The World as Evolving
Information
Carlos Gershenson1,2,3
1 Computer Sciences Department, Instituto de Investigaciones en
Matemáticas Aplicadas y en Sistemas
Universidad Nacional Autónoma de México1
Ciudad Universitaria, A.P. 20-726, 01000 México D.F. México
cgg@unam.mx http://turing.iimas.unam.mx/∼cgg
2 New England Complex Systems Institute
238 Main Street Suite 319 Cambridge, MA 02142, USA
3Centrum Leo Apostel, Vrije Universiteit Brussel
Krijgskundestraat 33 B-1160 Brussel, Belgium
This paper discusses the benefits of describing the world as information, especially
in the study of the evolution of life and cognition. Traditional studies encounter prob-
lems because it is difficult to describe life and cognition in terms of matter and energy,
since their laws are valid only at the physical scale. However, if matter and energy,
as well as life and cognition, are described in terms of information, evolution can be
described consistently as information becoming more complex.
The paper presents eight tentative laws of information, valid at multiple scales,
which are generalizations of Darwinian, cybernetic, thermodynamic, psychological,
philosophical, and complexity principles. These are further used to discuss the no-
tions of life, cognition and their evolution.
1Current affiliation. A considerable part of this work was developed while at other institu-
tions.
http://arxiv.org/abs/0704.0304v3
http://turing.iimas.unam.mx/~cgg
1 Introduction
Throughout history we have used concepts from our current technology as
metaphors to describe our world. Examples of this are the description of the
body as a factory during the Industrial Age, and the description of the brain as a
computer during the Information Age. These metaphors are useful because they
extend the knowledge acquired by the scientific and technological developments
to other areas, illuminating them from a novel perspective. For example, it is
common to extend the particle metaphor used in physics to other domains, such
as crowd dynamics [27]. Even when people are not particles and have very com-
plicated behaviour, for the purposes of crowd dynamics they can be effectively
described as particles, with the benefit that there is an established mathemati-
cal framework suitable for this description. Another example can be seen with
cybernetics [4, 28], where the system metaphor is used: everything is seen as a
system with inputs, outputs, and a control that regulates the internal variables
of the system under the influence of perturbations from its environment. Yet
another example can be seen with the computational metaphor [60], where the
universe can be modelled with simple discrete computational machines, such as
cellular automata or Turing machines.
Having in mind that we are using metaphors, this paper proposes to extend
the concept of information to describe the world: from elementary particles to
galaxies, with everything in between, particularly life and cognition. There is no
suggestion on the nature of reality as information [58]. This work only explores
the advantages of describing the world as information. In other words, there are
no ontological claims, only epistemological.
In the next section, the motivation of the paper is presented, followed by a
section describing the notion of information to be used throughout the paper. In
Section 4, eight tentative laws of information are put forward. These are applied
to the notions of life (Section 5) and cognition (Section 6). The paper closes
presenting future work and conclusions.
2 Why Information?
There is a great interest in the relationship between energy, matter, and infor-
mation [32, 54, 43]. One of the main reasons for this arises because this rela-
tionship plays a central role in the definition of life: Hopfield [30] suggests that
the difference between biological and physical systems is given by the meaning-
ful information content of the former ones. Not that information is not present
in physical systems, but—as Roederer puts it—information is passive in physics
and active in biology [49]. However, it becomes complicated to describe how this
information came to be in terms of the physical laws of matter and energy. In
this paper the inverse approach is proposed: let us describe matter and energy in
terms of information. If atoms, molecules and cells are described as information,
there is no need of a qualitative shift (from non-living to living matter) while
describing the origin and evolution of life: this is translated into a quantitative
shift (from less complex to more complex information).
There is a similar problem when we study the origin and evolution of cog-
nition [20]: it is not easy to describe cognitive systems in terms of matter and
energy. The drawback with the physics-based approach to the studies of life and
cognition is that it requires a new category, that in the best situations can be
referred to as “emergent”. Emergence is a useful concept, but it this case it is
not explanatory. Moreover, it stealthily introduces a dualist view of the world:
if we cannot relate properly matter and energy with life and cognition, we are
forced to see these as separate categories. Once this breach is made, there is
no clear way of studying or understanding how systems with life and cognition
evolved from those without it. If we see matter and energy as particular, simple
cases of information, the dualist trap is avoided by following a continuum in the
evolution of the universe. Physical laws are suitable for describing phenomena
at the physical scale. The tentative laws of information presented below aim at
being suitable for describing phenomena at any scale. Certainly, there are other
approaches to describe phenomena at multiple scales, such as general systems
theory and dynamical systems theory. These approaches are not exclusive, since
one can use several of them, including information, to describe different aspects
of the same phenomena.
Another benefit of using information as a basic descriptor for our world is
that the concept is well studied and formal methods have already been developed
[14, 46], as well as its philosophical implications have been discussed [19]. Thus,
there is no need to develop a new formalism, since information theory is well
established. I borrow this formalism and interpret it in a new way.
Finally, information can be used to describe other formalisms: not only par-
ticles and waves, but also systems, networks, agents, automata, and computers
can be seen as information. In other words, it can contain other descriptions
of the world, potentially exploiting their own formalisms. Information is an
inclusive formalism.
3 What Is Information?
Extending the notion of Umwelt [57], the following notion of information can be
given:
Notion 1 Information is anything that an agent can sense, perceive, or observe.
This notion is in accordance with Shannon’s [52], where information is seen
as a just-so arrangement, a defined structure, as opposed to randomness [12, 13],
and it can be measured in bits. This notion can be applied to everything that
surrounds us, including matter and energy, since we can perceive it—because it
has a defined structure—and we are agents, according to the following notion:
Notion 2 An agent is a description of an entity that acts on its environment
[22, p. 39].
Noticing that agents (and their environments) are also information (as they
can be perceived by other agents, especially us, who are the ones who describe
them as agents), an agent can be a human, a cell, a molecule, a computer
program, a society, an electron, a city, a market, an institution, an atom, or a
star. Each of these can be described (by us) as acting in their environment,
simply because they interact with it. However, not all information is an agent,
e.g. temperature, color, velocity, hunger, profit.
Notion 3 The environment of an agent consists of all the information interact-
ing with it.
Information will be relative to the agent perceiving it2. Information can exist
in theory “out there”, independently of an agent, but for practical purposes, it
can be only spoken about once an agent—not necessarily a human—perceives
/ interacts with it. The meaning of the information will be given by the use
the agent perceiving it makes of it [59], i.e. how the agent responds to it [7].
Thus, Notion 1 is a pragmatic one. Note that perceived information is different
from the meaning that an agent gives to it. Meaning is an active product of the
interaction between information and the agent perceiving it [13, 44].
Like this, an electron can be seen as an agent, which perceives other electrons
as information. The same description can be used for molecules, cells, and
animals. We can distinguish:
First order information is that which is perceived directly by an agent. For
example, the information received by a molecule about another molecule
Second order information is that which is perceived by an agent about in-
formation perceived by another agent. For example, the information per-
ceived by a human observer about a molecule receiving information about
another molecule.
Most of the scientific descriptions about the world are second order informa-
tion, as we perceive how agents perceive and produce information. The present
approach also introduces naturally the role of the observer in science, since ev-
erything is “observing” the (limited, first order) information it interacts with
from its own perspective. Humans would be second-level observers, observing
the information observed by information. Everything we can speak about is
observed, and all agents are observers.
Information is not necessarily conserved, i.e. it can be created, destroyed, or
transformed. These can take place only through interaction. Computation can
be seen as the change in information, be it creation, destruction, or transfor-
mation. Matter and energy can be seen as particular types of information that
cannot be created or destroyed, only transformed, along with the well-known
properties that characterize them.
2Shannon’s information [52] deals only with the technical aspect of the transmission of
information and not with its meaning, i.e. it neglects the semantic aspect of communication.
The amount of information required to describe a process, system, object,
or agent determines its complexity [46]. According to our current knowledge,
during the evolution of our universe there has been a shift from simple informa-
tion towards more complex information [2] (the information of an atom is less
complex than that of a molecule, than that of a cell, than that of a multicellular
organism, etc.). This “arrow of complexity”[11] in evolution can guide us to
explore general laws of information.
4 Tentative Laws of Information
Seeing the world as information allows us to describe general laws that can be
applied to everything we can perceive. Extending Darwin’s theory [15], the
present framework can be used to reframe “universal Darwinism” [17], which
explores the idea of evolution beyond biological systems. In this work, the laws
that describe the general behaviour of information as it evolves are introduced.
These laws are only tentative, in the sense that they are only presented with
arguments in favour of them, but they still need to be thoroughly tested.
4.1 Law of Information Transformation
Since information is relative to the agents perceiving it, information will poten-
tially be transformed as different agents perceive it. Another way of stating this
law is the following: information will potentially be transformed by interacting
with other information. This law is a generalization of the Darwinian principle
of random variation, and ensures novelty of information in the world. Even
when there might be static information, different agents can perceive it differ-
ently and interact with it, potentially transforming it. Through evolution, the
transformation of information generates a variety or diversity that can be used
by agents for novel purposes.
Since information is not a conserved quantity, it can increase (created), de-
crease (destroyed), or be maintained as it is transformed.
As an example, RNA polymerase (RNAP) can make errors while copying
DNA onto RNA strands. This slight random variation can lead to changes
in the proteins for which the RNA strands serve as templates. Some of these
changes will lead to novel proteins that might improve or worsen the function of
the original proteins.
The transformation of information can be classified as follows:
Dynamic. Information changes itself. This could be considered as “objective,
internal” change.
Static. The agent perceiving the information changes, but the information itself
does not change. There is a dynamic change but in the agent. This could
be considered as “subjective, internal” change.
Active. An agent changes information in its environment. This could be con-
sidered as an “objective, external” change.
Stigmergic. An agent makes an active change of information, which changes
the perception of that information by another agent. This could be con-
sidered as “subjective, external” or “intersubjective” change.
4.2 Law of Information Propagation
Information propagates as fast as possible. Certainly, only some information
manages to propagate. In other words, we can assume that different informa-
tion has a different “ability” to propagate, also depending on its environment.
The “fitter” information, i.e. that which manages to persist and propagate faster
and more effectively, will prevail over other information. This law generalizes
the Darwinian principle of natural selection, the maximum entropy production
principle [37] (entropy can also be described as information), and Kauffman’s
tentative fourth law of thermodynamics3. It is interesting that this law contains
the second law of thermodynamics, as atoms interact, propagating informa-
tion homogeneously. It also describes living organisms, where genetic informa-
tion is propagated across generations. And it also describes cultural evolution,
where information is propagated among individuals. Life is “far from thermo-
dynamic equilibrium” because it constrains [32] the (more simple) information
propagation at the thermodynamic scale, i.e. the increase of entropy, exploiting
structures to propagate (or maintain) the (more complex) information at the
biological scale.
In relation with the law of information transformation, as information re-
quires agents to perceive it, information will be potentially transformed. This
source of novelty will allow for the “blind” exploration of better ways of propa-
gating information, according to the agents perceiving it and their environments.
Extending the previous example, if errors in transcription made by RNAP are
beneficial for its propagation (which entails the propagation of the cell producing
RNAP), cells with such novel proteins will have better chances of survival than
their “cousins” without transcription errors.
The propagation of information can be classified as follows:
Autonomous. Information propagates by itself. Strictly speaking, this is not
possible, since at least some information is determined by the environment.
However, if more information is produced by itself than by its environment,
we can call this autonomous propagation (See Section 5).
Symbiotic. Different information cooperates, helping to propagate each other.
Parasitic. Information exploits other information for its own propagation.
Altruistic. Information promotes the propagation of other information at the
cost of its own propagation.
3“The workspace of the biosphere expands, on average, as fast as it can in this coconstruct-
ing biosphere” [32, p. 209]
4.3 Law of Requisite Complexity
Taking into account the law of information transformation, transformed infor-
mation can increase, decrease, or maintain its previous complexity, i.e. amount
[46]. However, more complex information will require more complex agents to
perceive, act on, and propagate it. This law generalizes the cybernetic law of
requisite variety [4]. Note that simple agents can perceive and interact with
part of complex information, but they cannot (by themselves) propagate it. An
agent cannot perceive (and thus contain) information more complex than itself.
For simple agents, information that is complex for us will be simple as well.
As stated above, different agents can perceive the same information in different
ways, giving it different meanings.
The so called “arrow of complexity” in evolution [11] can be explained with
this law. If we start with simple information, its transformation will produce by
simple drift [39, 41] increases in the complexity of information, without any goal
or purpose. This occurs simply because there is an open niche for information to
become more complex as it varies. But this also promotes agents to become more
complex to exploit novel (complex) information and propagate it. Evolution does
not need to favour complexity in any way: information just propagates to every
possible niche as fast as possible, and it seems that there is often an “adjacent
possible” [32] niche of greater complexity.
For example, it can be said that a protein (as an agent) perceives some
information via its binding sites, as it recognizes molecules that “fit” a site. More
complex molecules will certainly need more complex binding sites. Whether
complex molecules are better or worse is a different matter: some will be better,
some will be worse. But for those which are better, the complexity of the
proteins must match the complexity of the molecules perceived. If the binding
site perceives only a part of the molecule, then this might be confused with
other molecules which share the perceived part. Following the law of information
transformation, there will be a variety of complexities of information. The law
of requisite complexity just states that the increase in complexity of information
is determined by the ability of agents to perceive, act on, and propagate more
complex information.
Since more complex information will be able to produce more variety, the
speed of the complexity increase will escalate together with the complexity of
the information.
4.4 Law of Information Criticality
Transforming and propagating information will tend to a critical balance be-
tween its stability and its variability. Propagating information maintains itself
as much as possible, but transforming information varies it as much as possi-
ble. This struggle leads to a critical balance analogous to the “edge of chaos”
[36, 31], self-organized criticality [8, 1], and the “complexity from noise” princi-
ple [6]. The homeostasis of living systems can also be seen as the self-regulation
of information criticality.
This law can generalize Kauffman’s four candidate laws for the coconstruc-
tion of a biosphere [32, Ch. 8]. Their relationship with this framework demands
further discussion, which is out of the scope of this paper.
A well known example can be seen with cellular automata [36] and random
Boolean networks [31, 21, 23]: stable (ordered) dynamics limit considerably or
do not allow change of states so information cannot propagate, while variable
(chaotic) dynamics change the states too much, losing information. Following
the law of information propagation, information will tend to a critical state
between stability and variability to maximize its propagation: if it is too stable,
it will not propagate, and if it is too variable, it will be transformed. In other
words, “critical” information will be able to propagate better than stable or
variable one, i.e. as fast as possible (cf. law of information propagation).
4.5 Law of Information Organization
Information produces constraints that regulate information production. These
constraints can be seen as organization [32]. In other words, evolving information
will be organized (by transformation and propagation) to regulate information
production. According to the law of information criticality, this organization
will lie at a critical area between stability and variability. And following the
law of information propagation, the organization of information will enable it to
propagate as fast as possible.
This law can also be seen as information having a certain control over its
environment, since the organization of information will help it withstand pertur-
bations. It has been shown [33, 47, 34] that using this idea as a fitness function
can lead to the evolution of robust and adaptive agents, namely maximizing the
mutual information between sensors and environment.
A clear example of information producing its own organization can be seen
with living systems, which are discussed in Section 5.
4.6 Law of Information Self-organization
Information tends to its preferred, most probable state. This is actually a tautol-
ogy, since observers determine probabilities after observing tendencies of infor-
mation dynamics. Still, this tautology can be useful to describe and understand
phenomena. This law lies at the heart of probability theory and dynamical sys-
tems theory [5]. The dynamics of a system tend to a subset of its state space,
i.e. attractors, depending on its history. This simple fact reduces the possibility
space of information, i.e. a system will tend towards a small subset of all pos-
sible states. If we describe attractors as “organized”, then we can describe the
dynamics of information in terms of self-organization [25].
Pattern formation can be described as information self-organizing, and re-
lated to the law of information propagation. Information will self-organize in
“fit” patterns that are the most probable (defined a posteriori).
Understanding different ways in which self-organization is achieved by trans-
forming information can help us understand better natural phenomena [24] and
design artificial systems [22]. For example, random Boolean networks can be
said to self-organize towards their attractors [23].
4.7 Law of Information Potentiality
An agent can give different potential meanings to information. This implies
that the same information can have different meanings. Moreover, meaning—
while being information—can be independent of the information carrying it, i.e.
depend only on the agent observing it. Thus, different information can have the
same potential meaning. The precise meaning of information will be given by
an agent observing it within a specific context.
The potentiality of information allows the effective communication between
agents. Different information has to be able to acquire the same meaning
(homonymy), while the same information has to be able to acquire different
meanings (polysemy) [44]. The relationship between the laws of information
and communication is clear, but beyond the scope of this paper.
The law of information potentiality is related to a passive information trans-
formation, i.e. a change in the agent observing information.
In spite of information potentiality, not all meanings will be suitable for
all information. In other words, pure subjectivism cannot dictate meanings of
information. By the law of information propagation, some meanings will be
more suitable than others and will propagate. The suitability of meanings will
be determined by their use and context [59]. However, there is always a certain
freedom to subjectively transform information.
For example, a photon can be observed as a particle, as a wave, or as a
particle-wave. The suitability of each given meaning is determined by the context
in which the photon is described/observed.
4.8 Law of Information Perception
The meaning of information is unique for an agent perceiving it in unique, always
changing open contexts. If meaning of information is determined by the use an
agent makes of it, which is embedded in an open environment, we can go to
such a level of detail that the meaning will be unique. Certainly, agents make
generalizations and abstractions of perceptions in order to be able to respond to
novel information. Still, the precise situation and context will never be repeated.
This makes perceived information unique. The implication of this is that the
response to any given information might be “unexpected”, i.e. novelty can
arise. Moreover, the meaning of information can be to a certain extent arbitrary.
This is related with the law of information transformation, as the uniqueness
of meaning allows the same information perceived differently by the same or
different agents to be statically transformed.
This law is a generalization of the first law of human perception: “whatever
is perceived can be perceived only from a uniquely situated place in the overall
structure of points of view” [29, p. xxiv] (cited in [44, p. 250]). We can describe
agents perceiving information as filtering it. An advantage of humans and other
agents is that we can choose which filter to use to perceive. The suggestion
is not that “unpleasant” information should be solipsistically ignored, but that
information can be potentially actively transformed.
For example, T lymphocytes in an immune system can perceive foreign agents
and attack them. Even when the response will be similar for similar foreign
agents, each perception will be unique, a situation that always leaves space for
novelty.
Scales of perception
Different information is perceived at different scales of observation [9]. As the
scale tends to zero, then the information tends to infinite. For lower scales, more
information and details are perceived. The uniqueness of information perception
dominates at these very low (spatial and temporal) scales. However, as gener-
alizations are made, information is “compressed”, i.e. only relevant aspects of
information are perceived4. At higher scales, more abstractions and general-
izations are made, i.e. less information is perceived. When the scale tends to
infinite, the information tends to zero. In other words, no information is needed
to describe all of the universe, because all the information is already there. This
most abstract understanding of the world is in line with the “highest view” of
Vajrayana Buddhism [45]. Implications at this level of description cannot be
right or wrong, because there is no context. Everything is contained, but no
information is needed to describe it, since it is already there. This “maximum”
understanding is also described as vacuity, which leads to bliss [45, p. 42].
Following the law of information criticality, agents will tend to a balance
where the perceived information is minimal but maximally predictive [51] (at a
particular scale): few information is cheaper, but more information in general
entails a more precise predictability. The law of requisite complexity applies at
particular scales, since a change of scale will imply a change of complexity of
information [9].
5 On the Notion of Life
There is no agreed notion of life, which reflects the difficulty of defining the
concept. Still, many researchers have put forward properties that characterize
important aspects of life. Autopoiesis is perhaps the most salient one, which
notes that living systems are self-producing [55, 38]. Still, it has been argued
that autopoiesis is a necessary but not sufficient property for life [50]. The
relevance of autonomy [10, 42, 35] and individuality [40, 35] for life have also
been highlighted .
These approaches are not unproblematic, since no living system is completely
autonomous. This follows from the fact that all living systems are open. For
4The relevance is determined by the context, i.e. different aspects will be relevant for
different contexts.
example, we have some degree of autonomy, but we are still dependent on food,
water, oxygen, sunlight, bacteria living in our gut, etc. This does not mean that
we should abandon the notion of autonomy in life. However, we need to abandon
the sharp distinction between life and non-life [11, 35], as different degrees of
autonomy escalate gradually, from the systems we considered as non-living to
the ones we consider as living. In other words, life has to be a fuzzy concept.
Under the present framework, living and non-living systems are information.
Rather than a yes/no definition, we can speak about a “life ratio”:
Notion 4 The ratio of living information is the information produced by itself
over the information produced by its environment.
Being more specific—since all systems also receive information—a system
with a high life ratio produces more (first order) information about itself than
the one it receives from its environment. Following the law of information orga-
nization, this also implies that living information produces more of its own con-
straints (organization) to regulate itself than the ones produced by its environ-
ment, and thus it has a greater autonomy. All information will have constraints
from other (environmental) information, but we can measure (as second-order
information) the proportion of internal over external constraints to obtain the
life ratio. If this is greater than one, then the information regulates by itself more
than the proportion that is regulated by external information. In the opposite
case, the life ratio would be less than one.
Following the law of information propagation, evolution will tend to informa-
tion with higher life ratios, simply because this can propagate better, as it has
more “control” and autonomy over its environment. When information depends
more on its environment for its propagation, it has a higher probability of being
transformed as it interacts with its environment.
Note that the life ratio depends on spatial and temporal scales at which
information is perceived. For example, for some microorganisms observed at a
scale of years , the life ratio would be less than one, but if observed at a scale of
seconds, the life ration would be greater than one.
Certainly, some artificial systems would be considered as living under this
notion. However, we can make a distinction between living systems embodied
in or composed by biological cells [16], i.e. life as we know it, and the rest, i.e.
life as it could be. The latter ones are precisely those explored by artificial life.
6 On the Notion of Cognition
Cognition is certainly related with life [53]. The term has taken different mean-
ings in different contexts, but all of them can be generalized into a common
notion [20]. Cognition comes from the Latin cognoscere, which means “get to
know”. Like this,
Notion 5 A system is cognitive if it knows something [20, p.135].
From Notion 2, all agents are cognitive, since they “know” how to act on
their environment, giving (first order) meaning to their environmental informa-
tion. Thus, there is no boundary between non-cognitive and cognitive systems.
Throughout evolution, however, there has been a gradual increase in the com-
plexity of cognition [20]. This is because all agents can be described as possessing
some form of cognition, i.e. “knowledge” about the (first-order) information they
perceive5.
Following the law of requisite complexity, evolution leads to more complex
agents, to be able to cope with the complexity of their environment. This is
precisely what triggers the (second-order) increase in the complexity of cognition
we observe.
Certainly, there are different types of cognition6. We can say that a rock
“knows” about gravity because it perceives its information, which has an effect
on it, but it cannot react to this information. Throughout evolution, infor-
mation capable of maintaining its integrity has prevailed over that which was
not. Robust information is that which can resist perturbations to maintain its
integrity. The ability to react to face perturbations to maintain information
makes information adaptive, increasing its probability of maintenance. When
this reaction is made before it occurs, the information is anticipative7. As in-
formation becomes more complex (even if only by information transformation),
the mechanisms for maintaining this information also become more complex, as
stated by the law of requisite complexity. This has led gradually to the advanced
cognition that animals and machines posses.
7 Future Work
The ideas presented here still need to be explored and elaborated further. One
way of doing this would be with a simulation-based method. Being inspired by
ǫ-machines [51, 26], one could start with “simple” agents that are able to per-
ceive and produce information, but cannot control their own production. These
would be let to evolve, measuring if complexity increases as they evolve. The
hypothesis is that complexity would increase (under which conditions still re-
mains to be seen), to a point where “ǫ-agents” will be able to produce themselves
depending more on their own information than that of the environment. This
would be similar to the evolution in Tierra [48] or Avida [3] systems, only that
self-replication would not be inbuilt. The tentative laws of information presented
in Section 4 would be better defined if such a system was studied.
One important aspect that remains to be studied is the representation of
5One could argue that, since agency (and thus cognition) is already assumed in all agents,
this approach is not explanatory. But I am not trying to explain the “origins” of agency, since
I assume it to be there from the start. I believe that we can only study the evolution and
complexification of agency and cognition, not their “origins”.
6For example, human, animal, plant, bacterial, immune, biological, adaptive, systemic, and
artificial [20].
7For a more detailed treatment on robustness, adaptation, and anticipation, see [22]
thermodynamics in terms of information. This is because the ability to per-
form thermodynamic work is a characteristic property of biological systems [32].
This work can be used to generate the organization necessary to sustain life
(cf. law of information organization). It is difficult to describe life in terms
of thermodynamics, since it entails new characteristic properties not present in
thermodynamic systems. But if we see the latter ones as information, it will be
easier to describe how life—also described as information—evolves from them,
as information propagates itself at different scales.
A potential application of this framework would be in economy, considering
capital, goods, and resources as information (a non-conserved quantity) [18].
A similar benefit (of non-conservation) could be given in game theory: if the
payoff of games is given in terms of information (not necessarily conserved), non-
zero sum games could be easier to grasp than if the payoff is given in material
(conserved) goods.
It becomes clear that information (object), the agent perceiving it (subject)
and the meaning-making or transformation of information (action) are deeply
interrelated. They are part of the same totality, since one cannot exist without
the others. This is also in line with Buddhist philosophy. The implications of an
informational description of the world for philosophy have also to be addressed,
since some schools have focussed on partial aspects of the object-subject-action
trichotomy. Another potential application of the laws of information would be
in ethics, where value can be described accordingly to the present framework.
8 Conclusions
This paper introduced general ideas that require further development, extension
and grounding in particular disciplines. Still, a first step is always necessary, and
hopefully feedback from the community will guide the following steps of this line
of research.
Different metaphors for describing the world can be seen as different lan-
guages: they can refer to the same objects without changing them. And each
can be more suitable for a particular context. For example, English has several
advantages for fast learning, German for philosophy, Spanish for narrative, and
Russian for poetry. In other words, there is no “best” language outside a par-
ticular context. In a similar way, I am not suggesting that describing the world
as information is more suitable than physics to describe physical phenomena,
or better than chemistry to describe chemical phenomena. It would be redun-
dant to describe particles as information if we are studying only particles. The
suggested approach is meant only for the cases when the physical approach is
not sufficient, i.e. across scales, constituting an alternative worth exploring to
describe evolution.
It seems easier to describe matter and energy in terms of information than
vice versa. Moreover, information could be used as a common language across
scientific disciplines [56].
Acknowledgements
I should like to thank Irun Cohen, Inman Harvey, Francis Heylighen, David
Krakauer, Antonio del Rı́o, Marko Rodriguez, David Rosenblueth, Stanley
Salthe, Mikhail Prokopenko, Clément Vidal, and Héctor Zenil for their useful
comments and suggestions.
Bibliography
[1] Adami, Christoph, “Self-organized criticality in living systems”, Phys. Lett. A 203
(1995), 29–32.
[2] Adami, Christoph, “What is complexity?”, Bioessays 24, 12 (December 2002), 1085–
1094.
[3] Adami, Chris, and C. Titus Brown, “Evolutionary learning in the 2d artificial life system
”Avida””, Proc. Artificial Life IV (R. Brooks and P. Maes eds.), MIT Press (1994),
377–381.
[4] Ashby, W. Ross, An Introduction to Cybernetics, Chapman & Hall London (1956).
[5] Ashby, W. Ross, “Principles of the self-organizing system”, Principles of Self-
Organization (Oxford, ) (H. V. Foerster and G. W. Zopf, Jr. eds.), Pergamon (1962),
255–278.
[6] Atlan, H., “On a formal definition of organization”, J Theor Biol 45, 2 (June 1974),
295–304.
[7] Atlan, Henri, and Irun R. Cohen, “Immune information, self-organization and mean-
ing”, Int. Immunol. 10, 6 (1998), 711—717.
[8] Bak, Per, Chao Tang, and KurtWiesenfeld, “Self-organized criticality: An explanation
of the 1/f noise”, Phys. Rev. Lett. 59, 4 (July 1987), 381–384.
[9] Bar-Yam, Y., “Multiscale variety in complex systems”, Complexity 9, 4 (2004), 37–45.
[10] Barandarian, Xabier, “Behavioral adaptive autonomy. a milestone in the ALife route
to AI?”, Artificial Life IX Proceedings of the Ninth International Conference on the
Simulation and Synthesis of Living Systems (J. Pollack, M. Bedau, P. Husbands,
T. Ikegami, and R. A. Watson eds.), MIT Press (2004), 514–521.
[11] Bedau, Mark A., “Four puzzles about life”, Artificial Life 4 (1998), 125–140.
[12] Cohen, Irun R., Tending Adam’s Garden: Evolving the Cognitive Immune Self, Academic
Press London (2000).
[13] Cohen, Irun R., “Informational landscapes in art, science, and evolution”, Bulletin of
Mathematical Biology 68, 5 (July 2006), 1213–1229.
[14] Cover, Thomas M., and Joy A. Thomas, Elements of Information Theory, Wiley-
Interscience (July 2006).
[15] Darwin, Charles, The Origin of Species, Wordsworth (1998).
[16] De Duve, Christian, Live Evolving: Molecules, Mind, and Meaning, Oxford University
Press (2003).
[17] Dennett, Daniel, Darwin’s Dangerous Idea, Simon & Schuster (1995).
[18] Farmer, J. Doyne, and Neda Zamani, “Mechanical vs. informational components of price
impact”, EPJ B 55, 2 (2007), 189–200.
[19] Floridi, Luciano ed., The Blackwell Guide to Philosophy of Computing and Information,
Blackwell (2003).
[20] Gershenson, Carlos, “Cognitive paradigms: Which one is the best?”, Cognitive Systems
Research 5, 2 (June 2004), 135–156.
[21] Gershenson, Carlos, “Introduction to random Boolean networks”, Workshop and Tu-
torial Proceedings, Ninth International Conference on the Simulation and Synthesis of
Living Systems (ALife IX) (Boston, MA, ) (M. Bedau, P. Husbands, T. Hutton, S. Ku-
mar, and H. Suzuki eds.), (2004), 160–173.
[22] Gershenson, Carlos, Design and Control of Self-organizing Systems, CopIt Arxives
Mexico (2007), http://tinyurl.com/DCSOS2007.
[23] Gershenson, Carlos, “Guiding the self-organization of random Boolean networks”, The-
ory in Biosciences (In Press).
[24] Gershenson, Carlos, “The sigma profile: A formal tool to study organization and its
evolution at multiple scales”, Complexity (In Press).
[25] Gershenson, Carlos, and Francis Heylighen, “When can we call a system self-
organizing?”, Advances in Artificial Life, 7th European Conference, ECAL 2003 LNAI
2801 (Berlin, ) (W. Banzhaf, T. Christaller, P. Dittrich, J. T. Kim, and J. Ziegler
eds.), Springer (2003), 606–614.
[26] Görnerup, Olof, and James P. Crutchfield, “Hierarchical self-organization in the
finitary process soup”, Artificial Life In Press (2008), Special Issue on the Evolution of
Complexity.
[27] Helbing, Dirk, and Tamás Vicsek, “Optimal self-organization”, New Journal of Physics
1 (1999), 13.1–13.17.
[28] Heylighen, Francis, and Cliff Joslyn, “Cybernetics and second order cybernetics”,
Encyclopedia of Physical Science and Technology, (R. A. Meyers ed.) 3rd ed. vol. 4.
Academic Press New York (2001), pp. 155–170.
[29] Holquist, M., “Introduction”, Art and Answerability, (M. M. Bakhtin ed.). University
of Texas Press Austin (1990).
[30] Hopfield, J. J., “Physics, computation, and why biology looks so different”, Journal of
Theoretical Biology 171 (1994), 53–60.
[31] Kauffman, S. A., The Origins of Order, Oxford University Press (1993).
[32] Kauffman, Stuart A., Investigations, Oxford University Press (2000).
[33] Klyubin, Alexander S., Daniel Polani, and Chrystopher L. Nehaniv, “Organization of
the information flow in the perception-action loop of evolved agents perception-action loop
of evolved agents”, Proceedings of 2004 NASA/DoD Conference on Evolvable Hardware
(R. S. Zebulum, D. Gwaltney, G. Hornby, D. Keymeulen, J. Lohn, and A. Stoica
eds.), IEEE Computer Society (2004), 177–180.
[34] Klyubin, Alexander S., Daniel Polani, and Chrystopher L. Nehaniv, “Representations
of space and time in the maximization of information flow in the perception-action loop”,
Neural Computation 19 (2007), 2387–2432.
[35] Krakauer, D. C., and P. M. A. Zanotto, “Viral individuality and limitations of the
life concept”, Protocells: Bridging Nonliving and Living Matter, (S. Rasmussen, M. A.
Bedau, L. Chen, D. Deamer, D. C. Krakauer, N. Packard, and D. P. Stadler eds.).
MIT Press (2007).
[36] Langton, Christpher, “Computation at the edge of chaos: Phase transitions and emer-
gent computation”, Physica D 42 (1990), 12–37.
[37] Martyushev, L. M., and V. D. Seleznev, “Maximum entropy production principle in
physics, chemistry and biology”, Physics Reports 426, 1 (April 2006), 1–45.
[38] McMullin, Barry, “30 years of computational autopoiesis: A review”, Artificial Life 10,
3 (Summer 2004), 277–295.
[39] McShea, Daniel W., “Metazoan complexity and evolution: Is there a trend?”, Evolution
50 (1996), 477–492.
[40] Michod, Richard E., Darwinian Dynamics: Evolutionary Transitions in Fitness and
Individuality, Princeton University Press Princeton, NJ (2000).
[41] Miconi, T, “Evolution and complexity: the double-edged sword”, Artificial Life 14, 3
(Summer 2008), 325–344, Special Issue on the Evolution of Complexity.
[42] Moreno, Alvaro, and Kepa Ruiz-Mirazo, “The maintenance and open-ended growth of
complexity in nature: information as a decoupling mechanism in the origins of life”, Re-
framing Complexity: Perspectives from the North and South, (F. Capra, A. Juarrero,
P. Sotolongo, and J. van Uden eds.). ISCE Publishing (2006).
[43] Morowitz, Harold, and D. Eric Smith, “Energy flow and the organization of life”, Tech.
Rep. no. 06-08-029, Santa Fe Institute, (2006).
[44] Neuman, Yair, Reviving the Living: Meaning Making in Living Systems vol. 6 of Studies
in Multidisciplinarity, Elsevier Amsterdam (2008).
[45] Nydahl, Ole, The Way Things Are: A living Approach to Buddhism for today’s world.,
O Books (2008).
[46] Prokopenko, Mikhail, Fabio Boschetti, and Alex J. Ryan, “An information-theoretic
primer on complexity, self-organisation and emergence”, Complexity 15, 1 (2009), 11–28.
[47] Prokopenko, M., V. Gerasimov, and I. Tanev, “Evolving spatiotemporal coordination
in a modular robotic system”, From Animals to Animats 9: 9th International Conference
on the Simulation of Adaptive Behavior (SAB 2006) (S. Nolfi, G. Baldassarre, R. Cal-
abretta, J. C. T. Hallam, D. Marocco, J.-A. Meyer, O. Miglino, and D. Parisi
eds.), vol. 4095 of Lecture Notes in Computer Science, Springer (2006), 558–569.
[48] Ray, T. S., “An approach to the synthesis of life”, Artificial Life II, (C. Langton,
C. Taylor, J. D. Farmer, and S. Rasmussen eds.) vol. XI of Santa Fe Institute Studies
in the Sciences of Complexity. Addison-Wesley Redwood City, CA (1991), pp. 371–408.
[49] Roederer, Juan G., Information and its Role in Nature, Springer-Verlag Heidelberg
(May 2005).
[50] Ruiz-Mirazo, Kepa, and Alvaro Moreno, “Basic autonomy as a fundamnental step in
the synthesis of life”, Artificial Life 10, 3 (Summer 2004), 235–259.
[51] Shalizi, Cosma R., Causal Architecture, Complexity and Self-Organization in Time
Series and Cellular Automata, PhD thesis University of Wisconsin at Madison (2001).
[52] Shannon, C. E., “A mathematical theory of communication”, Bell System Technical
Journal 27 (July and October 1948), 379–423 and 623–656.
[53] Stewart, John, “Cognition = life : Implications for higher-level cognition”, Behavioural
processes 35 (1995), 311–326.
[54] Umpleby, Stuart, “Physical relationships among matter, energy and information”, Cy-
bernetics and Systems 2004 (Vienna, ) (R. Trappl ed.), vol. 1, Austrian Society for
Cybernetic Studies, (2004), 124–6.
[55] Varela, Francisco J., Humberto R. Maturana, and R. Uribe., “Autopoiesis: The
organization of living systems, its characterization and a model”, BioSystems 5 (1974),
187–196.
[56] von Baeyer, Hans Christian, Information: The New Language of Science, Harvard
University Press Cambridge, MA (2004).
[57] von Uexküll, Jakob, “A stroll through the worlds of animals and men”, Instinctive
Behavior: The Development of a Modern Concept, (C. H. Schiller ed.). International
Universities Press New York (1957), pp. 5–80.
[58] Wheeler, John Archibald, “Information, physics, quantum: the search for links”, Com-
plexity, Entropy, and the Physics of Information, (W. H. Zurek ed.) vol. VIII of Santa
Fe Institute Studies in the Sciences of Complexity. Perseus Books Reading, MA (1990).
[59] Wittgenstein, Ludwig, Philosophical Investigations 3rd ed., Prentice Hall (1999).
[60] Wolfram, Stephen, A New Kind of Sciene, Wolfram Media (2002).
	1 The World as Evolving Information
	1 Introduction
	2 Why Information?
	3 What Is Information?
	4 Tentative Laws of Information
	4.1 Law of Information Transformation
	4.2 Law of Information Propagation
	4.3 Law of Requisite Complexity
	4.4 Law of Information Criticality
	4.5 Law of Information Organization
	4.6 Law of Information Self-organization
	4.7 Law of Information Potentiality
	4.8 Law of Information Perception
	5 On the Notion of Life
	6 On the Notion of Cognition
	7 Future Work
	8 Conclusions

  This paper discusses the benefits of describing the world as information,
especially in the study of the evolution of life and cognition. Traditional
studies encounter problems because it is difficult to describe life and
cognition in terms of matter and energy, since their laws are valid only at the
physical scale. However, if matter and energy, as well as life and cognition,
are described in terms of information, evolution can be described consistently
as information becoming more complex.
  The paper presents eight tentative laws of information, valid at multiple
scales, which are generalizations of Darwinian, cybernetic, thermodynamic,
psychological, philosophical, and complexity principles. These are further used
to discuss the notions of life, cognition and their evolution.

Introduction
Throughout history we have used concepts from our current technology as
metaphors to describe our world. Examples of this are the description of the
body as a factory during the Industrial Age, and the description of the brain as a
computer during the Information Age. These metaphors are useful because they
extend the knowledge acquired by the scientific and technological developments
to other areas, illuminating them from a novel perspective. For example, it is
common to extend the particle metaphor used in physics to other domains, such
as crowd dynamics [27]. Even when people are not particles and have very com-
plicated behaviour, for the purposes of crowd dynamics they can be effectively
described as particles, with the benefit that there is an established mathemati-
cal framework suitable for this description. Another example can be seen with
cybernetics [4, 28], where the system metaphor is used: everything is seen as a
system with inputs, outputs, and a control that regulates the internal variables
of the system under the influence of perturbations from its environment. Yet
another example can be seen with the computational metaphor [60], where the
universe can be modelled with simple discrete computational machines, such as
cellular automata or Turing machines.
Having in mind that we are using metaphors, this paper proposes to extend
the concept of information to describe the world: from elementary particles to
galaxies, with everything in between, particularly life and cognition. There is no
suggestion on the nature of reality as information [58]. This work only explores
the advantages of describing the world as information. In other words, there are
no ontological claims, only epistemological.
In the next section, the motivation of the paper is presented, followed by a
section describing the notion of information to be used throughout the paper. In
Section 4, eight tentative laws of information are put forward. These are applied
to the notions of life (Section 5) and cognition (Section 6). The paper closes
presenting future work and conclusions.
2 Why Information?
There is a great interest in the relationship between energy, matter, and infor-
mation [32, 54, 43]. One of the main reasons for this arises because this rela-
tionship plays a central role in the definition of life: Hopfield [30] suggests that
the difference between biological and physical systems is given by the meaning-
ful information content of the former ones. Not that information is not present
in physical systems, but—as Roederer puts it—information is passive in physics
and active in biology [49]. However, it becomes complicated to describe how this
information came to be in terms of the physical laws of matter and energy. In
this paper the inverse approach is proposed: let us describe matter and energy in
terms of information. If atoms, molecules and cells are described as information,
there is no need of a qualitative shift (from non-living to living matter) while
describing the origin and evolution of life: this is translated into a quantitative
shift (from less complex to more complex information).
There is a similar problem when we study the origin and evolution of cog-
nition [20]: it is not easy to describe cognitive systems in terms of matter and
energy. The drawback with the physics-based approach to the studies of life and
cognition is that it requires a new category, that in the best situations can be
referred to as “emergent”. Emergence is a useful concept, but it this case it is
not explanatory. Moreover, it stealthily introduces a dualist view of the world:
if we cannot relate properly matter and energy with life and cognition, we are
forced to see these as separate categories. Once this breach is made, there is
no clear way of studying or understanding how systems with life and cognition
evolved from those without it. If we see matter and energy as particular, simple
cases of information, the dualist trap is avoided by following a continuum in the
evolution of the universe. Physical laws are suitable for describing phenomena
at the physical scale. The tentative laws of information presented below aim at
being suitable for describing phenomena at any scale. Certainly, there are other
approaches to describe phenomena at multiple scales, such as general systems
theory and dynamical systems theory. These approaches are not exclusive, since
one can use several of them, including information, to describe different aspects
of the same phenomena.
Another benefit of using information as a basic descriptor for our world is
that the concept is well studied and formal methods have already been developed
[14, 46], as well as its philosophical implications have been discussed [19]. Thus,
there is no need to develop a new formalism, since information theory is well
established. I borrow this formalism and interpret it in a new way.
Finally, information can be used to describe other formalisms: not only par-
ticles and waves, but also systems, networks, agents, automata, and computers
can be seen as information. In other words, it can contain other descriptions
of the world, potentially exploiting their own formalisms. Information is an
inclusive formalism.
3 What Is Information?
Extending the notion of Umwelt [57], the following notion of information can be
given:
Notion 1 Information is anything that an agent can sense, perceive, or observe.
This notion is in accordance with Shannon’s [52], where information is seen
as a just-so arrangement, a defined structure, as opposed to randomness [12, 13],
and it can be measured in bits. This notion can be applied to everything that
surrounds us, including matter and energy, since we can perceive it—because it
has a defined structure—and we are agents, according to the following notion:
Notion 2 An agent is a description of an entity that acts on its environment
[22, p. 39].
Noticing that agents (and their environments) are also information (as they
can be perceived by other agents, especially us, who are the ones who describe
them as agents), an agent can be a human, a cell, a molecule, a computer
program, a society, an electron, a city, a market, an institution, an atom, or a
star. Each of these can be described (by us) as acting in their environment,
simply because they interact with it. However, not all information is an agent,
e.g. temperature, color, velocity, hunger, profit.
Notion 3 The environment of an agent consists of all the information interact-
ing with it.
Information will be relative to the agent perceiving it2. Information can exist
in theory “out there”, independently of an agent, but for practical purposes, it
can be only spoken about once an agent—not necessarily a human—perceives
/ interacts with it. The meaning of the information will be given by the use
the agent perceiving it makes of it [59], i.e. how the agent responds to it [7].
Thus, Notion 1 is a pragmatic one. Note that perceived information is different
from the meaning that an agent gives to it. Meaning is an active product of the
interaction between information and the agent perceiving it [13, 44].
Like this, an electron can be seen as an agent, which perceives other electrons
as information. The same description can be used for molecules, cells, and
animals. We can distinguish:
First order information is that which is perceived directly by an agent. For
example, the information received by a molecule about another molecule
Second order information is that which is perceived by an agent about in-
formation perceived by another agent. For example, the information per-
ceived by a human observer about a molecule receiving information about
another molecule.
Most of the scientific descriptions about the world are second order informa-
tion, as we perceive how agents perceive and produce information. The present
approach also introduces naturally the role of the observer in science, since ev-
erything is “observing” the (limited, first order) information it interacts with
from its own perspective. Humans would be second-level observers, observing
the information observed by information. Everything we can speak about is
observed, and all agents are observers.
Information is not necessarily conserved, i.e. it can be created, destroyed, or
transformed. These can take place only through interaction. Computation can
be seen as the change in information, be it creation, destruction, or transfor-
mation. Matter and energy can be seen as particular types of information that
cannot be created or destroyed, only transformed, along with the well-known
properties that characterize them.
2Shannon’s information [52] deals only with the technical aspect of the transmission of
information and not with its meaning, i.e. it neglects the semantic aspect of communication.
The amount of information required to describe a process, system, object,
or agent determines its complexity [46]. According to our current knowledge,
during the evolution of our universe there has been a shift from simple informa-
tion towards more complex information [2] (the information of an atom is less
complex than that of a molecule, than that of a cell, than that of a multicellular
organism, etc.). This “arrow of complexity”[11] in evolution can guide us to
explore general laws of information.
4 Tentative Laws of Information
Seeing the world as information allows us to describe general laws that can be
applied to everything we can perceive. Extending Darwin’s theory [15], the
present framework can be used to reframe “universal Darwinism” [17], which
explores the idea of evolution beyond biological systems. In this work, the laws
that describe the general behaviour of information as it evolves are introduced.
These laws are only tentative, in the sense that they are only presented with
arguments in favour of them, but they still need to be thoroughly tested.
4.1 Law of Information Transformation
Since information is relative to the agents perceiving it, information will poten-
tially be transformed as different agents perceive it. Another way of stating this
law is the following: information will potentially be transformed by interacting
with other information. This law is a generalization of the Darwinian principle
of random variation, and ensures novelty of information in the world. Even
when there might be static information, different agents can perceive it differ-
ently and interact with it, potentially transforming it. Through evolution, the
transformation of information generates a variety or diversity that can be used
by agents for novel purposes.
Since information is not a conserved quantity, it can increase (created), de-
crease (destroyed), or be maintained as it is transformed.
As an example, RNA polymerase (RNAP) can make errors while copying
DNA onto RNA strands. This slight random variation can lead to changes
in the proteins for which the RNA strands serve as templates. Some of these
changes will lead to novel proteins that might improve or worsen the function of
the original proteins.
The transformation of information can be classified as follows:
Dynamic. Information changes itself. This could be considered as “objective,
internal” change.
Static. The agent perceiving the information changes, but the information itself
does not change. There is a dynamic change but in the agent. This could
be considered as “subjective, internal” change.
Active. An agent changes information in its environment. This could be con-
sidered as an “objective, external” change.
Stigmergic. An agent makes an active change of information, which changes
the perception of that information by another agent. This could be con-
sidered as “subjective, external” or “intersubjective” change.
4.2 Law of Information Propagation
Information propagates as fast as possible. Certainly, only some information
manages to propagate. In other words, we can assume that different informa-
tion has a different “ability” to propagate, also depending on its environment.
The “fitter” information, i.e. that which manages to persist and propagate faster
and more effectively, will prevail over other information. This law generalizes
the Darwinian principle of natural selection, the maximum entropy production
principle [37] (entropy can also be described as information), and Kauffman’s
tentative fourth law of thermodynamics3. It is interesting that this law contains
the second law of thermodynamics, as atoms interact, propagating informa-
tion homogeneously. It also describes living organisms, where genetic informa-
tion is propagated across generations. And it also describes cultural evolution,
where information is propagated among individuals. Life is “far from thermo-
dynamic equilibrium” because it constrains [32] the (more simple) information
propagation at the thermodynamic scale, i.e. the increase of entropy, exploiting
structures to propagate (or maintain) the (more complex) information at the
biological scale.
In relation with the law of information transformation, as information re-
quires agents to perceive it, information will be potentially transformed. This
source of novelty will allow for the “blind” exploration of better ways of propa-
gating information, according to the agents perceiving it and their environments.
Extending the previous example, if errors in transcription made by RNAP are
beneficial for its propagation (which entails the propagation of the cell producing
RNAP), cells with such novel proteins will have better chances of survival than
their “cousins” without transcription errors.
The propagation of information can be classified as follows:
Autonomous. Information propagates by itself. Strictly speaking, this is not
possible, since at least some information is determined by the environment.
However, if more information is produced by itself than by its environment,
we can call this autonomous propagation (See Section 5).
Symbiotic. Different information cooperates, helping to propagate each other.
Parasitic. Information exploits other information for its own propagation.
Altruistic. Information promotes the propagation of other information at the
cost of its own propagation.
3“The workspace of the biosphere expands, on average, as fast as it can in this coconstruct-
ing biosphere” [32, p. 209]
4.3 Law of Requisite Complexity
Taking into account the law of information transformation, transformed infor-
mation can increase, decrease, or maintain its previous complexity, i.e. amount
[46]. However, more complex information will require more complex agents to
perceive, act on, and propagate it. This law generalizes the cybernetic law of
requisite variety [4]. Note that simple agents can perceive and interact with
part of complex information, but they cannot (by themselves) propagate it. An
agent cannot perceive (and thus contain) information more complex than itself.
For simple agents, information that is complex for us will be simple as well.
As stated above, different agents can perceive the same information in different
ways, giving it different meanings.
The so called “arrow of complexity” in evolution [11] can be explained with
this law. If we start with simple information, its transformation will produce by
simple drift [39, 41] increases in the complexity of information, without any goal
or purpose. This occurs simply because there is an open niche for information to
become more complex as it varies. But this also promotes agents to become more
complex to exploit novel (complex) information and propagate it. Evolution does
not need to favour complexity in any way: information just propagates to every
possible niche as fast as possible, and it seems that there is often an “adjacent
possible” [32] niche of greater complexity.
For example, it can be said that a protein (as an agent) perceives some
information via its binding sites, as it recognizes molecules that “fit” a site. More
complex molecules will certainly need more complex binding sites. Whether
complex molecules are better or worse is a different matter: some will be better,
some will be worse. But for those which are better, the complexity of the
proteins must match the complexity of the molecules perceived. If the binding
site perceives only a part of the molecule, then this might be confused with
other molecules which share the perceived part. Following the law of information
transformation, there will be a variety of complexities of information. The law
of requisite complexity just states that the increase in complexity of information
is determined by the ability of agents to perceive, act on, and propagate more
complex information.
Since more complex information will be able to produce more variety, the
speed of the complexity increase will escalate together with the complexity of
the information.
4.4 Law of Information Criticality
Transforming and propagating information will tend to a critical balance be-
tween its stability and its variability. Propagating information maintains itself
as much as possible, but transforming information varies it as much as possi-
ble. This struggle leads to a critical balance analogous to the “edge of chaos”
[36, 31], self-organized criticality [8, 1], and the “complexity from noise” princi-
ple [6]. The homeostasis of living systems can also be seen as the self-regulation
of information criticality.
This law can generalize Kauffman’s four candidate laws for the coconstruc-
tion of a biosphere [32, Ch. 8]. Their relationship with this framework demands
further discussion, which is out of the scope of this paper.
A well known example can be seen with cellular automata [36] and random
Boolean networks [31, 21, 23]: stable (ordered) dynamics limit considerably or
do not allow change of states so information cannot propagate, while variable
(chaotic) dynamics change the states too much, losing information. Following
the law of information propagation, information will tend to a critical state
between stability and variability to maximize its propagation: if it is too stable,
it will not propagate, and if it is too variable, it will be transformed. In other
words, “critical” information will be able to propagate better than stable or
variable one, i.e. as fast as possible (cf. law of information propagation).
4.5 Law of Information Organization
Information produces constraints that regulate information production. These
constraints can be seen as organization [32]. In other words, evolving information
will be organized (by transformation and propagation) to regulate information
production. According to the law of information criticality, this organization
will lie at a critical area between stability and variability. And following the
law of information propagation, the organization of information will enable it to
propagate as fast as possible.
This law can also be seen as information having a certain control over its
environment, since the organization of information will help it withstand pertur-
bations. It has been shown [33, 47, 34] that using this idea as a fitness function
can lead to the evolution of robust and adaptive agents, namely maximizing the
mutual information between sensors and environment.
A clear example of information producing its own organization can be seen
with living systems, which are discussed in Section 5.
4.6 Law of Information Self-organization
Information tends to its preferred, most probable state. This is actually a tautol-
ogy, since observers determine probabilities after observing tendencies of infor-
mation dynamics. Still, this tautology can be useful to describe and understand
phenomena. This law lies at the heart of probability theory and dynamical sys-
tems theory [5]. The dynamics of a system tend to a subset of its state space,
i.e. attractors, depending on its history. This simple fact reduces the possibility
space of information, i.e. a system will tend towards a small subset of all pos-
sible states. If we describe attractors as “organized”, then we can describe the
dynamics of information in terms of self-organization [25].
Pattern formation can be described as information self-organizing, and re-
lated to the law of information propagation. Information will self-organize in
“fit” patterns that are the most probable (defined a posteriori).
Understanding different ways in which self-organization is achieved by trans-
forming information can help us understand better natural phenomena [24] and
design artificial systems [22]. For example, random Boolean networks can be
said to self-organize towards their attractors [23].
4.7 Law of Information Potentiality
An agent can give different potential meanings to information. This implies
that the same information can have different meanings. Moreover, meaning—
while being information—can be independent of the information carrying it, i.e.
depend only on the agent observing it. Thus, different information can have the
same potential meaning. The precise meaning of information will be given by
an agent observing it within a specific context.
The potentiality of information allows the effective communication between
agents. Different information has to be able to acquire the same meaning
(homonymy), while the same information has to be able to acquire different
meanings (polysemy) [44]. The relationship between the laws of information
and communication is clear, but beyond the scope of this paper.
The law of information potentiality is related to a passive information trans-
formation, i.e. a change in the agent observing information.
In spite of information potentiality, not all meanings will be suitable for
all information. In other words, pure subjectivism cannot dictate meanings of
information. By the law of information propagation, some meanings will be
more suitable than others and will propagate. The suitability of meanings will
be determined by their use and context [59]. However, there is always a certain
freedom to subjectively transform information.
For example, a photon can be observed as a particle, as a wave, or as a
particle-wave. The suitability of each given meaning is determined by the context
in which the photon is described/observed.
4.8 Law of Information Perception
The meaning of information is unique for an agent perceiving it in unique, always
changing open contexts. If meaning of information is determined by the use an
agent makes of it, which is embedded in an open environment, we can go to
such a level of detail that the meaning will be unique. Certainly, agents make
generalizations and abstractions of perceptions in order to be able to respond to
novel information. Still, the precise situation and context will never be repeated.
This makes perceived information unique. The implication of this is that the
response to any given information might be “unexpected”, i.e. novelty can
arise. Moreover, the meaning of information can be to a certain extent arbitrary.
This is related with the law of information transformation, as the uniqueness
of meaning allows the same information perceived differently by the same or
different agents to be statically transformed.
This law is a generalization of the first law of human perception: “whatever
is perceived can be perceived only from a uniquely situated place in the overall
structure of points of view” [29, p. xxiv] (cited in [44, p. 250]). We can describe
agents perceiving information as filtering it. An advantage of humans and other
agents is that we can choose which filter to use to perceive. The suggestion
is not that “unpleasant” information should be solipsistically ignored, but that
information can be potentially actively transformed.
For example, T lymphocytes in an immune system can perceive foreign agents
and attack them. Even when the response will be similar for similar foreign
agents, each perception will be unique, a situation that always leaves space for
novelty.
Scales of perception
Different information is perceived at different scales of observation [9]. As the
scale tends to zero, then the information tends to infinite. For lower scales, more
information and details are perceived. The uniqueness of information perception
dominates at these very low (spatial and temporal) scales. However, as gener-
alizations are made, information is “compressed”, i.e. only relevant aspects of
information are perceived4. At higher scales, more abstractions and general-
izations are made, i.e. less information is perceived. When the scale tends to
infinite, the information tends to zero. In other words, no information is needed
to describe all of the universe, because all the information is already there. This
most abstract understanding of the world is in line with the “highest view” of
Vajrayana Buddhism [45]. Implications at this level of description cannot be
right or wrong, because there is no context. Everything is contained, but no
information is needed to describe it, since it is already there. This “maximum”
understanding is also described as vacuity, which leads to bliss [45, p. 42].
Following the law of information criticality, agents will tend to a balance
where the perceived information is minimal but maximally predictive [51] (at a
particular scale): few information is cheaper, but more information in general
entails a more precise predictability. The law of requisite complexity applies at
particular scales, since a change of scale will imply a change of complexity of
information [9].
5 On the Notion of Life
There is no agreed notion of life, which reflects the difficulty of defining the
concept. Still, many researchers have put forward properties that characterize
important aspects of life. Autopoiesis is perhaps the most salient one, which
notes that living systems are self-producing [55, 38]. Still, it has been argued
that autopoiesis is a necessary but not sufficient property for life [50]. The
relevance of autonomy [10, 42, 35] and individuality [40, 35] for life have also
been highlighted .
These approaches are not unproblematic, since no living system is completely
autonomous. This follows from the fact that all living systems are open. For
4The relevance is determined by the context, i.e. different aspects will be relevant for
different contexts.
example, we have some degree of autonomy, but we are still dependent on food,
water, oxygen, sunlight, bacteria living in our gut, etc. This does not mean that
we should abandon the notion of autonomy in life. However, we need to abandon
the sharp distinction between life and non-life [11, 35], as different degrees of
autonomy escalate gradually, from the systems we considered as non-living to
the ones we consider as living. In other words, life has to be a fuzzy concept.
Under the present framework, living and non-living systems are information.
Rather than a yes/no definition, we can speak about a “life ratio”:
Notion 4 The ratio of living information is the information produced by itself
over the information produced by its environment.
Being more specific—since all systems also receive information—a system
with a high life ratio produces more (first order) information about itself than
the one it receives from its environment. Following the law of information orga-
nization, this also implies that living information produces more of its own con-
straints (organization) to regulate itself than the ones produced by its environ-
ment, and thus it has a greater autonomy. All information will have constraints
from other (environmental) information, but we can measure (as second-order
information) the proportion of internal over external constraints to obtain the
life ratio. If this is greater than one, then the information regulates by itself more
than the proportion that is regulated by external information. In the opposite
case, the life ratio would be less than one.
Following the law of information propagation, evolution will tend to informa-
tion with higher life ratios, simply because this can propagate better, as it has
more “control” and autonomy over its environment. When information depends
more on its environment for its propagation, it has a higher probability of being
transformed as it interacts with its environment.
Note that the life ratio depends on spatial and temporal scales at which
information is perceived. For example, for some microorganisms observed at a
scale of years , the life ratio would be less than one, but if observed at a scale of
seconds, the life ration would be greater than one.
Certainly, some artificial systems would be considered as living under this
notion. However, we can make a distinction between living systems embodied
in or composed by biological cells [16], i.e. life as we know it, and the rest, i.e.
life as it could be. The latter ones are precisely those explored by artificial life.
6 On the Notion of Cognition
Cognition is certainly related with life [53]. The term has taken different mean-
ings in different contexts, but all of them can be generalized into a common
notion [20]. Cognition comes from the Latin cognoscere, which means “get to
know”. Like this,
Notion 5 A system is cognitive if it knows something [20, p.135].
From Notion 2, all agents are cognitive, since they “know” how to act on
their environment, giving (first order) meaning to their environmental informa-
tion. Thus, there is no boundary between non-cognitive and cognitive systems.
Throughout evolution, however, there has been a gradual increase in the com-
plexity of cognition [20]. This is because all agents can be described as possessing
some form of cognition, i.e. “knowledge” about the (first-order) information they
perceive5.
Following the law of requisite complexity, evolution leads to more complex
agents, to be able to cope with the complexity of their environment. This is
precisely what triggers the (second-order) increase in the complexity of cognition
we observe.
Certainly, there are different types of cognition6. We can say that a rock
“knows” about gravity because it perceives its information, which has an effect
on it, but it cannot react to this information. Throughout evolution, infor-
mation capable of maintaining its integrity has prevailed over that which was
not. Robust information is that which can resist perturbations to maintain its
integrity. The ability to react to face perturbations to maintain information
makes information adaptive, increasing its probability of maintenance. When
this reaction is made before it occurs, the information is anticipative7. As in-
formation becomes more complex (even if only by information transformation),
the mechanisms for maintaining this information also become more complex, as
stated by the law of requisite complexity. This has led gradually to the advanced
cognition that animals and machines posses.
7 Future Work
The ideas presented here still need to be explored and elaborated further. One
way of doing this would be with a simulation-based method. Being inspired by
ǫ-machines [51, 26], one could start with “simple” agents that are able to per-
ceive and produce information, but cannot control their own production. These
would be let to evolve, measuring if complexity increases as they evolve. The
hypothesis is that complexity would increase (under which conditions still re-
mains to be seen), to a point where “ǫ-agents” will be able to produce themselves
depending more on their own information than that of the environment. This
would be similar to the evolution in Tierra [48] or Avida [3] systems, only that
self-replication would not be inbuilt. The tentative laws of information presented
in Section 4 would be better defined if such a system was studied.
One important aspect that remains to be studied is the representation of
5One could argue that, since agency (and thus cognition) is already assumed in all agents,
this approach is not explanatory. But I am not trying to explain the “origins” of agency, since
I assume it to be there from the start. I believe that we can only study the evolution and
complexification of agency and cognition, not their “origins”.
6For example, human, animal, plant, bacterial, immune, biological, adaptive, systemic, and
artificial [20].
7For a more detailed treatment on robustness, adaptation, and anticipation, see [22]
thermodynamics in terms of information. This is because the ability to per-
form thermodynamic work is a characteristic property of biological systems [32].
This work can be used to generate the organization necessary to sustain life
(cf. law of information organization). It is difficult to describe life in terms
of thermodynamics, since it entails new characteristic properties not present in
thermodynamic systems. But if we see the latter ones as information, it will be
easier to describe how life—also described as information—evolves from them,
as information propagates itself at different scales.
A potential application of this framework would be in economy, considering
capital, goods, and resources as information (a non-conserved quantity) [18].
A similar benefit (of non-conservation) could be given in game theory: if the
payoff of games is given in terms of information (not necessarily conserved), non-
zero sum games could be easier to grasp than if the payoff is given in material
(conserved) goods.
It becomes clear that information (object), the agent perceiving it (subject)
and the meaning-making or transformation of information (action) are deeply
interrelated. They are part of the same totality, since one cannot exist without
the others. This is also in line with Buddhist philosophy. The implications of an
informational description of the world for philosophy have also to be addressed,
since some schools have focussed on partial aspects of the object-subject-action
trichotomy. Another potential application of the laws of information would be
in ethics, where value can be described accordingly to the present framework.
8 Conclusions
This paper introduced general ideas that require further development, extension
and grounding in particular disciplines. Still, a first step is always necessary, and
hopefully feedback from the community will guide the following steps of this line
of research.
Different metaphors for describing the world can be seen as different lan-
guages: they can refer to the same objects without changing them. And each
can be more suitable for a particular context. For example, English has several
advantages for fast learning, German for philosophy, Spanish for narrative, and
Russian for poetry. In other words, there is no “best” language outside a par-
ticular context. In a similar way, I am not suggesting that describing the world
as information is more suitable than physics to describe physical phenomena,
or better than chemistry to describe chemical phenomena. It would be redun-
dant to describe particles as information if we are studying only particles. The
suggested approach is meant only for the cases when the physical approach is
not sufficient, i.e. across scales, constituting an alternative worth exploring to
describe evolution.
It seems easier to describe matter and energy in terms of information than
vice versa. Moreover, information could be used as a common language across
scientific disciplines [56].
Acknowledgements
I should like to thank Irun Cohen, Inman Harvey, Francis Heylighen, David
Krakauer, Antonio del Rı́o, Marko Rodriguez, David Rosenblueth, Stanley
Salthe, Mikhail Prokopenko, Clément Vidal, and Héctor Zenil for their useful
comments and suggestions.
Bibliography
[1] Adami, Christoph, “Self-organized criticality in living systems”, Phys. Lett. A 203
(1995), 29–32.
[2] Adami, Christoph, “What is complexity?”, Bioessays 24, 12 (December 2002), 1085–
1094.
[3] Adami, Chris, and C. Titus Brown, “Evolutionary learning in the 2d artificial life system
”Avida””, Proc. Artificial Life IV (R. Brooks and P. Maes eds.), MIT Press (1994),
377–381.
[4] Ashby, W. Ross, An Introduction to Cybernetics, Chapman & Hall London (1956).
[5] Ashby, W. Ross, “Principles of the self-organizing system”, Principles of Self-
Organization (Oxford, ) (H. V. Foerster and G. W. Zopf, Jr. eds.), Pergamon (1962),
255–278.
[6] Atlan, H., “On a formal definition of organization”, J Theor Biol 45, 2 (June 1974),
295–304.
[7] Atlan, Henri, and Irun R. Cohen, “Immune information, self-organization and mean-
ing”, Int. Immunol. 10, 6 (1998), 711—717.
[8] Bak, Per, Chao Tang, and KurtWiesenfeld, “Self-organized criticality: An explanation
of the 1/f noise”, Phys. Rev. Lett. 59, 4 (July 1987), 381–384.
[9] Bar-Yam, Y., “Multiscale variety in complex systems”, Complexity 9, 4 (2004), 37–45.
[10] Barandarian, Xabier, “Behavioral adaptive autonomy. a milestone in the ALife route
to AI?”, Artificial Life IX Proceedings of the Ninth International Conference on the
Simulation and Synthesis of Living Systems (J. Pollack, M. Bedau, P. Husbands,
T. Ikegami, and R. A. Watson eds.), MIT Press (2004), 514–521.
[11] Bedau, Mark A., “Four puzzles about life”, Artificial Life 4 (1998), 125–140.
[12] Cohen, Irun R., Tending Adam’s Garden: Evolving the Cognitive Immune Self, Academic
Press London (2000).
[13] Cohen, Irun R., “Informational landscapes in art, science, and evolution”, Bulletin of
Mathematical Biology 68, 5 (July 2006), 1213–1229.
[14] Cover, Thomas M., and Joy A. Thomas, Elements of Information Theory, Wiley-
Interscience (July 2006).
[15] Darwin, Charles, The Origin of Species, Wordsworth (1998).
[16] De Duve, Christian, Live Evolving: Molecules, Mind, and Meaning, Oxford University
Press (2003).
[17] Dennett, Daniel, Darwin’s Dangerous Idea, Simon & Schuster (1995).
[18] Farmer, J. Doyne, and Neda Zamani, “Mechanical vs. informational components of price
impact”, EPJ B 55, 2 (2007), 189–200.
[19] Floridi, Luciano ed., The Blackwell Guide to Philosophy of Computing and Information,
Blackwell (2003).
[20] Gershenson, Carlos, “Cognitive paradigms: Which one is the best?”, Cognitive Systems
Research 5, 2 (June 2004), 135–156.
[21] Gershenson, Carlos, “Introduction to random Boolean networks”, Workshop and Tu-
torial Proceedings, Ninth International Conference on the Simulation and Synthesis of
Living Systems (ALife IX) (Boston, MA, ) (M. Bedau, P. Husbands, T. Hutton, S. Ku-
mar, and H. Suzuki eds.), (2004), 160–173.
[22] Gershenson, Carlos, Design and Control of Self-organizing Systems, CopIt Arxives
Mexico (2007), http://tinyurl.com/DCSOS2007.
[23] Gershenson, Carlos, “Guiding the self-organization of random Boolean networks”, The-
ory in Biosciences (In Press).
[24] Gershenson, Carlos, “The sigma profile: A formal tool to study organization and its
evolution at multiple scales”, Complexity (In Press).
[25] Gershenson, Carlos, and Francis Heylighen, “When can we call a system self-
organizing?”, Advances in Artificial Life, 7th European Conference, ECAL 2003 LNAI
2801 (Berlin, ) (W. Banzhaf, T. Christaller, P. Dittrich, J. T. Kim, and J. Ziegler
eds.), Springer (2003), 606–614.
[26] Görnerup, Olof, and James P. Crutchfield, “Hierarchical self-organization in the
finitary process soup”, Artificial Life In Press (2008), Special Issue on the Evolution of
Complexity.
[27] Helbing, Dirk, and Tamás Vicsek, “Optimal self-organization”, New Journal of Physics
1 (1999), 13.1–13.17.
[28] Heylighen, Francis, and Cliff Joslyn, “Cybernetics and second order cybernetics”,
Encyclopedia of Physical Science and Technology, (R. A. Meyers ed.) 3rd ed. vol. 4.
Academic Press New York (2001), pp. 155–170.
[29] Holquist, M., “Introduction”, Art and Answerability, (M. M. Bakhtin ed.). University
of Texas Press Austin (1990).
[30] Hopfield, J. J., “Physics, computation, and why biology looks so different”, Journal of
Theoretical Biology 171 (1994), 53–60.
[31] Kauffman, S. A., The Origins of Order, Oxford University Press (1993).
[32] Kauffman, Stuart A., Investigations, Oxford University Press (2000).
[33] Klyubin, Alexander S., Daniel Polani, and Chrystopher L. Nehaniv, “Organization of
the information flow in the perception-action loop of evolved agents perception-action loop
of evolved agents”, Proceedings of 2004 NASA/DoD Conference on Evolvable Hardware
(R. S. Zebulum, D. Gwaltney, G. Hornby, D. Keymeulen, J. Lohn, and A. Stoica
eds.), IEEE Computer Society (2004), 177–180.
[34] Klyubin, Alexander S., Daniel Polani, and Chrystopher L. Nehaniv, “Representations
of space and time in the maximization of information flow in the perception-action loop”,
Neural Computation 19 (2007), 2387–2432.
[35] Krakauer, D. C., and P. M. A. Zanotto, “Viral individuality and limitations of the
life concept”, Protocells: Bridging Nonliving and Living Matter, (S. Rasmussen, M. A.
Bedau, L. Chen, D. Deamer, D. C. Krakauer, N. Packard, and D. P. Stadler eds.).
MIT Press (2007).
[36] Langton, Christpher, “Computation at the edge of chaos: Phase transitions and emer-
gent computation”, Physica D 42 (1990), 12–37.
[37] Martyushev, L. M., and V. D. Seleznev, “Maximum entropy production principle in
physics, chemistry and biology”, Physics Reports 426, 1 (April 2006), 1–45.
[38] McMullin, Barry, “30 years of computational autopoiesis: A review”, Artificial Life 10,
3 (Summer 2004), 277–295.
[39] McShea, Daniel W., “Metazoan complexity and evolution: Is there a trend?”, Evolution
50 (1996), 477–492.
[40] Michod, Richard E., Darwinian Dynamics: Evolutionary Transitions in Fitness and
Individuality, Princeton University Press Princeton, NJ (2000).
[41] Miconi, T, “Evolution and complexity: the double-edged sword”, Artificial Life 14, 3
(Summer 2008), 325–344, Special Issue on the Evolution of Complexity.
[42] Moreno, Alvaro, and Kepa Ruiz-Mirazo, “The maintenance and open-ended growth of
complexity in nature: information as a decoupling mechanism in the origins of life”, Re-
framing Complexity: Perspectives from the North and South, (F. Capra, A. Juarrero,
P. Sotolongo, and J. van Uden eds.). ISCE Publishing (2006).
[43] Morowitz, Harold, and D. Eric Smith, “Energy flow and the organization of life”, Tech.
Rep. no. 06-08-029, Santa Fe Institute, (2006).
[44] Neuman, Yair, Reviving the Living: Meaning Making in Living Systems vol. 6 of Studies
in Multidisciplinarity, Elsevier Amsterdam (2008).
[45] Nydahl, Ole, The Way Things Are: A living Approach to Buddhism for today’s world.,
O Books (2008).
[46] Prokopenko, Mikhail, Fabio Boschetti, and Alex J. Ryan, “An information-theoretic
primer on complexity, self-organisation and emergence”, Complexity 15, 1 (2009), 11–28.
[47] Prokopenko, M., V. Gerasimov, and I. Tanev, “Evolving spatiotemporal coordination
in a modular robotic system”, From Animals to Animats 9: 9th International Conference
on the Simulation of Adaptive Behavior (SAB 2006) (S. Nolfi, G. Baldassarre, R. Cal-
abretta, J. C. T. Hallam, D. Marocco, J.-A. Meyer, O. Miglino, and D. Parisi
eds.), vol. 4095 of Lecture Notes in Computer Science, Springer (2006), 558–569.
[48] Ray, T. S., “An approach to the synthesis of life”, Artificial Life II, (C. Langton,
C. Taylor, J. D. Farmer, and S. Rasmussen eds.) vol. XI of Santa Fe Institute Studies
in the Sciences of Complexity. Addison-Wesley Redwood City, CA (1991), pp. 371–408.
[49] Roederer, Juan G., Information and its Role in Nature, Springer-Verlag Heidelberg
(May 2005).
[50] Ruiz-Mirazo, Kepa, and Alvaro Moreno, “Basic autonomy as a fundamnental step in
the synthesis of life”, Artificial Life 10, 3 (Summer 2004), 235–259.
[51] Shalizi, Cosma R., Causal Architecture, Complexity and Self-Organization in Time
Series and Cellular Automata, PhD thesis University of Wisconsin at Madison (2001).
[52] Shannon, C. E., “A mathematical theory of communication”, Bell System Technical
Journal 27 (July and October 1948), 379–423 and 623–656.
[53] Stewart, John, “Cognition = life : Implications for higher-level cognition”, Behavioural
processes 35 (1995), 311–326.
[54] Umpleby, Stuart, “Physical relationships among matter, energy and information”, Cy-
bernetics and Systems 2004 (Vienna, ) (R. Trappl ed.), vol. 1, Austrian Society for
Cybernetic Studies, (2004), 124–6.
[55] Varela, Francisco J., Humberto R. Maturana, and R. Uribe., “Autopoiesis: The
organization of living systems, its characterization and a model”, BioSystems 5 (1974),
187–196.
[56] von Baeyer, Hans Christian, Information: The New Language of Science, Harvard
University Press Cambridge, MA (2004).
[57] von Uexküll, Jakob, “A stroll through the worlds of animals and men”, Instinctive
Behavior: The Development of a Modern Concept, (C. H. Schiller ed.). International
Universities Press New York (1957), pp. 5–80.
[58] Wheeler, John Archibald, “Information, physics, quantum: the search for links”, Com-
plexity, Entropy, and the Physics of Information, (W. H. Zurek ed.) vol. VIII of Santa
Fe Institute Studies in the Sciences of Complexity. Perseus Books Reading, MA (1990).
[59] Wittgenstein, Ludwig, Philosophical Investigations 3rd ed., Prentice Hall (1999).
[60] Wolfram, Stephen, A New Kind of Sciene, Wolfram Media (2002).
	1 The World as Evolving Information
	1 Introduction
	2 Why Information?
	3 What Is Information?
	4 Tentative Laws of Information
	4.1 Law of Information Transformation
	4.2 Law of Information Propagation
	4.3 Law of Requisite Complexity
	4.4 Law of Information Criticality
	4.5 Law of Information Organization
	4.6 Law of Information Self-organization
	4.7 Law of Information Potentiality
	4.8 Law of Information Perception
	5 On the Notion of Life
	6 On the Notion of Cognition
	7 Future Work
	8 Conclusions
