Evolución abierta:
Perspectives from the OEE
Workshop in York
Abstract We describe the content and outcomes of the First
Workshop on Open-Ended Evolution: Recent Progress and Future
Milestones (OEE1), held during the ECAL 2015 conference at the
University of York, Reino Unido, in July 2015. We briefly summarize the
content of the workshopʼs talks, and identify the main themes that
emerged from the open discussions. Two important conclusions
from the discussions are: (1) the idea of pluralism about OEE—it
seems clear that there is more than one interesting and important kind
of OEE; y (2) the importance of distinguishing observable
behavioral hallmarks of systems undergoing OEE from hypothesized
underlying mechanisms that explain why a system exhibits those
hallmarks. We summarize the different hallmarks and mechanisms
discussed during the workshop, and list the specific systems that were
highlighted with respect to particular hallmarks and mechanisms. Nosotros
conclude by identifying some of the most important open research
questions about OEE that are apparent in light of the discussions.
The York workshop provides a foundation for a follow-up OEE2
workshop taking place at the ALIFE XV conference in Cancún,
México, in July 2016. Additional materials from the York workshop,
including talk abstracts, presentation slides, and videos of each talk,
are available at http://alife.org/ws/oee1.
‡
‡
††
‡‡
‡‡‡
†††
Tim Taylor*,†
Mark Bedau
Alastair Channon§
David Ackley
Wolfgang Banzhaf
Guillaume Beslon§§
Emily Dolson***
Tom Froese
Simon Hickinbotham
Takashi Ikegami
Barry McMullin§§§
Norman Packard****
††††
Steen Rasmussen
Nathaniel Virgo
Eran Agmon§§§§
†
Edward Clark
Simon McGregor*****
Charles Ofria***
†††††
Glen Ropella
Lee Spector
Kenneth O. Stanley§§§§§
Adam Stanton§
Christopher Timperley
Anya Vostinar***
Michael Wiser***
‡‡‡‡‡
‡‡‡‡
†
Palabras clave
Open-ended evolution, ongoing evolution,
perpetual novelty, adaptive evolution, dynamical
jerarquías, major transitions
** This paper was primarily written by the first three co-authors [T.T., M.B., A.C.] based upon material presented and discussed by
participants of the First Workshop on Open-Ended Evolution (OEE1) during the European Conference on Artificial Life 2015 at the Uni-
versity of York, Reino Unido. Additional material and comments were provided by the other presenters at the workshop [D.A., W.B., GB., E.D., T.F.,
S.H., T.I., B.M., N.P., S.R., and N.V.] and by the other coauthors, who contributed to discussions at the workshop or afterwards online.
* Contact author.
† University of York, Reino Unido. Correo electrónico: tim@tim-taylor.com (T.T.)
‡ Reed College, EE.UU. Correo electrónico: mab@reed.edu (M.B.)
§ Keele University, Reino Unido. Correo electrónico: a.d.channon@keele.ac.uk (A.C.)
†† University of New Mexico, EE.UU.
‡‡ Memorial University of Newfoundland, Canada.
§§ Université de Lyon, Francia.
*** Michigan State University, EE.UU.
††† National Autonomous University of Mexico.
‡‡‡ University of Tokyo, Japón.
§§§ Dublin City University, Irlanda.
**** ProtoLife, Cª, EE.UU.
†††† University of Southern Denmark.
‡‡‡‡ Earth-Life Science Institute, Japón.
§§§§ Indiana University, EE.UU.
***** University of Sussex, Reino Unido.
††††† Tempus Dictum, Cª, EE.UU.
‡‡‡‡‡ Hampshire College, EE.UU.
§§§§§ University of Central Florida, EE.UU.
© 2016 Instituto de Tecnología de Massachusetts. Artificial Life 22: 408–423 (2016) doi:10.1162/ARTL_a_00210
Publicado bajo una atribución Creative Commons
3.0 no portado (CC POR ) licencia.
yo
D
oh
w
norte
oh
a
d
mi
d
F
r
oh
metro
h
t
t
pag
:
/
/
d
i
r
mi
C
t
.
metro
i
t
.
mi
d
tu
a
r
t
yo
/
/
yo
a
r
t
i
C
mi
–
pag
d
F
/
/
/
/
2
2
3
4
0
8
1
6
6
6
2
9
7
a
r
t
yo
/
_
a
_
0
0
2
1
0
pag
d
.
F
b
y
gramo
tu
mi
s
t
t
oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3
t. Taylor et al.
Evolución abierta: Perspectives from the OEE Workshop in York
1 Introducción
From the first experiments with digital evolution in the 1950s to the increasingly sophisticated
simulations of the present day, the concept of open-ended evolution (OEE) has been a central concern
for artificial life (ALife) investigadores [51]. Loosely defined, an open-ended evolutionary system is one
that is capable of producing a continual stream of novel organisms1 rather than settling on some
quasi-stable state beyond which nothing fundamentally new occurs. Some definitions of OEE further
require that the maximum complexity of organisms in the system increases over time, or that eco-
system complexity increases. Understanding open-ended evolution remains a holy grail in ALife—and
yet there remains little agreement within the community on precise definitions and measures.
There has been progress on a variety of fronts concerning OEE in the past decade. To take stock
of and document recent work, and to identify key milestones for the immediate future, a workshop
on Open-Ended Evolution: Recent Progress and Future Milestones (OEE1) was held at the European
Conference on Artificial Life (ECAL 2015) at the University of York, Reino Unido, in July 2015.2 The work-
shop aimed to create a common framework for discussing and evaluating research on open-ended
evolution, and to catalyze further progress. En particular, a follow-up workshop (OEE2) will take
place at the ALIFE XV conference in Cancún, Mexico in July 2016.3 The Cancún workshop will
be followed by a special issue on open-ended evolution in the Artificial Life journal, including a
comprehensive review paper on work on OEE.
The York workshop had two sessions. The first session consisted of 14 short presentations that
addressed (one or more of) five tasks:4
1. Define key concepts concerning open-ended evolution.
2. Produce actual models that do (or do not) generate interesting kinds of open-ended
evolution.
3. Find and use operational empirical—and, ideally, quantitative—measures of key kinds of
open-ended evolution.
4. Demonstrate examples of (kinds of) open-ended evolution in models or natural systems.
5. Identify critical future research milestones in open-ended evolution.
The second session was an open discussion among the speakers and other attendees about open-
ended evolution, focusing on the key hallmarks of various kinds of OEE and the hypothetical
mechanisms that could produce those hallmarks. Sección 2 below briefly summarizes the short pre-
sentaciones, y Sección 3 highlights some central themes that emerged from the open discussion.
2 Summary of Short Presentations
In “Karl Popper, artificial life, and the curious tale of the hopeful behavioral monster,” Barry
McMullin highlighted Karl Popperʼs philosophy of evolutionary epistemology and its relevance
to ALife and OEE. Focusing particularly on Popperʼs work “Evolution and the Tree of Knowledge”
(a chapter of his book Objective Knowledge: An Evolutionary Approach [37]) based upon a lecture
delivered in 1961, McMullin outlined Popperʼs thought experiment on how mutations in an agentʼs
“central propensity structure” (a hierarchical control system defining its set of skills and behaviors)
1 We use to term organism here to include both biological organisms and individuals in artificial evolutionary systems in software,
hardware, or wetware.
2 http://alife.org/ws/oee1
3 http://alife.org/ws/oee2
4 Videos and presentation slides from this session can be accessed from the workshop website.
Artificial Life Volume 22, Número 3
409
yo
D
oh
w
norte
oh
a
d
mi
d
F
r
oh
metro
h
t
t
pag
:
/
/
d
i
r
mi
C
t
.
metro
i
t
.
mi
d
tu
a
r
t
yo
/
/
yo
a
r
t
i
C
mi
–
pag
d
F
/
/
/
/
2
2
3
4
0
8
1
6
6
6
2
9
7
a
r
t
yo
/
_
a
_
0
0
2
1
0
pag
d
.
F
b
y
gramo
tu
mi
s
t
t
oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3
t. Taylor et al.
Evolución abierta: Perspectives from the OEE Workshop in York
could guide the future evolution of the agent, leading to apparently “goal-directed” evolution. Este
perspective suggests that the existence of hierarchical control organization and the continuing
feasibility of inheritable change at the highest control levels (including emergence of higher, newly
dominating levels) may be critical to the substantive openness of evolution of complex function.
The presentation by Wolfgang Banzhaf on “Open-Endedness and Novelty in Evolution” started with
the observation that the notion of novelty in a system must be defined with respect to a particular model.
Banzhaf identified three different types of novelty: (1) novelty within a model (variación), (2) novelty that
changes the model (innovation), y (3) novelty that changes the meta-model5 (emergence). He then addressed
the question of whether OEE requires unbounded novelty or unbounded complexity. Observing that the universe
is limited and hence cannot afford an unbounded increase in levels of complexity, and also that all combina-
torial possibilities at any one level are bounded, he argued that novelty can still be practically unbounded if
the number of levels of complexity in the system is allowed to grow (as novelties grow exponentially with
complejidad). Por eso, Banzhafʼs position is that OEE does not require unbounded complexity, but that
unbounded novelty is sufficient. He concluded with some comments on the competing roles of expo-
nential growth and competition due to resource constraints in natural selection, and the analogous situ-
ation in hierarchical systems whereby climbing the levels of complexity introduces exponentially more
possibilities, but exploration of these possibilities is restricted by resource constraints on the number of
individuals that can populate higher levels. Banzhafʼs talk was based upon a forthcoming paper [3].
After highlighting some of the many different concepts associated with the term OEE in the literature, en
“Requirements for Open-Ended Evolution in Natural and Artificial Systems” Tim Taylor proposed a
high-level classification of these issues in the form of five basic requirements for a system to exhibit OEE:
(1) robustly reproductive individuals, (2) individuals capable of producing more complex offspring,
(3) mutational pathways to other viable individuals, (4) a medium allowing the possible existence of a
practically unlimited diversity of individuals and interactions, y (5) drive for continued evolution. Para
each requirement, Taylor explained why it was important, what theoretical issues it encompassed, y
what practical issues were involved in implementing a system to meet the requirement. The talk was
based upon a paper [50] presented at the EvoEvo Workshop6 at the same ECAL 2015 conference.
Guillaume Beslon started his talk “Is Biological Evolution Open-Ended?” by observing that the
vast majority of literature on OEE comes from the ALife community and not from evolutionary
biology. Históricamente, the mathematical models of evolutionary biologists have focused on stable
estados. Además, selection is thought to commonly act as a stabilizing force on genetic diversity.
Sin embargo, although the concept of OEE is largely lacking in the biological literature, the concept of
novelty pervades it in many forms. Of all kinds of biological novelty, Beslon identified coevolution and
major transitions as the two being most closely related to the concept of OEE. He proposed that the
most important idea of open-endedness was the emergence of novelty leading to new levels of
individuality (es decir., major transitions). Sin embargo, he conjectured that biology cannot be open-ended
with regard to major transitions, arguing that as higher levels of organization are inevitably popu-
lated by smaller population sizes, this leads to decreasing probability of fixation of beneficial
mutations. A saving grace for computational systems, according to Beslon, was that this limitation
could be overcome by tricks such as suitable fitness landscapes (although whether they were open-
ended would still be an open question). A version of Beslonʼs argument can also be found in the
forthcoming paper mentioned by Banzhaf [3].
In “Normalised Evolutionary Activity Statistics and the Need for Phenotypic Evidence,” Alastair
Channon noted that there was widespread agreement that OEE involves the continued evolution
of new adaptive traits. As this can be achieved trivially,7 he argued that OEE must also involve a
5 A meta-model is a description of the kinds of things that might be present in a model.
6 http://evoevo.liris.cnrs.fr/evoevo-workshop/
7 Por ejemplo, evolving variable-length bit strings in a selective environment that favors longer strings would result in the continued
evolution of new adaptive traits.
410
Artificial Life Volume 22, Número 3
yo
D
oh
w
norte
oh
a
d
mi
d
F
r
oh
metro
h
t
t
pag
:
/
/
d
i
r
mi
C
t
.
metro
i
t
.
mi
d
tu
a
r
t
yo
/
/
yo
a
r
t
i
C
mi
–
pag
d
F
/
/
/
/
2
2
3
4
0
8
1
6
6
6
2
9
7
a
r
t
yo
/
_
a
_
0
0
2
1
0
pag
d
.
F
b
y
gramo
tu
mi
s
t
t
oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3
t. Taylor et al.
Evolución abierta: Perspectives from the OEE Workshop in York
sustained increase in some measure of accumulated adaptive success. Sin embargo, he questioned the
inclusion of increasing complexity as a hallmark of OEE, as this would preclude the possibility of
addressing important questions such as whether or not OEE can be the cause of increasing maximal
complejidad (ya sea individual, grupo, or system complexity) or what conditions might be necessary
or sufficient for this. Channon then described his Geb system [15], based upon Harveyʼs SAGA
principios [19] with the addition of coevolutionary feedback arising via biotic selection rather than
being specified by abiotic fitness functions, followed by a description of Bedau et al.ʼs work on
evolutionary activity statistics [4, 14]. Two useful features of these measures, he suggested, eran
that they are widely applicable, and that the key metric (cumulative evolutionary activity, Residencia en
adaptive persistence) is “a measure of the continual adaptive success of the components in the system”
[7], that is a measure of accumulated adaptive success. When applied to Geb, these measures classify
the system as producing unbounded evolutionary dynamics (OEE). Despite that, it becomes increas-
ingly difficult (over evolutionary time) to visually observe the behaviors that evolve. Channon iden-
tified three critical future milestones for the field: (1) more systems classified as OEE according to
the evolutionary activity statistics, in order to refine definitions and tests for the hallmarks of OEE;
(2) evidence of complex artifacts or behaviors arising from evolutionary changes (rather than from a
very small number of mutations from a hard-coded ancestor ); y (3) evidence of long evolutionary
sequences of evolved artefacts or behaviors (a result that has not been conclusively observed in
work to date).
In “Indefinite Scalability for Open-Ended Evolution,” David Ackley agreed with Beslon that
major transitions are the most important aspect of OEE, and that if we accept that, then a finite
system cannot be open-ended: Successive major transitions produce larger and slower individuals
until ultimately producing a population size of one that lives forever. He argued that conven-
tional component-based evolutionary activity measures of OEE are problematic because they
require us to identify the components of interest beforehand—if we treat components as priors
rather than observables, we will be unable to detect major transitions. To avoid this problem and
treat evolutionary components as observables, models should be defined at the level of physics
and chemistry, not at the level of biological components. But this raises the question, what kind
of physics and chemistry is appropriate? Ackleyʼs answer is that satisfactory models should, en
principle, be indefinitely scalable. This rules out the whole class of deterministic, synchronous models (semejante
as Game-of-Life-type systems), and suggests that OEE models should embrace nondeterminism. Este
approach could create a unifying research strand between different ALife projects that implemented
different kinds of indefinitely scalable systems. Ackley concluded his talk by proposing a research chal-
lenge to develop a statistical OEE measure based upon identifying potential evolutionary components at
a given scale by near-perfect spatial autocorrelation of elements, study of the phase space defined by the
“life lines” of such components over time, and application of the same technique at different scales in
the system. Ackley presented further details of his concept of indefinitely scalable architectures in a
paper at the main ECAL 2015 conference [1].
In “Emergence of Emergence,” Norman Packard discussed current work with Nicholas
Guttenberg and others on the evolution of coding in the transition from prebiotic systems to biotic
evolutionary systems. The central question being tackled is: What dynamical processes lead gener-
ically to sequestration of information into units that have long-term stability, control fast time-scale
dinámica, and can serve as evolvable elements? The goal is to understand this transition well
enough to be able to engineer systems that will naturally implement information sequestration,
evolvability, and robustness. Packard observed that evolutionary dynamics is very different from
attractor dynamics: It behaves somewhat like an attractor on the short term, but over a longer term,
instabilities lead to the generation of innovation. Their work augments the language of dynamical
systems theory with concepts capable of describing such phenomena. En particular, the concept of
dynamical canals is used in place of attractors. A mechanism that seems to produce this kind of
system generically is one involving an alternation between unstable (or neutrally stable) dynamics and
contracting, fixed-point dynamics. Alternation forces the system to produce information bottlenecks,
Artificial Life Volume 22, Número 3
411
yo
D
oh
w
norte
oh
a
d
mi
d
F
r
oh
metro
h
t
t
pag
:
/
/
d
i
r
mi
C
t
.
metro
i
t
.
mi
d
tu
a
r
t
yo
/
/
yo
a
r
t
i
C
mi
–
pag
d
F
/
/
/
/
2
2
3
4
0
8
1
6
6
6
2
9
7
a
r
t
yo
/
_
a
_
0
0
2
1
0
pag
d
.
F
b
y
gramo
tu
mi
s
t
t
oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3
t. Taylor et al.
Evolución abierta: Perspectives from the OEE Workshop in York
which seem to imply the emergence of informationally stable components that become proto-code. En
addition to Packardʼs own work on this origin-of-life transition, colleagues are working on applying
these ideas to other transitions, including multicellularity, ecological niche formation, and the evolution
of cognitive mechanisms.
The central claim of Nathaniel Virgo in “Open-Ended Fitness Landscapes” was that open-
endedness is a property of fitness landscapes, and not of the process of evolution itself. He character-
ized OEE in terms of increasing phenotypic complexity, and argued that ecological factors (p.ej.,
changing environments, coevolution, and niche construction) might not be necessary for OEE, or at
least that this is a hypothesis worth taking seriously. To evaluate this claim, he suggested we should
focus on understanding how to create more “lifelike” fitness landscapes of high-dimensionality,
containing many qualitatively different “solutions,” where fitter solutions also generally tend to
be more complex, and where those solutions can be reached through a sequence of small changes.
By comparison with the biological-physical case, Virgo argued that this kind of fitness landscape
required the existence of many degrees of freedom (DoFs), which he characterized as the “capacity [de un
sistema] to be changed in some nontrivial way.” Complex systems with many DoFs, he suggested,
enable the existence of many qualitatively different solutions and the capacity to move between
those solutions. Virgo then hypothesized that many nontrivial landscapes have small regions of
evolvability, and that evolutionary systems might evolve towards such regions through a process
of the evolution of evolvability. His tentative conclusion was that the requirements for OEE in com-
putational systems might just involve larger search spaces, more nontrivial fitness functions, más grande
poblaciones, weaker selection pressure, and more computer time.
In “Empirical Measurements of Door-Opening Evolution of Technology,” Mark Bedau described
recent work with colleagues on studying open-ended evolution within the context of cultural rather
than biological evolution. Específicamente, the work investigates the evolution of human technology. Tech-
nological evolution differs from biological evolution in many ways, including the presence of hyper-
parental reproduction, intentionally directed progress, and indirect (human-mediated) reproduction.
Además, one can identify populations of technology adopters, technology designers, technological
innovaciones, and technological products as four distinct, but interrelated components. Technological evo-
lution is therefore different from biological evolution in nontrivial ways, pero, Bedau argued, we should
not restrict ourselves to studying OEE just in biological systems. He identified the concept of reach—
intuitively, the idea of an invention that has descendants that are very different to itself—as an impor-
tant aspect of technological OEE. In recent preliminary work, the evolution of technological innova-
tions was studied by using text-mining techniques on historical patent records to extract relevant traits
in each record. Dimensionality reduction and clustering techniques were used to study the reach of
different traits in particular genealogies of technologies. These new results build on earlier work on
operationalizing the study of technological evolution [43, 11, 13], and they open the door to the em-
pirical study of many questions about open-ended evolution in nature.
In “The OEE Measure—Will It Blend?” Simon Hickinbotham questioned whether existing evo-
lutionary activity methods reduced the complexity of a system too much to produce a simple mea-
sure, and whether they really highlight the relevant features relating to a systemʼs open-endedness.
He argued that (mejorado) evolutionary activity measures were useful for making sense of the huge
amounts of data produced by computational evolutionary systems, y, more importantly, ellos
allow us to rigorously compare different systems and thereby demonstrate when improvements
in ALife systems have been achieved. Hickinbotham then introduced his new quantitative non-neutral
(QNN) evolutionary activity measure. He highlighted some of its attractive features as being that
(1) it produces a single numerical value, (2) it is based solely on population data (like other evo-
lutionary measures), y (3) it can be applied to systems with intrinsic or extrinsic fitness. The ap-
plication of the QNN measure to the Tierra and Stringmol systems was described, with discussion
of how it was used to guide improvements in the design of each system: Further details can be
found in papers presented during the main conference [23, 22] and in a subsequently published
artículo [20]. Hickinbotham concluded by suggesting that we need more measures to address different
412
Artificial Life Volume 22, Número 3
yo
D
oh
w
norte
oh
a
d
mi
d
F
r
oh
metro
h
t
t
pag
:
/
/
d
i
r
mi
C
t
.
metro
i
t
.
mi
d
tu
a
r
t
yo
/
/
yo
a
r
t
i
C
mi
–
pag
d
F
/
/
/
/
2
2
3
4
0
8
1
6
6
6
2
9
7
a
r
t
yo
/
_
a
_
0
0
2
1
0
pag
d
.
F
b
y
gramo
tu
mi
s
t
t
oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3
t. Taylor et al.
Evolución abierta: Perspectives from the OEE Workshop in York
aspects of OEE, and that once we had developed an adequate suite of such measures, había
the potential to create a meta-evolver for OEE.8
Steen Rasmussen in “Minimal Life and Open-Ended Evolution” conjectured that high-dimensional
systems with rich object complexity and/or diversity enable the emergence of higher-order function-
alities, and that these are necessary for OEE. Sin embargo, simply adding complexity and diversity is not a
sufficient condition. He stresses the existence of two different ways to increase complexity in a physical
sistema: through the aggregation of things from the environment, and through the evolution of new en-
coded entities. His view is based upon his work over many years with protocells—minimal self-reproducing
molecular machines comprising a metabolism, genes, and a container in a given environment. Protocells
utilize self-organization and self-assembly processes to maintain their organization, and are driven by a
metabolism feeding on free energy and resources from the environment. A full chemical protocell sys-
tem has not yet been synthesized in the lab, but simulation results show that published protocell designs
apparently lack the ability to evolve in an open-ended manner. Por ejemplo, in the case of Rasmussenʼs
own protocell design, simulations show that the systemʼs evolution is limited to the optimization of its
metabolic rate. Both experimental and simulation results show that a richer environment is necessary
to expand the systemʼs evolutionary potential. Real chemical systems demonstrate the emergence of
higher-order functionality at multiple hierarchical levels, and Rasmussen described simulation results
in which similar higher-order functionality had emerged [38]. This was achieved by adding to the com-
plexity of the lowest-level elements in the system. A similar approach might therefore be viable in the
protocell systems. Sin embargo, Rasmussen pointed out that just adding complexity to the system in an
unprincipled way was likely to lead to “black tar” rather than any interesting higher-order behavior—
the addition of complexity must be done with care. This leads to an as yet unanswered question: Are
there principles to guide us in adding complexity at the right places in the system, or are we essentially
left to experiment by trial and error?
Emily Dolson started her presentation “Understanding Complexity Barriers in Evolving Systems” with
an informal definition of open-endedness as the ability of a system to “keep doing interesting things.”
Dolson discussed how we might more accurately define both “keep doing” and “interesting things.”
She suggested that there is fairly general agreement that “keep doing” means unbounded rather than
asymptotic behavior of a measure. With regard to what measures to use, eso es, what constitutes
“interesting things,” she argued that it might be productive to flip the question around, and ask what
kinds of barriers might prevent a system from exhibiting open-endedness. Dolson proceeded to describe
four barriers that she and her colleagues had come up with: (1) change potential—how much we expect the
population composition to change during an interval; (2) novelty potential—how many entirely new strat-
egies we expect to arise during an interval; (3) complexity potential—how much we expect the greatest
individual complexity to increase during an interval; y (4) ecosystem potential—how much we expect
“meaningful” diversity to increase during an interval. This breaks down the concept of OEE into
separate aspects, each of which suggests more clearly focused lines of research for advancing our
understanding of open-endedness. Dolson went on to discuss the relationships between these factors:
To have novelty potential, a system requires change potential, and to have complexity potential or
ecosystem potential, a system requires novelty potential. She acknowledged that other barriers
might also exist, y, En particular, she and her colleagues are currently considering including a barrier
of major transition potential in their picture.9
In “A New Design Principle for Open-Ended Evolution,” Takashi Ikegami discussed evolution in
the context of web-based systems. Específicamente, he reported work with colleagues on studying
the dynamics of a social network site10 where users can upload photos and other users can attach
8 Eso es, an evolutionary system in which the individuals were themselves evolutionary systems, with selection based upon the individual
systemsʼ capacities for OEE.
9 Dolson and her collaborators elaborated on these issues, including major transition potential, in a web article published after the
taller, https://thewinnower.com/papers/2309-what-s-holding-artificial-life-back-from-open-ended-evolution.
10 http://roomclip.jp/
Artificial Life Volume 22, Número 3
413
yo
D
oh
w
norte
oh
a
d
mi
d
F
r
oh
metro
h
t
t
pag
:
/
/
d
i
r
mi
C
t
.
metro
i
t
.
mi
d
tu
a
r
t
yo
/
/
yo
a
r
t
i
C
mi
–
pag
d
F
/
/
/
/
2
2
3
4
0
8
1
6
6
6
2
9
7
a
r
t
yo
/
_
a
_
0
0
2
1
0
pag
d
.
F
b
y
gramo
tu
mi
s
t
t
oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3
t. Taylor et al.
Evolución abierta: Perspectives from the OEE Workshop in York
tags to the photos to describe their content. By using a variety of mathematical techniques to analyze
the use and evolution of tags in the system over a period of three years, Ikegami argued that the
increasing vocabulary of tags observed over time generated a self-maintaining system in which
certain types of tags stimulate users to create new combinations, and which prompted users to
upload new photos to be annotated by those tags. Además, a phase-transition-like event
was observed in the systemʼs activity, involving a sudden increase both in typical social network
size and in tagging activity of each user. Based upon these observations, Ikegami and colleagues
hypothesize that OEE in such systems can be driven by the usersʼ collective activities. The work
discussed in this talk is described in more detail in a late-breaking paper presented at the main
ECAL 2015 conference [33].
Tom Froese began his talk, “Groundlessness Avoids Openness Reduction in Hierarchies of Emer-
gence,” with the observation that problems of OEE are of interest to origin-of-life researchers (incluso
if not being addressed by biologists more broadly, as claimed by Beslon). En particular, he highlighted
recent work by Peter Strazewski on evolution in chemical systems: Strazewski argues that OEE is
more likely if we move away from well-defined systems to messy systems with many possible variants
(in chemical composition, propiedad, reactivity, forma, tamaño, etc.) [46]. Froese attempted to formalize
this intuition by characterizing OEE in terms of a systemʼs emergence of new degrees of freedom
(DoFs). He argued that if emergence is defined as collective dynamics resulting from nonlinear cou-
pling between two or more components, then the number of DoFs of the emergent phenomena
cannot, in principle, be greater than the sum of the numbers of DoFs of its components. Froese
argued that this suggests that as we climb to higher hierarchical levels of complexity in a system, nosotros
inevitably witness a decrease in the number of DoFs of the system at those levels. This might not be
a problem in systems that have sufficient complexity (many DoFs) at the bottom level. Sin embargo, un
alternative approach to avoiding this limitation would be to assume there is no bottom level—that
the system is groundless (a line of thought inspired by Michel Bitbol [8]). Froese suggested that an
explanation of OEE in the real world might therefore require us to conceptualize reality as a ground-
less system.
3 Themes from the Open Discussions
Substantial time in the workshop was left for open discussion, and a couple of important con-
clusions emerged. One was pluralism about OEE. It seems clear that there is more than one inter-
esting and important kind of OEE. This means that those discussing OEE should whenever
possible be explicit and precise about the kind of OEE of interest. Every kind of OEE should
be identified and defined as precisely as possible, taking care not to lose those kinds that have in-
tuitive appeal but cannot be precisely defined. Each successful definition should be operational and
quantitative. But no definition is the one and only right definition of OEE if there is more than one
kind of OEE. Some people might be especially interested in one kind of OEE, and others in another
kind. Later in this section we attempt to identify the broad categories of kinds of OEE discussed at
the workshop.
“Open-ended evolution” refers to a distinctive kind of behavior exhibited by some evolving sys-
tems, and different kinds of OEE correspond to somewhat different kinds of behavior. The work-
shop discussion highlighted the importance of distinguishing observable behavioral hallmarks of
systems undergoing OEE from hypothesized underlying mechanisms that explain why a system exhibits
those hallmarks. These “mechanisms” might be merely causally necessary conditions for OEE, o
necessary boundary conditions. A sufficiently large population, or a sufficiently long duration, o un
sufficiently large evolutionary search space have all been proposed as necessary conditions for the
appearance of OEE. Perhaps no single mechanism is causally sufficient to produce OEE, but pre-
sumably each kind of OEE is produced by something like a set of individually necessary and jointly
sufficient mechanisms. Identifying these key mechanisms for (each kind of) OEE is the question
driving much of the research on OEE.
414
Artificial Life Volume 22, Número 3
yo
D
oh
w
norte
oh
a
d
mi
d
F
r
oh
metro
h
t
t
pag
:
/
/
d
i
r
mi
C
t
.
metro
i
t
.
mi
d
tu
a
r
t
yo
/
/
yo
a
r
t
i
C
mi
–
pag
d
F
/
/
/
/
2
2
3
4
0
8
1
6
6
6
2
9
7
a
r
t
yo
/
_
a
_
0
0
2
1
0
pag
d
.
F
b
y
gramo
tu
mi
s
t
t
oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3
t. Taylor et al.
Evolución abierta: Perspectives from the OEE Workshop in York
Both hallmarks of OEE and mechanisms for OEE are important, but they are important for
different reasons. The hallmarks identify the important distinctive observable signs of (diferente
kinds of) OEE. A given kind of OEE might have more than one behavioral hallmark, and different
kinds will have somewhat different hallmarks, so the list of hallmarks of OEE can be expected to be
somewhat heterogeneous.
The York workshop constructed an initial list of behavioral hallmarks of OEE. The focus was to
generate a comprehensive list, so possible dependences among the hallmarks were secondary. El
set of behavioral hallmarks of systems undergoing OEE emphasized in the workshop include these:
1. Ongoing adaptive novelty is one important kind of OEE. Novel adaptations come in many
kinds, including new properties of entities, new interactions among entities, and new global
patterns of behavior. In each case the focus is usually on the evolution of novel adaptations.
Here are examples of some specific kinds of adaptive novelties:
(a) Ongoing generation of new adaptations is a very simple kind of OEE, and detecting it was
the motivation for the original evolutionary activity statistics of Bedau and Packard [5,
4]. New adaptations could arise through a combination of different evolutionary and
ecological mechanisms, such as competitive exclusion, random drift among neutral
variants, and kin selection. Adaptation comes in different forms; Por ejemplo,
sometimes it is possible and important to distinguish new instances of a familiar kind
of adaptation from qualitatively new kinds of adaptations, and the ongoing generation
of qualitatively new kinds of adaptations is more interesting and more challenging
to understand. The ongoing generation of new adaptations might seem to involve
populations of agents with an unlimited number of different basic adaptive traits.
Sin embargo, practical considerations often impose a finite ceiling on the number of
different basic adaptations distinguished in computer models or natural systems.
Sin embargo, if evolution can produce finite combinations (conjuntos) of adaptive traits,
then the number of potential new adaptive combinations increases dramatically.
(b) Ongoing generation of new kinds of entities is one way to bring about the ongoing generation
of new kinds of adaptations. The emergence of dynamical hierarchies described
by Rasmussen et al. [38] is one mechanism for generating new kinds of entities with
new kinds of properties. Sin embargo, since the underlying mechanism in dynamical
hierarchies can be merely chemical and physical self-assembly and self-organization,
the properties of the entities at different levels in the hierarchy might not be
adaptations. But if a dynamical hierarchy incorporates new material and information
from the environment, and if the whole hierarchy can reproduce similar daughter
jerarquías, then adaptive evolution could arise and start to shape a population of new
kinds of entities.
(C) A major transition in evolution involves the emergence of a dynamical hierarchy and
does involve adaptive evolution, and ongoing major transitions in evolution constitute another
kind of OEE. The major transitions in evolution discussed by Maynard Smith and
Szathmáry [31] (and recently revisited by Szathmáry [47]) are an especially interesting
form of dynamical hierarchies, and they are special because each new level in the
hierarchy consists of a new population of reproducing and evolving entities. A major
transition in evolution is preceded by the evolution of one or several distinct kinds
of reproducing entity. Eventually certain groups of those entities come to interact very
tightly, and they become members of a new population of higher-level reproducing
wholes. Entities in the old lower-level population become parts of the new wholes,
but they cannot reproduce independently. Now the process repeats once more.
Certain groups in the population of new wholes come to interact very tightly, y ellos
become new even-higher-level wholes that reproduce and form an even-higher-level
Artificial Life Volume 22, Número 3
415
yo
D
oh
w
norte
oh
a
d
mi
d
F
r
oh
metro
h
t
t
pag
:
/
/
d
i
r
mi
C
t
.
metro
i
t
.
mi
d
tu
a
r
t
yo
/
/
yo
a
r
t
i
C
mi
–
pag
d
F
/
/
/
/
2
2
3
4
0
8
1
6
6
6
2
9
7
a
r
t
yo
/
_
a
_
0
0
2
1
0
pag
d
.
F
b
y
gramo
tu
mi
s
t
t
oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3
t. Taylor et al.
Evolución abierta: Perspectives from the OEE Workshop in York
población, etcétera. Maynard Smith and Szathmáry [31] conclude that the major
transitions in evolution they survey are quite contingent; they could easily not have
happened, and there may be no more major transitions.11 So, the existence of
some major transitions in evolution is not necessarily any kind of OEE. But major
transitions can spur many further adaptations and help make evolution open-ended.
And ongoing major transitions would be an especially impressive kind of OEE.12
(d) Since major transitions in evolution typically create new kinds of entities with new
kinds of adaptations, the transitions are one way in which the ability to evolve
can itself evolve. But there are many other ways in which the ability to evolve can
itself evolve. De este modo, the ongoing evolution of evolvability is another kind of OEE. Uno
especially critical step in the evolution of evolvability is the very first step: the emergence
of the ability to evolve at all (the subject of Packardʼs talk at the workshop).
2. Ongoing growth of complexity of an evolving system is another kind of OEE, and there are
at least two different kinds of complexity to distinguish.
(a) One focus is the complexity of the entities in an evolving population, and one kind of
OEE is the ongoing growth in complexity of entities in the evolving population. El
property of interest here is the complexity of the most complex entities, en vez de
entities with mean or modal complexity [18]. Further kinds of OEE involve ongoing
growth of other global properties of the evolving population, such as diversity or
disparity [17]. Note that growth of entity complexity is a side effect of major
transitions in evolution, when the old evolving entities become parts of the new ones.
But other mechanisms could also produce entities that are more and more complex.
(b) Another way in which an evolving system can become more complex is for the interactions
among the entities in it to become more complex. Ongoing growth of complexity of
interactions among entities is another kind of OEE. Even if the internal properties of
the entities in a system remain the same, the interactions among entities can become
more and more complex, as when food webs among species become more complex.
yo
D
oh
w
norte
oh
a
d
mi
d
F
r
oh
metro
h
t
t
pag
:
/
/
d
i
r
mi
C
t
.
metro
i
t
.
mi
d
tu
a
r
t
yo
/
/
yo
a
r
t
i
C
mi
–
pag
d
F
/
/
/
/
2
2
3
4
0
8
1
6
6
6
2
9
7
a
r
t
yo
/
The emphasis on “ongoing” novelty itself deserves a brief mention. “Ongoing” is better than
another common expression used in this context—“perpetual” novelty—because OEE is not actu-
ally perpetual although it is ongoing. The discussion in York focused partly on David Ackleyʼs idea
of indefinite scalability, after this concept was emphasized in his talk. Ackley [2] defines indefinite
scalability “as supporting open-ended computational growth without requiring substantial re-
engineering.” The key criteria for indefinite scalability is that, should an upper bound be reached
(p.ej., in the number of novel entities encountered over the course of evolution or in the diversity
or complexity of entities), increasing the values of physical limits (p.ej., available matter, población
tamaño, or memory) should enable an unbounded sequence of greater upper bounds to be achieved
(after sufficient increases in the limits). Sin embargo, it is not possible in finite system time to establish
that a metric is truly unbounded.13 And it is not possible—over a finite number of increases in
system parameter(s)—to establish that a metric is truly indefinitely scalable. Más, an increase
in parameter(s) may require a longer system time before a greater scale (higher value metric) es
_
a
_
0
0
2
1
0
pag
d
.
F
b
y
gramo
tu
mi
s
t
t
oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3
11 For alternative perspectives on major transitions, see also [12].
12 A pesar de, as discussed in several of the talks, each successive major transition produces organisms requiring more resources and
existing in smaller populations. Por eso, there is an inevitable limit in any finite system on the extent to which the occurrence of successive
major transitions can be ongoing.
13 Some workshop participants (p.ej., McGregor ) felt that a sensible null hypothesis might be to assume that ALife systems were
unbounded by default, eso es, they might just need more time and larger environments to display OEE (an additional complicating factor
here is the role of contingency in determining the outcome of any one specific run [52]). The research program then becomes a matter of
identifying reasons why this might not be the case—a view that resonates with Dolsonʼs talk. Por otro lado, otros (including various
other speakers discussed in Section 2) felt that these systems were missing important enabling conditions for OEE.
416
Artificial Life Volume 22, Número 3
t. Taylor et al.
Evolución abierta: Perspectives from the OEE Workshop in York
logrado. Claims about systems can, aunque, be expressed and evaluated in terms such as a metric
increasing without bound up to a certain system time (or number of generations, etc.); or a metric
increasing as system parameter(s) are increased up to certain value(s), where it was necessary to
increase these to establish increases in the metricʼs maximum observed value over successive runs.
Además, we can define boundedness of a metric within a system in a rigorous way by fitting
mathematical functions to the data and using statistics to ascertain which function is the best fit
(p.ej., ver [57]). If the best-fit function is unbounded, that is a good indication that the system is
exhibiting unbounded behavior.
Clarifying the hallmarks of OEE is a crucial step in clearly identifying and distinguishing the
different kinds of OEE. After the hallmarks are clear, another crucial step is identifying and testing
possible mechanisms that would produce and explain each of the kinds of OEE. Different mecha-
nisms might be proposed to produce or explain a given kind of OEE; the mechanisms could provide
competing explanations, or they could provide cooperating mechanisms. También, a single mechanism
might be involved in the explanation of more than one kind of OEE. So, the list of hypothetical
mechanisms for a given kind of OEE could be rather heterogeneous. Además, OEE pluralism
means that different kinds of OEE could have different underlying mechanisms. Some mechanisms
might be necessary for one kind but not another kind of OEE; other mechanisms might be neces-
sary for every kind.
The discussion in York was weighted towards hallmarks of OEE, but some mechanisms for
OEE were also mentioned and discussed. Por ejemplo, one might think that the evolution of
the genetic code is the mechanism behind OEE. Certain mechanisms are very obvious, but often
insufficient by themselves. Por ejemplo, since OEE involves adaptive evolution, natural selection
helps explain it, and we already know a lot about evolution by natural selection. The participants in
the discussion were divided about whether we already know enough to explain each kind of OEE,
with some conjecturing that a fundamentally new mechanism is required for some kinds of OEE,
such as major transitions in evolution.
Note that major transitions, the evolution of the genetic code, and the evolution of evolvability in
general, are both kinds of OEE and mechanisms for kinds of OEE. This shows how one and the same
thing can appear on the lists of both hallmarks of (one kind of) OEE and mechanisms for (otro
kind of ) OEE.
An important research goal is to document examples of each hallmark and requirement of OEE,
both in computer models and in natural systems. Positive examples that demonstrate a kind of OEE
in a model or natural system are especially critical, but also important are negative examples of model
or natural systems that do not demonstrate some kind of OEE.
Open-ended evolution is an ongoing process, so a single instance of the behavioral hallmarks of
OEE falls short of being genuinely open-ended. A single new adaptation is not OEE, neither is the
growth in complexity of one organism, nor one instance of the evolution of evolvability, nor one
major transition in evolution. Sin embargo, it can be a significant scientific achievement to document
even single instances of some especially challenging hallmarks, such as major transitions in evolution.
The following list summarizes the specific systems described by the speakers and participants in
york (and some closely related systems) and the claims made of them regarding OEE:14
(cid:129) Earthʼs biosphere has been classified, through fossil data sets at the level of taxonomic families,
as exhibiting open-ended evolutionary dynamics according to Bedau and Packardʼs
evolutionary activity measures [6, 7]. Bedau et al. reasoned that it was not necessary to
include a shadow mechanism in this analysis, as “the mere fact that a family appears in the
fossil record is good evidence that its persistence reflects its adaptive significance” [7].
14 While this report focuses on research discussed during the York workshop, a task for future work is to compile a more compre-
hensive list of achievements in OEE to date. Such a list is planned to form part of a comprehensive review paper on OEE to be produced
after the OEE2 workshop.
Artificial Life Volume 22, Número 3
417
yo
D
oh
w
norte
oh
a
d
mi
d
F
r
oh
metro
h
t
t
pag
:
/
/
d
i
r
mi
C
t
.
metro
i
t
.
mi
d
tu
a
r
t
yo
/
/
yo
a
r
t
i
C
mi
–
pag
d
F
/
/
/
/
2
2
3
4
0
8
1
6
6
6
2
9
7
a
r
t
yo
/
_
a
_
0
0
2
1
0
pag
d
.
F
b
y
gramo
tu
mi
s
t
t
oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3
t. Taylor et al.
Evolución abierta: Perspectives from the OEE Workshop in York
(cid:129) The Long-Term Evolution Experiment (LTEE) (Lenski et al.). The LTEE [28] is the most
extensive laboratory study of ongoing biological evolution. Publications from this highly
tractable system exhibit dynamics in evolving populations of E. coli that appear to be
open-ended. Específicamente, the LTEE has shown continuous increases in fitness that are best
described by an unbounded power law function [57, 29]. Individual populations have shown
continuous generation of novelty, como: new portions of the fitness landscape continually
being explored [53], numerous selective sweeps [30], new diversity arising after sweeps [9], y
epistatic interactions among mutations where later benefits depend upon earlier mutations [56].
Finalmente, multiple populations in the LTEE exhibit frequency dependence [42, 24, 41, 30],
including a special case [10, 9, 54] shown to be driven by ecological specialization and
crossfeeding [55]. Most prominently, the LTEE gained substantial attention when a
drastically new phenotype appeared, giving rise to what amounted to a new species [10, 9].
(cid:129) Tierra (Rayo). Tierra [39] is perhaps the most well-known example of an early ALife system in
which digital organisms—self-replicating computer programs—were free to evolve in
an open-ended manner without the guidance of an explicit fitness function. Sin embargo,
each particular run of the system would eventually reach a state of stasis where only
selectively neutral variations were seen to emerge [40, 49]. Bedau and colleagues analyzed
the dynamics of a Tierra-like system named Evita (but not Tierra itself), and found it
to have qualitatively different evolutionary dynamics from those displayed in biological
evolution as evidenced by the fossil record [6].15
(cid:129) Avida (Ofria et al.). Avida [32] is currently the most widely used digital evolution system, y
is used to study a wide range of evolutionary and ecological dynamics in populations
of self-replicating computer programs. Avida has enabled the evolution of qualitatively novel
behaviors such as complex features completely absent in the ancestor organism (ongoing
generation of new adaptations) [27], novel collaboration strategies among organisms
(ongoing growth of complexity of interactions) [16], and novel ecological interactions
through coevolution promoting even greater levels of complexity [58]. Dolson and
collaborators are actively testing their complexity barriers in this system, as well as analyzing
evolutionary activity statistics. Initial results of the boundedness of fitness growth in simple
to complex environments in Avida indicate that fitness continues to increase without an
asymptote in the default environment. Many ongoing projects use Avida to evolve
cooperation, ecosystems, sexual reproduction, parasitism/mutualism, pleiotropy,
intelligence, evolvability, and complexity.
(cid:129) Geb (Channon). Geb was the first ALife system to be classified as exhibiting openended
evolutionary dynamics according to Bedau and Packardʼs evolutionary activity measures [7]
and is the only one to have been classified as such according to an enhanced version of
these measures developed by Channon [14, 15].16 Novel adaptations reported in Geb
include behaviors such as following, lucha, fleeing, mimicking, and novel artifacts such as
matching I&O channels in agentsʼ neurocontrollers. Preliminary (unpublished) resultados
presented at the Artificial Life XI conference in 2008 further indicated that component
diversity (a simple measure of system complexity) may be indefinitely scalable (although that
term was not yet in use); a more complete study of this is now planned.
(cid:129) Pichler ʼs computational ecosystem [35, 36, 34] is the only other ALife system to date to have been
classified as exhibiting open-ended evolutionary dynamics according to Bedau and Packardʼs
15 Específicamente, they found that the Evita model was bounded in their component diversity measure, whereas the fossil data was unbounded.
In a separate analysis, Taylor also found that his Cosmos system—an elaboration of the basic Tierra design—also showed evolutionary
dynamics similar to those of Evita [48, páginas. 122–127].
16 The enhanced versions of the measures were found by Stout and Spector [45] to be of particular importance to the testʼs robustness
against attempts to “break” the test by achieving unbounded dynamics in “intuitively unlifelike” systems.
418
Artificial Life Volume 22, Número 3
yo
D
oh
w
norte
oh
a
d
mi
d
F
r
oh
metro
h
t
t
pag
:
/
/
d
i
r
mi
C
t
.
metro
i
t
.
mi
d
tu
a
r
t
yo
/
/
yo
a
r
t
i
C
mi
–
pag
d
F
/
/
/
/
2
2
3
4
0
8
1
6
6
6
2
9
7
a
r
t
yo
/
_
a
_
0
0
2
1
0
pag
d
.
F
b
y
gramo
tu
mi
s
t
t
oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3
t. Taylor et al.
Evolución abierta: Perspectives from the OEE Workshop in York
evolutionary activity measures. A lo mejor de nuestro conocimiento, it has not been subjected to
the enhanced test.
(cid:129) Stringmol (Hickinbotham et al.). Stringmol is an artificial chemistry system that has been
shown to exhibit the ongoing appearance of new chemical species [21]. In some cases the
system has been shown to evolve multi-species hypercycles that persist for prolonged
periods. De este modo, Stringmol demonstrates the ongoing generation of new adaptations. Estos
adaptations affect a speciesʼ binding affinity to other species, as well as its reaction rules.
Quantitative novelties are certainly arising in the system (p.ej., in binding affinities), a pesar de
it is yet to be established whether any qualitative novelties are arising at the level of the
individual chemical species. The appearance of hypercycles also demonstrates growth of
complexity of interactions, and a qualitatively new organization of the system.
(cid:129) Novelty search (Lehman y Stanley). Mentioned in Dolsonʼs talk, Lehman and Stanleyʼs
novelty search technique has attracted considerable interest in recent years [25, 26]. El
approach has been shown to generate ongoing generation of new adaptations. Sin embargo, este
is achieved by employing a selection mechanism that specifically looks for novel phenotypes.
Por eso, by design the approach will produce the hallmark of ongoing generation of new
adaptations if (and only if) the system has implemented the necessary mechanisms for the
ongoing generation of such adaptations. Novelty search, by itself, does not take a stand
on what kinds of mechanisms are required. Además, it requires a measure of phenotypic
distance between individuals to tell whether two individuals exhibit sufficiently different
behaviors. Like a fitness function in traditional EAs, this definition of phenotypic uniqueness
needs to be carefully chosen. Defining a more general measure, applicable to OEE, appears to
be a major research challenge, but potentially a rewarding one. Further work is required
to understand the similarities and differences between novelty search and OEE; one line
of research along these lines has recently been initiated by Soros and Stanley [44].
(cid:129) Dynamical hierarchies (Rasmussen et al.). Rasmussen and colleagues reported results in a model of
a physicochemical system that exhibited dynamical hierarchies. They demonstrated the
emergence of two higher orders of entities and interactions on top of the basic first-order
elements built into the system. This work was based upon a model of self-assembly rather
than evolution; to be of direct relevance to OEE it would need to be augmented with
mechanisms for self-replication, variación, and selection of the emergent dynamical hierarchies.
Enabling populations of newly emerging dynamical hierarchies to undergo adaptive evolution
would unify the processes of self-assembly and self-organization with the process of
adaptive evolution, and this could explain one kind of novelty in OEE: the evolution of
new kinds of wholes with new kinds of properties. In this context one mechanism driving
the ongoing generation of novelty is the availability in the environment of new materials
that can aggregate and generate novel properties.
(cid:129) Emergence of coding (Packard and Guttenberg). In his talk, Packard described preliminary work
on a model in which alternation between unstable and fixed-point dynamics produced
conditions suitable for the emergence of informationally-stable components. Mientras
preliminary, the results have relevance for the pre-biotic transition to information-driven
sistemas. Packard reported that colleagues are applying these ideas to models of other
major evolutionary transitions too. This work has not yet been published.
(cid:129) Patented technology (Bedau et al.). Bedau suggested that the actual evolution of technology
(detected in the patent record) is a real-world system that exhibits a form of OEE that
he termed ongoing door-opening evolution [11], which occurs when one technological
innovation enables a whole new kind of technology to arise and diversify. Bedau conjectured
that door-opening innovations are an important mechanism behind the ongoing generation
of new kinds of adaptations, and he proposed some first steps to observing and measuring
door-opening innovations in the patent record.
Artificial Life Volume 22, Número 3
419
yo
D
oh
w
norte
oh
a
d
mi
d
F
r
oh
metro
h
t
t
pag
:
/
/
d
i
r
mi
C
t
.
metro
i
t
.
mi
d
tu
a
r
t
yo
/
/
yo
a
r
t
i
C
mi
–
pag
d
F
/
/
/
/
2
2
3
4
0
8
1
6
6
6
2
9
7
a
r
t
yo
/
_
a
_
0
0
2
1
0
pag
d
.
F
b
y
gramo
tu
mi
s
t
t
oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3
t. Taylor et al.
Evolución abierta: Perspectives from the OEE Workshop in York
(cid:129) Social media tags (Ikegami). In his talk, Ikegami argued that the social media tag system
he described represented an example of OEE. With respect to the evolution of new
combinations of tags, this would be ongoing generation of new adaptations (at a quantitative
nivel). The role of human users as an integral part of the system, who both supply the new
tags and upload new images to be tagged, is a complicating factor in this case.
Of the example systems discussed by the speakers in York, many focused on the ongoing generation
of quantitatively new adaptations, where “quantitatively new” means that the adaptations are novel, pero
identifiable within a determined class of possibilities, and as a result of their identifiability, they may be
statistically quantified. A diferencia de, qualitatively new adaptations lie outside any predetermined class of
possibilities. It is clear that qualitatively new adaptations are a part of natural evolutionary processes, pero
less clear whether and how they might occur in example systems considered so far—indeed, clearer
criteria are required for what counts as qualitative, rather than quantitative, novelty in these systems.
Sharpening this distinction should lead toward progress in understanding how open-ended evolution
manifests properties such as growth of complexity of interactions, ongoing generation of new entities,
ongoing generation of new functionalities, and major evolutionary transitions.
4 Conclusión
The workshop in York closed with a better appreciation of what remains to be learned about open-
ended evolution, and a clearer picture of the most important open research questions about OEE.
These include:
1. What is the best way to categorize, define, and operationally observe each kind of OEE,
and how are the different kinds related?
2. What are the most important candidate mechanisms (or necessary conditions) para
producing each kind of OEE? Which mechanisms are most plausible?
3. Which kinds of OEE can be demonstrated in specific systems, including analytical models,
computer models, laboratory experimental systems, or natural biological communities?
What has already been shown in each type of system?
The state of the art on these and other fundamental questions about OEE are the focus of the
Second Workshop on Open-Ended Evolution (OEE2),17 to be held at the ALIFE XV conference in
Cancún, Mexico in July 2016.
Expresiones de gratitud
We gratefully acknowledge use of Overleaf, the free, online collaborative LaTeX authoring tool
(https://www.overleaf.com/), in preparing the draft of this paper.
Referencias
1. Ackley, D. h., & Ackley, mi. S. (2015). Artificial life programming in the robust-first attractor. In P. Andrews,
l. Caves, R. Doursat, S. h. F. Polack, S. Stepney, t. taylor, & j. Timmis (Editores.), Actas de la
European Conference on Artificial Life 2015 (páginas. 554–561). Cambridge, MAMÁ: CON prensa.
2. Ackley, D. h., & Pequeño, t. R. (2014). Indefinitely scalable computing = artificial life engineering.
En H. Sayama, j. Rieffel, S. Risi, R. Doursat, & h. Lipson (Editores.), Artificial life 14: Actas de
the Fourteenth International Conference on the Synthesis and Simulation of Living Systems (páginas. 606–613). Cambridge,
MAMÁ: CON prensa.
3. Banzhaf, w., Baumgaertner, B., Beslon, GRAMO., Doursat, r., Foster, j. A., McMullin, B., de Melo,
V. v., Miconi, T., Spector, l., Stepney, S., & Blanco, R. (2016). Defining and simulating open-ended
novelty: Requirements, pautas, and challenges. Theory in Biosciences (in press).
17 http://alife.org/ws/oee2
420
Artificial Life Volume 22, Número 3
yo
D
oh
w
norte
oh
a
d
mi
d
F
r
oh
metro
h
t
t
pag
:
/
/
d
i
r
mi
C
t
.
metro
i
t
.
mi
d
tu
a
r
t
yo
/
/
yo
a
r
t
i
C
mi
–
pag
d
F
/
/
/
/
2
2
3
4
0
8
1
6
6
6
2
9
7
a
r
t
yo
/
_
a
_
0
0
2
1
0
pag
d
.
F
b
y
gramo
tu
mi
s
t
t
oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3
t. Taylor et al.
Evolución abierta: Perspectives from the OEE Workshop in York
4. Bedau, METRO. A., & Marrón, C. t. (1999). Visualizing evolutionary activity of genotypes. Artificial Life,
5(1), 17–35.
5. Bedau, METRO. A., & Packard, norte. h. (1991). Measurement of evolutionary activity, teleology, y la vida.
In C. Langton, C. taylor, D. Farmer, & S. Rasmussen (Editores.), Artificial life II, volumen. X of Santa Fe Institute
Studies in the Sciences of Complexity (páginas. 431–461). Bostón, MAMÁ: Addison-Wesley.
6. Bedau, METRO. A., Snyder, MI., Marrón, C. T., & Packard, norte. h. (1997). A comparison of evolutionary activity
in artificial evolving systems and in the biosphere. In P. Husbands & I. harvey (Editores.), Actas de la
Fourth European Conference on Artificial Life (páginas. 125–134). Cambridge, MAMÁ: CON prensa.
7. Bedau, METRO. A., Snyder, MI., & Packard, norte. h. (1998). A classification of long-term evolutionary dynamics.
In C. Adami, R. k. Belew, h. Kitano, & C. mi. taylor (Editores.), Artificial life VI: Proceedings of the Sixth International
Conference on Artificial Life (páginas. 228–237). Cambridge, MAMÁ: CON prensa.
8. Bitbol, METRO. (2007). Ontology, matter and emergence. Phenomenology and the Cognitive Sciences, 6(3), 293–307.
9. Blount, z. D., Barrick, j. MI., Davidson, C. J., & Lenski, R. mi. (2012). Genomic analysis of a key innovation
in an experimental Escherichia coli population. Naturaleza, 489(7417), 513–518. http://dx.doi.org/10.1038/
nature11514.
10. Blount, z. D., Borland, C. Z., & Lenski, R. mi. (2008). Historical contingency and the evolution of a
key innovation in an experimental population of Escherichia coli. Proceedings of the National Academy of Sciences of
the USA, 105(23), 7899–7906. http://www.pnas.org/content/105/23/7899.abstract.
11. Buchanan, A., Packard, norte. h., & Bedau, METRO. A. (2011). Measuring the evolution of the drivers of
technological innovation in the patent record. Artificial Life, 17(2), 109–122.
12. Calcott, B., & Sterelny, k. (Editores.) (2011). The major transitions in evolution revisited. Cambridge, MAMÁ: CON prensa.
13. Chalmers, D., Francisco, C. C., Pepper, NORTE., & Bedau, METRO. A. (2010). High-content words in patent records
reflect key innovations in the evolution of technology. En H. Fellermann, METRO. Dörr, METRO. METRO. Hanczy,
l. l. Laursen, S. Maurer, D. Merkle, P.-A. Monnard, k. Støy, & S. Rasmussen (Editores.), Artificial life XII:
Proceedings of the Twelfth International Conference on the Synthesis and Simulation of Living Systems (páginas. 838–845).
Cambridge, MAMÁ: CON prensa.
14. Channon, A. (2003). Improving and still passing the ALife test: Component-normalised activity statistics
classify evolution in Geb as unbounded. En R. k. Standish, METRO. A. Bedau, & h. A. Abbass (Editores.), Artificial life
VIII: Proceedings of the Eighth International Conference on Artificial Life (páginas. 173–181). Cambridge, MAMÁ:
CON prensa.
15. Channon, A. (2006). Unbounded evolutionary dynamics in a system of agents that actively process and
transform their environment. Genetic Programming and Evolvable Machines, 7(3), 253–281. http://dx.doi.org/
10.1007/s10710-006-9009-3.
16. Goldsby, h. J., Dornhaus, A., Kerr, B., & Ofria, C. (2012). Task-switching costs promote the evolution
of division of labor and shifts in individuality. Proceedings of the National Academy of Sciences of the USA,
109(34), 13686–13691.
17. Gould, S. j. (1989). Wonderful life: The Burgess Shale and the nature of history. Nueva York: W.. W.. norton.
18. Gould, S. j. (1996). Full house: The spread of excellence from Plato to Darwin. Nueva York: Harmony Books.
(Published in the UK under the title Lifeʼs Grandeur.)
19. harvey, I. (2001). Artificial evolution: A continuing saga. In T. Gomi (Ed.), Evolutionary robotics. De
intelligent robotics to artificial life, volumen. 2217 of Lecture Notes in Computer Science (páginas. 94–109). Berlina: Saltador.
http://dx.doi.org/10.1007/3-540-45502-7_5.
20. Hickinbotham, S., clark, MI., Nellis, A., Stepney, S., Clarke, T., & Joven, PAG. (2016). Maximizing the
adjacent possible in automata chemistries. Artificial Life, 22(1), 49–75.
21. Hickinbotham, S., clark, MI., Stepney, S., Clarke, T., Nellis, A., Pay, METRO., & Joven, PAG. (2010). Diversity from
a monoculture: Effects of mutation-on-copy in a string-based artificial chemistry. En H. Fellermann, METRO.
Dörr, METRO. METRO. Hanczy, l. l. Laursen, S. Maurer, D. Merkle, P.-A. Monnard, k. Støy, & S. Rasmussen (Editores.),
Artificial life XII: Proceedings of the Twelfth International Conference on the Synthesis and Simulation of Living Systems
(páginas. 24–31). Cambridge, MAMÁ: CON prensa.
22. Hickinbotham, S., & Stepney, S. (2015). Conservation of matter increases evolutionary activity. In P. Andrews,
l. Caves, R. Doursat, S. h. F. Polack, S. Stepney, t. taylor, & j. Timmis (Editores.), Proceedings of the European
Conference on Artificial Life 2015 (páginas. 98–105). Cambridge, MAMÁ: CON prensa.
Artificial Life Volume 22, Número 3
421
yo
D
oh
w
norte
oh
a
d
mi
d
F
r
oh
metro
h
t
t
pag
:
/
/
d
i
r
mi
C
t
.
metro
i
t
.
mi
d
tu
a
r
t
yo
/
/
yo
a
r
t
i
C
mi
–
pag
d
F
/
/
/
/
2
2
3
4
0
8
1
6
6
6
2
9
7
a
r
t
yo
/
_
a
_
0
0
2
1
0
pag
d
.
F
b
y
gramo
tu
mi
s
t
t
oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3
t. Taylor et al.
Evolución abierta: Perspectives from the OEE Workshop in York
23. Hickinbotham, S., & Stepney, S. (2015). Environmental bias forces parasitism in Tierra. In P. Andrews,
l. Caves, R. Doursat, S. h. F. Polack, S. Stepney, t. taylor, & j. Timmis (Editores.), Proceedings of the European
Conference on Artificial Life 2015 (páginas. 294–301). Cambridge, MAMÁ: CON prensa.
24. Le Gac, METRO., Plucain, J., Hindré, T., Lenski, R. MI., & Schneider, D. (2012). Ecological and evolutionary
dynamics of coexisting lineages during a long-term experiment with Escherichia coli. Actas de la
National Academy of Sciences of the USA, 109(24), 9487–9492. http://www.pnas.org/content/109/24
/9487.abstract.
25. Lehman, J., & Stanley, k. oh. (2008). Exploiting open-endedness to solve problems through the search
for novelty. In S. Bullock, j. Noble, R. A. watson, & METRO. A. Bedau (Editores.), Artificial life XI: Actas de la
Eleventh International Conference on the Simulation and Synthesis of Living Systems. Cambridge, MAMÁ: CON prensa.
26. Lehman, J., & Stanley, k. oh. (2011). Abandoning objectives: Evolution through the search for novelty
solo. Computación evolutiva, 19(2), 189–223. http://www.mitpressjournals.org/doi/pdf/10.1162/
EVCO_a_00025.
27. Lenski, R. MI., Ofria, C., Pennock, R. T., & Adami, C. (2003). The evolutionary origin of complex features.
Naturaleza, 423(6936), 139–144. http://www.nature.com/nature/journal/v423/n6936/abs/nature01568.
html.
28. Lenski, R. MI., Rose, METRO. r., Simpson, S. C., & Tadler, S. C. (1991). Long-term experimental evolution
in Escherichia coli. I. Adaptation and divergence during 2,000 generaciones. The American Naturalist, 138(6),
1315–1341. http://www.jstor.org/stable/2462549.
29. Lenski, R. MI., Wiser, METRO. J., Ribeck, NORTE., Blount, z. D., Nahum, j. r., morris, j. J., Zaman, l., Tornero,
C. B., Wade, B. D., Maddamsetti, r., Burmeister, A. r., Baird, mi. J., Bundy, J., Grant, norte. A., Card,
k. J., Rowles, METRO., Weatherspoon, K., Papoulis, S. MI., sullivan, r., clark, C., Mulka, j. S., & Hajela,
norte. (2015). Sustained fitness gains and variability in fitness trajectories in the Long-Term Evolution
Experiment with Escherichia coli. Proceedings of the Royal Society of London B: Ciencias Biologicas, 282(1821).
http://rspb.royalsocietypublishing.org/content/282/1821/20152292.abstract.
30. Maddamsetti, r., Lenski, R. MI., & Barrick, j. mi. (2015). Adaptation, clonal interference, and frequency-
dependent interactions in a long-term evolution experiment with Escherichia coli. Genetics, 200(2), 619–631.
http://www.genetics.org/content/200/2/619.abstract.
31. Maynard Smith, J., & Szathmáry, mi. (1995). The major transitions in evolution. Oxford, Reino Unido: Universidad de Oxford
Prensa.
32. Ofria, C., & Wilke, C. oh. (2004). Avida: A software platform for research in computational evolutionary
biology. Artificial Life, 10(2), 191–229. http://dx.doi.org/10.1162/106454604773563612.
33. Oka, METRO., Hashimoto, y., & Ikegami, t. (2015). An open-ended evolution in a web system. In Late breaking
papers at the European Conference on Artificial Life. https://www.cs.york.ac.uk/nature/ecal2015/paper-159.
html.
34. Pichler, P.-P. (2009). Natural selection, adaptive evolution and diversity in computational ecosystems. Doctor. tesis,
University of Hertfordshire.
35. Pichler, P.-P., & Canamero, l. (2007). An evolving ecosystems approach to generating complex agent
behaviour. In Proceedings of the First IEEE Symposium on Artificial Life (páginas. 303–310). Nueva York: IEEE.
36. Pichler, P.-P., & Canamero, l. (2008). Evolving morphological and behavioral diversity without predefined
behavior primitives. In S. Bullock, j. Noble, R. A. watson, & METRO. A. Bedau (Editores.), Artificial life XI: Actas
of the Eleventh International Conference on the Simulation and Synthesis of Living Systems (páginas. 404–411). Cambridge,
MAMÁ: CON prensa.
37. Popper, k. (1972). Objective knowledge: An evolutionary approach. Oxford, Reino Unido: prensa de la Universidad de Oxford.
38. Rasmussen, S., Baas, norte. A., Mayer, B., Nilsson, METRO., & Olesen, METRO. W.. (2001). Ansatz for dynamical
jerarquías. Artificial Life, 7(4), 329–353.
39. Rayo, t. S. (1991). An approach to the synthesis of life. In C. GRAMO. Langton, C. taylor, D. Farmer, &
S. Rasmussen (Editores.), Artificial life II (páginas. 371–408). Bostón, MAMÁ: Addison-Wesley.
40. Rayo, t. S. (1992). Evolución, ecology and optimization of digital organisms. Reporte técnico 92-08-942. Santa Fe Institute.
41. Ribeck, NORTE., & Lenski, R. mi. (2015). Modeling and quantifying frequency-dependent fitness in microbial
populations with cross-feeding interactions. Evolución, 69(5), 1313–1320. http://dx.doi.org/10.1111/
evo.12645.
422
Artificial Life Volume 22, Número 3
yo
D
oh
w
norte
oh
a
d
mi
d
F
r
oh
metro
h
t
t
pag
:
/
/
d
i
r
mi
C
t
.
metro
i
t
.
mi
d
tu
a
r
t
yo
/
/
yo
a
r
t
i
C
mi
–
pag
d
F
/
/
/
/
2
2
3
4
0
8
1
6
6
6
2
9
7
a
r
t
yo
/
_
a
_
0
0
2
1
0
pag
d
.
F
b
y
gramo
tu
mi
s
t
t
oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3
t. Taylor et al.
Evolución abierta: Perspectives from the OEE Workshop in York
42. Rozen, D. MI., & Lenski, R. mi. (2000). Long-term experimental evolution in escherichia coli. VIII. Dynamics
of a balanced polymorphism. The American Naturalist, 155(1), 24–35. http://www.jstor.org/stable/
10.1086/303299.
43. Skusa, A., & Bedau, METRO. A. (2003). Towards a comparison of evolutionary creativity in biological and
cultural evolution. En R. k. Standish, METRO. A. Bedau, & h. A. Abbass (Editores.), Artificial life VIII: Actas
of the Eighth International Conference on Artificial Life (páginas. 233–242). Cambridge, MAMÁ: CON prensa.
44. Soros, l. B., & Stanley, k. oh. (2014). Identifying necessary conditions for open-ended evolution through
the artificial life world of chromaria. En H. Sayama, j. Rieffel, S. Risi, R. Doursat, & h. Lipson (Editores.),
Artificial life 14: Proceedings of the Fourteenth International Conference on the Synthesis and Simulation of Living Systems
(páginas. 793–800). Cambridge, MAMÁ: CON prensa.
45. Stout, A., & Spector, l. (2005). Validation of evolutionary activity metrics for long-term evolutionary
dinámica. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2005) (páginas. 137–142).
Berlina: Saltador.
46. Strazewski, PAG. (2015). Omne vivum ex vivo … omne? How to feed an inanimate evolvable chemical system
so as to let it self-evolve into increased complexity and life-like behaviour. Israel Journal of Chemistry, 55(8),
851–864.
47. Szathmáry, mi. (2015). Toward major evolutionary transitions theory 2.0. Actas de la Academia Nacional de
Sciences of the USA, 112(33), 10104–10111. http://www.pnas.org/content/112/33/10104.abstract.
48. taylor, t. (1999). From artificial evolution to artificial life. Doctor. tesis, Division of Informatics, Universidad de
Edimburgo. Available online at http://www.tim-taylor.com/papers/thesis/.
49. taylor, t. (2013). Evolution in virtual worlds. En m. Grimshaw (Ed.), The Oxford handbook of virtuality (cap. 32).
Oxford, Reino Unido: prensa de la Universidad de Oxford.
50. taylor, t. (2015). Requirements for open-ended evolution in natural and artificial systems. In EvoEvo
Workshop at the 13th European Conference on Artificial Life (ECAL 2015), University of York, Reino Unido. http://arxiv.org/
abs/1507.07403.
51. taylor, T., Dorin, A., & Cesta, k. (2014). Digital genesis: Computadoras, evolution and artificial life. In 7th
Munich–Sydney–Tilburg Philosophy of Science Conference: Evolutionary thinking. http://arxiv.org/abs/1512.02100.
52. taylor, T., & Hallam, j. (1998). Replaying the tape: An investigation into the role of contingency in
evolution. In C. Adami, R. k. Belew, h. Kitano, & C. mi. taylor (Editores.), Artificial life VI: Actas de la
Sixth International Conference on Artificial Life (páginas. 256–265). Cambridge, MAMÁ: CON prensa.
53. Tenaillon, o., Barrick, j. MI., Ribeck, NORTE., Deatherage, D. MI., Blanchard, j. l., Dasgupta, A., Wu, GRAMO. C.,
Wielgoss, S., Cruveiller, S., Medigue, C., Schneider, D., & Lenski, R. mi. (2016). Tempo and mode of
genome evolution in a 50,000-generation experiment. bioRviv. http://biorxiv.org/content/early/2016/01/
15/036806.abstract.
54. Tornero, C. B., Blount, z. D., & Lenski, R. mi. (2015). Replaying evolution to test the cause of extinction of
one ecotype in an experimentally evolved population. PLoS ONE, 10(11), e0142050.
55. Tornero, C. B., Blount, z. D., mitchell, D. h., & Lenski, R. mi. (2015). Evolution and coexistence in
response to a key innovation in a long-term evolution experiment with Escherichia coli. bioRviv. http://
www.biorxiv.org/content/early/2015/06/17/020958.abstract.
56. Wielgoss, S., Barrick, j. MI., Tenaillon, o., Wiser, METRO. J., Dittmar, W.. J., Cruveiller, S., Chane-Woon-Ming, B.,
Médigue, C., Lenski, R. MI., & Schneider, D. (2013). Mutation rate dynamics in a bacterial population reflect
tension between adaptation and genetic load. Proceedings of the National Academy of Sciences of the USA, 110(1),
222–227. http://www.pnas.org/content/110/1/222.abstract.
57. Wiser, METRO. J., Ribeck, NORTE., & Lenski, R. mi. (2013). Long-term dynamics of adaptation in asexual populations.
Ciencia, 342(6164), 1364–1367. http://www.sciencemag.org/content/342/6164/1364.abstract.
58. Zaman, l., Meyer, j. r., Devangam, S., Bryson, D. METRO., Lenski, R. MI., & Ofria, C. (2014). Coevolution
drives the emergence of complex traits and promotes evolvability. PLoS Biol, 12(12), e1002023.
Artificial Life Volume 22, Número 3
423
yo
D
oh
w
norte
oh
a
d
mi
d
F
r
oh
metro
h
t
t
pag
:
/
/
d
i
r
mi
C
t
.
metro
i
t
.
mi
d
tu
a
r
t
yo
/
/
yo
a
r
t
i
C
mi
–
pag
d
F
/
/
/
/
2
2
3
4
0
8
1
6
6
6
2
9
7
a
r
t
yo
/
_
a
_
0
0
2
1
0
pag
d
.
F
b
y
gramo
tu
mi
s
t
t
oh
norte
0
7
S
mi
pag
mi
metro
b
mi
r
2
0
2
3
Descargar PDF