Etxeverria, A., Moreno, A. (2009) on Biological Relevance

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Adaptive Behavior

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DOI: 10.1177/1059712309340842

2009 17: 306Adaptive BehaviorArantza Etxeberria and Alvaro Moreno

On Biological Relevance

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306

On Biological Relevance

Arantza Etxeberria, Alvaro Moreno

University of the Basque Country

Barbara Webbs article advocates using artificial

agents and robots to model real animals. The method-

ology of building artificial creatures to study adaptive

behavior brings her close to other people in the field

of ALife or adaptive behavior (AB) using a similar

synthetic methodology, but far from them in the ulti-

mate goal, as she defines it: whereas hers is in mode-

ling the real animal (the real target), others in the

field work without any pre-established target. Webb

feels that as far as work with robots aims to be a con-

tribution to biology, models of invented animals

should be avoided, because the biological relevance of

the models is dependent on the existence and extent

of the mapping between [the] agent and real cognitive

systems (sec. 4.1).

If her claim is a statement in favor of building

some artificial agents and robots in such a way that

they constitute good classical models of certain phe-

nomena that are interesting for biology, then we may

agree. Even if it is an encouragement to others to drawon empirical data, we concur. But we do not think the

solution to the problem of the relation of the charac-

teristically abstract or systemic models of the field

with empirical data is as straightforward as she main-

tains: although the goal of developing robots that will

purport contributions to biology is an important one,

we find the way Webb advises improving the scien-

tific relevance of ALife/robotic models problematic.

The focus of ALife/AB is in the development of a

research program capable of a naturalist understand-

ing of some capacities of autonomous agents in theirenvironments, enabled by their physical architectures

and the interactions in which they engage. This implies

the questioning of some conceptual aspects of prior

biological and cognitive theories, specifically con-

cerning the kinds of the relevant primitives required

(such as symbols or representations) and how they

relate to one another. In a way, the goal of this type of

model is different from that of the classical ones

(including in this term the new robotic or computa-

tional models), in that they appear to try to make

progress in meta-theoretical issues. This implies work

to generate new theories about the biological basis of

adaptation and cognition which may in their novelty

challenge in some ways the style of traditional empiri-

cal sciences and may pose problems about how to map

them into an empirical domain. Concretely they may

appear to be highly abstract and in need of connection

to the empirical domain. In our opinion Webbs article

uncovers this problem but does not provide good or

useful clues about how to proceed about it, mainly

because she dismisses the epistemological peculiarities

of the field and tries to solve the problems encountered

suggesting a return to a classical conception of the role

of science and modeling. We shall come back to thisquestion at the end.

Work on adaptive systems needs to be systemic

and requires focusing on how to solve problems of

organization. Our claim is that the lack of empirical

ground may be an issue for this field, but only after (not

before) some of these problems have been analyzed

conceptually through abstract/mathematical models.

However, as we shall explain later, these kinds of

models are quite different from the traditional analyti-

cal models typical of physicochemical sciences.

Rather than deducing the state of a system for a giventime value, these mathematical models work by syn-

thetically reproducing in the computer the holistic prop-

erties of an abstract system through numerical methods,

consisting in a fine-grained step-by-step update and

Copyright 2009 International Society for Adaptive Behavior

(2009), Vol 17(4): 306308.

DOI: 10.1177/1059712309340842

Correspondence to: Arantza Etxeberria, University of the Basque

Country, Avenida de Tolosa 70, San Sebastin 20080, Spain.

E-mail: [email protected]. Tel.: +34 943 01 5507

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Etxeberria & Moreno On Biological Relevance 307

record of the state of all the interrelated variables of

the system. In this way, the evolution of the system is

synthetically reproduced in the course of the simula-

tion. Under these conditions, the prediction of the glo-

bal properties requires an enormous amount of parallel

computation, where the complexity of the computa-

tional simulation is almost equivalent to that of the

simulated system.

In fact, the development of such models and con-

ceptualizations has already made a relevant contribution

to biology (although in need of closer combination

with empirical data). Then an epistemological task for

the future of the field is to find how empirical data will

specifically help the study of organizations already

studied through abstract models. To attempt a clarifi-

cation, we need to explain how we agree and disagree

with Webb.

Firstly, there is a way in which it is not conten-tious to say that animats are biological models of

some sort: as Webb herself states at the end of the arti-

cle, even Braitenbergs vehicles intend biological rel-

evance, and we do not think there is dispute on this.

However, when she says that animats should also be

viewed and evaluated as models (sec. 3) she implies

a correspondence, as a representational relation,

between them and a target phenomenon in the world

(It is relevant to biology to the extent that its compo-

nents and behavior can be directly compared with that

animal. sec. 3). We think that biological relevance inthis sense of direct correspondence with the world

may be too narrow for the aims of the field.

Webb is probably right that it is important to have

models that look at real cases. Apart from the general

sense that all work on neural networks or complex

dynamical systems, however abstractly defined, is

always inspired in biology in its very ground, there are

of course evolutionary and developmental arguments to

defend that work should be grounded in biology. The

general organizational patterns of neural networks and

other biological structures, as well as interaction behav-

iors, have been shaped through evolution according to

morphogenetic principles largely based on the nature

of materials and interactions, different forms of inher-

itance or transmission, and many contingencies. Then

the range of variations existing in nature is limited,

although huge, and should be taken into account at the

time of generating artificial systems.

However, these generic properties need to be dis-

covered by studying the mechanisms responsible for

their generation. To investigate their properties, many

abstract theoretical models have been extremely use-

ful. It is true that the properties and results of dynami-

cal systems (e.g., patterns of connectivity, attractors,

etc.) may be thought not to speak directly of the

empirical ground, but work done so far has been

important for discovering and operationally defining

many mechanisms. Now new methodologies need to be

devised to precisely find their epistemological nature,

to improve the empirical relevance of the models, and

maybe to obtain new kinds of empirical data.

Secondly, ALifes stress has been on producing

instantiations of living and cognitive phenomena to

expand the ontology of entities of that kind (living,

cognitive) in the world; such instantiations are based

on a multiple understanding of the concept of a model

beyond the idea of a model as representation of a real

phenomenon that Webb exploits. But expectations ofstudying life-as-it-could-be by expanding the ontolog-

ical realm of life in other media have failed in many

ways. ALife has embraced synthesis to explore the

possible, but theories in biology are not univocal and

it is very difficult to explore possible life without a

good understanding of actual life. The lack of success

of that research program obliges us to rethink the rela-

tion of the field with biology. However, the field does

not need to renounce the aim of producing models as

examples or instances of the phenomena under study;

that is to say, entities that are not only representations,but objects of study themselves. The tremendous com-

plexity of the activity of model making in science

these days prevents taking the intent of a model as the

only ground for its evaluation.

As a matter of fact, artificial agents and robots

need to be considered and evaluated as something

more than replicas of real animals, as one of the main

goals of the field is to study their capacities as agents

that interact with the environment (including the

human modeler).

It is precisely in this sense of model (as an

instance of an agent) that robots are instrumental for

studying and discussing very general theoretical prim-

itives, or for grounding notions, including their role as

existence proofs of possible relations or phenomena

that may later be further investigated in real systems.

Besides going from real phenomena to models, this

work also provides knowledge in the opposite direc-

tion from models to reality. Thus, it may be the case

that Webbs appeal to real data may be required in a



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308 Adaptive Behavior 17(4)

sense opposite to the one she demands: maybe it is not

that the models need to conform to data but that we

have to devise ways to see if and how some theoreti-

cal abstract results (impossible to obtain starting from

raw data, however exact, without the theoretical help

of abstract models) have correspondence in nature.

In this sense, the development of abstract systems

does contribute to biology at least in that they pro-

vide new ways to think about the organic world and

may be developed into new ways of assessing results

in nature.

If what we are looking for is to assess highly

abstract theoretical claims, then the requirement of

building models testable against real, specific empiri-

cal objects may be secondary. What matters, instead,

is a research strategy aiming to improve, through an

indirect experimental technologically mediated meth-

odology, our epistemological and meta-theoreticalassumptions about a given empirical domain. Namely,

making explicit and testing the validity of what post-

positivist philosophers of science have considered as

metaphysical (but inevitable) assumptions of scien-

tific theories. How can this task be done? Models and

theories can be used to improve each other by estab-

lishing a kind of hermeneutic circularity: the models

are used for the elucidation of theories about the

empirical world, and these are then used to interpret

the models. In our case, since ALife or animat models

are used for the elucidation of theories about the empir-

ical world, then the reformulated theories can be in turn

re-used to interpret the models, and so on. In other

words, the process of empirical validation is highly

indirect and complex.

To conclude, our main disagreement with Webbs

position has to do with her views on what constitutes a

contribution to biology. The development of models

that are abstract in the sense she analyzes (neural net-

works, dynamical systems theory, etc.) has provided a

tremendous contribution to science in general and to

biology in particular, even if many issues about how

to relate them to reality are not straightforward and are

yet to come. Then, the relevance of ALife models and

animats should be considered according to their

capacity to make explicit, dissonant, or inconsistent

some of the components of the conceptual frame-

works of biological and cognitive theories, thus lead-ing to improve these theories. It is true that the use of

abstract dynamical theoretical models to study biolog-

ical phenomena has been lately contested in many

fields: systems biology, evo-devo, and so forth, as

models lacking empirical ground. But, at the same

time, this research has doubtless fuelled very impor-

tant aspects of contemporary science. The difficulty of

finding clear empirical counterparts for these models

is an important challenge that should be met without

forgetting their theoretical significance for biology.


Etxeverria, A., Moreno, A. (2009) on Biological Relevance

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