#  On the nature of information - an evolutionary perspective

Wouter van der Wijngaart contact<EMAIL_ADDRESS>Department of Intelligent
Systems, KTH Royal Institute of Technology, Stockholm, Sweden

###### Abstract

This Perspective explores the origins and persistence of recurrent structures
and patterns throughout the known Universe. We start with a first fundamental
question:

1\. Considering that all information consists of patterns in physical
structure but not all physical patterns constitute information, what is the
fundamental relation between these two?

We first explore the materialistic nature of structures and information,
detailing how they can form through spontaneous or templated processes and
evolve into complex structures, including self-replicators. We posit that all
recurring structures emerge either spontaneously de novo or based on
underlying information. A main implication is that all information must be
understood as both a product and a driver of evolution. We further observe
that the three carriers of information underpin the emergence of three main
layers of self-organisation: genes coded in DNA for the biological layer,
ideas stored in neural structure for the cultural layer, and records written
on innate objects for the civilisation layer. This gives rise to two
additional questions, which we subsequently address:

2\. What can we anticipate about the future development of self-organizing
layers given the role of information in their emergence?

3\. What is the universality of information and its evolution throughout the
Universe?

This manuscript aims to offer a fresh perspective and a universal framework
for information and the origin of structures by extending and unifying
concepts from physics, biology, and information theory.

###### keywords:

Emergence, Evolution, Information, Patterns, Self-organisation

## Physical processes*, structures* and their evolution

Information is always composed of specific arrangements of matter components,
which we refer to as constituents. These constituents themselves are matter.
We place matter and its constituents at the centre of this perspective.111In
comparison to Signal Theory focusing on how information is signalled, i.e.,
translated through space, we here focus on how information is stored, i.e.,
translated through time.

### Matter assemblies*

We start from a nested definition of matter: matter consists of sets of
constituents, referred to as "assemblies*"222Words in this manuscript marked
with an asterisk (*) have specific definitions that may slightly differ from
their conventional meanings. The use of specific terminology with unique
definitions may make the text less easy to read, but I opt for this approach
to prepare for stringent and rigorous analysis in future efforts., which
constituents themselves are matter. An assembly* is a set of matter
constituents within a system, typically forming a composite entity. The
overall properties of an assembly* are significantly influenced by the
relationships between its constituents and can be described as a 2-tuple of i)
its pose—defined by a spatiotemporal position vector (x) and orientation
vector (Φ)—and ii) the relative poses and properties of its constituents. The
physical properties of an assembly* emerge from the relative poses and
properties of its constituents. In systems where constituents are conserved,
assemblies* cannot be created or destroyed, though their properties may evolve
if the relative poses of constituents change.

### Processes*

Processes* are a relation between an assembly* at two different time points.
Processes* represent the laws of physics acting on an assembly*, altering or
maintaining the assembly* pose or properties. The assembly* before the
process* is termed the input, and following the process*, the output.

In any process*, constituents are categorized as either active* or passive*.
Active* constituents include those in the input or output that are altered by
the process*, or those altering other structures* during the process* via
mechanisms like force field modifications or mechanical interactions. Passive*
constituents do not influence the process*’s outcome. Moving forward,
references to assembly* constituents in processes* will pertain specifically
to active* constituents, unless stated otherwise.

### Structures*

We define a structure* as an assembly* with a persistent internal
configuration of constituents.333”Persistent” can be interpreted as existing
significantly longer than the diffusion timescale of its constituents. In this
manuscript, a structure* is considered to retain its properties unless noted
otherwise. Processes* are more likely to occur persistently if part of the
input assembly* is structured*.

We define a Structure* category as the set of all structures* having
identical444Physical processes* are inherently chaotic, although attractors,
i.e., states that systems evolve towards and stabilize, may guide systems to
predictable states despite initial variations.(Poincaré,, 1899) Such
attractors signify shifts in stability derived from the chaotic dynamics of
physical processes*. Such dynamics are crucial for understanding how self-
regulating or self-organizing processes* can produce predictable outcomes, as
illustrated by the stability of dynamic systems amidst external disturbances
(Prigogine and Stengers,, 1984) or patterns in complex systems (Haken,, 1977).
Throughout this manuscript, the notions ”identical” or ”same” when referring
to structures* or processes* must be understood as ”being in the same
attractor”. constituents and constituent poses. (Categories are denoted with a
capital; instances of a category with a versal.) A structure* transitions to a
different Structure* category when its constituents reconfigure.

This perspective underscores that assemblies* and structures* are tangible
material systems, with processes* embodying the laws of physics acting upon
them.

### De-novo-structuring*, replicating* and mutating*

Of specific interest in this Perspective are those processes* producing new
structures*. We will refer to such new structures* as the product* of the
process*. When a process* increases the number of instances of a Structure*
category, this increase is either from zero instances in the input or from at
least one instance in the input. We define the former as de-novo-structuring*
and the latter as replicating*.

Replicating* processes may from time to time produce replicas* with enough
variation to form new attractors. These new attractors indicate the system’s
evolution toward a new, stable structure*: essentially, a mutation*. In
crystallisation, for example, defects in the crystal structure* are often
replicated in subsequent layers as the crystal grows.(Kittel,, 2005) In the
biological context, Kauffman conceptualizes genetic regulatory networks as
dynamical systems that can stabilize into various attractors, which represent
stable gene expression patterns capable of enduring over time, even amidst
external disruptions.(Kauffman,, 1993)

### Spontaneous* vs Templating* Processes* and Complex Structures*

Instances of Structure* categories that are active* in a process* but neither
created nor destroyed are called templates*. Processes* that involve
templates* are called templating processes*. Conversely, processes* that do
not use templates* are called spontaneous processes*.

Templates* can actively influence processes* through a variety of mechanisms,
such as modifying force fields, creating zones of minimal energy, establishing
passive inert boundaries (walls, recipients, membranes), dynamic mechanical
manipulation (by tools or machines) or catalysing outcomes. Specific
templates* can consistently direct the formation of structures*, although
results may vary due to external influences or inherent system dynamics.

The formation of complex structures* through spontaneous de-novo-processing*
is limited by probability. The infinite monkey theorem illustrates the
improbability of complex structures*, such as the complete works of William
Shakespeare, emerging from a spontaneous process* (sha,, 2002). For instance,
amino acids (Oro,, 1961) and fullerenes (Kroto et al.,, 1985; Foing and
Ehrenfreund,, 1994), are among the most complex molecules of extraterrestrial
origin detected.

In contrast, certain templating* processes can consistently generate highly
complex output structures*. These processes* rely on the complexity inherent
in the templates* themselves. For instance, the electronic structure* of atoms
and molecules acts as a template* in chemical reactions, directing the
formation of specific molecular structures* based on principles of minimal
energy configurations (Pauling,, 1960). We define complex structures* as those
more complex than what spontaneous* processes* produce and note that complex
structures* are produced in templating processes*.

### Information*, phenotype*, and self-replicators*

We define an information* as a structure* that can mutate*. This means that
information* is an element of a set of structures* (all possible mutations)
for which a replication process* exists that can replicate* all elements of
that set, and, additionally, that mutating processes* exist for each element
of the set that can reconfigure that element into another. Mutations* have an
evolutionary benefit because they can be replicated* using the same process*,
unlike structures* that must rely on different replication processes* when
altered. In genes, neural memory, and computer memory, mutations typically
involve changes in the orientation or type of structural* constituents while
preserving the overall internal order of the constituents.

This definition of information* and its processing aligns with key concepts in
Information Science, such as information, sensing and signalling. Consider an
information* with a set of $i=1...n$ matter constituents that are active*
during processing*. The amount of information in such information*, its
Shannon entropy $H=-\sum p_{i}\cdot\log p_{i},$ equals the Boltzmann entropy
of this set, $S=-k_{B}\sum p_{i}\cdot\log p_{i}$, with an offset factor
$k_{B}$. Signalling from a sender A to a receiver B can be described as a
process* where information* at B is reconfigured into a mutated form,
templated* by the information* at A. Signalling over a noisy channel
additionally induces random reconfigurations to the information* at B. Sensing
is a process* in which an environmental condition (process* or assembly*)
reconfigures an information* using a sensor as a template*.

Note that information* differs from our conventional understanding of
information. Let’s consider measuring the presence of rain with a rain sensor
as a simple example. Conventionally, we consider the presence or absence of
rain as information. However, in the reinterpretation of this manuscript, rain
is a structure* and its presence or absence is not information*; rather, the
information* here is the configured data storage structure* that can be
altered by the rain sensor based on the absence/presence of rain.

We define phenotype* as structures* produced in a process* templated* by
information*. We term such phenotype* production "translating*". Through
translating*, information* gains its essence as an agent of change, as is
obvious in biological, technological, social, and cognitive systems.

The crucial difference between information* and phenotype* structures* is
that, although both may undergo (random) reconfiguration of their
constituents, only changes in information* can be inherited through
replication* processes*.

We define a self-replicator* as any set of structures* produced in processes*
templated* by other structures* within the set. Our definition of a self-
replicator* thus generalises the definition of an autocatalytic set.

Among all (autocatalytic) self-replicating sets, those containing information*
have a higher probability of prevailing due to their ability to undergo
mutations that provide evolutionary adaptation. Note that self-replicators*
containing information* are, themselves, information*. To distinguish between
the two, from this point on, references to information* will exclude self-
replicators* unless explicitly stated otherwise.

While building on foundational concepts of self-organisation and evolution
from Kauffman’s seminal work ’The Origins of Order,’ (Kauffman,, 1993) this
manuscript extends these ideas by exploring the role of information in not
just biological systems but also in cultural, civilisational, and emerging
cybernetic layers of self-organisation.

## The evolutionary nature of information*

Our definition of information* as "mutatable* structure*" closely links to
what Richard Lewontin refers to as "any entities in nature that have
variation, reproduction, and heritability" in the statement: "The generality
of the principles of natural selection means that any entities in nature that
have variation, reproduction, and heritability may evolve," from the foreword
to his seminal work, "The Units of Selection" (Lewontin,, 1970). This wording
highlights the essential nature of all information: it forms the core of
evolution.

The evolutionary nature of information* is often overlooked, despite its three
significant consequences: 1) its ubiquity, 2) its meaning, and 3) its impact
on the surrounding environment. Understanding information* through the lens of
evolution is crucial for assessing its significance and value.

Ubiquity of Information*:

  * •

The ubiquity of information* is based on the autopoietic nature of self-
replicators* (Maturana and Varela,, 1980). Self-replicators* form systems with
the potential to fill their environment with replicas of themselves, typically
at an exponential rate if unhindered. Moreover, self-replicators* containing
information* have an evolutionary advantage. Evolution, therefore, leads to
the emergence of self-replicators* that contain information* among their
constituting structures*.

  * •

Templated* replicating* can be subject to evolutionary pressure, improving the
evolutionary fitness* of processes* that consistently form stable structures*.
The universality of this concept is known as universal Darwinism (Hodgson,,
2005). Complexity arises as natural selection accumulates small, advantageous
mutations that enhance functionality and adaptation, a process detailed by
Dawkins (Dawkins,, 1986).

  * •

The emergence of self-replicating entities is critical in studying the origin
of life. Kauffman proposed that life may start from self-organized networks of
molecular interactions that become autocatalytic (Kauffman,, 1993). Mossel and
Steel provided a framework to study conditions under which biochemical
networks achieve autocatalysis (Mossel and Steel,, 2005). Vasas et al. showed
that multiple autocatalytic subsets can lead to evolutionary processes,
including competition and selection (Vasas et al.,, 2012). Hordijk suggested
extending autocatalytic sets to fields beyond biology, such as sociology,
ecology, and economics (Hordijk,, 2013).

  * •

Through their autopoietic nature, self-replicators* and their constituting
information* and associated phenotype* constitute a large fraction of the
structures* in our environment.

Meaning of Information*:

  * •

The meaning of information* is encoded in the type or pose of its
constituents, a result of evolutionary processes*. Once generated through
probabilistic processes*, self-replicators* and their information* evolve to
become increasingly well-adapted to their environment. Natural selection
shapes information* to enhance the survival and reproduction of its own
structure* or any self-replicator* it belongs to.

  * •

The "meaning" of information* is not a representation of an external reality
but rather its evolutionary fitness in this external reality. Donald Hoffman
explores this evolutionary aspect in his book "The Case Against Reality: Why
Evolution Hid the Truth from Our Eyes," where he argues that our perception is
a user interface crafted by natural selection to support survival and
reproduction, rather than to disclose the truth (Hoffman,, 2019). This thesis
challenges conventional views on perception, suggesting that our understanding
of reality is more about enhancing evolutionary fitness than providing a true
reflection of the external world. Where Hoffman deals mainly with biological
perceptual systems, we propose that this principle applies universally to all
types of information*, across all domains. This includes not only brain
structures but also other systems of information processing and storage.

Information* transforming its environment:

  * •

Information* alters its environment by proliferating copies of itself, its
phenotypes*, or any associated self-replicators*. We refer to the ensemble of
structures* originating from information* as "Life*". These processes* and
their interactions create the foundation for continuously evolving layers of
self-organization, which will be explored in more detail in the following
section.

## Structure* originates in emerging layers of self-organisation

The world around us is abundant with patterns. These patterns emerge either
from spontaneous* de-novo-structuring* or from the processing* of
information*. These diverse systems of pattern formation interact within an
evolutionary framework, where various structure*-forming processes* can
enhance or constrain one another, while natural selection continuously refines
and adapts information*-driven structures*.

We can observe four distinct classes of information* based on the medium that
forms their structure*: 1) the absence of information; 2) genes*, which are
DNA structures and their methylation; 3) ideas*, which are those structures*
in a neural system that have the potential to replicate* through social
interactions; and 4) records*, defined as any information* stored on an
inanimate carrier, such as a book or computer memory. These four classes
underpin each a respective primary layer of self-organisation: the physical,
biological, cultural, and civilizational layers.

### The physical layer

In the absence of information*, de-novo-structures* emerge from the physical
laws forming entities via symmetry breaking555Symmetry breaking is a process
where a system transitions from symmetry to asymmetry due to perturbations. It
is crucial in physical and chemical transformations such as phase transitions,
where uniformity is disrupted, or in dynamic instabilities, where systems
become unstable and transition to complex, unpredictable patterns. Star and
planetary disk formation form an example of symmetry breaking, where an
initial uniform isotropic molecular cloud collapses under gravity, whereupon
rotational forces flatten it into a spinning disk, breaking the spherical
symmetry. This results in a dense central star and a protoplanetary disk where
planets form. Particles in the disk coalesce through accretion, growing into
planets and moons, organizing into a structured solar system with clear
orbital patterns. or self-organisation666Spontaneous self-organization refers
to processes* by which system components interact according to intrinsic rules
without external guidance, resulting in organized structures emerging from
local interactions. Reaction-diffusion systems are a particular example of
spontaneous self-organization, where the interaction and diffusion of chemical
substances form stable patterns like stripes or spirals, essential for
biological morphogenesis.(Turing,, 1952) Fractal growth is another form,
characterized by iterative, scale-invariant patterns formed through repetitive
interactions, as in the branching patterns of rivers.. Examples include
galaxies, planetary systems, sand dunes, volcanoes, meanders, snowflakes, and
simple molecules.

### The biological layer

Originating from abiotic chemical reactions, the biological layer may have
evolved under the influence of natural selection and environmental pressures
(Kauffman,, 1993). The constituent information* in this layer is encoded
within the structures of DNA and its methylation, forming the genetic
blueprint that guides all biological functions. Cells and organisms are self-
replicators* in this layer. The phenotype* includes all biological
structures*.

### The cultural layer

In the cultural layer, the constituent information* resides in transferable
ideas* stored in the neural structure* of individuals. Culture can be defined
as information* capable of affecting individuals’ behaviour that they acquire
from other individuals through teaching, imitation and other forms of social
transmission processes*.(Boyd and Richerson,, 1985) Examples of self-
replicators* in the cultural layer include language, myths, musical tradition,
ceremonies and cuisine. The phenotype* in this layer consists of the tangible
cultural artifacts and social structures that arise from these ideas*, such as
community practices or simple tools, which are concrete manifestations of the
underlying information*.

### The civilizational layer

The emergence of symbolic systems of communication in the form of written
language, i.e., records*, marks the beginning of history. Written
communication can store extensive information and underpin large, lasting
infrastructures, and is therefore a necessity for the development of complex
societies characterized by the development of the state, social stratification
and urbanization.(Harari,, 2015)

Examples of records* include: the master plates and original design
specifications for the U.S. dollar bills in the US Bureau of Engraving and
Printing; the Constitution of the United States in the National Archives;
commercial trademarks in patent offices; Papal bulls and encyclicals in the
Vatican archives, and; the source code for Windows 95 in encrypted
repositories on secure servers managed by Microsoft’s internal IT
infrastructure. Examples of self-replicators* in this layer include
multinational corporations, religious organizations, and political parties,
which replicate their structures through franchising, doctrine propagation, or
political campaigning, respectively.

Harari (Harari,, 2015) uses Peugeot to illustrate how modern human societies
are built on "shared myths" that allow for unprecedented levels of cooperation
and organization among humans underpinning civilisation. What Harari calls
"myths" are, in essence, specific structures*: human ideas*, which he
describes as "imagined realities", and records*, including the company’s
articles of incorporation, patents for its technology, engineering plans,
branding materials, and financial records. The phenotype* consists of the
tangible products and services produced by Peugeot, such as cars, marketing
campaigns, and customer service operations. Evolution progresses through the
adaptation of these products and services to changing market demands (mutation
of branding materials), technological advancements (mutation of engineering
plans), and regulatory environments, driven by competition and innovation
within the automotive industry. The replication* of a car manufacturer as a
corporate entity occurs through several processes. Firstly, it happens through
the establishment of new manufacturing plants, dealerships, and service
centers, which expand the company’s physical presence and operational
capacity. Secondly, replication occurs through the continuous development and
release of new car models and technologies, which propagate the brand and its
market influence. Lastly, replication is facilitated by corporate mergers and
acquisitions, as well as partnerships and alliances with other companies,
which enable Peugeot to integrate new capabilities and expand into new
markets.

## Future layers of self-organisation

We can speculate on potential future layers of self-organization by
considering the material types underpinning information* and phenotype*.

In the biological and cultural layers, the earliest templates* and phenotypes*
were of biological origin. Non-biological templates* and phenotypes* emerged
later, proving advantageous in terms of natural selection. For example, sand
acts as a template* during plant root development in the biological layer,
while shelter constructions serve as templates* in the cultural layer.
Similarly, calcified skeletons and simple tools are innate phenotypes* in
these layers.

Newer layers increasingly rely on non-biological material structures*. The
Industrial, Energy, and Digital Revolutions illustrate this trend, driven by
the greater abundance and diversity of properties in non-biological materials
compared to biological ones. The rise of the civilizational layer was
particularly marked by the adoption of information* in non-biological
records*. Interestingly, a connection remains between the cultural and
civilizational layers: information* stored in neural structures* and inanimate
objects can be partially replicated* and translated* between these layers.
Moreover, the replicators* in both layers—cultures and civilizations—can
evolve into each other, as seen in the rise and fall of civilizations.

The second half of the 21st century marks the emergence of what could become a
new layer, the cybernetic layer, in which machines not only perform tasks but
also communicate, adapt, and make decisions independently. Starting from the
theoretical foundations of cybernetics in the mid-20th century, this layer
developed through the rise of the internet, AI growth, and the advent of IoT.
Today, it represents a mature stage where machines autonomously communicate,
process* information*, and adapt without human intervention. Nevertheless,
evolutions (innovations) in this layer still rely heavily on mutations* in
human ideas* rather than machine-coded records*.

One evolutionary aspect yet to emerge is self-replicators* independent of
biological constraints. Such self-replicators* could be autonomous robotic
systems capable of self-reproduction using environmental raw materials,
mirroring biological self-replication. This concept, initially proposed by Von
Neumann as universal constructors (von Neumann,, 1966), has been further
explored by Freitas and Merkle (Freitas Jr. and Merkle,, 2004). Such evolution
would involve moving beyond gene-centered evolution, where genes are the units
of selection (Williams,, 1966). Synthetic self-replicators*, once dominant,
could evolve their own cultural and civilizational layers of self-
organization.

The evolution of such a new organizational layer hinges on the evolutionary
fitness of synthetic self-replicators*. Here, materials and evolutionary
processes* mark a clear divide between the layers of self-organization.
Biological systems are bounded by the inherent limitations of biological
materials, whereas abiotic systems can have much higher mechanical strength
and support much higher energy conversion rates. From an evolutionary
perspective, the biological layer evolved over billions of years to become
highly energy-efficient and resilient to environmental changes. Genetic
evolution follows small steps (local gradients) in the environmental fitness
landscapes. In contrast, cognitive functions in the cultural layer and higher
enable intelligent design to bypass fitness barriers in such landscapes.
Humans, for instance, have been particularly adept at this, developing
technologies such as wheels, which are unfeasible in biological evolution but
achievable in the civilizational layer driven by human ingenuity.

The emergence of a new layer will likely depend on how effectively intelligent
design tools, such as AI, can accelerate evolution and whether these tools
will predominantly benefit civilizational evolution or self-replicating
robots. For self-replicating* machines to establish a new layer, they must
first prove beneficial to human civilization to avoid being outcompeted by
existing structures*. Only once their evolutionary fitness surpasses that of
civilizations can they become dominant.

## Universality of information* and the nature of science

In this last section, we speculate about the ubiquity of information*-based
structure* evolution throughout the Universe, including extraterrestrial
life*. While our perspective does not address the probability of such
processes*, it provides insight into the nature of potential extraterrestrial
intelligent life*, if it exists.

Let us first review intelligent life on Earth and examine how our perspective
aligns with philosophical views and the relationship between mathematics,
science, and the physical world. Kant’s "Critique of Pure Reason" Kant, (1781)
posits that inherent mental structures shape our understanding of reality,
suggesting that our cognitive frameworks influence how we perceive and
understand the world. Similarly, Wigner’s "The Unreasonable Effectiveness of
Mathematics in the Natural Sciences" (Wigner,, 1960) highlights the surprising
success of mathematics in describing natural phenomena, implying that our
mathematical constructs might be inherently aligned with the universe’s
patterns.

We propose that the effectiveness of mathematics and science arises because
both the biological phenotype* of our brains and our cultural scientific and
mathematical ideas* have evolved to enhance evolutionary fitness. This
evolutionary perspective aligns with Kant’s notion of pre-set organizational
methods and suggests that our brain structures* are fine-tuned to the
universe’s inherent patterns. While this supports the intertwined nature of
mathematics and physical understanding, it contrasts with Tegmark’s idea that
the universe is inherently mathematical (Tegmark,, 2014). Instead, we see
mathematical ideas* as evolutionary tools that emerge and propagate based on
their utility.

If intelligent life* evolves elsewhere, it would likely be driven by natural
selection within its environment. Observations of convergent evolutionary
processes reveal that certain traits, such as predation, "neural" structures*
adapted for sensory input processing, and curiosity, may independently emerge
multiple times (McGhee,, 2011). These traits are evolutionarily advantageous
and may lead to intelligent extraterrestrials whose information* structures*
are finely tuned to their environments.

Given the universality of the laws of physics, one would expect that these
"neural" structures* would evolve a fitness to these laws. Consequently,
extraterrestrials might develop their own forms of "physics" and
"mathematics". While these would differ from the human equivalents, their
structures* would likely bear similarities, much like the independently
evolved optical structures* in the eyes of insects, moluscs, and mammals show
both differences and similarities (Nilsson and Pelger,, 1994). Ultimately,
extraterrestrial "physics" and "mathematics" would be shaped to optimize
extraterrestrial evolutionary fitness.

## Outlook

This perspective integrates concepts from various disciplines — systems
biology, information theory, complex systems theory, cybernetics, integrated
information theory, and network science — while offering a broad view of
systems and information interaction. It emphasizes the fundamental role of
information*, both resulting from and as the driver of, natural selection,
structure formation, and the emergence of complexity across different scales.
While this work provides a general framework, aspects that could benefit from
further exploration include the development of more rigorous, quantifiable
metrics, enhanced explanations for specific emergent behaviours and unique
complexities of biological systems, and a deeper consideration of the
subjective aspects of information processing or detailed network interactions.
We hope this manuscript provides a fresh view on complex systems and inspires
future research to refine these concepts, perhaps leading to innovative
applications in biomimicry, adaptive system design, or the development of
predictive models.

## Acknowledgements

I could not have developed this perspective without the substantial assistance
of ChatGPT, which provided overviews, challenged my ideas, and helped
formulate sentences that spanned across multiple disciplines of science.

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