in living nature, are efficient
tools for solving some problems, like for example optimizations, hardware learning or the search of complex
transfer functions and pattern recognition. The question is how to use mentioned software techniques for modeling
the processes, the imitation of which they are. One thing is to seek a solution of a certain problem and use
algorithms, which have some characteristics analogous with the processes in living nature, but it is totally different
when we expect these computer simulations of the part of the natural process to simulate the entire process or a
living organism.
If we want to simulate a process in living nature, we have to face some software-related and also
epistemological issues. It can be very dangerous or at least unsuccessful to model a living process if the model is
reduced to some – according to programmer’s opinion – essential parameters and then we expect that other
parameters have “negligible influence”. Living biological processes are full of “hidden parameters” and interacting
intertwinements, which often essentially influence their dynamics.
The rest of the text is devoted to snares, which we have to be attentive to when attempting to model
biological processes and especially entire living organisms. In this section I am going to list some of the most
important software elements, which a simulation of a biological system should contain. In the next (3 rd) section I am
going to examine also some more philosophical characteristics of living organisms which are usually neglected or
are realized by software with difficulty and therefore present serious problems when living systems are modeled. In
section 3 some of the problems indicated in this section will be dealt with in detail.
When choosing basic guidelines for the composition of artificial simulation of living processes our
epistemological beliefs are crucial.
Classic “computer” view of the nature of the operation of loving organisms (rare are those who doubt it) sees
living mechanisms as Turing’s machines which copy input information into outputs in a certain more or less
complex manner, One of the guidelines, which can be found in the texts of almost all authors of computer
simulations of living processes, is the feasibility of Turing’s machine algorithm. The basis for this is ChurchTuring’s thesis ((10),(11)), which serves as an assurance that modeling of living (and all other) processes is possible.
There are also other points of view. Cybernetics of the second order (Bateson, von Foerster), for example,
does not recognize the strict separation of the system and the environment. It considers that what we usually call the
environment is another system, so that we cannot speak of inputs and outputs, but only of changes of border
conditions or perturbations. Such point of view means also the rejection of classic informational theory as the
receiver and transmitter of information do not exist nor the channel of transfer (more on this in section 3). Most of
the opponents of the classic viewpoint are not so radical; they only want to pint out that it is not so self-evident.
Roch (3), for example, believes that the Church-Turing thesis from the point of view of mathematics is undoubtedly
valid. However, the tendency, which attempts to use this thesis to justify the reduction of the functional level of
living systems (that we want to model) to lower levels, is questionable. The viewpoint of Roch is that the only way
to understand functions of systems is their comparison and study on the level of interest.
A more precise examination of the basic presumption therefore reveals the need to attentively study every
step when composing a model of a process in living nature. In the continuation I will attempt to list some common
guideline, which can be found in most of the successful simulations 2.
 The first demand is that the model is general. When we compose, we have to take care not to include a
border condition, which is valid only for a certain system, which we think of, in the simulation. Therefore simplicity
of basic components (1) is highly recommended.
 The system’s topology is very important. The artificial system must be constructed so that basic elements are
parallely distributed and enable the development of complex dynamics out of basic components. It seems that
models which allow changing topology in the real time of simulation are very successful.
 The concept of a complex system dynamics must be codes in each basic element, so that self-organization
1
I am not going to deal with the classic artificial intelligence in this text, which does not mean that some of its results could not be classifies into
its context.
2
As it clear from the above-mentioned, I cannot claim that the listed guidelines are indispensable.
can occur when topology is suitable. Living systems obviously do not have a central unit, which would control and
regulate individual basic components. There are systems like for example nervous or endocrine which regulate some
function on a higher level. The development of cells and similar basic processes happen globally in harmony (selforganized), but entirely without external “directions”. A good example is the transformation of a caterpillar into a
butterfly. It would be ideal if control mechanisms could develop “by themselves” inside the simulation (as certain
neurons can develop into cardinal neurons in neural networks).
 Simulation of a biological process or system must be flexible. This means that it has to be immune to illdefined problems, ill-defined border values and missing input values. Real living systems function (for a certain
time) also when external stimulation is missing. This is related with the dynamic stability of living processes. This is
the characteristic that in spite of certain exemptions in border values and starting parameters (quantity) we always
get the same result (quality). Regardless of whether we put apple seed into the black, moisty soil or into the dry soil,
the result is always an apple tree. Exemptions of course should not be too great, but nevertheless nothing else can
grow out of an apple seed that an apple tree (dynamic stability).
 Living nature is capable of producing new, original forms of organization. Therefore the stochastic
component of a simulation is almost an indispensable component. It gives the algorithm many degrees of freedom.
It can also be useful for solving some problems mentioned under the previous point,
 Durations of individual processes must be in realistic proportions; therefore the definition of the time of the
system’s dynamics is necessary. Such approach enable harmonized operation of the parallely distributed structure
of a system and supervision of the order of individual processes (especially if parallely operating hardware is not at
our disposal, evolware, for example, does not require it). For example the basic time unit should be defined.
The above list of guidelines is not complete, but most of the important notes are certainly listed which
characterize a new paradigm in the artificial modeling of biological systems. It can be compared to a similar list that
Jefferson and al. (12) (listed under (1)) have proposed.
3. SOME IMPORTANT CHARACTERISTICS OF LIVING SYSTEMS
In this section I will discuss some characteristics of living systems, which can rarely (or never) be found in
artificial simulations. There basic problems of modeling living systems should be solved before we can deal with
formal treatment. “Basic” problems mean problems that are not software-related, but belong mostly to the domain of
philosophy, theoretical biology and cognitive science. There is no clear division among them in living world
simulations.
3.1. The goal of evolution and survival
In nature new generations develop through the process of reproduction where genotypes (perfect descriptions
of individual’s plan of physical characteristics) of individual subjects are transferred to posterity. Realized physical
characteristics of an individual organism (phenotype) are during the course of their life submitted to changes that are
a consequence organism interactions with the environment. The environment allows survival only to well-adapted
organisms, which allows a positive selection and constant genotype improving. All these processes allow the flow of
evolution, which does not have a central control mechanism (which was discussed in section 2), nor a certain final
goal3. The criterion of successfulness of an individual organism is implicity (13) determined by its capacity to
survive and reproduce itself in a certain environment.
In artificial systems the criterion is determined by the user according to the task given to the system. For
evolutionary simulations we can speak about directed evolution (1) in comparison with open-ended evolution for
real living populations.
If a certain artificial (software) systems has certain condition for evolutionary success of subject, it is very
improbable that we could satisfactorily simulate natural processes. Despite this the majority of evolutionary
simulations are directed, as the user has no control over undirected systems. The danger of divergence appears in the
latter. It can happen that due to the imperfect determination of border parameters, the system “strays” into the
direction, which is not related with the natural example. One of rare undirected evolutionary systems is virtual world
named Tierra (14). This is made of computer programmes, capable of evolution. These programmes named also
“Tierra beings” have no externally determined criteria, which would determine direction of their development. They
are programmed to compete for “natural stock” (access to CPE and the memory quantity) of the computer-generated
virtual world where they live. Creators of Tierra have observed a formation of a real ecosystem with beings of
different size and with different attributes.
3.2. Need for an integral concept of the process of life
In cases when the purpose of an artificial model is a phenomenological simulation of an individual process in
the living nature (evolution, self-organization, learning, …) we can afford isolation of the observed process from the
3
This is the viewpoint of the majority, Hegel, for example, would disagree.
whole4. When observing a functional level of living systems, a reduction to lower levels, the idealized basic
elements of which we can simulate, is inadmissible. As already mentioned, it seems that only way to understand the
functions of systems is to study them on the functional level, the operation of which we are interested in. This does
not mean that simulation should not be based on simple basic components. Systems of inanimate nature can be
divided into components and the perfect description of components allows a perfect description of the system. If we
want to model a car, it is enough to know the operation of all of its parts. If we could model the learning process,
reproduction process, locomotory system operation, immune system operation, etc., this would not be enough to
model the living function of the entire organism.
3.3. Operational closure of living organisms
What is the difference between a car and a living organism which prevents us to model them in the same
way? A car is mentioned above as an example of an alopoietic system, this is a system that can be defined only
according to a context. The identity of an alopoietic system depends on the observer and is not determined beyond
its operation, because its product is something else than itself. The product of computer activity, for example, is not
the activity of a computer, but is the “output”, due to which (or due to its relation with input) we call a certain
system a computer.
Living systems are different from alopoietic systems and belong among autopoietic systems, for which selfcreation (autopoiesis) (16) is characteristic. The constant of an autopoietic system is its organization; the main
attribute of its organization is orientation towards the conservation of the autopoietic nature of the system. The
structure of an autopoietic system can be changed, all changes are submitted to the maintenance of its own
organization. Biologists Maturana and Varela (16) name this attribute operational closure and Patee (17) calls it
semantic closure. The latter term gives more emphasis to non-material (symbolic) part of the irreducible closed
circle: measurement – control – symbolic instructions.
In formal symbolic systems syntax is separated from semantics (meaning). Such systems get a meaning only
inside the integrite: symbols (and rules) – meaning (determined by users or creators of a system). If we lose
regulations, which give semantic value to individual symbols (i.e. we open the circle and lose the semantic
function), the syntactical part becomes meaningless.
Living systems are different from formal systems due to the lack of external definition of the semantics of
their elements (3) (no programme). Dynamics of living systems totally depends on the (self-) organization of their
parts. Output of such processes are not result of time-independent linear (sequential) logical calculations, but depend
on the interaction of the cooperating physical processes, where time distribution plays an essential role. Physical
processes which occur inside an operationally closed system totally depend on the inside meaning of material
elements of which the system is composed. We can say that the meaning is an “internal matter” of living systems.
Such systems are closed in their semantic space.
Nervous system do not “get information” from the environment, as we often hear. On the contrary, it
generates the world by determining which changes in the environment will cause (which) changes in organism.
Broad comparison, which describe the brain as the “data processing appliance “is therefore not only ambiguous but
also obviously false.” (16).
Such systems certain cannot be modeled by simple transfer function between input and output. Von Foerster
(19) suggested algorithms solution in 1969, which includes self-reference in the sense of semantic closure, called
cognitive tile.
4. CONCLUSION
Cognitive tiles do not imitate a certain element in the living world but a function (operational or semantic
closure), which is obviously one of the basic characteristics of all living organisms. In this text I pointed out some
other characteristics of the living world, which escape the simple algorithmic description. One of this is that
evolution is not striving for a particular goal but is an open, continuous flow of change. Further speculations, which
exceed the set objectives, would lead to the analysis of the attributes of organisms according to the development
stage of the central nervous system. It seems to be a turning point in the evolutionary orientation of an individual
organism: from adaptation for the survival of the species to the adaptation for the survival of an individual.
_______________________
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