Luisa Damiano* - Specialized Research AI at MIT

Luisa Damiano*
IULM University
Research Group on the
Epistemology of the Sciences
of the Artiﬁcial
Department of Communication,
Arts, and Media
luisa.damiano@iulm.it

Pasquale Stano
University of Salento
Department of Biological and
Environmental Sciences and
Technologies

Keywords
Artiﬁcial intelligence, synthetic biology,
epistemology of synthetic models,
taxonomy of synthetic models, synthetic
method, criteria of relevance

Explorative Synthetic Biology in
AI: Criteria of Relevance and a
Taxonomy for Synthetic Models of
Living and Cognitive Processes

Abstract This article tackles the topic of the special issue “Biology
in AI: New Frontiers in Hardware, Software and Wetware Modeling
of Cognition” in two ways. It addresses the problem of the relevance
of hardware, software, and wetware models for the scientiﬁc
understanding of biological cognition, and it clariﬁes the
contributions that synthetic biology, construed as the synthetic
exploration of cognition, can offer to artiﬁcial intelligence (AI). The
research work proposed in this article is based on the idea that the
relevance of hardware, software, and wetware models of biological
and cognitive processes—that is, the concrete contribution that
these models can make to the scientiﬁc understanding of life and
cognition—is still unclear, mainly because of the lack of explicit
criteria to assess in what ways synthetic models can support the
experimental exploration of biological and cognitive phenomena.
Our article draws on elements from cybernetic and autopoietic
epistemology to deﬁne a framework of reference, for the synthetic
study of life and cognition, capable of generating a set of assessment
criteria and a classiﬁcation of forms of relevance, for synthetic
models, able to overcome the sterile, traditional polarization of their
evaluation between mere imitation and full reproduction of the target
processes. On the basis of these tools, we tentatively map the forms
of relevance characterizing wetware models of living and cognitive
processes that synthetic biology can produce and outline a
programmatic direction for the development of “organizationally
relevant approaches” applying synthetic biology techniques to the
investigative ﬁeld of (embodied) AI.

1 Explorative Synthetic Biology and AI

Within the context of experimental research in biology, the development of synthetic biology (SB)
(Chiarabelli et al., 2009; Endy, 2005; Morange, 2009; Schwille & Diez, 2009) represents one of the
most relevant novelties. The interest of this emerging sci-tech area relies on its hybrid nature. SB
arose at the beginning of the new millennium, based on the interbreeding of biology and engi-
neering, primarily to design and build biological parts or systems not existing in nature to achieve
practical goals (biosynthesis of ﬁne chemicals, biofuels, pharmaceutics, etc.). However, increasingly

* Corresponding author.

© 2023 Massachusetts Institute of Technology.
Published under a Creative Commons Attribution
4.0 International (CC BY 4.0) license.

Artiﬁcial Life 29: 367–387 (2023) https://doi.org/10.1162/artl_a_00411

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Explorative Synthetic Biology in AI

often, SB overcomes merely applicative purposes and contributes in a new way to addressing the
scientiﬁc exploration of life. Current frontier techniques of (bio)chemical synthesis, assembly, and
molecular and supramolecular manipulation allow SB not only to modify effectively extant biological
cells but also to construct from scratch synthetic (or artiﬁcial) cells (SCs/ACs) (Buddingh’ & van
Hest, 2017; Gaut & Adamala, 2021; Guindani et al., 2022; Luisi, 2002; Luisi et al., 2006; Mansy &
Szostak, 2009; Salehi-Reyhani et al., 2017), which enables SB to fully express an original explorative
vein. The latter inquiring approach can be recognized as a variant of what Christopher Langton
(1989), while deﬁning Artiﬁcial Life (AL), called the “synthetic approach to biology.” Indeed, SCs
can be built as means to model primitive cells, simpliﬁed (minimal) cells, or even alternative cellular
forms and thus to study at an experimental level the fundamental features of life by “put[ting] living
systems together” from scratch—that is, from (bio)chemical molecules—“rather than taking [them]
apart” (Langton, 1989, p. 40; Liu & Fletcher, 2009). On the basis of the rapid growth of related re-
search directions, contemporary SB is now overcoming the mere status of a branch of engineering
and is deﬁning itself as a science: a synthetic science of life, aiming at deepening the scientiﬁc under-
standing of life through the construction and the experimental exploration of molecular models of
biological systems and processes.

However, this is not the only potential that the synthetic approach to life can provide. As we have
shown in detail previously (Damiano & Stano, 2018a, 2021a, 2021b), the synthetic exploration of
biological processes allows SB to produce scientiﬁc contributions of interest outside the traditional
perimeter of biology and, speciﬁcally, within the domain of the cognitive sciences. Following the
emergence of the embodied approach (e.g., Clark, 1997; Varela et al., 1991), the cognitive sciences
are increasingly recognizing, as well as focusing on, the biological dimensions of cognitive processes
and developing synergies with the sciences of life. As we have argued (Damiano & Stano, 2018a),
in the context of this general process of transdisciplinary interconnection, explorative SB appears
as an artiﬁcial science of life capable of involving AI—in particular, embodied AI—in a process of
cross-fertilization, favored by methodological and experimental preconditions that are already set.

From the methodological point of view, as it happens in AL, explorative SB implements, in its
speciﬁc research context, and, in this sense, in a speciﬁc way, the method of inquiry characterizing
the lines of AI engaged in studying natural cognitive systems and processes. This is the so-called
synthetic method, which AI inherited from cybernetics and its precursor—“proto-cybernetic”—
research lines (Cordeschi, 2002) and has been applied, since the 1950s, to the study of natural cog-
nition through the construction of software and hardware models. Often, starting from the late 1990s,
the synthetic method is designated within AI by the slogan “understanding-by-building” (Pfeifer &
Scheier, 1999) and presented to the scientiﬁc community as the method characterizing investiga-
tions that intend to contribute to the scientiﬁc understanding of natural processes through forms
of “artiﬁcial” or “synthetic” modeling—in other words, the creation and the experimental study
of artifacts that reproduce target natural processes on the basis of scientiﬁc hypotheses and thus
can be considered “material models” of these processes, namely, functioning physical artifacts al-
lowing science to test these hypotheses experimentally. In their scientiﬁc literature, specialists in
explorative SB explicitly indicate the understanding-by-building approach as their methodological
framework of reference and deﬁne the chemical artifacts produced based on its implementation
as “chemical models” of the target biological processes. On these grounds, philosophical analyses
conceptualize these models, frequently deﬁned “wetware models” (e.g., Bedau, 2003), as a third type
of model that, together with software and hardware models, contemporary science produces
through the synthetic method (Damiano & Stano, 2018a, 2018b).

Such a methodological convergence prepares cross-disciplinary collaboration between SB and

AI and, speciﬁcally, embodied AI (EAI), that is, the form of AI closest to the sciences of life.

From the experimental point of view,

in SB, the implementation of the understanding-
by-building approach can generate concrete research scenarios to study, at an empirical level, pro-
cesses of emergence of minimal living organisms through the synthesis of artiﬁcial chemical systems.
As we have discussed in the series of works mentioned earlier, on the basis of these scenarios, SB
can pave the way to the synthetic study of biological adaptive dynamics through chemical models

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of minimal biological systems and processes—in other words, a form of synthetic investigation of
minimal expressions of natural cognition that will be based not on software or hardware but on
wetware models. The related programmatic idea conceptualizes it as a new “chemical explorative AI”
directed to support EAI in advancing the scientiﬁc understanding of the biological dimensions of
cognitive processes.

Indeed, at present, the transition of SB to the status of a full-ﬂedged synthetic science of life
and cognition still appears as a programmatic project whose complete accomplishment appears
uncertain. As we discuss later, the bottom-up construction of complex-enough molecular systems
presents several challenges. However, technical issues are just one side of the coin. SB’s ability to
produce effective contributions to the scientiﬁc understanding of target biological and cognitive
processes does not depend only on the soundness of technical solutions. It depends also, and pri-
marily, on the possibility for SB to effectually address the epistemological critical questions that
today affect the synthetic modeling of life and cognition, which, if left unanswered, threaten the ef-
fective acceptance of this approach among the methodological strategies that the pertinent scientiﬁc
communities recognize as capable of producing valid insights. Among such epistemological issues,
a critical one concerns the relevance of synthetic models of biological and cognitive processes, under-
stood as the contribution(s) that they can make to the scientiﬁc description of the target processes
(Stano & Damiano, 2023). Can systems endowed with artiﬁcial “embodiments” and “embedments”
be considered effective models of natural living and cognitive processes? In what conditions and in
what sense can exploring synthetic models of biological and cognitive phenomena provide signiﬁ-
cant advancements in biological and cognitive sciences?

The issue of relevance is not limited to wetware models but also affects software and hardware
models. Typically, the literature of the artiﬁcial disciplines engaged in investigating natural pro-
cesses, including SB, deﬁnes the synthetic systems produced for exploratory purposes as models
of the target systems but does not support this deﬁnition through epistemological inquiries on the
relationship between these models and the target processes, nor on concrete ways in which syn-
thetic models can contribute to understanding their target processes. The problem is particularly
critical because, as we discuss, synthetic models often appear to have a merely “imitative” value
whose contribution to advancing scientiﬁc knowledge of the target processes is unclear. Further-
more, the current evaluation of synthetic models is often polarized in the rigid, sterile alternative
between, on one side, the mere behavioral imitation and, on the other side, the full reproduction
of target processes: a clear-cut opposition between the simple reproduction of the target processes,
based on biologically implausible mechanisms, and the perfect re-creation of the “real thing”—
whose relevance, as we will argue, is not less problematic (and often coincident with idle technical
virtuosity).

We believe that effectively addressing the problem of the relevance of synthetic models, and
transforming the aforementioned binary alternative into a productive space for the synthetic explo-
ration of life and cognition, is a precondition for the development of SB from an ancillary discipline
to a (synthetic) science of life and cognition. To support the fulﬁllment of this preliminary require-
ment, we present in this article an epistemological inquiry into the relevance of synthetic models.
The main goal is to generate a conceptual framework to determine and assess the different forms
of relevance that (hardware, software, and wetware) models can have for the scientiﬁc understand-
ing of life and cognition, in order to clarify the contributions that SB can offer to AI construed as
the synthetic exploration of cognition and, on these grounds, pave the way to the development of
research approaches in SB able to productively investigate the territory of EAI.

The present article is organized as follows. In section 2, we show the limits of the alternative
“mere imitation/complete reproduction” for evaluating the relevance of synthetic models and the
need for assessment criteria that overcome the imitation paradigm. In the third section, we pave
the way to an alternative paradigm of assessment by deﬁning an epistemological framework of
reference for the synthetic study of life and cognition, grounded in autopoietic cognitive biology
and proposing a related set of criteria to evaluate the relevance of synthetic models. In the fourth
section, we use these criteria to deﬁne a range of different forms of relevance for synthetic models,

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and we tentatively map SB models based on them. In the ﬁfth and concluding section, we draw
a programmatic direction for “organizationally relevant” SB–AI approaches and ﬁnish by brieﬂy
recapitulating our path and prospecting future work.

2 Mere Imitation and Complete Reproduction: From an

Alternative to a Workspace

The alternative “mere imitation/complete reproduction” has historical signiﬁcance with respect to
the issue of evaluating the relevance of synthetic models. Indeed, its thematization is at the heart
of the cybernetic literature on the synthetic approach and, in particular, of the three groundbreaking
works dated 1943 that are usually recognized as having laid the epistemological foundations of the
synthetic modeling of life and cognition: “Behavior, Purpose and Teleology” by Rosenblueth and
colleagues (1943), “A Logical Calculus of the Ideas Immanent in Nervous Activity” by McCulloch
and Pitts (1943), and The Nature of Explanation by Craik (1943). Their epistemological coverages of
the sciences of the artiﬁcial converge in questioning the approach to the assessment of synthetic
models introduced by “proto-cybernetics,” that is, by the scientists and engineers who, between
the ﬁrst and the third decades of the last century, began to develop the synthetic method (e.g.,
Cordeschi, 2008). Actually, to evaluate their models of living and cognitive processes, these pre-
cursors of cybernetics set up a “proof of sufﬁciency” approach, which, for the purpose of the sci-
entiﬁc explanation of a target natural process, proposes to consider relevant synthetic models all
artiﬁcial systems capable of reproducing the target process, independently of their biological plausi-
bility. According to this approach, the empirical demonstration that a human-made mechanism can
produce behaviors typical of an organism is enough to ensure the possibility of providing for them a
mechanistic—and, in this sense, a scientiﬁc—explanation. The founders of cybernetics’ unanimous
criticism of this approach was directed to elevating the ambitions of the synthetic exploration of life
and cognition. The core idea was that, when artiﬁcial systems do not incorporate scientiﬁc hypothe-
ses on what functional organization—that is, on what speciﬁc mechanisms—generates the target
behaviors in nature, they are of no use with respect to the scientiﬁc explanation of such behaviors.
In other words, to be considered relevant models of a target natural process, artiﬁcial systems have
to reenact this process based on the operationalization of scientiﬁc hypotheses of how it is generated
in natural systems.

In Rosenblueth et al. (1943), this position is presented in connection with two compelling re-
marks, both central to the purposes of the present article. The ﬁrst suggests that chemical artiﬁcial
systems (i.e., wetware systems), having a physical realization similar to that of biological systems,
may be better candidates than electromechanical systems (i.e., hardware systems) for a successful
artiﬁcial modeling of natural cognitive processes:

If an engineer were to design a robot, roughly similar in behavior to an animal organism,
he would not attempt at present to make it out of proteins and other colloids. He would
probably build it out of metallic parts, some dielectrics and many vacuum tubes. The
movement of the robot could readily be much faster and more powerful than those of
the original organism. Learning and memory, however, would be quite rudimentary. In
future years, as the knowledge of colloids and protein increases, future engineers may
attempt the design of robots not only with a behavior, but also with a structure similar to
that of a mammal. (p. 23)

The development of hardware models is recognized by Rosenblueth et al. (1943) as useful to
express aspects of the behavior of natural systems, but not to instantiate and explore the underlying
mechanisms, for which it is considered a better option to build wetware models. This view was
shared by other protagonists of early cybernetics, such as Donald M. MacKay (1951), who proposed
that, to generate deep models of cognitive processes, “one would have to go in for mechanisms
in protoplasm instead of mechanisms in copper” (p. 221).

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The second remark associates the introduction of this visionary perspective to a cautionary com-
ment related to the limits of such a realistic approach. Rosenblueth et al. (1943) present it through a
joke, famously stating that “the ultimate model of a cat is of course another cat, whether it is born of
still another cat or synthetized in a laboratory” (p. 23). From an epistemological point of view, this
appears as a warning targeting “over-realist” strategies of implementation for the synthetic method.
If merely imitative, or “underdetermined,” models have no utility for the scientiﬁc explanation of
target behaviors, the same can be said for models that would fully reproduce the target systems.
Indeed, these would be “overdetermined” models, whose level of detail, instead of clarifying princi-
ples and mechanisms underlying life and cognition, would hide them. As Roberto Cordeschi (2008),
condensing the related debate, points out,

while, at one extreme, models based on simple functional equivalence turn out to be
underdetermined, models that would go, say, down to the level of the most detailed
physicochemical characteristics . . . [of the target systems] might turn out to be
“overdetermined,” as Stevan Harnad has effectively put it, in the sense that, in order to be
as realistic as possible, the models would end up including physical and functional
properties that might be irrelevant or nonessential to the researcher’s understanding of
the phenomenon under consideration: in this case the models would end up obscuring
those principles or assumptions that they are called upon to elucidate. Restrictions do
indeed increase the chance that the model will tell us something about the real
phenomenon by approximating it (perhaps by forward motion), but we cannot delude
ourselves that the rule is always and only: the more restrictions, the better. Unless we
have to conclude, with Norbert Wiener’s quip, that the best model of the cat is the cat
itself. (p. 188)

It is beyond the scope of this article to take up the entire debate on this topic, for which we refer
the reader to the relevant scientiﬁc literature (e.g., Boccignone & Cordeschi, 2007; Harnad, 1994;
Webb, 2006). For the aims of this article, it is sufﬁcient to bring into focus the central message of
this debate: In a nutshell, with regard to the purpose of the scientiﬁc description of target natural
processes, a synthetic model is signiﬁcant if it lies between the extremes of mere imitation and full
reproduction of those processes.

This epistemological insight, although diffused in the debate since its cybernetic origins, has been
mostly neglected in the work of specialists engaged in modeling biological and cognitive processes
synthetically. Since the 1950s, the community of the sciences of the artiﬁcial, to evaluate its mod-
els, refers to the Turing test, which focuses the assessment on their ability to imitate the target
system’s manifest behavior. Introduced by Alan Turing (1950) in “Computing Machinery and Intel-
ligence” for other purposes, on the basis of the “imitation game,” this test proposes that an artifact
is ascribed the property of intelligence—construed as the ability to think like a human—when it is
impossible for a human observer to distinguish such a machine from another human agent based
on the answers it and that agent give to the human’s questions. Despite multiple critiques and re-
lated reformulations, for which we refer to the extensive related literature (Copeland, 2000; French,
1990, 2000; Hernandez-Orallo, 2000), this test still constitutes a paradigmatic reference for assessing
the relevance of synthetic models. Currently, not only is it still at the center of the debate in AI but
it is acquiring a central role in the other sciences of the artiﬁcial. Recently, in the ﬁeld of explorative
SB, the Turing test has been at the basis of one of the ﬁrst attempts of assessing the life-likeness
of synthetic cells (Cronin et al., 2006). A recent investigation explicitly refers to the Turing test
for SCs while discussing the result of a bidirectional chemical communication (via the exchange of
signaling molecules and the corresponding activation of genes) between SCs and bacteria (Lentini
et al., 2017). In particular, experimental data were employed to (cautiously) deﬁne the life-likeness
of the SCs employed in the study, at approximately 40% (for a commentary, see Damiano & Stano,
2020).

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Among the most severe criticisms of the Turing test, questioning its paradigmatic role as a tool
for assessing the relevance of synthetic models of cognitive processes, is the Chinese Room argu-
ment presented by John Searle in his 1980 article “Minds, Brains, and Programs.” This argument,
based on a thought experiment that reenacts key features of the Turing test, points out the limita-
tions of its assessment approach and, at the same time, suggests that, when a form of AI focuses
on imitation exclusively, it turns out incapable of reproducing deep, essential features of natural
cognition. Searle delineates this view by discussing speciﬁcally the case of classical or computation-
alist AI, whose synthetic models of human cognition, he argues, are able to simulate, but not to
re-create, its most distinctive aspect, that is, understanding—more in general, its semantic dimen-
sion. In his 1980 article, Searle traces this impossibility to classical AI’s tendency to synthetically
model manifest aspects of cognitive behaviors and not their physical realization nor related underly-
ing mechanisms. These are the grounds on which Searle denounces classical AI for being affected by
the traditional mind–body dualism and offered theoretical and epistemological bases for developing
EAI, that is, the biologically inspired form of AI that arose in the late 1980s, bringing forth a posi-
tive emphasis on the role the biological body plays in cognitive processes (e.g., Pfeifer & Bongard,
2006). However, Searle’s attack on classical AI, beyond Searle’s intentions, and despite his critique
of the imitation paradigm, has been favoring a further polarization of the debate on the relevance of
synthetic models, around the duality “mere simulation–complete reproduction” of the target cogni-
tive processes. Indeed, in his 1980 article, Searle grounds his position in the conceptual alternative
“weak AI/strong AI,” which, to this day, remains a paradigmatic reference point for assessing the
relevance of synthetic models in terms of the alternative mere simulation–complete reproduction of
the target natural processes. In fact, several EAI lines, such as android science (Ishiguro, 2016), use
in this polarizing way Searle’s distinction in the context of research work producing frameworks to
assess the relevance of synthetic models (Damiano & Dumouchel, 2020; Kahn et al., 2007).

As we mentioned earlier, one of the main goals of this article is to provide the scientiﬁc com-
munity with epistemological tools useful to overcome the mere simulation–complete reproduc-
tion alternative in assessing synthetic models. The ambition is to help make operational, within the
synthetic modeling practice, the central message of the cybernetic and subsequent debate on the
relevance of synthetic models. In view of a positive contribution of synthetic modeling to the scien-
tiﬁc understanding of life and cognition, “imitation” and “full reproduction” of the target processes
can be more fruitful if construed not as terms of a choice but as limits of a wide and plural ﬁeld of
exploration. In the next section, to support the operationalization of this epistemological perspec-
tive into synthetic modeling practice, we propose research work dedicated to generating assessment
criteria, for synthetic models, allowing us to distinguish for them a variety of different forms of
relevance (which include, but are not limited to, mere imitation and complete reproduction of target
processes) and related forms of usefulness in the context of the synthetic exploration of life and
cognition.

3 Two Criteria of Relevance for Synthetic Models

The research work presented in this section takes as its general frame of reference the autopoietic
epistemology developed by Humberto Maturana and Francisco Varela (1973). This choice stems
from the recognition of the strong connection between the synthetic approach and autopoietic cog-
nitive biology, relying on the fact that the latter, besides proposing a systemic theory of life and
cognition effectively inspiring the production of synthetic models for 50 years, also provides an
explicit theory of scientiﬁc knowledge that deﬁnes the synthetic approach as the proper method
to investigate biological and cognitive processes both at the theoretical and the experimental lev-
els (Damiano, 2009). On these grounds, we consider that the notions and principles in which the
autopoietic thematization of the synthetic approach is expressed can be seen as useful elements to
provide a shared epistemological framework to the synthetic study of life and cognition (Damiano
et al., 2011).

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Coherently with this view, to elaborate criteria of relevance for synthetic models to be proposed
to the whole synthetic modeling community, we extracted from autopoiesis two epistemological
principles. In what follows, we propose a schematic description of them and of the way we ground
in them the two criteria of relevance for synthetic models that we intend to propose to innovate the
assessment approach of the synthetic modeling of life and cognition.1

3.1 Explaining as Constructing
The ﬁrst principle we extracted from the autopoietic epistemology introduces an operational deﬁni-
tion of the scientiﬁc explanation, which claims that explaining a phenomenon amounts to proposing
a mechanism able to produce it (cf. Maturana & Varela, 1987, chap. 1). The epistemological ambition
is redeﬁning the traditional view of the scientiﬁc explanation, more speciﬁcally, shifting from the
classical “explaining = predicting” perspective to an operational approach, which, by proposing the
“explaining = constructing” notion of the scientiﬁc explanation, can be applied to systems exceed-
ing the predictive ability of science. Asking for models that are able not to predict but to generate
the natural target processes, the “explaining = constructing” principle focuses the scientiﬁc explana-
tion not on actual but on possible behaviors of the systems explored. In this way, it deﬁnes a form
of scientiﬁc explanation that is characterized by two main advantages. On one side, it is particularly
suitable for biological and cognitive processes, as it cannot be affected by their unpredictability; on
the other, it is able to ground a “general” science of life and cognition—as Christopher Langton
(1989) would put it, a science of life and cognition as they are and could be (see also Damiano,
2009; Damiano et al., 2011).

Through its operational deﬁnition of the scientiﬁc explanation, the autopoietic cognitive biol-
ogy generates the epistemological structure underlying the synthetic modeling of life and cognition.
Schematically, target of the inquiry = natural phenomena untreatable through the classical pre-
dictive modeling; general approach = to scientiﬁcally understand means to build the target process;
heuristic strategy = elaborating operational descriptions of the target natural processes; procedure =
deﬁnition of (a set of) mechanisms able to generate the target natural phenomenology, and experi-
mental exploration of the phenomenology they produce.

Also in Maturana and Varela’s (1987) literature, this kind of scientiﬁc endeavor is articulated
around the notion “synthetic.” Indeed, the notion of synthesis deﬁnes explicitly the entire autopoi-
etic biology’s methodological orientation. On the basis of their principle of scientiﬁc explanation,
Maturana and Varela target a new deﬁnition of life that, instead of listing the main features of
biological systems, speciﬁes a mechanism apt at producing the whole biological phenomenology
(Damiano, 2009; Damiano & Luisi, 2010). They distinguish this form of deﬁnition from the tradi-
tional “analytic” deﬁnitions of life, consisting of lists of properties, by characterizing it as “synthetic”
and by assigning it a condition to satisfy to be considered an appropriate operational explanation
of life. The mechanism that the synthetic deﬁnition proposes has to manifest the ability to create,
from a set of elemental components, the entire biological domain as we know it. In other words, the
synthetic deﬁnition of life has to be able to generate, through the dynamical coordination of a set
of elements, a minimal form of life—a minimal cellular system—and its characteristic phenomenol-
ogy. Maturana and Varela (1987) include in this phenomenology not only self-production, but, on
this basis, also reproduction and evolution, which, in principle, make the minimal living unit able to
generate a differentiated living domain, as complex and populated as the terrestrial one.

This kind of scientiﬁc description captures the main traits of the synthetic modeling of life and, in
particular, the traits of one of its most interesting expressions. We refer to Langton’s (1989) program
of AL, which intends to implement this approach in the form of an experimental construction: the
synthesis of “any and all biological phenomena, from viral self-assembly to the evolution of the
entire biosphere” (p. 53), without restriction to carbon-chain chemistry. Along with Maturana and
Varela (1987), Langton (1989) associates the synthetic approach to a constructive and universal
biology. His program meets autopoietic biology not only in the operational principle of the scientiﬁc

1 A partial, preliminary version of the research work presented in this paragraph can be found in Damiano et al. (2011).

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explanation but also in a related “principle of universalization” of biology, according to which “life is
. . . a result of organization of matter, rather than something that inheres in the matter itself” (p. 53).

3.2 Organization and Structure
The second autopoietic principle we consider relevant for the epistemological grounding of the
synthetic approach is a theoretical element endowed with a signiﬁcant epistemological value: the
distinction between the notion of organization and the notion of structure. A simpliﬁcation of
Maturana and Varela’s (1987, chap. 2) theoretical deﬁnition of this distinction puts it as follows: The
organization of a biological system is its relational frame, that is, the network of relations deﬁning
the system as a unity of components; the structure of a biological system is its materialization,
constituted of the actual components and the interconnections between them of which, in every
instant, the system is made.

This theoretical distinction, far from being a theoretical novelty by Maturana and Varela (1987),
was introduced by Jean Piaget (1967, chap. 4), who deﬁned it as the theoretical key to understanding
the speciﬁcity of the dynamic of biological systems. According to Piaget, indeed, while the organi-
zation is the invariant aspect of living systems, the structure is their variant aspect, because in these
systems, all the elementary components permanently change, while the systems as wholes—that is,
as relational unities of components—remain. As Piaget pointed out, this can be afﬁrmed at both
the ontogenetic and the phylogenetic levels. The relational unity keeps unchanged not only in the
metabolic ﬂux of physical-chemical components characterizing biological organisms but also during
the ontogenetic changes making the living system unrecognizable from one observation to the next.
Furthermore, the relational unity is what is transmitted through reproduction and stays unchanged
through different generations. Being in this sense the invariant of the biological dynamics, the rela-
tional unity is the lowest common denominator of living systems. Hence operating the theoretical
distinction of this invariant relational frame from the changeable materializations of living systems,
and realizing its scientiﬁc description, means to deﬁne the common element of the whole class of
biological systems—in other words, to deﬁne life itself.

The epistemological relevance of the distinction between organization and structure relies here
and is at least twofold. First, this distinction allows one to characterize the mechanism underlying the
biological dynamics in terms of a mechanism creating organizational invariance through permanent
structural variation, which opens the possibility of providing an operational explanation of life in
line with the autopoietic principle of the scientiﬁc explanation. Second, this distinction provides
insights about the relevance of the synthetic approach for the study of natural living and cognitive
processes, as it implies that (a) in principle, the material realization (structure) of living systems can
be manifold and that (b) artiﬁcial systems displaying the same organization as living systems, and
materializing it in different structures, belong to the class of living systems.

In this sense, by reproposing the Piagetian distinction between organization and structure of
biological systems, the autopoietic cognitive biology offers theoretical grounds to the thesis—“the
big claim”—through which Langton (1989) expresses the ambition of the synthetic approach to
biology:

A properly organized set of artiﬁcial primitives carrying out the same functional roles as
the bio-molecules in natural living systems will support a process that will be “alive” in the
same way that natural organisms are alive. Artiﬁcial Life will therefore be genuine life—it
will simply be made of different stuff than the life that has evolved here on Earth. (p. 69)

3.3 Autopoiesis and the Extension of the Synthetic Approach to the

Domain of Cognition

In line with the Piagetian approach, which treats the problem of cognition and the problem
of life jointly, Maturana and Varela (1987) have emphasized that the process of metabolic self-
production (i.e., autopoiesis) characterizing biological systems corresponds to a permanent dynamics

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of interaction with the environment and other systems, which they called “structural coupling” and
conceptualized as a symmetric relationship made of reciprocal dynamics of perturbations triggering
endogenous self-regulations. The thematization of this process as a cognitive process is at the basis
of the cognitive biology that Maturana and Varela have developed as an integral part of their the-
ory of life, paving the way to radical approaches to embodied cognitive science (e.g., Clark, 1999).
Based on this view, the phenomenology that has to be produced by the autopoietic synthetic def-
inition of life, to provide a scientiﬁc explanation of the living, includes not only all the biological
but also all the cognitive phenomenology—lato sensu (Maturana & Varela, 1987). Hence the au-
topoietic operational principle of the scientiﬁc explanation and the autopoietic distinction between
organization and structure, considered together, offer a grounding framework for both the synthe-
tic study of life and the synthetic study of cognition.

3.4 Two Criteria of Relevance for the Synthetic Approach
The two autopoietic principles presented in the preceding pages generate two criteria useful for
evaluating the relevance of synthetic models for the scientiﬁc exploration of life and cognition.

3.4.1 C1: Phenomenological Relevance
From the autopoietic operational principle of the scientiﬁc explanation (P1: To explain scientiﬁcally
is to provide a mechanism able to produce the phenomenology to be explained) can be derived a criterion of
phenomenological relevance for synthetic models of living and cognitive phenomena, according to which
(C1) A synthetic model is relevant at a phenomenological level if it provides a mechanism that produces (according to
explicit parameters) the same phenomenology as the target living and/or cognitive phenomenology.

We use the expression “phenomenological relevance” to emphasize that this criterion requires
only a relation of identity, deﬁned by explicit parameters, between the phenomenology generated
synthetically and the target natural phenomenology. In other words, C1 does not impose any con-
straints on the biological plausibility of the synthetic mechanism used to reproduce the target phe-
nomenology. Hence, in case C1 is not integrated with a criterion requiring the biological plausibility
of synthetic models, and deﬁning this plausibility, then C1 is not able to warrant that synthetic mod-
els express a biologically plausible operational explanation. In this sense, by itself, C1 assesses the
capability of a model to imitate the target natural phenomenology without referring to a biologically
plausible generative mechanism.

However, the autopoietic theory of the scientiﬁc explanation helps in addressing this limit by
proposing a tool to distinguish between different phenomenologically—or imitatively—relevant
models based on their operational explanatory power. This epistemological tool is a principle intro-
duced by Maturana and Varela to orient the choice between different models describing the same
phenomenological domain (cf. Maturana, 1988; Maturana & Varela, 1987, chap. 1). According to
this principle, a better scientiﬁc explanation speciﬁes a mechanism able to generate not only the
target phenomenology but also other phenomena belonging to the same domain that were not con-
sidered in the context of the deﬁnition of the mechanism. In this way, this principle associates the
operational explanatory power of a model to what we can call its “progressive” character—its ca-
pability of producing supplementary relevant phenomena. On the basis of this principle, we dis-
tinguish two basic kinds of phenomenologically relevant models: (a) basic phenomenological models,
which produce only the target phenomenology, and in this sense have a basic operational explana-
tory power, and (b) progressive phenomenological models, which produce, together with the target phe-
nomenology, other phenomena belonging to the same domain. The latter are characterized by an
operational explanatory power proportional to the supplementary phenomena that they produce.

Importantly, the evolution toward higher-level progressive phenomenological models generates
models endowed with a higher operational explanatory power but that are not necessarily plausible
from a biological point of view. Indeed, although a greater operational explanatory power can be
considered a clue of greater biological plausibility, the latter remains uncertain in the absence of a
criterion deﬁning this form of plausibility.

As we emphasize in the next section, the lack of full-ﬂedged biological plausibility does not imply
that basic and progressive phenomenological—or imitative—models are not useful tools for the

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scientiﬁc investigation of life and cognition. When the exploration of the target processes does not
yet have consolidated and detailed theories of reference, attracting at least a partial consensus within
the scientiﬁc community, these models can be precious sources of inspiration for the production
of hypotheses about the mechanisms underlying the target phenomenology. In particular, when a
synthetic model does not produce the target phenomenology, not only will the “failure” indirectly
guide the research to more productive directions but also can help understanding “how not” and
“why not” questions related to the mechanisms under inquiry.

3.4.2 C2: Organizational Relevance
As mentioned in section 3.2, the autopoietic distinction between organization and structure implies
that (a) all living systems share the same organization, but not necessarily the same structure, and
thus that (b) synthetic systems displaying a different structure but the same organization as living
systems have to be considered as belonging to the class of living systems. On these grounds, the
organization–structure distinction produces a strong criterion of relevance for synthetic models.
Indeed, according to (b), when synthetic models share the organization of living systems, they can
be considered deep models of them, because they constitute specimens of the class of living systems.
We can refer to this criterion as a criterion of organizational relevance, which warrants the biological
plausibility of synthetic models. Considering the continuity between life and cognition advanced
by the theory of autopoiesis, this criterion can be formulated as follows: (C2) Synthetic models are
organizationally relevant if they display (according to an explicit theory of the living-cognitive organization that is
coherent with the distinction between organization and structure) the same organization as living-cognitive systems.

To be met, this criterion requires the scientiﬁc community to engage in attempts of implement-
ing, in artiﬁcial models, theories of the biological-cognitive organization, which represents an under-
taking at the limits of realizability. Indeed, these attempts involve a series of difﬁculties that, far from
being limited to technical problems, include critical epistemological obstacles, such as the irreducible
multiplicity of the possible interpretations of a target theory, the different levels of abstraction at
which each interpretation can be realized synthetically, and the related constraints limiting the pos-
sibilities of these implementations.

Although schematic, this overview of the challenges involved in meeting C2 is enough to empha-
size that “relevance in the proper sense” cannot correspond to a “complete reproduction” of the
target processes, which, to be attained, would require the availability of a deﬁnitive, exhaustive, uni-
vocally interpretable and perfectly implementable theory of the biological-cognitive organization.
However, in this impossibility lies the interest of C2. As we show in the next pages, C2 encour-
ages the adoption of a pluralist approach to the synthetic modeling of life and cognition, which, at
the scientiﬁc level, appears more generative than a (over-)realist approach aiming at the complete
reproduction of the target natural systems and processes–as Rosenblueth et al. (1943) suggested
through their quip about modeling a cat (cf. section 2 of this article). A pluralist approach (Stano
& Damiano, 2023) would indeed multiply the possibilities of generating interesting insights by en-
gaging the sciences of the artiﬁcial not only in implementing a variety of theories of biological-
cognitive organization, but also in exploring, with regard to each of them, a variety of different
ways of implementation, based on diverse interpretations of the theory of reference and multiple
options related to the choice of the level of abstraction deﬁning the synthetic realization.

4 A Taxonomy for Synthetic Models: Forms of Relevance and

Uses in Scientiﬁc Research

The criteria we introduced present two main advantages. First, they can be applied to hardware,
software, and wetware models and thus provide the community of the synthetic modeling with a
framework of assessment able to extend across the contributions generated by all the sciences of
the artiﬁcial. Second, these criteria allow an evaluation of the relevance of synthetic models that,
while overcoming the classical focus on their capability of imitating the target processes, avoids

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Figure 1. A taxonomy for synthetic models: forms of phenomenological and/or organizational relevance.

falling into the traditional pure imitation/authentic reproduction polarization. Indeed, the two cri-
teria we propose generate a taxonomy of synthetic models that accounts for a variety of forms of
phenomenological and organizational relevance, as well as their combinations (Figure 1).

Indeed, within the assessment space opened by C1 and C2, the polarization between “weak”
and “strong” AI mentioned earlier, which is typically used to discuss the relevance of synthetic
models within and beyond the ﬁeld of AI, reﬂects a simplifying approach that recognizes only two
extreme cases. Weak AI deals with purely phenomenologically relevant models, whereas strong AI
targets models that are perfectly organizationally relevant and that, as a consequence, are considered
phenomenologically relevant as well. Here the implicit hypothesis is that organizational relevance
necessarily brings about phenomenological relevance, implying a hierarchy between the two poles
of this classic opposition. On the contrary, including all the combinations of phenomenological and
organizational relevance involves other, more articulated and interesting relations between them
than a simple hierarchy and proposes to the synthetic modeling of life and cognition a wide and
diversiﬁed map of possible development paths.

In this section, we explore this space—which, as we said, is inherently accessible to hardware,
software, and wetware synthetic explorations of life and cognition—by focusing on wetware models,
to clarify the concrete contributions that SB can offer to the synthetic modeling and show a map of
potential paths for developing explorative SB by means of a synergic growth in basic and applied
sciences.

4.1 Forms of Phenomenological Relevance
As mentioned, the criterion of phenomenological relevance C1 states that a synthetic model is
phenomenologically relevant if it exhibits, coherently with well-deﬁned parameters, the same phe-
nomenology as displayed by the target system, independently of the plausibility of the particular
mechanism used to achieve this. The focal point is the ability of a synthetic system to reproduce the
behavior of a natural system, regardless of how that behavior is generated. In this sense, models that
satisfy C1 are only imitative or “superﬁcial” models of the target phenomena (Breazeal, 2003). They
offer evidence of the sufﬁciency of the mechanisms implemented for the generation of the target
processes, but in general, they say nothing about how these processes are generated in nature.

When theories of reference are missing, or paradigms are uncertain, these models and their im-
plemented mechanisms can function as a drive to further investigate by trial and error the generative
mechanisms of the phenomenology under inquiry. However, it is sometimes difﬁcult to spot, with-
out deeper knowledge of the ﬁnest details of the mechanism of the imitative models and the one of
the biological targets, whether or not there is a partial correspondence between them.

In the context of explorative SB, several attempts to generate cell-like particles can be counted
in the class of phenomenologically relevant models, satisfying C1 only. Reports on the genera-
tion of “sulphobes,” coacervates, membrane-less droplets, “jeevanu,” proteinoid microspheres, and
so on (for a review, see Hanczyc, 2009) ﬁll the annals of science. Usually these cell-like particles,
which form spontaneously under certain conditions, serve the aim of investigating the structure

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and, occasionally, some rudimentary behavioral aspects of “primitive cells,” as well as their spon-
taneous formation from very simple chemicals. These approaches succeed in reconstituting the
phenomenology of micrometric cell-like structures, their physical stability, capability of solute re-
tention, semipermeability, and generation by molecular self-assembly. On these bases, they embody
the concept of self-distinction from the surrounding environment but generally fail to capture the
topological structure of cells. This explains the superior relevance of lipid vesicles as modern-cell
models, even if they are characterized by other limitations. In case the target phenomenology refers
to primitive cells, the question of identifying suitable models is much more open; in this domain, a
very partial phenomenological relevance (e.g., failure to produce stable-enough particles or genera-
tion of particles unable to grow and divide) can anyway help to progress the ﬁeld and gain knowledge
in an etiological sense, for example, Why this and not that (Bolli et al., 1997; Luisi, 2011)? In general,
these purely imitative approaches to the generation of cell-like particles are viable as “biomimetic”
systems and have a long tradition in experimental contexts. Quite often, experiments with these
cell-like systems are carried out without claiming that the model provides an explanation about a
target biological phenomenology (i.e., the origin of early cells). In most cases, the explicit inten-
tion refers to their employment as novel materials, exploiting novel chemistries and exploring novel
reactivities for very diverse purposes, including those of applied science.

Another very interesting aspect of phenomenologically relevant models concerns the possibil-
ity of using them to investigate interactions between the target processes, which they imitatively
reproduce, and the environment of reference. From the point of view of scientiﬁc research, this
investigative function of basic imitative models can have particularly effective applications when
the environment of reference, in which the models are situated for exploratory purposes, includes
natural systems of interest for the ongoing inquiry. In this case, the possibility exists that the mod-
els, through their imitation of the target behaviors, engage natural systems in interactive dynamics
that are of scientiﬁc interest for the investigation in progress. In these circumstances, the models
constitute, for the scientiﬁc research on life and cognition, synthetic tools useful to explore and
manipulate experimentally, in the natural systems with which they interact, dynamics of interest.

The signiﬁcant value of this kind of model makes it worth distinguishing their speciﬁc form of
phenomenological relevance, which we call interactive phenomenological relevance. Accordingly, we deﬁne
artiﬁcial systems as interactive phenomenological models when they are able to synthetically produce the
phenomenology under inquiry and, through the production of this phenomenology, engage natural
biological-cognitive systems in interactive dynamics that (according to some explicit parameter) are
germane to the scientiﬁc exploration of the target processes conducted through these models.

This form of phenomenological relevance ﬁnds an example in chemical cells (“chells”; Gardner
et al., 2009) interacting with natural cells. Chells have been built by encapsulating some chemical
components that generate the so-called formose reaction inside lipid vesicles. As a result of the
reaction, a set of sugar-like molecules is produced; one of them, once released in the medium and
transformed by another additional reaction, becomes a “signal molecule”—like species that can elicit
a biological response in a nearby population of bacteria. Phenomenologically, the entire process
looks like the biological process of (unidirectional) signaling between cells, but the sender cells (the
chells) are artiﬁcial, and indeed they are just superﬁcial analogs of biological cells, both in structure
and in function. In particular, while the signaling event has a meaning for the bacteria, it makes no
sense for the chells. Nevertheless, chells participate in the signal transmission event in a functional
way, generating a behavior in the receiving biological cells that is similar to what happens in biology
(chemiluminescence activation).

A third kind of imitative model are those characterized by the progressive form of phenomenological
relevance introduced in section 3.4. Progressive phenomenological models are artiﬁcial systems that, besides
reproducing the target phenomena, exhibit unexpected behaviors pertinent to the inquiry on the
target processes.

An example can be found in a series of reports that describe the formation of solute-rich li-
posomes intended as primitive cell models (Luisi et al., 2010; Pereira de Souza et al., 2009). In
this case, the target phenomenology was referred to modeling primitive cells that host nontrivial,

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network-like reactions in their lumen, generating liposomes in aqueous solutions where the solutes
of interest were present—without any special interventions. The phenomenological relevance of
this model is progressive because of some additional unexpected observations. In particular, the
model not only showed that liposomes could host the target complex reaction successfully but also
revealed that such an event occurred in demanding conditions too. In particular, the successful out-
come was demonstrated either when liposomes were extremely small or when the chemicals were
diluted—two conditions wherein, a priori, it should not have happened for the adverse statis-
tics. This synthetic model of primitive cells, in addition to reproducing the target phenomenology,
generates another phenomenology (the co-encapsulation of chemicals inside liposomes in adverse
conditions), contributing to the development of investigations about the onset of conditions to
support protometabolism thanks to the local concentration enhancement that can occur in some
primitive cells.

A further type of phenomenological relevance combines the last two forms considered—that is,
interactive and progressive phenomenological relevance. It refers to artiﬁcial systems that exhibit
unexpected interactive behaviors relevant to the investigation within which they are used as mod-
els. An example in SB is given by SCs that were designed to send a chemical message to bacteria
(Rampioni et al., 2018). In addition to the reproduction of the target (interactive) phenomenology,
the model generated a phenomenology with progressive relevance as it resulted in an unexpected
predator behavior (bacteria attacked and destroyed SCs, probably due to an unintended but very ef-
fective chemotactic signaling). In terms of explanatory power, the progressive aspect of this model
converges with currently accepted views by showing, in a rather dramatic manner, why the de novo
spontaneous generation of simple “defenseless” cells can occur only in natural scenarios where life
is not preexisting.

Not intended to be exhaustive, the examples from SB proposed in this section show that the
forms of phenomenological relevance deﬁned by our taxonomy have concrete expressions in the
current synthetic modeling research in the wetware domain. Further examples could have been
made based on an epistemological analysis of already published studies about software and hard-
ware models of biological-cognitive processes. This is not the case for the forms of (full-ﬂedged)
organizational relevance we are about to introduce, for which we believe contemporary research
cannot provide concrete examples.

4.2 Forms of Organizational Relevance
As mentioned, the criterion of organizational relevance C2 states that a synthetic model is orga-
nizationally relevant if its organization reproduces the target system’s organization, according to a
pertinent scientiﬁc theory. This second criterion shifts the focus from phenomenology to organiza-
tion, that is, from the target natural systems’ behavior to their underlying mechanisms. Schematically,
a synthetic model is phenomenologically relevant if it reproduces the behavior observable in the target
systems, and it is organizationally relevant if it reproduces the target systems’ organization and, in this
sense, the organizational mechanisms that underlie that behavior.

Concerning organizationally relevant models, our taxonomy introduces a possibility that is typi-
cally excluded by the pure imitation/authentic reproduction polarization and hence by Searle’s dichotomy
of weak versus strong AI. This option covers the case of synthetic models that are organizationally
relevant but not phenomenologically relevant, in other words, models whose reproduction of the
target organizational mechanisms gives rise to new behaviors, different from those of the target
natural systems. By taking into account this case, our taxonomy leaves open the possibility that the
synthetic modeling may lead to human-made variants of natural systems that manifest different behav-
iors. This possibility, which we deﬁne through the label of basic organizational relevance, corresponds
to the synthetic creation of new forms of life and biological cognition. Models characterized by this
type of relevance would represent the most basic case of what Christopher Langton (1989), in the
“big claim” about AL’s ambition (see section 3.2 above), proposes, that is, Artiﬁcial Life as “genuine
life . . . made of different stuff than the life that has evolved here on Earth” (p. 69) and, we add,
displaying different behaviors.

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An example that could express this possibility can be found in the programmatic design of an
artiﬁcial system dubbed Los Alamos Bug, a protocellular model studied extensively but not yet fully
realized experimentally (DeClue et al., 2009; Maurer et al., 2011). It is interesting to note that its de-
sign was focused on the thermodynamic coupling between the three functional structures (container,
metabolism, and genes), which are themselves deﬁned in an alternative way—when compared with
usual biological models. An artiﬁcial peptide-nucleic-acid (PNA) genetic component was selected
for an easier physical binding with the hydrophobic container, which in turn was a small, nonhollow
structure (e.g., a micelle) and thus hosting reactions on its surface. An organic photosensitizer an-
chored to the template (or template precursor) is introduced to harvest the light energy and generate
a redox-based proto-metabolism (Rasmussen et al., 2003).

The second form of organizational relevance generated by the criteria we proposed refers to
models that satisfy both C2 and C1, that is, basic phenomenologically relevant models that are also
organizationally relevant. We can deﬁne their form of relevance as basic relevance in the proper sense,
as these models, at the same time, would reproduce the behavior observable in the target sys-
tems and the organization underlying that behavior. In this sense, they would correspond to artiﬁ-
cial re-creations of living-cognitive systems, but not necessarily to those synthetic re-creations that
Rosenblueth and colleagues, in their 1943 article, deﬁned as “ultimate” models in their quip on the
cat—“born of still another cat or synthetized in a laboratory”—quoted earlier. As argued, within the
context of the synthetic modeling, reproducing living-cognitive systems’ organization means to at-
tempt to embody theories of biological-cognitive organization in artiﬁcial systems. Related modeling
procedures, far from enduring spans of time compatible with those characterizing the evolution of
life and cognition on Earth, would be those typical of our biological lab procedures, which plausibly
excludes the possibility of generating the kind of biological-cognitive structures that evolutionary
processes of adaptation (and exaptation) could produce. In this perspective, a synthetic model en-
dowed with both organizational and phenomenological relevance would probably be a much simpler
system than the “real thing”—its target living-cognitive system.

This is the reason for the high scientiﬁc importance of models that, although organized according
to the same principles that we found in known biological systems, express their behaviors via similar
generative mechanisms, but at a lower or minimal level of complexity. It is generally supposed that
part of living beings’ complexity comes from adaptation mechanisms and actually represents a map
of the evolutive history of the organism. It is probable that in stable and controlled environments,
the same organizational principles can be realized in a simpliﬁed manner.

A third form of organizational relevance refers to organizationally relevant artiﬁcial systems that
are characterized by an interactive form of phenomenological relevance and thus can engage natural
systems in dynamics that are germane for the study in which they are used as models. This form of
relevance, which we deﬁne as interactive relevance in the proper sense, would characterize synthetic models
that could be used as tools to explore interaction dynamics between natural living-cognitive systems
and their human-made reproductions. This kind of investigation could be particularly interesting
when these artiﬁcial systems, as suggested earlier, would express simpliﬁed versions of the target
natural systems.

A fourth form of organizational relevance covered by the taxonomy that we are proposing
reﬂects the possibility of organizationally relevant artiﬁcial systems that are characterized by a pro-
gressive form of phenomenological relevance. This option introduces the case of synthetic mod-
els that, while reproducing the organization of their target systems, would generate unexpected
phenomena, supplementary with regard to those observed in the target systems. Hence the pos-
sibility of progressive relevance in the proper sense refers to synthetic models that would bring into evi-
dence aspects of the target natural living-cognitive systems that are still unknown or genuinely new
living-cognitive phenomena belonging to these artiﬁcial systems exclusively. In this second case,
models that would be progressively relevant in the proper sense, as basic organizationally relevant
models, would open to science the possibility of exploring “life as it could be,” as well as “cog-
nition as it could be,” according to the ambition that Langton (1989) ascribed to the synthetic
approach.

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As mentioned at the end of section 4.1, models with phenomenological relevance can be found
in current research based on software and hardware systems (not discussed in this article). On the
other hand, it is a question whether organizational relevance can characterize software and hardware
models–an issue that currently is debated in various ways. We believe, in line with the intuitions from
early cybernetics (see section 2), that synthetic models endowed with organizational relevance can
be built only within the wetware domain. Indeed, the biological-cognitive organization and phe-
nomenology, which express autopoiesis as the distinctive property of living-cognitive systems, are
based on a continuous production and transformation of their components that cannot take place
in software or hardware systems. Chemical networks dynamically embody the set of relations of re-
ciprocal production and transformation that characterizes autopoietic systems and their responses
to environmental perturbations.

4.3 Partial Forms of Phenomenological and Organizational Relevance
We can also consider the case of partial ways of meeting the proposed criteria and thus of partial
forms of relevance characterizing synthetic models. Partial phenomenological relevance refers to synthetic
models that partially reproduce, according to well-deﬁned parameters, the phenomenology under
inquiry. These kinds of models can be particularly interesting, from the scientiﬁc point of view,
to study complex interactions between the target processes and their environment of reference,
because they allow the exploration of speciﬁc aspects of these interactions. Partial organizational rel-
evance instead expresses the capability of a synthetic model to reproduce part of the organization
of the target living-cognitive system. A particularly interesting case, from the point of view of sci-
entiﬁc research, would be that of partial organizational relevance in the proper sense, in which the part
of the target system’s organization, incorporated in the artiﬁcial system, would correspond to the
underlying mechanisms of the phenomenology that the model reproduces. In this case, the syn-
thetic system would not be fully plausible from the biological point of view but would generate
the target phenomenology through organizationally relevant mechanisms. This kind of artiﬁcial
model, offering a partially realistic implementation of the target phenomenology, could be useful
in the context of progressive approaches to constructing organizationally relevant models in the
proper sense.

We believe that these categories cover the large majority of contemporary experimental wetware
approaches, which pragmatically face the complexity of biological systems by generating models
intended as milestones for a stepwise approximation of the full phenomenological or organizational
targets. For example, the early reports on chemical autopoiesis, owing to Pier Luigi Luisi and his
group in the 1990s (e.g., autopoietic reverse micelles, micelles, and vesicles, made by a handful of
simple chemical compounds; Bachmann et al., 1990, 1991; Luisi & Varela, 1989; Walde et al., 1994),
evidence partial forms of phenomenological and organizational relevance. These models were able
to capture both aspects of the phenomenon of the growth and division of cell-like particles (whose
structure was also relevant) and aspects of the corresponding generative mechanism (production of
compounds of the structure by internal reactions). In another known example, production and con-
sumption processes were balanced to achieve a phenomenological and organizational relevant, yet
rudimentary, form of chemical homeostasis (Zepik et al., 2001). It is not surprising, then, that these
pioneer efforts inspired, in renovated forms, attempts based on nucleic acids and proteins (Luisi
et al., 2002, 2006; Oberholzer et al., 1999; Yu et al., 2001), which later ﬂowed into contemporary
SB research.

4.4 Between Underdetermination and Overdetermination
The criteria and taxonomy of forms of relevance here proposed articulate the space between
the traditional poles of pure imitation and complete reproduction in a wide multiplicity of dif-
ferent forms of relevance for synthetic models, related to a variety of different research purposes,
paths, and uses for the scientiﬁc research on life and cognition. On these bases, the approach we are
proposing can be seen as an epistemological tool to make operational the warning through which

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Rosenblueth et al. (1943) suggested the scientiﬁc community adopt the synthetic approach to avoid
the unproductive shift from the problem of underdetermined models, whose explanatory power and
production of insights are limited by their imitative value, to the problem of overdetermined models,
whose proximity to the “real thing” itself would obscure, instead of clarifying, the principles at the
basis of life and cognition that this community intends to explore.

The approach we are advancing, on one side, provides the synthetic modeling with a broad,
concrete workspace between underdetermination and overdetermination and, on the other side,
proposes to make it generative based on the pluralist operative hypothesis according to which there
are many different ways to model the living-cognitive organization and, in this sense, to create
artiﬁcial systems grounded in the same principles of life and cognition as the living-cognitive systems
populating Earth.

In our view, it is in this space that exploratory SB can deﬁne new frontiers for EAI. Hence we
refer to this workspace to organize a research program to ground, in SB techniques, a minimal EAI
capable of overcoming the paradigm of imitation in the synthetic modeling of life and cognition.

5 Perspectives for Future Research and Concluding Remarks

How to advance SB research by designing and fabricating wetware systems characterized by forms
of basic, interactive, or progressive organizational relevance? The position we put forward in this
article is that a wide range of possibilities exists, lying between the extremes of pure imitation and
complete reproduction of biological and cognitive processes.

We have insisted on how the theory of autopoiesis provides a valid epistemological and op-
erational framework for designing synthetic systems that have chances of being relevant models
of living-cognitive systems. However, as we argued in previous works, autopoietic mechanisms
are very complex and thus highly difﬁcult to re-create artiﬁcially. Natural autopoietic—that is,
living-cognitive—systems have been shaped by evolution and, in their ontogenesis, are immersed
in a process of permanent coconstitution and codeﬁnition (through the terms of the autopoietic
theory: “structural coupling”) with their environment, which means that they originated and con-
stantly emerge from a coevolutionary path. Synthetic models, on the other hand, are generated by a
designer (the experimenter) who is bound to create for them internal mechanisms that, owing to the
scientiﬁc need to work within the experimental reach, represent simpliﬁed—often oversimpliﬁed—
versions of the target system (or of speciﬁc aspects of it) even when largely inspired by autopoi-
etic cognitive biology. Typically, the limits imposed by the experimental reach lead to the search of
minimal levels of complexity, either in the system’s parts or in its organization.

As a consequence, synthetic models currently produced can cope only with a narrow range of
environmental variations—imposed by the designer too. In other words, artiﬁcial systems have no
mechanisms and no time to evolve—or better, to coevolve—with an environment, also because, in
most cases, the environment itself is under the experimenter’s control. Synthetic systems are born
with prepacked instructions that are embodied not only in their structure but also in their rules of
functioning (e.g., the chemical reaction network in the case of wetware models), valid for a spe-
ciﬁc (over-)simpliﬁed environment only. This constraint implies an obvious but rarely mentioned
aspect of explorative SB research, namely, that the designer actually does not fabricate only a syn-
thetic model (an agent) but a “supersystem” composed by the synthetic model situated in a certain
environment—that is, a “synthetic ecology.” In this sense, what “makes sense” to wetware synthetic
models, and what does not, has actually been decided in advance. Within this experimentally acces-
sible scenario, the “cognitive domain” of such synthetic models is also predeﬁned at the time of
their design and construction. Only unexpected adaptations can broaden the horizon of what the
synthetic models can perceive and react to.

On the basis of these considerations, we would like to highlight one possible perspective for
future research concerning wetware SB models of cells (SCs and systems alike). Side by side with
the constantly reported advancements in the number of reconstituted functions, which ultimately

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serve to approach a lifelike behavior typically identiﬁed with a self-reproduction by growth divi-
sion, a productive direction for targeting organizationally relevant models could focus on explor-
ing processes potentially able to generate autonomous adaptive systems, characterized by a high
level of plasticity. This, in our view (Damiano & Stano, 2018a, 2018b), means concentrating the
synthetic modeling on mechanisms of self-regulation supporting self-maintenance, as these mecha-
nisms, from an autopoietic point of view, are at the basis of sensemaking—biological cognition.

One of the most productive tendencies in current SB modeling approaches makes use of highly
evolved elements, such as DNA and proteins, to build synthetic models of cells. These elements
are conveniently used for their high performance in terms of speciﬁcity and reliability, because of
the signiﬁcant constraints they generate on the system dynamics. The other side of the coin is that
the resulting system is signiﬁcantly stiff, in the sense that it is made of specialized elements—a
feature that is certainly a plus for a bioengineering perspective that aims at artiﬁcial systems that
perform accurately predetermined operations; indeed, synthetic biologists refer to this aspect as
the “programmability” of artiﬁcial systems (Fu, 2006; Kobayashi et al., 2004). A possible strategy
for introducing regulation mechanisms would be based on recruiting a sensorial layer, for example,
by implementing chemical neural networks in SCs (Gentili & Stano, 2022). This layer, however,
needs to be designed properly to evidence context-dependent responses and a form of adaptivity
or even plasticity. And it is an open question whether these goals can be achieved by employing
molecular devices like two-component signaling systems (Hellingwerf et al., 1995), possibly coupled
with self-referential gene expression.

However, other implementations can perhaps meet the organizational relevance criterion C2
differently, and should be explored too. For example, chemical networks and chemical systems
with autopoietic traits (e.g., even simple ones, as the possible revisitation of the aforementioned
example of micelles; Bachmann et al., 1990, 1991) could have the potential to respond to nonpre-
deﬁned environmental perturbations in a systemic, adaptive way—speciﬁc investigations must be
devised for exploring these new scenarios. As mentioned, experimenters need to learn to interpret
network behavior according to a wholeness perspective. Here the long tradition of AL research can
strongly contribute, providing conceptual and technical tools that would enrich SB approaches.
Frontier ﬁelds of chemistry, such as the so-called systems chemistry (Ashkenasy et al., 2017;
Ludlow & Otto, 2007; Ruiz-Mirazo et al., 2014; Szostak, 2009), can further contribute to this vision
with new strategies.

Taking into consideration the vast ﬁeld of experimental possibilities and the increasing interest
that wetware synthetic models are attracting per se, we plan future investigations on these (and
related) subjects. In particular, by relying on the proposed criteria (C1 and C2), and on the taxonomy
that they generate (Figure 1), we intend to offer a ﬁrst classiﬁcation of the forms of relevance
characterizing the variegate existing models, and from this starting point, we plan to move forward
to deﬁne a research program to advance the ﬁeld (Damiano & Stano, 2018a, 2021a, 2021b).

We believe that identifying the (forms/degrees of) phenomenological and organizational rele-
vance is in itself a tool that helps to deﬁne new systems and modeling projects of interest. We expect
that elaborating a set of theoretical models based on selected theories of the biological organization,
and realistic ways of material implementation, is the next critical step for advancing research and
related scientiﬁc discoveries in the ﬁeld of explorative SB applied to AI. This will also limit the
risks of stumbling on issues already met in the sciences of the artiﬁcial operating in other domains.
Wetware synthetic models can indeed leak into the discussion on diffused analogy dyads, such as
machine–organism, computer–mind, and similar concepts, by providing insights that uniquely stem
from the very nature of chemical systems, whereby the distinction between operations and operands
tends to vanish and circular casualties can often be recognized—especially in networks.

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