Emergence in Artificial Life
Abstract Even when concepts similar to emergence have been
used since antiquity, we lack an agreed definition. Sin embargo,
emergence has been identified as one of the main features of
complex systems. Most would agree on the statement “life is
complex.” Thus understanding emergence and complexity should
benefit the study of living systems. It can be said that life emerges
from the interactions of complex molecules. But how useful is this
to understanding living systems? Artificial Life (ALife) ha sido
developed in recent decades to study life using a synthetic approach:
Build it to understand it. ALife systems are not so complex, be they
soft (simulations), hard (robots), or wet (protocells). De este modo, podemos
aim at first understanding emergence in ALife, to then use this
knowledge in biology. I argue that to understand emergence and life,
it becomes useful to use information as a framework. In a general
sense, I define emergence as information that is not present at one
scale but present at another. This perspective avoids problems of
studying emergence from a materialist framework and can also be
useful in the study of self-organization and complexity.
Carlos Gershenson
Universidad Nacional
Autánoma de México
Instituto de Investigaciones en
Matemáticas Aplicadas y en
Sistemas
Centro de Ciencias de la Complejidad
Lakeside Labs GmbH
Santa Fe Institute
cgg@unam.mx
Palabras clave
Emergence, vida, información,
self-organization, downward causation
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1 Emergence
The idea of emergence is far from new (Wimsatt, 1986). It has certain analogies, with Aristotle refer-
ring to the whole as being more than the sum of its parts. It had some development in the nineteenth
siglo, starting with John Stuart Mill, in what is known as British emergentism (McLaughlin, 1992;
Mengal, 2006). Sin embargo, given the success of reductionist approaches, interest in emergence dimin-
ished in the early twentieth century.
Only in recent decades has it been possible to study emergence systematically, because we lacked
the proper tools to explore models of emergent phenomena before digital computers were devel-
oped (Pagels, 1989).
In parallel, limits of reductionism have surfaced (Gershenson, 2013a; Heylighen et al., 2007;
Morin, 2007). While successful in describing phenomena in isolation, reductionism has been in-
adequate for studying emergence and complexity. As Murray Gell-Mann argues, reductionism is
correcto, but incomplete. (Gell-Mann, 1994, pag. 118–119).
This was already noted by Anderson (1972) y otros, as phenomena at different scales exhibit
properties and functionalities that cannot be reduced to lower scales (Gu et al., 2009). Thus the
reductionist attempt of basing all phenomena in the “lowest” scale (or level) and declaring only
that as reality, while everything else is epiphenomena, has failed miserably. Sin embargo, it still has
several followers, as a coherent alternative has yet to emerge (pun intended).
I blame the failure of reductionism on complexity (Bar-Yam, 1997; De Domenico et al., 2019;
Ladyman & Wiesner, 2020; mitchell, 2009). Complexity is characterized by relevant interactions
© 2023 Instituto de Tecnología de Massachusetts.
Publicado bajo una atribución Creative Commons
4.0 Internacional (CC POR 4.0) licencia.
Artificial Life 29: 153–167 (2023) https://doi.org/10.1162/artl_a_00397
C. Gershenson
Emergence in Artificial Life
(Gershenson, 2013b). These interactions generate novel information that is not present in initial
or boundary conditions. Thus predictability is inherently limited, owing to computational irreducibility
(Chaitin, 2013; Wolfram, 2002): There are no shortcuts to the future, as information and computa-
tion produced by the dynamics of a system must go through all intermediate states to reach a final
estado. The concept of computational irreducibility was already suggested by Leibniz in 1686 (Chaitin,
2013), but its implications have been explored only recently (Wolfram, 2002). An integrated theory
of complexity is lacking, but its advances have been enough to prompt the abandonment of the re-
ductionist enterprise. I do not see the goal of complexity as fulfilling the expectations of a Laplacian
worldview, where everything can be predictable only if we have enough information and computing
fuerza. On the contrary, complexity is shifting our worldview (Heylighen et al., 2007; Morin, 2007)
so that we are understanding the limits of science and seek not only prediction but also adaptation
(Gershenson, 2013a). Instead of attempting to dominate Nature for our purposes, we are learning
to take our place in it.
Thus we are slowly accepting emergence as real, in the sense that emergent properties have causal
influence in the physical world (see later concerning downward causation). Sin embargo, we still
lack an agreed-on definition of emergence (Bedau & Humphreys, 2008; Feltz et al., 2006). This might
seem problematic, but we also lack agreed-on definitions of complexity, vida, intelligence, and consciousness.
Sin embargo, this has not prevented advances in complex systems, biology, and cognitive sciences.
In a general sense, we can understand emergence as information that is not present at one scale but
present at another. Por ejemplo, life is not present at the molecular scale, but it is at the cellular and
organism scales. When scales are spatial or organizational, emergence can be said to be synchronic,
whereas emergence can be diachronic for temporal scales (Rueger, 2000).
It could be argued that the preceding definition of emergence is not sharp. I do not believe that
emergence—similar to life—is or is not. We should speak about different degrees of emergence,
and this can be useful to compare “more” or “less” emergence in different conditions and systems.
Another argument against the definition could be that of vagueness. Still, sharp definitions tend to
be useful only for particular contexts. This definition is general enough to be applicable in a broad
variety of contexts, and particular, sharper notions of emergence can be derived from it for specific
situations, (p.ej., Abrahão & Zenil, 2022; Cooper, 2009; Neuman & Vilenchik, 2019).
Different types of emergence have been proposed, but we can distinguish mainly weak emergence
and strong emergence. Weak emergence (Bedau, 1997) only requires computational irreducibility, entonces
it is easier to accept for most people. The “problem” with strong emergence (Bar-Yam, 2004a) es
that it implies downward causation (Bitbol, 2012; Campbell, 1974; Farnsworth et al., 2017; Flack, 2017).
Usually, emergent properties are considered to occur at higher/slower scales, arising from the in-
teractions occurring at lower/faster scales. Still, I argue that emergence can also arise in lower/faster
scales from interactions at higher/slower scales, as exemplified by downward causation. This is also
related to “causal emergence” (Hoel, 2017; Hoel et al., 2013). Taking again the example of life, el
organization of a cell restricts the possibilities of its molecules (Kauffman, 2000). Most biological
molecules would not exist without life to produce them. Molecules in cells do not violate the laws
of physics or chemistry, but these are not enough to describe the existence of all molecules, como
información (and emergence) may flow across scales in either direction.
Note that not all properties are emergent—only those that require more than one scale to be
descrito (thus the novel information). Por ejemplo, in a crowd, there might be some emergent
propiedades (p.ej., coherent “Mexican wave” in a stadium), but not all of the crowd properties are use-
fully described as emergent (p.ej., traffic flow at low densities, as it can be described fully from the
behavior of drivers). In the same sense, a society can produce emergent properties in its individuals
(p.ej., social norms that guide or restrict individual behavior), but not all properties of individuals
are necessarily described as emergent in this (downward) way (p.ej., performance during a workout).
Complexity occurs when novel information is produced through interactions between the compo-
nents of a system. Emergence occurs when novel information is produced across scales.
One might wonder, entonces, whether all macroscopic properties are emergent. They are not.
If they can be fully derived from a microscopic description (información), then we can call them
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C. Gershenson
Emergence in Artificial Life
differently for convenience, but they can be practically reduced to the information of the mi-
croscale. If novel information is produced at the macroscale, then that information can be said to
be emergent.
Note that it is difficult to decide on the ontological status of emergence, eso es, whether it “really
exists” (independently of an observer). I am aiming “just” for an epistemology of emergence, cual
can be understood as answering the question, When is it useful to describe something as emergent?
The answer to this question can certainly change with context so something might be usefully
considered emergent in one context but not in another.
Because emergence is closely related to information, I explain more about their relationship in
sección 3, but before doing so, I discuss the role of emergence in Artificial Life.
2 (Artificial) Life
Life has several definitions, but none with which everyone agrees (Bedau, 2008; Zimmer, 2021).
We could say simply that “life is emergent,” but this does not explain much. Still, we can abstract
the substrate of living systems and focus on the organization and properties of life. This was done
already in cybernetics (Gershenson et al., 2014; Heylighen & Joslyn, 2001) but became central in
Artificial Life (Adami, 1998; Langton, 1997). Beginning in the mid-1980s, ALife has studied living
systems using a synthetic approach: building life to understand it better (Aguilar et al., 2014). Por
having more precise control of ALife systems than of biological systems, we can study emergence
in ALife, increasing our general understanding of emergence. With this knowledge, we can go back
to biology. Entonces, emergence might actually become useful to understanding life.
ALife and its methods have had a considerable influence on the cognitive sciences (Beer, 2014b;
Froese & Stewart, 2010; harvey, 2019). It has yet to have an explicit impact on biology, tal vez
because biological life is more complex than ALife, and biologists tend to study living systems
directly. Still, computational models in biology are becoming increasingly commonplace (Noble,
2002), so it could be said that the methods developed in Artificial Life have been absorbed into
biology (Kamm et al., 2018) and other disciplines (Barbrook-Johnson & Penn, 2021; Lazer et al.,
2009; Rahwan et al., 2019; Seth, 2021; Trantopoulos et al., 2011).
Because one of the central goals of ALife is to understand the properties of living systems, él
does not matter whether these are software simulations, robots, or protocells (representatives of
“soft,” “hard,” and “wet” ALife) (Gershenson et al., 2020). These approaches allow us to explore
the principles of a “general biology” (Kauffman, 2000) that is not restricted to the only life we
saber, based on carbon, DNA, and cells.
2.1 Soft ALife
Mathematical and computational models of living systems have the advantage and disadvantage of
simplicity: One can abstract physical and chemical details and focus on general features of life.
A classical example is Conway’s Game of Life (Berlekamp et al., 1982). In this cellular automa-
tonelada, cells on a grid interact with their neighbors to decide on the life or death of each cell. Incluso
when rules are very simple, different patterns emerge, including some that exhibit locomotion, pre-
dación, y, one could even say, cognition (Beer, 2014a). Cells interact to produce higher-order
emergent structures that can be used to build logic gates and even universal computation.
Another popular example is “boids” (Reynolds, 1987): Particles follow simple rules, depending
on their neighbors (try not to crash, try to keep average velocity, try to keep close). The interactions
lead to the emergence of patterns similar to flocking, schooling, herding, and swarming. There have
been several other models of collective dynamics of self-propelled agents (Sayama, 2009; Vicsek &
Zafeiris, 2012), but the general idea is the same: Local interactions lead to the emergence of global
patrones.
Soft ALife has also been used to study open-ended evolution (OEE) (Adams et al., 2017; Pattee
& Sayama, 2019; Standish, 2003; Taylor et al., 2016). Por ejemplo, Hernández-Orozco et al. (2018)
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C. Gershenson
Emergence in Artificial Life
showed that undecidability and irreducibility are conditions for OEE. I would argue that OEE is
an example of emergence, but not vice versa. Under the broader notion of emergence used in this
artículo, undecidability and irreducibility are not conditions for emergence.
Emergence in soft ALife is perhaps the easiest to observe, precisely because of its abstract nature.
Even when most examples deal with “upward emergence,” there are also cases of “downward
emergence” (p.ej., Escobar et al., 2019; Hoel et al., 2013), where information at a higher scale leads
to novel properties at a lower scale.
2.2 Hard ALife
One of the advantages and disadvantages of robots is that they are embedded and situated in a
physical environment. The positive side is that they are realistic and thus can be considered closer
to biology than soft ALife. The negative side is that they are more difficult to build and explore.
Emergence can be observed at the individual robot level, where different components interact
to produce behavior that is not present in the parts, (p.ej., Braitenberg, 1986; walter, 1950, 1951),
or also at the collective level, where several robots interact to achieve goals that individuals are
unable to fulfill (Dorigo et al., 2004; Halloy et al., 2007; Rubenstein et al., 2014; Vásárhelyi et al.,
2018; Zykov et al., 2005).
Understanding emergent properties of robots and their collectives is giving us insight into the
emergent properties of organisms and societies. And as we better understand organisms and so-
cieties, we will be able to build robots and other artificial systems that exhibit more properties of
living systems (Bedau et al., 2009; Bedau et al., 2013; Gershenson, 2013C).
2.3 Wet ALife
The advantage and disadvantage of wet ALife is that it deals directly with chemical systems to
explore the properties of living systems. By using chemistry, we are closer to biological life with
wet ALife than we are with soft or hard ALife. Sin embargo, the potential space of chemical reactions
is so great, and its exploration is so slow, that it seems amazing that there have been any advances
at all using this approach.
One research avenue in wet ALife is to attempt to build “protocells” (Hanczyc et al., 2003;
Rasmussen et al., 2003; Rasmussen et al., 2004; Rasmussen et al., 2008): chemical systems with
some of the features of living cells, such as membranes, metabolism, information codification,
locomotion, and reproduction. Dynamic formation and maintenance of micelles and vesicles
(Bachmann et al., 1990; Bachmann et al., 1992; Luisi & Varela, 1989; Walde et al., 1994) predate
the protocell approach, while more recently, the properties of active droplets or “liquid robots”
( ˇCejková et al., 2017) have been an intense area of study. These include the emergence of locomo-
ción ( ˇCejková et al., 2014; Hanczyc et al., 2007) and complex morphology ( ˇCejková et al., 2018).
Recent work has also studied collective properties of protocells (Qiao et al., 2017) and droplets
( ˇCejková et al., 2017), where the interactions between the chemical entities lead to the emergence of
global patterns.
The recently developed “xenobots” (Blackiston et al., 2021; Blackiston et al., 2022; Kriegman
et al., 2020)—multicellular entities designed using artificial evolution and constructed from frog
embryonic cells—can also be considered wet ALife.
3 Información
Our species has lived through three major revolutions: agricultural, industrial, and informational.
We can say that the first one dealt mainly with control of matter, the second with control of energy,
and third and current one with the control of, obviamente, información. This does not mean that we
did not manipulate information beforehand (Gleick, 2011), only that we lacked the tools to do so
at the scales we have since the development of electronic computers.
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C. Gershenson
Emergence in Artificial Life
shannon (1948) proposed a measure of information in the context of telecommunications. Este
has been useful, but also many sophistications have been derived from it. Shannon’s information
can be seen as a measure of a “just so” arrangement, so it can also be used to measure organization.
Still, it is “simply” a probabilistic measure that assumes that the meaning of a message is shared
by sender and receiver. But of course, the same message can acquire different meanings, depending
on the encoding used (Haken & Portugali, 2015).
Living systems process information (Farnsworth et al., 2013; Hopfield, 1994). Thus understand-
ing information might improve our understanding of life (Kim y cols., 2021). It has been challeng-
ing to describe information in terms of physics (matter and energy) (Kauffman, 2000), especially
when we are interested in the meaning of information (Haken & Portugali, 2015; Neuman, 2008;
Scharf, 2021).
One alternative is to describe the world in terms of information, including matter and energy
(Gershenson, 2012). Everything we perceive can be described in terms of information—particles,
fields, átomos, molecules, cells, organisms, viruses, societies, ecosystems, biospheres, galaxies, y
so on—simply because we can name them. If we can name them, then they can be described as
información. If we could not name them, then we should not be able to even speak about them
(Wittgenstein, 2013). All of these have physical components. Sin embargo, other nonphysical phe-
nomena can also be described in terms of information, such as interactions, ideas, valores, conceptos,
and money. This gives information the potential to bridge physical and nonphysical phenomena,
avoiding dualisms. This does not mean that other descriptions of the world are “wrong.” One can
have different, complementary descriptions of the same phenomenon, and this does not affect the
fenómeno. The question is how useful these descriptions are for a particular purpose. I claim
that information is useful for describing general principles of our universe, as an information-based
formalism can be applied easily across scales. Thus general “laws” of information can be explored,
generalizing principles from physics, biology, cybernetics, complejidad, psicología, and philosophy
(Gershenson, 2012). These laws can be used to describe and generalize known phenomena within
a common framework. Además, as von Baeyer (2005) suggested, information can be used as a
language to bridge disciplines.
One important aspect of information is that it is not necessarily conserved (as matter and en-
ergy). Information can be created, destroyed, or transformed. We can call this computation (Denning,
2010; Gershenson, 2010). I argue that some of the “problems” of emergence arise because of con-
servation assumptions, which dissolve when we describe phenomena in terms of information. Para
ejemplo, meaning can change passively (Gershenson, 2012), eso es, independently of its substrate.
One instance of this is the devaluation of money: Prices might change, while the molecules of a
bill or the atoms of a coin remain unaffected by this.
Several measures of emergence have been proposed (p.ej., Bersini, 2006; Fuentes, 2014;
Prokopenko et al., 2009). In the context posed in this article, it becomes useful to explore the no-
tion of emergence in terms of information, because it can be applied to everything we perceive.
We have proposed a measure of emergence that is actually equivalent to Shannon’s information
(Fernández et al., 2014; shannon, 1948) (which is also equivalent to Boltzmann–Gibbs entropy).
Shannon was interested in a function to measure how much information a process “produces.”
De este modo, if we understand emergence as “new” information, we can also measure emergence E
(diachronic or synchronic, weak or strong) as well with information:
E = −K
norte(cid:2)
i=i
pi log pi,
(1)
where K is a positive constant that can be adjusted to normalize E to the interval [ 0, 1], depend-
ing on the “alphabet” of length n. If we use log2, entonces
K = 1
(2)
.
log2 n
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This normalization allows us to use the same measure and explore how different information
is produced at different scales. If E = 0, then there is no new information produced: We know
the future from the past, one scale from another. If E = 1, then we have maximum information
produced: We have no way of knowing the future from the past, or one scale from another; tenemos
to observe them.
In this context, “new information” does not imply something that has never been produced
before but a pattern that deviates from the probability distribution of previous patterns: If previous
information tells you nothing about future information (as in the case of fair coin tosses), then each
symbol will bring maximum information; if future symbols can be predicted from the past (cual
occurs when only one symbol has maximum probability of occurring and all the others never occur),
then these “new” symbols carry no information at all.
If we already have information at one scale, but observe “new” information at another scale,
eso es, that cannot be derived from the information at the first scale, then we can call this infor-
mation emergent. This measure of emergence has successfully applied in different contexts (Amoretti
& Gershenson, 2016; Correa, 2020; Febres et al., 2015; Fernández et al., 2017; Morales et al., 2018;
Ponce-Flores et al., 2020; Ramírez-Carrillo et al., 2020; Zapata et al., 2020; Zubillaga et al., 2014).
Still, this does not imply that other measures of emergence are “wrong” or not useful, as usefulness
depends on the contexts in which a measure is applied (Gershenson, 2002). Además, the goal of
this article is not to defend a measure of emergence but to explore the concept of emergence.
A better understanding of emergence has been and can be useful for soft, hard, and wet ALife. En
all of them, we are interested in how properties of the living emerge from simpler components. En
fewer cases, we are also interested in how systems constrain and promote behaviors and properties
of their components (downward emergence).
Emergence is problematic only in a physicalist, reductionist worldview. In an informational,
complex worldview, emergence is natural to accept. Interactions are not necessarily described by
física, but they are “real” in the sense that they have causal influence on the world. We can again
use the example of money. The value of money is not physical but informational. The physical
properties of shells, seeds, coins, bills, or bits do not determine their value. This is very clear with art,
the value of which comes from the interactions among humans who agree on it. The transformation
of a mountain excavated for open-pit mining does not violate the laws of physics; sin embargo, usando
only the laws of physics, one cannot predict whether humans might decide to give value to some
mineral under the mountain and transform matter and energy to extract it. En este sentido, información
(interactions, dinero) has a (downward) causal effect on matter and energy.
Using an informationist perspective, one also avoids problems with downward causation, como esto
can be seen as simply the effect of a change in scale (Bar-Yam, 2004b). In the Game of Life, one can
describe gliders (higher scale) as emerging from cell rules (lower scale) but also describe cell states as
emerging from the movement of the glider in its environment. In a biological cell (higher scale), uno
can describe life as emerging from the interactions of molecules (lower scale) but also as molecule
states and behavior emerging from the cell’s constraints. In many cases, biological molecules would
simply degrade if they were not inside an organism that produces and provides the conditions for
sustaining them. In a society (higher scale), one can describe culture and values as emerging from
the interactions of people (lower scale) but also describe individual properties and behaviors as
emerging from social norms.
Like with special and general relativity in physics, one cannot define one “real” scale of observa-
ción (frame of reference). Scales are relative to an observer, as is the information perceived at each
escala. Information is relative to the agent perceiving it. En otras palabras, as mentioned, different meanings
can be implied by the same messages.
4 Self-Organization
Emergence is one of the main features of complex systems (De Domenico et al., 2019). Otro
is self-organization (Ashby, 1947, 1962; Atlan & cohen, 1998; Gershenson & Heylighen, 2003;
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Emergence in Artificial Life
Heylighen, 2003), and it has also had great influence in ALife (Gershenson et al., 2020). A system
can be usefully described as self-organizing when global patterns or behaviors are a product of the
interactions of its components (Gershenson, 2007).
One might think that self-organization requires emergence, y viceversa, or at least, that they
go hand in hand. Sin embargo, self-organization and emergence are better understood as opposites
(Fernández et al., 2014; Lopez-Ruiz et al., 1995).
Self-organization can be seen as an increase in order. This implies a reduction of entropy
(Gershenson & Heylighen, 2003). If emergence leads to an increase of information, which is anal-
ogous to entropy and disorder, self-organization should be anticorrelated with emergence, as more
organization requires less information. We can thus simply measure self-organization, S, como
S = 1 − E.
(3)
This equation assumes that the dynamics are internal so that the organization is self-produced.
De lo contrario, we might be measuring “exo-organization.” Minimum S = 0 occurs for maximal en-
tropy: There is only change. In a system where all of its states have the same probability, hay
no organization. Maximum S = 1 occurs when order is maximum: There is no change. Only one
state is possible, and we can call this state “organized” (Ashby, 1962). Note that this measure is not
useful for deciding whether a system is self-organizing (Gershenson & Heylighen, 2003); bastante, el
purpose of the measure is to compare different levels of organization in a specific context.
También, it should be noted that information (and thus emergence and self-organization as con-
sidered here) can have different dynamics at different scales. Por ejemplo, there can be more
self-organization at one scale (spatial or temporal) and more emergence at another scale.
5 Complexity
As mentioned earlier, complex systems tend to exhibit both emergence and self-organization. Ex-
treme emergence implies chaos, whereas extreme self-organization implies immutability (orden).
Complexity requires a balance between emergence and self-organization (Lopez-Ruiz et al., 1995).
Therefore we can measure complexity, C, con
C = 4 · E · S,
(4)
donde el 4 is added to normalize C to [ 0, 1]. C will be close to zero if either emergence or
self-organization dominates, and it will increase as these become more balanced (ver figura 1).
This measure of complexity, C, is maximal at phase transitions in random Boolean networks
(Gershenson & Fernández, 2012), the Ising model, and other dynamical systems characterized by
criticidad (Amoretti & Gershenson, 2016; Febres et al., 2015; Franco et al., 2021; Pineda et al., 2019;
Ramírez-Carrillo et al., 2018; Zubillaga et al., 2014). Recientemente, we have found that different types of
heterogeneity increase the parameter regions of high complexity for a variety of models (López-Díaz
et al., 2022; Sánchez-Puig et al., 2022).
Curiosamente, typical examples of emergence and self-organization are not extreme cases of either.
Perhaps this is the case because if we had only emergence or only self-organization, then these would
not be distinguishable from the full system. It is easier to provide examples in contrast to another
propiedad. So, Por ejemplo, a flock of birds is a good example of emergence, self-organization, y
complexity because the flock produces novel information, self-organizes, and has interactions at
al mismo tiempo. If it had E = 1, S = 0, C = 0, then only information would be produced constantly
(no complexity or organization). If the flock had E = 0, S = 1, C = 0, then it would be static and
fully organized, without change (no complexity or emergence).
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C. Gershenson
Emergence in Artificial Life
Cifra 1. Emergence, mi, self-organization, S, and complexity, C, depending on the probability of having ones, PAG(X), en un
binary string (Fernández et al., 2014).
6 Discusión
Emergence is partially subjective, in the sense that the “emergentness” of a phenomenon can change
depending on the frame of reference of the observer. This is also the case with self-organization
(Gershenson & Heylighen, 2003) and complexity (Bar-Yam, 2004b). De hecho, anything we per-
ceive, all information, might change with the context (Gershenson, 2002) in which it is used. De
curso, this does not mean that one cannot be objective about emergence, self-organization, com-
plejidad, vida, cognition (Gershenson, 2004), etcétera. We just have to agree on the context (marco
of reference) primero.
Therefore the question is not whether something is emergent. The question becomes, In which
contexts it is useful to describe something as emergent? If the context focuses only one scale, él
does not make sense to speak of emergence. But if the context implies more than one scale, y
how phenomena/information at one scale affects phenomena/information at another scale, entonces
emergence becomes relevant.
De este modo, is emergence an essential aspect of (artificial) vida? It depends. If we are interested in
life at a single scale, we can do without emergence. But if we are interested in the relationships
across scales in living systems, then emergence becomes a necessary condition for life: Life has to
be emergent if we are interested in explaining living systems from nonliving components. Sin
emergence, we would fall into dualisms. A similar argument can be made for the study of cognition.
information has been proposed to measure how “living” a system might be
(Farnsworth et al., 2013; Fernández et al., 2014; Kim y cols., 2021). This view also suggests that there
is no sharp transition between the nonliving and the living; bastante, there is a gradual increase in
how much information is produced by an organism compared to how much of its information is
produced by its environment (Gershenson, 2012).
Además,
One implication is that materialism becomes insufficient to study life, artificial or biological.
Better said, materialism was always insufficient to study life. Only now are we developing an alter-
native. It remains to be seen whether it is a better one.
There are inherent limitations to formal systems (Gödel, 1931; Turing, 1936). These limitations
also apply to artificial intelligence (mitchell, 2019) and soft and hard ALife. Simply described, a sys-
tem cannot change its own axioms. One can always define a metasystem, where change will be
posible (in a predefined way), but there will be new axioms that will not be possible to change.
160
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Emergence in Artificial Life
Because scientific theories are also formal, they are also limited in this way. This is one of the rea-
sons why emergence and downward causation are difficult to accept for some: There is no hope
of a grand unified theory, as the emergent future cannot be prestated (Kauffman, 2008) and down-
ward causation can change axioms of our theories. Thus the traditional approach has been to deny
emergence and downward causation. I believe that this is untenable (Wolpert, 2022) y que nosotros
have to develop a scientific understanding of phenomena that—even when we know they cannot
be complete—can be always evolving.
Expresiones de gratitud
I am grateful to Edoardo Arroyo, Manlio De Domenico, Nelson Fernández, Bernardo Fuentes
Herrera, Hiroki Sayama, Vito Trianni, Justin Werfel, and David Wolpert for useful discus-
siones. Anonymous referees provided comments that improved the article. I acknowledge sup-
port from UNAM-PAPIIT (IN107919, IV100120, IN105122) and from the PASPA program of
UNAM-DGAPA.
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