To appear in Leonardo.
The scientific and philosophical scope of artificial life
Mark Bedau
Department of Philosophy, Reed College, Portland OR 97202, USA
mab@reed.edu, http://www.reed.edu/~mab
Abstract
The new interdisciplinary science of artificial life has had a
connection with the arts from its inception. This paper provides an
overview of artificial life, reviews its key scientific challenges, and
discusses its philosophical implications. It ends with a few words
about the implications of artificial life for the arts.
Artificial life is a young interdisciplinary collection of research activities aimed at
understanding the fundamental behavior of life-like systems by synthesizing that
behavior in artificial systems. As befits a journal for artists who use science and
developing technologies, papers discussing artificial life regularly appear in the
pages of Leonardo. There is also traffic in the other direction; for example, the
biennial International Conference on Artificial Life is the primary vehicle for
publishing all the latest scientific developments in artificial life, but more than
five percent of the articles published in the proceedings of the last conference [1]
concerned the application of artificial life to art and music [2-5]. People in both
communities believe that the arts and artificial life have much to offer each other.
Given this, it would useful for the two communities to know each other better.
The opportunity to counteract the hype and misleading publicity surrounding
artificial life is also welcome. The truth is often more interesting and surprizing
than fiction, and it is always more valuable.
This paper aims primarily to provide an overview of artificial life,
explaining its approach to science and technology and outlining its main open
problems, and sketching its broader philosophical implications. It ends with a
few words about the implications of artificial life for the arts.
Overview of artificial life
Life is an interconnected web of adaptive systems produced spontaneously by
the process of evolution. Living systems exhibit impressively robust and flexible
functionality at many levels of analysis. Examples range from the genomic and
proteomic regulatory systems that control how biological organisms develop and
function, to the evolving ecological networks through which members of
different species interact. Man-made adaptive systems like the myriad
communication networks that span the globe are beginning to approach the
complexity of adaptive systems found in nature. Learning how to engineer
flexible and robust adaptive complexity is one of the biggest challenges facing
society in the twenty-first century.
Traditionally, adaptive systems of different kinds were modeled
independently in different disciplines. Artificial life is now bringing together
biologists, physicists, chemists, psychologists, economists, and anthropologists
with computer scientists and philosophers to create a unified understanding of
adaptive systems of all types. Artificial life (also known as “ALife”) studies life
and life-like processes by synthesizing them in artificial media, most often using
computer technology. The goals of this activity include modeling and even
creating life and life-like systems; the goals also include developing practical
applications involving new technologies that exploit intuitions and methods
taken from living systems. The phrase “artificial life” was coined by Christopher
Langton. He envisioned a study of life as it could be in any possible setting, and
he organized the first conference that explicitly recognized this field [6]. There
has since been a regular series of conferences on artificial life and a number of
academic journals have been launched to publish work in this new field.
Artificial life borrows from other, older disciplines, especially computer
science, cybernetics, biology, and the study of complex systems in physics. Its
closest intellectual cousin is artificial intelligence (AI). But there is a crucial
difference between the modeling strategies AI and ALife typically employ. Most
traditional AI models are top-down-specified serial systems involving a
complicated, centralized controller that makes decisions based on access to all
aspects of global state. The controller’s decisions have the potential to affect
directly any aspect of the whole system. On the other hand, many natural living
systems exhibiting complex autonomous behavior are parallel, distributed
networks of relatively simple low-level “agents” that simultaneously interact
with each other. Each agent’s decisions are based on information about only its
own local situation, and its decisions directly affect only its own local situation.
ALife’s models follow nature’s example. The models themselves are bottom-upspecified
parallel systems of simple agents interacting locally. The local
interactions are repeatedly iterated and the resulting global behavior is observed.
The whole system’s behavior is represented only indirectly. It arises out of the
interactions of a collection of directly represented parts.
The synthetic methodology of artificial life has several virtues. The
discipline of expressing a theory synthetically, especially in computer code,
forces precision and clarity. It also insures that hypothesized mechanisms are
feasible. Computer models also facilitate the level of abstraction required for
maximally general models of phenomena. The bottom-up architecture of
artificial life models creates an additional virtue. Allowing micro-level entities
continually to affect the context of their own behavior introduces a realistic
complexity that is missing from analytically studied mathematical models.
Analytically solvable mathematical models can reveal little about the global
effects that emerge from a web of simultaneous nonlinear interactions. The
obvious way to study the effects of these interactions is to build bottom-up
models and then empirically investigate their emergent global behavior through
computer simulations.
Many artificial life models are designed not to represent known biological
systems but to generate wholly new and extremely simple instances of life-like
phenomena. The simplest example of such a system is the famous cellular
automaton called the “Game of Life”, devised by the mathematician John
Conway in the 1960s [7]. Computer simulation is crucial for the study of complex
adaptive systems. It plays the role that observation and experiment play in more
conventional science. The complex self-organizing behavior of the Game of Life
would never have been discovered without simulating thousands of generations
for millions of sites. The same holds for virtually all other systems studied by
artificial life.
Rather than merely producing computer simulations, some artificial life
research aims to implement system in the real world. The products of this
activity are physical devices such as robots that exhibit characteristic life-like
behavior. Some of these implementations are motivated by the concern to
engineer practical devices that have some of the useful features of living systems,
such as robustness, flexibility, and autonomy. But some of this activity is
primarily theoretical, motivated by the belief that the best way to confront the
hard questions about how life occurs in the physical world is to study real
physical systems. Examples range from evolvable hardware, which attempts to
use biologically-inspired adaptive processes to shape the configuration of microelectronic
circuitry, to biologically-inspired robotics, such as using evolutionary
algorithms to automate the design of robotic controllers and swarms of robots
communicating locally to achieve some collective goal.
Grand challenges in artificial life
A good way to understand a scientific community is to grasp its central aims.
The fact that a second generation of scientists is commencing work in artificial
life prompted the organizers of the last International Conference on Artificial Life
to publish list of grand challenges [8]. Since there is still so much unknown about
the emergence and evolution of living systems, the list emphasizes scientific
understanding rather than applications, and the challenges are unabashedly long
term. This section reviews those challenges.
The challenges fall into three broad categories: the origin of life, life’s
evolutionary potential, and life’s connection to mind and culture.
A. How does life arise from the non-living?
1. Generate a molecular proto-organism in vitro.
2. Achieve the transition to life in an artificial chemistry in silico.
3. Determine whether fundamentally novel living organizations can arise
from inanimate matter.
4. Simulate a unicellular organism over its entire lifecycle.
5. Explain how rules and symbols are generated from physical dynamics
in living systems.
B. What are the potentials and limits of living systems?
6. Determine what is inevitable in the open-ended evolution of life.
7. Determine minimal conditions for evolutionary transitions from specific
to generic response systems.
8. Create a formal framework for synthesizing dynamical hierarchies at all
scales.
9. Determine the predictability of evolutionary manipulations of
organisms and ecosystems.
10. Develop a theory of information processing, information flow, and
information generation for evolving systems.
C. How is life related to mind, machines, and culture?
11. Demonstrate the emergence of intelligence and mind in an artificial
living system.
12. Evaluate the influence of machines on the next major evolutionary
transition of life.
13. Provide a quantitative model of the interplay between cultural and
biological evolution.
14. Establish ethical principles for artificial life.
Challenges in the third category are more speculative, and some are interwoven
with non-scientific issues. Some areas in which artificial life plays a significant
role, such as robotics and art, do not appear on the list. In part this is simply a
practical expedient to shorten the list as much as possible. In the rest of this
section I will briefly explain a representative selection of these challenges. More
information about them all can be found in the original source.
The first challenge involves no less than constructing a novel life form in
the laboratory from scratch. The first targets should be the simplest possible
forms of life—self-reproducing molecular structures that construct and maintain
themselves in a simple environment and evolve. The environment would involve
only simple forms of energy and material, and the goal would be to create an
encapsulated biochemical system that can derive energy from simple chemicals
or light and use information carried in primitive genes. The attempt to create a
proto-organism that self-replicates and evolves, using energy and nutrients from
its environment, illustrates artificial life’s concern with understanding life by
synthesizing it. It also shows that artificial life’s interests are not just fanciful
abstractions. A fundamental understanding of real life in the real world is a key
part of what artificial life hopes to provide.
Few questions concerning living systems are as fundamental as the
spontaneous generation of life, and the second challenge explores this issue in
artificial chemistries. Artificial chemistries are computer-based model systems
comprised of objects (abstractions of molecules) which are assembled by
collisions among simpler objects according to predefined interaction rules. The
chemistry must be constructive rather than merely descriptive, with rules that
determine arbitrarily complex products from arbitrarily complex collisions.
Furthermore, the chemical interaction rules should be simple compared with the
ultimate products that they create. This challenge illustrates artificial life’s
emphasis on understanding the amazing spontaneous emergence of structure
and hierarchy that characterizes life. It also shows how artificial life uses
abstraction to capture the essence of such a process.
Life as we know it encodes information about hierarchically organized
spatially localized individuals in genetic structures. The third challenge involves
determining whether this or any other particular form of organization is
necessary for life. The question is relevant to the search for extra-terrestrial life in
the universe. Examples of fundamentally different organizations include those
without a genetic code, without spatially localized individuals, without
hierarchical organization, without a genotype-phenotype distinction or, indeed,
without any symbolic representation scheme. The debate about what
organizations are “fundamentally different” will clarify our understanding of the
nature of life, and pursuing this challenge will expand our horizons and
challenge our preconceptions about life.
The sixth challenge concerns life’s contingency. Artificial life is trying to
discern the features common to all evolutionary processes, or to broad classes of
evolutionary processes. It aims to determine whether different kinds of
evolutionary processes have different potential creativity. Artificial life expects
that many of the most fundamental features of the evolution of life on Earth are
independent of the physical media that happen to embody the process. Digital
information processing in computers is a very different medium from molecular
biology, yet artificial life has been building digital organisms based on genetic
and cellular principles from its inception. Digital media provide considerable
scope to vary the “physics” underlying the evolutionary process, so it is
straightforward to investigate evolutionary contingency in that context. But we
do not know whether digital systems and physical systems have the same
potential for evolutionary innovation. Artificial life’s commitment to a synthetic
methodology shows itself here. Not content with mere verbal speculation about
kinds of evolutionary creativity, artificial life insists on making systems that
actually demonstrate those capacities.
The forms of life that we know all have a complex organization that
enables them to act autonomously and in their own interests. Organisms can be
transgenically manipulated to express different genes, but the evolutionary
consequences and limits of such manipulations are unknown. This raises
artificial life’s ninth challenge: determining how well we can predict the
evolutionary consequences of making new forms of life. To what extent can one
redesign organisms to fulfill novel functions without disrupting their viability? Is
there a tradeoff between size of modification and viability? Better understanding
of the genetics of development will enable us to create novel multicellular
organisms, but they might not flourish or they might unleash unanticipated and
uncontrollable ecological consequences. Perhaps major changes to organisms can
be perfected only by lengthy coevolutionary optimization. Along with genetic
engineering, artificial life must confront questions like these since it unleashes
novel autonomous beings with lives of their own. Furthermore, artificial life is
ideally poised to address such questions, since it can synthesize all kinds of
genetic manipulation in isolated digital contexts.
Once life originated, biological evolution underwent a number of major
evolutionary transitions, such as the origin of eukaryotes, the origin of
multicellular life, and the origin of culture. Presumably there will be more major
transitions in the future. Once culture originates, it has the capacity to evolve on
its own. The past century has seen the explosion of technological culture
including the creation of computing machines and complex distributed networks
connecting them. Many agree it is only a matter of time before artificial life
creates machines that are alive, intelligent, reproduce their own kind, have their
own purposes, set their own goals, and evolve autonomously. These machines
will be part of our world and their evolution will affect our future. Think of how
machines currently influence the nature and rate of human communication and
interconnection. All this suggests that machines might play an unprecedented
role in the next major evolutionary transition. Artificial life’s twelfth challenge is
to predict and explain this role. Machines will certainly play at least a supporting
role in the next major evolutionary transition since they provide an infrastructure
that influences the rate and direction of change. They might even be central
players, if autonomously evolving machines have proliferated. This will again
push the boundaries of what it means to be alive and embody new forms of the
unbounded creativity of evolution.
Culture is one of the products of human existence, and culture itself
evolves. Artificial life’s thirteenth challenge is to understand the connection
between biological and cultural evolution. Examples of cultural evolution
include the development of economic markets, the changes in technological
infrastructure (see the previous challenge), and growth and revolution in
scientific opinion. Some treatments of cultural evolution (e.g., sociobiology and
evolutionary psychology) consider how cultural traits evolve due to their impact
on biological fitness. But one can also consider how cultural traits evolve in their
own right, as Dawkins did when he coined the word “meme” [9]. This sort of
“pure” cultural evolution is driven by mechanisms similar to those behind
biological evolution, but there are important differences. In each case traits
exhibit variation, heritability, and differential fitness, but cultural traits are
transmitted not genetically but psychologically, and their fitness concerns not
biological survival and reproduction but retention in and proliferation across
minds. One question concerns the similarities and differences in the behavior of
biological and cultural evolution. Do both exhibit the same kind of creative
explosions, and for similar reasons? Another question concerns how they are
interconnected. Confronting these questions invites us to reconceptualize life,
culture, and technology. Artificial life gives us an increasingly constructive role
in our future. Even if we do not try to shape our future to fit our current
preconceptions of what is possible, artificial life can help us to understand and
appreciate the open-ended creative process in which we are all embedded.
Philosophical implications of artificial life
Artificial life is not just a scientific and engineering enterprise. It offers a new
perspective on the essential nature of many fundamental aspects of reality like
life, adaptation, and creation. Thus is has rich implications for a number of broad
philosophical issues. In fact, philosophy and artificial life are natural intellectual
partners, for a variety of reasons. Both seek to understand phenomena at a level
of generality that is sufficiently deep to ignore contingencies and reveal essential
natures. In addition, by creating wholly new kinds of life-like phenomena,
artificial life continually forces us to reexamine what it is to be alive, intelligent,
creative, etc. Furthermore, artificial life’s computational methodology is a direct
and natural extension of philosophy’s traditional methodology of a priori thought
experiment. In the attempt to capture the simple essence of vital processes,
artificial life models abstract away as many details of living systems as possible.
These models are thought experiments that are explored with the help of a
computer. Like the traditional armchair thought experiments, artificial life
simulations attempt to answer “What if X?” questions, but the premises they
pose are complicated enough that their implications can be explored only by
computer simulation; armchair analysis is simply inconclusive. Synthesizing
thought experiments on a computer brings a new kind of clarity and constructive
evidence to philosophy. In this section I illustrate artificial life’s broad
implications for a handful of philosophical topics: emergence, evolution, life, and
mind.
Emergence. One of life’s amazing features is how the whole is more than
the sum of the parts. This is called emergence. In general, emergent phenomena
share two broad hallmarks: they both depend on and are autonomous from
underlying phenomena. Although apparent emergent phenomena are all around
us, the two hallmarks of emergence seem inconsistent or philosophically
illegitimate. How can something be autonomous from underlying phenomena if
it depends on them? This is the traditional philosophical problem of emergence.
A solution to this problem would both dissolve the appearance of illegitimate
metaphysics and show how emergence plays a constructive in scientific
explanations of phenomena involving life and mind.
The aggregate global behavior of complex systems studied in artificial life
offers a new way to view of emergence. On this view, a system’s macrostate is
emergent just in case it can be derived from the system’s boundary conditions
and its micro-level dynamical process but only through the process of iterating
and aggregating all the micro-level effects [10]. This new view explains the two
hallmarks of emergence. Micro-level phenomena clearly depend on macro-level
phenomena; think of how a bottom-up artificial life model works. At the same
time, macro-level phenomena are autonomous because the micro-level
interactions in the bottom-up models produce such complex macro-level effects
that the only way to recognize or predict them is by observing macro-level
behavior. This form of emergence is common in complex systems found in
nature, and artificial life’s models also exhibit it. This view attributes the
unpredictability and unexplainability of emergent phenomena to the complex
consequences of myriad, non-linear and context-dependent local micro-level
interactions. Emergent phenomena can have causal powers on this view, but
only by means of aggregating micro-level causal powers. There is nothing
inconsistent or metaphysically illegitimate about underlying processes
constituting and generating phenomena by iteration and aggregation.
Furthermore, this form of emergence is prominent in scientific accounts of
exactly the natural phenomena like life and mind that apparently involve
emergence.
This shows how artificial life will play an active role in future
philosophical debates about emergence and related notions like explanation,
reduction, complexity, and hierarchy. Living systems are one of the primary
sources of emergent phenomena, and artificial life’s bottom-up models generate
impressive macro-level phenomena wholly out of micro-level interactions.
Artificial life expands our sense of what is possible, and it provides a
constructive way to explore it.
Evolution. The evolution of life has shown a remarkable growth in
complexity. Simple prokaryotic one-celled life lead to more complex eukaryotic
single-celled life, which then lead to multicellular life, then to large-bodied
vertebrate creatures with sophisticated sensory processing capacities, and
ultimately to highly intelligent creatures that use language and develop
sophisticated technology. This illustration of evolution’s creative potential leads
to a deep question about evolution’s creative potential: Does evolution have an
inherent tendency to create greater and greater adaptive complexity, or is the
complexity of life just a contingent and accidental by-product of evolution?
Stephen Jay Gould [11] devised a clever way to address this issue: the
thought experiment of replaying the tape of life. Imagine that the process of
evolution were recorded on a tape. The thought experiment is to rewind the
evolutionary process backward in time, erasing the tape, and then playing it
forward again but allowing it to be shaped by wholly different contingencies. It
is not clear what the outcome of the thought experiment is. Gould himself
suggests that “any replay of the tape would lead evolution down a pathway
radically different from the road actually taken.” He concludes that the
contingency of evolution destroys any possibility of a necessary growth in
adaptive complexity. Daniel Dennett [12] draws exactly the opposite conclusion.
He argues that complex features like sophisticated sensory processing provide
such a distinct adaptive advantage that natural selection will almost inevitably
discover it in one form or another. Dennett concludes that replaying life’s tape
will almost inevitably produce highly intelligent creatures that use language and
develop sophisticated technology.
Artificial life can make a number of contributions to this debate.
Experience in artificial life has shown time and again that armchair expectations
about the outcome of thought experiments like replaying life’s tape are highly
fallible. The only sure way to know what to expect is to create the relevant
system and observe the results of repeated simulation. In fact, artificial life is
exactly where the activity of creating and studying such systems occurs.
However, we cannot yet conduct the experiment of replaying life’s tape because
no one has been able to create a system that exhibits continual open-ended
evolution. Achieving this goal is a key open problem in artificial life, related to its
sixth grand challenge. All conjectures about evolution’s inherent creativity will
remain unsettled until we actually study what happens when the tape of life is
replayed.
Life. Philosophers from Aristotle to Kant have addressed the nature of
life. But philosophers today ignore the issue, perhaps because it seems too
scientific. At the same time, most biologists also ignore the issue, perhaps
because it seems too philosophical. The advent of artificial life has revitalized the
question. This is partly because one can simulate or synthesize living systems
only if one has some idea what life is. Artificial life’s self-conscious aim to discern
the essence of life encourages liberal experimentation with novel life-like
organizations and processes. Thus, artificial life both fosters a broad perspective
on life. In the final analysis, the question of the nature of life will be settled by
whatever perspective provides the best explanation of the rich range of natural
phenomena that living systems exhibit. Better understanding of how to explain
these phenomena will also help resolve a cluster of puzzles about life, such as
whether life admits of degrees, how the notion of life applies at different levels in
the biological hierarchy, and the relationship between the material embodiment
of life and the dynamical processes in which those materials participate.
Artificial life highlights the question whether artificial constructions,
especially purely digital systems existing in computers, could ever literally be
alive. This question will be easier to answer once there is agreement about the
nature of life; but that agreement should not be expected until we have
experienced a much broader range of possibilities. So the debate over whether
real but artificial life is possible continues. Some people complain that it is a
simple category mistake to confuse a computer simulation of life with a real
instance of it [13]. A flight simulation for an airplane, no matter how detailed and
realistic, does not really fly. A simulation of a hurricane does not create real rain
driven by real gale-force winds. Similarly, a computer simulation of a living
system produces merely a symbolic representation of the living system. The
intrinsic ontological status of this symbolic representation is nothing more than
certain electronic states inside the computer (e.g., patterns of high and low
voltages). This constellation of electronic states is no more alive than is a series of
English sentences describing an organism. It seems alive only when it is given an
appropriate interpretation. But this charge of category mistake can be blunted.
Artificial life systems are typically not simulations or models of any familiar
living system but new digital worlds. Conway’s Game of Life, for example, is not
a simulation or model of any real biochemical system but a digital universe that
exhibits spontaneous macroscopic self-organization. So, when the Game of Life is
actually running in a computer, the world contains a new physical instance of
self-organization. Processes like self-organization and evolution are multiply
realizable and can be embodied in a wide variety of different media, including
the physical media of suitably programmed computers. So, to the extent that the
essential properties of living systems involve processes like self-organization and
evolution, suitably programmed computers will actually be novel realizations of
life.
Mind. Life forms are sensitive to the environment in various ways, and
this environmental sensitivity affects their behavior in various ways. So forms of
life have broadly mental capacities. Furthermore, the sophistication of these
mental capacities seems to correspond to the complexity of those forms of life. So
it is natural to ask whether life and mind have some deep connection. Since all
forms of life must cope in one way or another with a complex, dynamic, and
unpredictable world, perhaps this adaptive flexibility inseparably connects life
and mind.
It is well known in the philosophy of mind and artificial intelligence that
the emergent dynamical patterns among human mental states are especially
difficult to describe and explain. Descriptions of these patterns must be qualified
by “ceteris paribus” clauses, as the following example illustrates: If someone
wants a goal and believes that performing a certain action is a means to that goal,
then ceteris paribus they will perform that action. For example, if someone wants
a beer and believes that there is one in the kitchen, then he will go get
one—unless, as the “ceteris paribus” clause signals, he does not want to miss any
of the conversation, or he does not want to offend his guest by leaving in
midsentence, or he does not want to drink beer in front of his mother-in-law, or
he thinks he had better flee the house since it is on fire, etc. This pattern exhibits a
special property which I will call “suppleness”. Suppleness is involved in a
distinctive kind of exceptions to the patterns in our mental lives—specifically,
those exceptions that reflect our ability to act appropriately in the face of an openended
range of contextual contingencies. These exceptions to the norm occur
when we make appropriate adjustment to contingencies. The ability to adjust our
behavior appropriately in context is a central component of the capacity for
intelligent behavior.
A promising strategy for explaining mental suppleness is to follow the
lead set by artificial life [14]. For there is a similar suppleness in vital processes
such as metabolism, adaptation, and even flocking. For example, a flock
maintains its cohesion not always but only for the most part, only ceteris paribus,
for the cohesion can be broken when the flock flies into an obstacle (like a tree).
In such a context, the best way to “preserve” the flock might be for the flock to
divide into subflocks. Artificial life models of flocking exhibit just this sort of
supple flocking behavior. Or consider another example concerning the process
of adaptation itself. Successful adaptation depends on the ability to explore an
appropriate number of viable evolutionary alternatives; too many or too few can
make adaptation difficult or even impossible. In other words, success requires
striking a balance between the competing demands for “creativity” (trying new
alternatives) and “memory” (retaining what has proved successful).
Furthermore, as the context for evolution changes, the appropriate balance
between creativity and memory can shift in a way that resists precise and
exceptionless formulation. Nevertheless, artificial life models can show a supple
flexibility in how they balance creativity and novelty.
Implications for the arts
Artificial life’s central aim is to develop a coherent theory of life in all its
manifestations. It embraces the possibility of discovering life in unfamiliar
settings and creating unfamiliar forms of life. In the long run artificial life will
contribute to the development of practical adaptive systems in many fields of
application, such as software development and management, design and
manufacture of robots including distributed swarms of autonomous agents,
automated trading in financial markets, pharmaceutical design, ecological
sustainability, and extraterrestrial exploration. The economic potential of
harnessing natural adaptive systems can be compared with cracking the genetic
code. Natural adaptive systems vastly exceed the complexity of anything
humans have yet created. Understanding and harnessing life’s adaptive
creativity will spawn a wealth of new technologies and entrepreneurial
opportunities.
Artificial life also has aesthetic applications. There are at least three ways
in which artists might find artificial life useful. First, artificial life technology can
be used for a variety of aesthetic purposes. They range from commercial
applications in computer animations of life forms to new kinds of active art,
evolving art, and interactive art (e.g., [3, 4, 15, 16]). Second, artificial life is
radically changing human culture and technology, and art often responds to and
comments on such changes (e.g., [17, 18]). Third, art has a long tradition of
representing and responding to our understanding of nature, so new insights
about life revealed by artificial life can spark new aesthetic objects (e.g., [19, 20,
21]).
Just as artificial life can be beneficial for artists, artists can provide
complementary benefits to artificial life. For one thing, artists that use artificial
life techniques and insights can be counted among the consumers of the product
that artificial life produces, and one spur to producing better products is
consumer demand. Scientists can also gain a broader perspective on their own
scientific activity when artists explore the implications of the science and subject
it to commentary and social criticism. Finally, human aesthetic activity is itself
one distinctive manifestation of the creative potential contained within life. It
would behoove those who want to understand nature’s creative potential to keep
an eye on the latest aesthetic developments.
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