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This chapter is forthcoming in Handbook of Philosophy of Information, edited by
Pieter Adriaans and Johan van Benthem in the series Handbooks of the Philosophy
of Science (Elsevier)
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1
Luciano
Floridi
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Date: 2006.09.03 16:33:51 +01'00'
Final version
Trends in the Philosophy of Information
Luciano Floridi
Dipartimento di Scienze Filosofiche, Università degli Studi di Bari; Faculty of
Philosophy and IEG, OUCL, University of Oxford.
Address for correspondence: St Cross College, OX1 3LZ, Oxford, UK;
luciano.floridi@philosophy.oxford.ac.uk
1. Introduction
“I love information upon all subjects that come in my way, and especially upon
those that are most important.” Thus boldly declares Euphranor, one of the
defenders of Christian faith in Berkley’s Alciphron.1 Evidently, information has
been an object of philosophical desire and puzzlement for some time, well before the
computer revolution, Internet or the dot.com pandemonium. Yet what does
Euphranor love, exactly? What is information?
As with many other field-questions (consider for example “what is being?”,
“what is morally good?” or “what is knowledge?”), “what is information?” is to be
taken not as a request for a dictionary definition, but as a means to demarcate a wide
area of research. The latter has recently been defined as the philosophy of
information (Floridi [2002], Floridi [2003b]). The task of this chapter is to review
some interesting research trends in the philosophy of information (henceforth also
PI). This will be achieved in three steps. We shall first look at a definition of PI. On
this basis, we shall then consider a series of open problems in PI on which
philosophers are currently working.2 The conclusion will then highlight the
innovative character of this new area of research.
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2. Defining the Philosophy of Information
The philosophy of information may be defined as the philosophical field concerned
with
a) the critical investigation of the conceptual nature and basic principles of
information, including its dynamics, utilisation and sciences, and
b) the elaboration and application of information-theoretic and computational
methodologies to philosophical problems.3
The first half of the definition concerns the philosophy of information as a
new field. PI appropriates an explicit, clear and precise interpretation of the classic,
Socratic question “ti esti...?” (“what is...?”), namely “What is the nature of
information?”. This is the clearest hallmark of a new field. PI provides critical
investigations that are not to be confused with a quantitative theory of data
communication (information theory). On the whole, we shall see that its task is to
develop an integrated family of theories that analyse, evaluate and explain the
various principles and concepts of information, their dynamics and utilisation, with
special attention to systemic issues arising from different contexts of application and
the interconnections with other key concepts in philosophy, such as knowledge,
truth, meaning and reality.
By “dynamics of information” the definition refers to:
i) the constitution and modelling of information environments, including their
systemic properties, forms of interaction, internal developments, applications etc.;
ii) information life cycles, i.e. the series of various stages in form and functional
activity through which information can pass, from its initial occurrence to its final
utilisation and possible disappearance;4 and
iii) computation, both in the Turing-machine sense of algorithmic processing, and in
the wider sense of information processing. This is a crucial specification. Although a
very old concept, information has finally acquired the nature of a primary
phenomenon only thanks to the sciences and technologies of computation and ICT
(Information and Communication Technologies). Computation has therefore
attracted much philosophical attention in recent years. Nevertheless, PI privileges
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“information” over “computation” as the pivotal topic of the new field because it
analyses the latter as presupposing the former. PI treats “computation” as only one
(although very important) of the processes in which information can be involved.
From an environmental perspective, PI is critical and normative about what
may count as information, and how information should be adequately created,
processed, managed and used. Methodological and theoretical choices in ICS are
also profoundly influenced by the kind of PI a researcher adopts more or less
consciously. It is therefore essential to stress that PI critically evaluates, shapes and
sharpens the conceptual, methodological and theoretical basis of ICS, in short that it
also provides a philosophy of ICS, as this has been plain since early work in the area
of philosophy of AI (Colburn [2000]).
It is worth stressing here that an excessive concern with contemporary issues
may lead one to miss the important fact that it is perfectly legitimate to speak of a
philosophy of information even in authors who lived before the information
revolution, and hence that it will be extremely fruitful to develop a historical
approach and trace PI’s diachronic evolution, as long as the technical and conceptual
frameworks of ICS are not anachronistically applied, but are used to provide the
conceptual method and privileged perspective to evaluate in full reflections that
were developed on the nature, dynamics and utilisation of information before the
digital revolution. This is significantly comparable with the development undergone
by other philosophical fields like the philosophy of language, the philosophy of
biology, or the philosophy of mathematics.5
The second half of the definition indicates that PI is not only a new field, but
provides an innovative methodology as well. Research into the conceptual nature of
information, its dynamics and utilisation is carried on from the vantage point
represented by the methodologies and theories offered by ICS and ICT (Grim et al.
[1998] and Greco et al. [2005]). This perspective affects other philosophical topics
as well. Information-theoretic and computational methods, concepts, tools and
techniques have already been developed and applied in many philosophical areas,
• to extend our understanding of the cognitive and linguistic abilities of
humans and animals and the possibility of artificial forms of intelligence
(e.g. in the philosophy of AI; in information-theoretic semantics; in
information-theoretic epistemology and in dynamic semantics);
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• to analyse inferential and computational processes (e.g. in the philosophy of
computing; in the philosophy of computer science; in information-flow
logic; in situation logic; in dynamic logic and in various modal logics);
• to explain the organizational principles of life and agency (e.g. in the
philosophy of artificial life; in cybernetics and in the philosophy of
automata; in decision and game theory);
• to devise new approaches to modelling physical and conceptual systems (e.g.
in formal ontology; in the theory of information systems; in the philosophy
of virtual reality);
• to formulate the methodology of scientific knowledge (e.g. in model-based
philosophy of science; in computational methodologies in philosophy of
science);
• to investigate ethical problems (in computer and information ethics and in
artificial ethics), aesthetic issues (in digital multimedia/hypermedia theory, in
hypertext theory and in literary criticism) and psychological, anthropological
and social phenomena characterising the information society and human
behaviour in digital environments(cyberphilosophy).
Indeed, the previous examples and the various chapters in this volume show that PI,
as a new field, provides a unified and cohesive, theoretical framework that allows
further specialisation.
3. Open Problems in the Philosophy of Information
PI possesses one of the most powerful conceptual vocabularies ever devised in
philosophy. This is because we can rely on informational concepts whenever a
complete understanding of some series of events is unavailable or unnecessary for
providing an explanation. In philosophy, this means that virtually any issue can be
rephrased in informational terms. This semantic power is a great advantage of PI
understood as a methodology (see the second half of the definition). It shows that we
are dealing with an influential paradigm, describable in terms of an informational
philosophy. But it may also be a problem, because a metaphorically pan-
informational approach can lead to a dangerous equivocation, namely thinking that
since any x can be described in (more or less metaphorically) informational terms,
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then the nature of any x is genuinely informational. And the equivocation obscures
PI’s specificity as a philosophical field with its own subject. PI runs the risk of
becoming synonymous with philosophy. The best way of avoiding this loss of
identity is to concentrate on the first half of the definition. PI as a philosophical
discipline is defined by what a problem is (or can be reduced to be) about, not by
how the latter is formulated. Although many philosophical issues seem to benefit
greatly from an informational analysis, in PI one presupposes that a problem or an
explanation can be legitimately and genuinely reduced to an informational problem
or explanation. So the criterion to test the soundness of the informational analysis of
x is not to check whether x can be formulated in informational terms but to ask what
would be like for x not to have an informational nature at all. With this criterion in
mind, we shall now review some of the most interesting problems in PI.
For reasons of space, only some research trends and issues could be included
and even those selected are only briefly outlined and not represented with adequate
depth, sophistication and significance. This is not only because of space, but also
because the interested reader will find a wealth of further material in the other
chapters of this Handbook. The issues included have been privileged because they
represent macroproblems, that is, they are the hardest to tackle but also the ones that
have the greatest influence on clusters of microproblems to which they can be
related as theorems to lemmas. Some microproblems are mentioned whenever they
seem interesting enough, but especially in this case the list is far from exhaustive.
Some problems are new, others are developments of old problems, and in some
cases philosophers have already begun to address them, but the review does not
concern old trends and problems that have already received their due philosophical
attention. There is also no attempt at keeping a uniform level of scope. Some
problems are very general, others more specific. All of them have been chosen
because they well indicate how vital and useful the new paradigm is in a variety of
philosophical areas. Finally, whenever possible I have indicated which chapters in
the Handbook are relevant to the problem under discussion.
4. The nature of information
This is the hardest and most central question in PI. It has received many answers in
different fields but, unsurprisingly, several surveys do not even converge on a
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single, unified definition of information (see for example Braman [1989], Losee
[1997], Machlup and Mansfield [1983], Debons and Cameron [1975], Larson and
Debons [1983]). Information is notoriously a polymorphic phenomenon and a
polysemantic concept so, as an explicandum, it can be associated with several
explanations, depending on the level of abstraction adopted and the cluster of
requirements and desiderata orientating a theory. Claude E. Shannon, for one, was
very cautious: “The word ‘information’ has been given different meanings by
various writers in the general field of information theory. It is likely that at least a
number of these will prove sufficiently useful in certain applications to deserve
further study and permanent recognition. It is hardly to be expected that a single
concept of information would satisfactorily account for the numerous possible
applications of this general field. (italics added)” (Shannon [1993], p. 180). Thus,
following Shannon, Weaver [1949] supported a tripartite analysis of information in
terms of (1) technical problems concerning the quantification of information and
dealt with by Shannon’s theory; (2) semantic problems relating to meaning and
truth; and (3) what he called “influential” problems concerning the impact and
effectiveness of information on human behaviour, which he thought had to play an
equally important role. And these are only two early examples of the problems
raised by any analysis of information.
Indeed, the plethora of different analyses can be confusing. Complaints about
misunderstandings and misuses of the very idea of information are frequently
expressed, even if to no apparent avail. Sayre [1976], for example, already criticised
the “laxity in use of the term ‘information’” in Armstrong [1968] (see now
Armstrong [1993]) and in Dennett [1969] (see now Dennett [1986]), despite
appreciating several other aspects of their work. More recently, Harms [1998]
pointed out similar confusions in Chalmers [1996], who “seems to think that the
information theoretic notion of information [see section 3, my addition] is a matter
of what possible states there are, and how they are related or structured […] rather
than of how probabilities are distributed among them” (p. 480).
Information remains an elusive concept. This is a scandal not by itself, but
because so much basic theoretical work, both in science and in philosophy, relies on
a clear grasping of the nature of information and of its cognate concepts. We know
that information ought to be quantifiable (at least in terms of partial ordering),
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additive, storable and transmittable. But apart from this, we still do not seem to have
a much clearer idea about its specific nature.
Information is often approached from three perspectives: information as reality
(e.g. as patterns of physical signals, which are neither true nor false), also known as
ecological information; information about reality (semantic information, which is
alethically qualifiable and an ingredient in the constitution of knowledge); and
information for reality (instruction, like genetic information, algorithms and
recipes). Many extensionalist approaches to the definition of information as/about
reality provide different starting points. The following list contains only some of the
most philosophically interesting or influential, and I shall say a bit more about each
of them presently. They are not to be taken as necessarily alternative, let alone
incompatible:
1. the communication theory approach (mathematical theory of codification and
communication of data/signals (Shannon and Weaver [1949 rep. 1998]; see also
the chapter by Topsøe and Harremoës) defines information in terms of
probability space distribution;
2. the algorithmic approach (also known as Kolmogorov complexity, Li and
Vitâanyi [1997]; see also the chapters by Grunwald and Vitâanyi and by
Adriaans) defines the information content of X as the size in bits of the smallest
computer program for calculating X (Chaitin [2003]);
3. the probabilistic approach (Bar-Hillel and Carnap [1953], Bar-Hillel [1964],
Dretske [1981]; see also the chapter by Dretske), is directly based on (1) above
and defines semantic information in terms of probability space and the inverse
relation between information in p and probability of p;
4. the modal approach defines information in terms of modal space and
in/consistency (the information conveyed by p is the set of possible worlds
excluded by p);
5. the systemic approach (situation logic, Barwise and Perry [1983], Israel and
Perry [1990], Devlin [1991]) defines information in terms of states space and
consistency (information tracks possible transitions in the states space of a
system);
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6. the inferential approach defines information in terms of inferences space
(information depends on valid inference relative to a person’s theory or
epistemic state);
7. the semantic approach (Floridi [2004c], Floridi [2005b]) defines information in
terms of data space (semantic information is well-formed, meaningful and
truthful data).
Each extentionalist approach can be given an intentionalist reading by interpreting
the relevant space as a doxastic (i.e. belief-related) space, in which information is
seen as a reduction in the degree of uncertainty or level of surprise given a state of
knowledge of the informee (see the chapters by Baltag, Moss and van Ditmarsch and
by Rott).
Communication theory in (1) approaches information as a physical phenomenon,
syntactically. It is not interested in the usefulness, relevance, meaning, interpretation
or reference of data, but in the level of detail and frequency in the uninterpreted data
(signals or messages). It provides a successful mathematical theory because its
central question is whether and how much data, not what information is conveyed.
The algorithmic approach in (2) is equally quantitative and solidly based on
probability theory. It interprets information and its quantities in terms of the
computational resources needed to specify it.
The remaining approaches all address the question “what is semantic
information?”. They seek to give an account of information as semantic content,
usually adopting a propositional orientation (they analyse examples like “The earth
has only one moon”). Do (1) or (2) provide the necessary conditions for any theory
of semantic information in (3)-(7)? Are all the remaining semantic approaches
mutually compatible? Is there a logical hierarchy? Do any of the previous
approaches provide a clarification of the notion of data as well? Most of the
problems in PI acquire a different meaning depending on how we answer this cluster
of questions. Indeed, positions might be more compatible than they initially appear
owing to different interpretations of the concept(s) of information involved.
Once the concept of information is clarified, each of the previous approaches
needs to address the following question.
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5. The dynamics of information
The question does not concern the nature of management processes (information
seeking, data acquisition and mining, information harvesting and gathering, storage,
retrieval, editing, formatting, aggregation, extrapolation, distribution, verification,
quality control, evaluation, etc.) but, rather, information processes themselves,
whatever goes on between the input and the output phase. Communication theory, as
the mathematical theory of data transmission, provides the necessary conditions for
any physical communication of information, but is otherwise of only marginal help.
The information flow⎯understood as the carriage and transmission of information
by some data about a referent, made possible by regularities in a distributed
system⎯has been at the centre of logical studies for some time (Barwise and
Seligman [1997], Van Benthem [2003]), but still needs to be fully explored. How is
it possible for something to carry information about something else? The problem
here is not yet represented by the “aboutness” relation, which needs to be discussed
in terms of meaning, reference and truth. The problem here concerns the nature of
data as vehicles of information. In this version, the problem plays a central role in
semiotics, hermeneutics and situation logic. It is closely related to the problem of the
naturalisation of information. Various other logics, from classic first order logic to
epistemic, erotetic and dynamic logic, provide useful approaches with which to
analyse the logic of information, but there is still much work to be done (Van
Benthem and Van Rooy [2003], Allo [forthcoming], Allo and Floridi [forthcoming],
Floridi [forthcoming]).
Information processing, in the general sense of information states transitions,
includes at the moment effective computation (computationalism, Newell [1980];
Pylyshyn [1984]; Fodor [1975];[1987]; Dietrich [1990]), distributed processing
(connectionism, Smolensky [1988]; Churchland and Sejnowski [1992]), and
dynamical-system processing (dynamism, Van Gelder [1995]; Van Gelder and Port
[1995]; Eliasmith [1996]). The relations between the current paradigms remain to be
clarified (Minsky [1990], for example, argues in favour of a combination of
computationalism and connectionism in AI, as does Harnad [1990] in cognitive
science), as do the specific advantages and disadvantages of each, and the question
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as to whether they provide complete coverage of all possible internalist information
processing methods.
The two previous questions in §§ 4 and 5 and are closely related to a third,
more general problem.
6. The challenge of unified theory of information
The reductionist approach holds that we can extract what is essential to
understanding the concept of information and its dynamics from the wide variety of
models, theories and explanations proposed. The non-reductionist argues that we are
probably facing a network of logically interdependent but mutually irreducible
concepts. The plausibility of each approach needs to be investigated in detail. Both
approaches, as well as any other solution in between, are confronted by the difficulty
of clarifying how the various meanings and phenomena of information are related,
and whether some concepts of information are more central or fundamental than
others and should be privileged. Waving a Wittgensteinian suggestion of family
resemblance means only acknowledging the problem, not solving it. The reader
interested in a positive answer the question may wish to read the essays collected in
Hofkirchner [1998]. A defence of a more skeptical view, following Shannon, can be
found in Floridi [2003a].
7. The data grounding problem: how data acquire their meaning
We have seen that most analyses of the nature of information tend to concentrate on
its semantic features, quite naturally. So it is useful to carry on our review of
problem areas in PI by addressing next the cluster of issues arising in informational
semantics. Their discussion is bound to be deeply influential in several areas of
philosophical research. But first, a warning. It is hard to formulate problems clearly
and in some detail in a completely theory-neutral way. So in what follows, the
semantic frame will be adopted (see above § 4, (7)), namely the view that semantic
information can be satisfactorily analysed in terms of well-formed, meaningful and
veridical data. This semantic approach is simple and powerful enough for the task at
hand. If the problems selected are sufficiently robust, it is reasonable to expect that
their general nature and significance are not relative to the theoretical vocabulary in
which they are cast but will be exportable across conceptual platforms.
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We have already encountered the issue of the nature of data. Suppose data are
intuitively described as uninterpreted differences (symbols or signals). How do they
become meaningful? This is the data grounding problem.
Searle [1990] refers to a specific version of the data grounding problem as the
problem of intrinsic meaning or “intentionality”. Harnad [1990] defines it as the
symbols grounding problem and unpacks it thus: “How can the semantic
interpretation of a formal symbol system be made intrinsic to the system, rather than
just parasitic on the meanings in our heads? How can the meanings of the
meaningless symbol tokens, manipulated solely on the basis of their (arbitrary)
shapes, be grounded in anything but other meaningless symbols?” (p. 335).
Arguably, the frame problem (how a situated agent can represent, and interact
with, a changing world satisfactorily) and its sub-problems are a consequence of the
data grounding problem (Harnad [1993], Taddeo and Floridi [2005]). In more
metaphysical terms, this is the problem of the semanticisation of being and it is
further connected with the problem of whether information can be naturalised.
8. The semantic problem: how meaningful data acquire their truth value
Once grounded, meaningful data can acquire different truth values, the question is
how. The question then gains new dimensions when asked within epistemology and
the philosophy of science. It also interacts with the way in which we approach both a
theory of truth and a theory of meaning, especially a truth-functional one (see the
chapter by Groenendijk, Kamp and Stokhof). Are truth and meaning understandable
on the basis of an informational approach, or is it information that needs to be
analysed in terms of non-informational theories of meaning and truth? To call
attention to this important set of issues it is worth asking two more place-holder
questions:
1) can information explain truth?
In this, as in the following question, we are not asking whether a specific theory
could be couched, more or less metaphorically, in some informational vocabulary.
This would be a pointless exercise. What is in question is not even the mere
possibility of an informational approach. Rather, we are asking
1.a) could an informational theory explain truth more satisfactorily than other
current approaches? And
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1.b), should (1.a) be answered in the negative, could an informational approach at
least help to clarify the theoretical constraints to be satisfied by other approaches?
The second major question mentioned above is:
2) can information explain meaning?
Several informational approaches to semantics have been investigated in
epistemology (Dretske [1981]; Dretske [1988]), situation semantics (Seligman and
Moss [1997]), discourse representation theory (Kamp [1984]) and dynamic
semantics (Muskens and Al. [1997]). Is it possible to analyse meaning not truth-
functionally but as the potential to change the informational context? Can semantic
phenomena be explained as aspects of the empirical world? Since the problem is
whether meaning can at least partly be grounded in an objective, mind- and
language-independent notion of information (naturalisation of intentionality), it is
strictly connected with the problem of the naturalisation of information.
9. Information processing and the study of cognition
Information and its dynamics are central to the foundations of AI and of cognitive
science (see the chapters by McCarthy and Boden). Both discipline study cognitive
agents as informational systems that receive, store, retrieve, transform, generate and
transmit information. This is the information processing view. Before the
development of connectionist and dynamic-system models of information
processing, it was also known as the computational view. The latter expression was
acceptable when a Turing machine (Turing [1936]) and the machine involved in the
Turing test (Turing [1950]) were inevitably the same. It has recently become
misleading, however, because computation, when used as a technical term (effective
computation), refers now to the specific class of algorithmic symbolic processes that
can be performed by a Turing machine, that is recursive functions (Turing [1936],
Minsky [1967]; Floridi [1999]; Boolos et al. [2002]).
The information processing view of cognition, intelligence and mind
provides the oldest and best-known cluster of significant problems in PI.6 Some of
their formulations, however, have long been regarded as uninteresting. Turing
[1950] considered “can machines think?” a meaningless way of posing the otherwise
interesting problem of the functional differences between AI and NI (natural
intelligence). Searle [1990] has equally dismissed “is the brain a digital computer?”
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as ill-defined. The same holds true of the unqualified question “are naturally
intelligent systems information processing systems?”. Such questions are vacuous.
Informational concepts are so powerful that, given the right level of abstraction
(LoA; Floridi and Sanders [2004]; Floridi and Sanders [forthcoming]), anything can
be presented as an information system, from a building to a volcano, from a forest to
a dinner, from a brain to a company, and any process can be simulated
informationally - heating, flying and knitting. So pancomputationalist views have
the hard task of providing a credible answer to the question: what would it mean for
a physical system not to be an informational system (that is, a computational system,
if computation is used to mean information processing, see Chalmers [1996] and
Chalmers [online])? The task is hard because pancomputationalism does not seem
vulnerable to a refutation, in the form of a realistic token counterexample in a world
nomically identical to the one to which pancomputationalism is applied.7 A good
way of posing the problem is not: “is ‘x is y’ adequate?”, but rather “if ‘x is y’ at
LoA z, is z adequate?”.
10. Science and information modelling
In many contexts (probability or modal states and inferential spaces), we often adopt
a conditional, laboratory view. We analyse what happens in “a’s being (of type, or
in state) F is correlated to b being (of type, or in state) G, thus carrying for the
observer the information that b is G”(Barwise and Seligman [1997] provide a similar
analysis based on Dretske [1981]) by assuming that F(a) and G(b). In other words,
we assume a given model. The question asked here is: how do we build the original
model? Many approaches seem to be ontologically over-committed. Instead of
assuming a world of empirical affordances and constraints to be designed, they
assume a world already well-modelled, ready to be discovered. The semantic
approach to scientific theories (Suppes [1960]; Suppes [1962]; Van Fraassen [1980];
Giere [1988]; Suppe [1989]), on the other hand, argues that “scientific reasoning is
to a large extent model-based reasoning. It is models almost all the way up and
models almost all the way down.” (Giere [1999], 56).
Theories do not make contact with phenomena directly, but rather higher
models are brought into contact with other, lower models. These are themselves
theoretical conceptualisations of empirical systems, which constitute an object being
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modelled as an object of scientific research. Giere [1988] takes most scientific
models of interest to be non-linguistic abstract objects. Models, however, are the
medium, not the message. Is information the (possibly non-linguistic) content of
these models? How are informational models (semantically, cognitively and
instrumentally) related to the conceptualisations that constitute their empirical
references? What is their semiotic status, e.g. structurally homomorphic or
isomorphic representations or data-driven and data-constrained informational
constructs? What levels of abstraction are involved? Is science a social (multi-
agents), information-designing activity? Is it possible to import, in (the philosophy
of) science, modelling methodologies devised in information system theory? Can an
informational view help to bridge the gap between science and cognition? Answers
to these questions are closely connected with the discussion of the problem of an
informational theory of truth see above. The reader interested in some specific
applications will find them in the chapters by Devlin and Rosenberg, and by Collier.
The possibility of a more or less informationally constructionist philosophy
of science leads to our next cluster of problems, concerning the relation between
information and the natural world.
11. The ontological status of information
Barwise and Seligman [1997] have remarked that “If the world were a completely
chaotic, unpredictable affair, there would be no information to process. Still, the
place of information in the natural world of biological and physical systems is far
from clear.” (p. xi). This lack of clarity prompts a whole family of problems.
It is often argued that there is no information without (data) representation.
Following Landauer and Bennett [1985]; Landauer [1987]; Landauer [1991];
Landauer [1996], this principle is usually interpreted materialistically, as advocating
the impossibility of physically disembodied information, through the equation
“representation = physical implementation”. The view that there is no information
without physical implementation is an inevitable assumption, when working on the
physics of computation, since computer science must necessarily take into account
the physical properties and limits of the carriers of information. It is also the
ontological assumption behind the Physical Symbol System Hypothesis in AI and
cognitive science (Newell and Simon [1976]). However, the fact that information
15
requires a representation does not entail that the latter ought to be physically
implemented. Arguably, environments in which there are only noetic entities,
properties and processes (e.g. Berkeley, Spinoza), or in which the material or
extended universe has a noetic or non-extended matrix as its ontological foundation
(e.g. Pythagoras, Plato, Leibniz, Hegel), seem perfectly capable of upholding the
representationalist principle without also embracing a materialist interpretation (see
Floridi [2004a] for a defence of this view). The relata giving rise to information
could be monads, for example. So the problem here becomes: is the informational an
independent ontological category, different from the physical/material and
(assuming one could draw this Cartesian distinction) the mental? Wiener, for
example, thought that “Information is information, not matter or energy. No
materialism which does not admit this can survive at the present day” (Wiener
[1948], 132).
If the informational is not an independent ontological category, to which
category is it reducible? If it is an independent ontological category, how is it related
to the physical/material and the mental? Answers to these questions determine the
orientation a theory takes with respect to the following problem.
12. Naturalised information
The problem is connected with the semanticisation of data. It seems hard to deny
that information is a natural phenomenon, so this is not what one should be asking
here. Even elementary forms of life such as sunflowers survive because they are
capable of some chemical data processing. The problem here is whether there is
information in the world independently of forms of life capable to extract it and, if
so, what kind of information is in question (an informational version of the
teleological argument for the existence of God argues both that information is a
natural phenomenon and that the occurrence of environmental information requires
an intelligent source). If the world is sufficiently information-rich, perhaps an agent
may interact successfully with it by using “environmental information” directly,
without being forced to go through a representation stage in which the world is first
analysed informationally. “Environmental information” still presupposes (or perhaps
is identical with) some physical support but it does not require any higher-level
cognitive representation or computational processing to be immediately usable. This
16
is argued, for example, by researchers in AI working on animats (artificial animals,
either computer simulated or robotic). Animats are simple reactive agents, stimulus-
driven. They are capable of elementary, “intelligent” behaviour, despite the fact that
their design excludes the possibility of internal representations of the environment
and any effective computation (Mandik [2002] for an overview, the case for non-
representational intelligence is famously made by Brooks [1991]). So, are cognitive
processes continuous with processes in the environment? Is semantic content (at
least partly) external (Putnam)? Does “natural” or “environmental” information
pivot on natural signs (Peirce) or nomic regularities? Consider the typical example
provided by the concentric rings visible in the wood of a cut tree trunk, which may
be used to estimate the age of the plant. The externalist/extensionalist, who favours a
positive answer (e.g. Dretske and Barwise), is faced by the difficulty of explaining
what kind of information and how much of it saturates the world, what kind of
access to, or interaction with “information in the world” an informational agent can
enjoy, and how information dynamics is possible. The internalist/intentionalist (e.g.
Fodor and Searle), who privileges a negative answer, needs to explain in what
specific sense information depends on intelligence and whether this leads to an anti-
realist view.
The location of information is related to the question whether there can be
information without an informee, or whether information, in at least some crucial
sense of the word, is essentially parasitic on the meanings in the mind of the
informee, and the most it can achieve, in terms of ontological independence, is
systematic interpretability. Before the discovery of the Rosetta Stone, was it
legitimate to regard Egyptian hieroglyphics as information, even if their semantics
was beyond the comprehension of any interpreter? Admitting that computers
perform some minimal level of proto-semantic activity works in favour of a “realist”
position about “information in the world”.
Before moving to the next problem, it remains to be clarified whether the
previous two ways of locating information might not be restrictive. Could
information be neither here (intelligence) nor there (natural world) but on the
threshold, as it were, as a special relation or interface between the world and its
intelligent inhabitants (constructionism)? Or could it even be elsewhere, in a third
world, intellectually accessible by intelligent beings but not ontologically dependent
17
on them (Platonism)? The reader interested in the physics of information is adviced
to read the chapter by Bais and Farmer.
13. The It from Bit hypothesis
Can nature be informationalised? The neologism “informationalised” is ugly but
useful to point out that this is the converse of the previous problem. Here too, it is
important to clarify what the problem is not. We are not asking whether the
metaphorical interpretation of the universe as a computer is more useful than
misleading. We are not even asking whether an informational description of the
universe, as we know it, is possible, at least partly and piecemeal. This is a
challenging task, but formal ontologies already provide a promising answer (Smith
[2004]). We are asking whether the universe in itself could essentially be made of
information, with natural processes, including causation, as special cases of
information dynamics (e.g. information flow and algorithmic, distributed
computation and forms of emergent computation). Depending on how one
approaches the concept of information, it might be necessary to refine the problem
in terms of digital data or other informational notions.
Answers to this problem deeply affect our understanding of the distinction
between virtual and material reality, of the meaning of artificial life in the ALife
sense (Bedau [2004]), and of the relation between the philosophy of information and
the foundations of physics: if the universe is made of information, is quantum
physics a theory of physical information? Moreover, does this explain some of its
paradoxes? If nature can be informationalised, does this help to explain how life
emerges from matter, and hence how intelligence emerges from life? “Can we build
a gradualist bridge from simple amoeba-like automata to highly purposive
intentional systems, with identifiable goals, beliefs, etc.?” (Dennett [1998], 262).
14. Conclusion
Our brief survey ends here. We have had a quick look to many questions of a wide
variety of nature and scope. This should not be disheartening. On the contrary, we
saw at the beginning of this chapter that Berkeley-Euphranor loved “information
upon all subjects”. It has required several scientific, technological and social
transformations, but philosophers have finally begun to address the new intellectual
18
challenges arising from the world of information and the information society.
Michael Dummett recently acknowledged that ““Evans had the idea that there is a
much cruder and more fundamental concept than that of knowledge on which
philosophers have concentrated so much, namely the concept of information.
Information is conveyed by perception, and retained by memory, though also
transmitted by means of language. One needs to concentrate on that concept before
one approaches that of knowledge in the proper sense. Information is acquired, for
example, without one’s necessarily having a grasp of the proposition which
embodies it; the flow of information operates at a much more basic level than the
acquisition and transmission of knowledge. I think that this conception deserves to
be explored. It’s not one that ever occurred to me before I read Evans, but it is
probably fruitful. That also distinguishes this work very sharply from traditional
epistemology.” (Dummett [1993], p. 186) Dummett is arguably correct. PI evolves
out of the analytic movement, but does not seem to belong to it. It attempts to
expand the frontier of philosophical research, not by putting together pre-existing
topics, and thus reordering the philosophical scenario, but by enclosing new areas of
philosophical inquiry⎯which have been struggling to be recognised and may not
yet found room in the traditional philosophical syllabus⎯and by providing
innovative methodologies to address traditional problems from new perspectives.
Clearly, PI promises to be one of the most exciting and fruitful areas of
philosophical research of our time. As this volume proves, it is already affecting the
overall way in which new and old philosophical problems are being addressed,
bringing about a substantial innovation of the philosophical system. This represents
the information turn in philosophy.8
19
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Notes
1 Berkeley [1732], Dialogue 1, Section 5, Paragraph 6/10.
2 For a longer and more detailed discussion see Floridi [2004b].
3 The definition is first introduced in Floridi [2002]. The nature and scope of PI are further discussed
in Floridi [2003b] and Floridi et al. [2005]. Floridi [2003c] provides an undergraduate level
introduction to PI.
4 A typical life cycle includes the following phases: occurring (discovering, designing, authoring,
etc.), processing and managing (collecting, validating, modifying, organising, indexing, classifying,
filtering, updating, sorting, storing, networking, distributing, accessing, retrieving, transmitting etc.)
and using (monitoring, modelling, analysing, explaining, planning, forecasting, decision-making,
instructing, educating, learning, etc.).
5 See Adams [2003] for a reconstruction of the informational turn in philosophy and Young [2004]
for an analysis of Wittgenstein’s philosophy of information.
6 In 1964, introducing his influential anthology, Anderson wrote that the field of philosophy of AI
had already produced more than a thousand articles (Anderson [1964], p. 1). No wonder that
(sometimes overlapping) editorial projects have flourished. Among the available titles, the reader
may wish to keep in mind Ringle [1979] and Boden [1990], which provide two further good
collections of essays, and Haugeland [1981], which was expressly meant to be a sequel to Anderson
[1964] and was further revised in Haugeland [1997].
7 Chalmers [online] seems to believe that pancomputationalism is empirically falsifiable, but what he
offers is not (a) a specification of what would count as an instance of x that would show how x is not
to be qualified computationally (or information-theoretically, in the language of this paper) given the
nomic characterisation N of the universe, but rather (b) just a re-wording of the idea that
pancomputationalism might be false, i.e. a negation of the nomic characterisation N of the universe in
question: “To be sure, there are some ways that empirical science might prove it to be false: if it turns
out that the fundamental laws of physics are noncomputable and if this noncomputability reflects
itself in cognitive functioning, for instance, or if it turns out that our cognitive capacities depend
essentially on infinite precision in certain analog quantities, or indeed if it turns out that cognition is
mediated by some non-physical substance whose workings are not computable.” To put it simply, we
would like to be told something along the lines that a white raven would falsify the statement that all
ravens are black, but instead we are told that the absence of blackness or of ravens altogether would,
which it does not.
8 This chapter is based on Floridi [2002], Floridi [2004b] and Floridi [2005a]. I wish to acknowledge
the kind permission by Blackwell and by the Stanford Encyclopedia of Philosophy to reproduce parts
of the texts from those publications.
25
