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Abstract

There is no consensus yet on the definition of semantic information. This paper contributes to the current debate by criticising and revising the Standard Definition of semantic Information (SDI) as meaningful data, in favour of the Dretske-Grice approach: meaningful and well-formed data constitute semantic information only if they also qualify as contingently truthful. After a brief introduction, SDI is criticised for providing necessary but insufficient conditions for the definition of semantic information. SDI is incorrect because truth-values do not supervene on semantic information, and misinformation (that is, false semantic information) is not a type of semantic information, but pseudo-information, that is not semantic information at all. This is shown by arguing that none of the reasons for interpreting misinformation as a type of semantic information is convincing, whilst there are compelling reasons to treat it as pseudo-information. As a consequence, SDI is revised to include a necessary truth-condition. The last section summarises the main results of the paper and indicates some interesting areas of application of the revised definition.
Philosophy and Phenomenological Research
Vol. LXX, No. 2, March 2005
IS SEMANTIC INFORMATION MEANINGFUL DATA? 351
Is Semantic Information Meaningful
Data?
LUCIANO FLORIDI
Wolfson College
There is no consensus yet on the definition of semantic information. This paper contrib-
utes to the current debate by criticising and revising the Standard Definition of semantic
Information (SDI) as meaningful data, in favour of the Dretske-Grice approach:
meaningful and well-formed data constitute semantic information only if they also qual-
ify as contingently truthful. After a brief introduction, SDI is criticised for providing
necessary but insufficient conditions for the definition of semantic information. SDI is
incorrect because truth-values do not supervene on semantic information, and misin-
formation (that is, false semantic information) is not a type of semantic information, but
pseudo-information, that is not semantic information at all. This is shown by arguing that
none of the reasons for interpreting misinformation as a type of semantic information is
convincing, whilst there are compelling reasons to treat it as pseudo-information. As a
consequence, SDI is revised to include a necessary truth-condition. The last section
summarises the main results of the paper and indicates some interesting areas of appli-
cation of the revised definition.
1. Introduction
The concept of information has become central in most contemporary phi-
losophy. However, recent surveys have shown no consensus on a single, uni-
fied definition of semantic information.1 This is hardly surprising. Informa-
tion is such a powerful and elusive concept that, as an explicandum, it can be
associated with several explanations, depending on the cluster of requirements
and desiderata that orientate a theory.2 Claude Shannon, for example,
remarked that
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. From “The Lattice Theory of Information”, in Shan-
non (1993) p. 180.
1For a review of the literature and further information see Floridi [2002], [2003a],
[2003b].
2The point is made explicit and defended in Bar-Hillel and Carnap [1953], Szaniawski
[1984] and Floridi [2002].
Please note, this is the uncorrected text of the proofs.
Page numbers may not correspond to the printed
version.
352 LUCIANO FLORIDI
Polysemantic concepts such as information can be fruitfully analysed only in
relation to well-specified contexts of application. Following this localist
principle, only one crucial aspect of a specific type of information will be
analysed in this paper, namely the alethic nature of declarative, objective
and semantic (DOS) information (more on these qualifications in the next
section). The question addressed is whether alethic values are supervenient3 on
DOS information, as presumed by the standard definition of information
(SDI). The negative answer defended is that DOS information encapsulates
“truthfulness”, so that “true information” is simply redundant and “false
information”, i.e. misinformation, is merely pseudo-information. It follows
that SDI needs to be revised by adding a necessary truth-condition. Five areas
of application of the revised definition are briefly discussed in the last section.
2. The Standard Definition of Information
Intuitively, “information” is often used to refer to non-mental, user-independ-
ent, declarative (i.e. alethically qualifiable),4 semantic contents, embedded in
physical implementations like databases, encyclopaedias, web sites, televi-
sion programmes and so on, which can variously be produced, collected,
accessed and processed. The Cambridge Dictionary of Philosophy, for exam-
ple, defines information thus:
an objective (mind independent) entity. It can be generated or carried by messages (words,
sentences) or by other products of cognizers (interpreters). Information can be encoded and
transmitted, but the information would exist independently of its encoding or transmission.
The extensionalist analysis of this popular concept of DOS (declarative,
objective and semantic) information is not immediately connected to levels
of subjective uncertainty and ignorance, to probability distributions, to util-
ity-functions for decision-making processes, or to the analysis of communica-
tion processes. So the corresponding mathematical and pragmatic5 senses in
3This technical term is used here to mean, weakly, “coming upon something subsequently,
as an extraneous addition”. The term is not used with the stronger meaning according to
which “if a set of properties x supervenes on another set of properties y, this means that
there is no variation with respect to x without a variation with respect to y”. I am grateful
to Philipp Keller for having prompted me to add this clarification.
4There are many plausible contexts in which a stipulation (“let the value of x = 3” or
“suppose we discover the bones of a unicorn”), an invitation (“you are cordially invited
to the college party”), an order (“close the window!”), an instruction (“to open the box
turn the key”), a game move (“1.e2-e4 c7-c5” at the beginning of a chess game) may be
correctly qualified as kinds of information. These and other similar, non-declarative
meanings of “information” (e.g. to refer to a music file or to a digital painting) are not
discussed in this paper, where objective semantic information is taken to have a declara-
tive or factual value, i.e., it is suppose to be correctly qualifiable alethically.
5See Bar-Hillel and Carnap [1953]. A pragmatic theory of information addresses the
question of how much information a certain message carries for a subject S in a given
doxastic state and within a specific informational environment.
IS SEMANTIC INFORMATION MEANINGFUL DATA? 353
which one may speak of information are not relevant in this context and can
be disregarded.
Over the last three decades, most analyses have supported a definition of
DOS information in terms of data + meaning. Three quotations from a vari-
ety of influential texts well illustrate the popularity of the bipartite account:6
Information is data that has been processed into a form that is meaningful to the recipient.
Davis and Olson (1985), 200.
Data is the raw material that is processed and refined to generate information. Silver and Sil-
ver (1989), 6.
Information equals data plus meaning. Checkland and Scholes (1990), 303.
The bipartite account has gained sufficient consensus to become an opera-
tional standard in fields that tend to deal with data and information as reified
entities (consider, for example, the now common expression “data mining”),
especially Information Science; Information Systems Theory, Methodology,
Analysis and Design; Information (Systems) Management; Database Design;
and Decision Theory. More recently, the bipartite account has begun to influ-
ence the philosophy of computing and information as well (see for example
Chalmers [1996], Floridi [1999], Franklin [1995] and Mingers [1997]).
The practical utility of the bipartite account is indubitable. The question is
whether it is rigorous enough to be applied in the context of an information-
theoretical epistemology. We shall see that this is not the case, but before
moving any criticism, we need a more rigorous formulation.
2.1. An Analysis of the Standard Definition of Information
Situation logic (Israel and Perry [1990]; Devlin [1991]) provides a powerful
methodology for our task. Let us use the symbol σ and the term “infon” to
refer to discrete items of information, irrespective of their semiotic code and
physical implementation:
SDI) σ is an instance of DOS information if and only if:
SDI.1) σ consists of n data (d), for n 1;
SDI.2) the data are well-formed (wfd);
SDI.3) the wfd are meaningful (mwfd = δ).
Three comments are now in order.
6Many other sources endorse equivalent accounts as uncontroversial, see Floridi [2003a]
for references.
354 LUCIANO FLORIDI
First, SDI.1 indicates that information cannot be dataless, but it does not
specify which types of δ constitute information. Data can be of four types
(Floridi [1999]):
δ.1) primary data. These are what we ordinarily mean by, and perceive as,
the principal data stored in a database, e.g. a simple array of numbers, or the
contents of books in a library. They are the data an information management
system is generally designed to convey to the user in the first place;
δ.2) metadata. These are secondary indications about the nature of the pri-
mary data. They enable a database management system to fulfil its tasks by
describing essential properties of the primary data, e.g. location, format,
updating, availability, copyright restrictions, etc.;
δ.3) operational data. These are data regarding usage of the data themselves,
the operations of the whole data system and the system’s performance;
δ.4) derivative data. These are data that can be extracted from δ.1-δ.3, when-
ever the latter are used as sources in search of patterns, clues or inferential
evidence, e.g. for comparative and quantitative analyses (ideometry).
At first sight, the typological neutrality (TN) implicit in SDI.1 may
seem counterintuitive. A database query that returns no answer, for example,
still provides some information, if only negative information; and silence is a
meaningful act of communication, if minimalist, Grice docet, yet where are
the data in these cases? SD.1 and TN cannot be justified by arguing that
absence of data is usually uninteresting, because similar pragmatic consid-
erations are at least controversial, as shown by the previous two examples,
and in any case irrelevant, since in this context the analysis concerns only
DOS information, not interested information.7 Rather, SD.1 and TN are
justified by the following principle of data-types reduction (PDTR):
PDTR) σ consists of a non-empty set (D) of data δ; if D seems empty and σ
still seems to qualify as information, then
1. the absence of δ is only apparent because of the occurrence of some nega-
tive primary δ, so that D is not really empty; or
2. the qualification of σ as information consisting of an empty D is mislead-
ing, since what really qualifies as information is not σ itself but some
7Interested information is a technical expression. The pragmatic theory of interested
information is crucial in Decision Theory, where a standard quantitative axiom states
that, in an ideal context and ceteris paribus, the more informative σ is to S, the more S
ought to be rationally willing to pay to find out whether σ is true [Sneed [1967]].
IS SEMANTIC INFORMATION MEANINGFUL DATA? 355
non-primary information µ concerning σ, constituted by meaningful non-
primary data δ.2-δ.4 about σ.
So in either case there is information because there is some type of data.
Consider the two examples above. Suppose we are using the Routledge
Encyclopedia of Philosophy on CD-ROM (EREP). If the database provides
an answer, it will provide at least a negative answer, e.g. the EREP will
open a small window with the message “no search hits found”, so PDTR.1
applies: primary information is provided through explicit negative data. If the
database provides no answer, either it fails to provide any data at all (e.g. the
screen of the EREP remains unmodified), in which case no primary informa-
tion σ is available or, more likely, there is a way of monitoring or inferring
the problems encountered by the database to establish, for example, that the
EREP is not responding rather than being busy elaborating (one can try to
use the CTRL + ALT + DEL command, which will open a Window with
information about the performance of the programs currently open), in which
case PDTR.2 applies: there isn’t any primary information and the non-pri-
mary information gained is provided by some metadata. Take now the second
example. My wife’s silence could provide some primary information, e.g. a
tacit assent or denial. The datum is the silence itself, as long as it counts as a
difference (more on this in a moment). Or her silence could carry some non-
primary information µ, e.g. she has not heard my question. The fact that I do
not even know whether her silence provides some primary or non-primary
information (let alone being able to guess the specific meaning of her silence)
explains why, in any binary communication, we tend to adopt a “positive”
signal for a negative message: the computer will send me a 1 or a 0 (primary
negative information) or nothing at all (secondary negative information),
rather than a signal or nothing at all, although the latter code would still be
sufficient to communicate in ideal circumstances. This point can be further
clarified by a third example. Imagine a very boring device that can produce
only one symbol, like E. A. Poe’s raven, who can answer only “nevermore.”
This is called a unary device. The raven is the informer, we are the informee,
“nevermore” is the message, there is a coding and decoding procedure through
a language, a channel of communication, and perhaps some possible noise.
Informer and informee share the same background knowledge about the collec-
tion of usable symbols (the alphabet). Given this a priori knowledge, it is
obvious that a unary device produces zero amount of primary information.
Simplifying, we already know the outcome so our ignorance cannot be
decreased. Whatever the informational state of the system, asking appropriate
questions to the raven does not make any difference. The point that interests
us here is that a unary source like the raven answers every question all the
time with only one symbol, not with silence or symbol, since silence, if
possible, would count as a signal, i.e. as a 0. On the contrary, my wife’s
356 LUCIANO FLORIDI
silence provides primary information if it is like a tacit 0, that is, only if I
assume that she might have answered something else instead. A completely
silent source is equivalent to my wife not hearing the question and qualifies
as a unary source, which can provide only non-primary information. This
shows that although there is no dataless information, the presence of data,
e.g. “nevermore”, does not guarantee the presence of primary information.
To summarise, when apparent absence of δ is not reducible to the occur-
rence of negative primary δ (the equivalent of a zero), either there is no
information or what becomes available and qualifies as information is some
further non-primary information µ about σ, constituted by some non-primary
δ.2-δ.4. Now, differences in the reduction both of the absence of positive
primary δ to the presence of negative primary δ and of σ to µ (when D is
truly empty) warrant that there can be more than one σ that may (mislead-
ingly) appear to qualify as information and be equivalent to an apparently
empty D. Not all silences are the same. However, since SDI.1 defines infor-
mation in terms of δ, without any further restriction on the typological
nature of the latter, it is sufficiently general to capture primary (positive or
negative) δ.1 and non-primary data δ.2-δ.4 as well, and hence the correspond-
ing special classes of information just introduced. As far as SDI.1 is con-
cerned, SDI is correct: there can be no dataless information.
Second comment. According to SDI.1, σ can consist of only a single
datum. Information is usually conveyed by large clusters or patterns of well-
formed, codified data, often alphanumeric, which are heavily constrained syn-
tactically and already very rich semantically. However, in its simplest form a
datum can be reduced to just a lack of uniformity, that is, a difference between
the presence and the absence of e.g. silence or of a signal:
Dd) d = (x y)
The dependence of information on the occurrence of syntactically well-formed
clusters, strings or patterns of data, and of data on the occurrence of physi-
cally implementable differences, explains why information can be decoupled
from one type of physical support in favour of another. Interpretations of this
support-independence can vary quite radically, however, because Dd leaves
underdetermined not only the logical type to which the relata belong (see
TN), but also the classification of the relata (taxonomic neutrality) and the
kind of support that the implementation of their inequality may require (onto-
logical neutrality).
Consider the taxonomic neutrality (TaxN) first. A datum is usually classi-
fied as the entity exhibiting the anomaly, often because the latter is perceptu-
ally more conspicuous or less redundant than the background conditions.
However, the relation of inequality is binary and symmetric. A white sheet of
paper is not just the necessary background condition for the occurrence of a
IS SEMANTIC INFORMATION MEANINGFUL DATA? 357
black dot as a datum, it is a constitutive part of the datum itself, together
with the fundamental relation of inequality that couples it with the dot. Noth-
ing is a datum per se for being a datum is an external property. So SDI
endorses the following thesis:
TaxN) a datum is a relational entity.
Understood as relational entities, data are definable as constraining affor-
dances, exploitable by a system as input of adequate queries that correctly
semanticise them to produce information as output. In short, semantic infor-
mation can also be described erotetically as data + queries (Floridi [1999]).
Consider next the ontological neutrality (ON). By rejecting the possibil-
ity of dataless information, GDI endorses the following modest thesis:
ON) no information without data representation.
ON is often interpreted materialistically, as advocating the impossibility of
physically disembodied information, through the equation “representation =
physical implementation”, thus:
S.1) no information without physical implementation.
S.1 is an inevitable assumption when working on the physics of computa-
tion, since computer science must necessarily take into account the physical
properties and limits of the carriers of information.8 It is also the ontological
assumption behind the Physical Symbol System Hypothesis in AI and Cog-
nitive Science (Newell and Simon [1976]). However, ON does not specify
whether, ultimately, the occurrence of every discrete state necessarily requires
a material implementation of the data representations. Arguably, environ-
ments in which all entities, properties and processes are ultimately noetic
(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, Fichte, Hegel), seem perfectly capable of upholding ON
without embracing S.1. The relata in Dd could be monads, for example.
Indeed, the classic realism vs. antirealism debate can be reconstructed pre-
cisely in terms of reasonably acceptable interpretations of ON.
All this explains why SDI is also consistent with two other popular slo-
gans, this time favourable to the proto-physical nature of information and
hence completely antithetic to S.1:
S.2) “It from b it. Otherwise put, every “it”every particle, every field of
force, even the space-time continuum itselfderives its function, its mean-
8Landauer [1996]. The debate on S.1 has flourished especially in the context of quantum
computing.
358 LUCIANO FLORIDI
ing, its very existence entirelyeven if in some contexts indirectlyfrom
the apparatus-elicited answers to yes-or-no questions, binary choices, bits. “It
from bit” symbolizes the idea that every item of the physical world has at
bottoma very deep bottom, in most instancesan immaterial source and
explanation; that which we call reality arises in the last analysis from the
posing of yes-no questions and the registering of equipment-evoked
responses; in short, that all things physical are information-theoretic in ori-
gin and that this is a participatory universe.” Wheeler (1990), 5.
and
S.3) “[information is] a name for the content of what is exchanged with the
outer world as we adjust to it, and make our adjustment felt upon it.” Wiener
(1954), 17. “Information is information, not matter or energy. No material-
ism which does not admit this can survive at the present day” Wiener (1961),
132.
S.2 endorses an information-theoretic, metaphysical monism: the universe’s
essential nature is digital, being fundamentally composed of information as
data instead of matter or energy, with material objects as a complex secondary
manifestation. S.2 may, but does not have to endorse a computational view
of information processes. S.3 advocates a more pluralistic approach along
similar lines. Both are compatible with SDI.
The third and final comment concerns SDI.3 and can be introduced by dis-
cussing a fourth slogan:
S.4) “In fact, what we mean by information—the elementary unit of informa-
tion—is a difference which makes a difference”. Bateson (1973), 428.
S.4 is one of the earliest and most popular formulations of SDI (see for
example Franklin [1995], 34 and Chalmers [1996], 281). A “difference” is
just a discrete state, i.e. a datum, and “making a difference” simply means
that the datum is “meaningful”, at least potentially. How data can come to
have an assigned meaning and function in a semiotic system in the first place
is one of the hardest problems in semantics. Luckily, the semanticisation of
data need not detain us here because SDI.3 only requires the δ to be provided
with a semantics already. The point in question is not how but whether data
constituting semantic information can be correctly described as being mean-
ingful independently of an informee. The genetic neutrality (GN) supported
by SDI states that:
GN) δ can have a semantics independently of any informee.
IS SEMANTIC INFORMATION MEANINGFUL DATA? 359
Before the discovery of the Rosetta Stone, Egyptian hieroglyphics were
already regarded as information, even if their semantics was beyond the com-
prehension of any interpreter. The discovery of an interface between Greek and
Egyptian did not affect the hieroglyphics’ embedded semantics but its accessi-
bility. This is the weak, conditional-counterfactual sense in which SDI.3 can
speak of meaningful data being embedded in an information-carrier informee-
independently. GN supports the possibility of information without an
informed subject, to adapt Popper’s phrase. Meaning is not (at least not
only) in the mind of the user. GN is to be distinguished from the stronger,
realist thesis, supported for example by Dretske (1981), according to which
data could also have their own semantics independently of an intelligent pro-
ducer/informer. This is also known as environmental information, and a
typical example is supposed to be 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.
To summarise, insofar as SDI provides necessary conditions for σ to
qualify as DOS information, it also endorses four types of neutrality: TN,
TaxN, ON and GN. These features represent an obvious advantage, as they
make SDI perfectly scalable to more complex cases, and hence reasonably
flexible in terms of applicability. However, by specifying that SDI.1-SDI.3
are also sufficient conditions, SDI further endorses a fifth type of alethic
neutrality (AN) which turns out to be problematic. Let us see why.
3. Alethic neutrality
According to SDI, alethic values are not embedded in, bu t supervene on
semantic information:
AN) meaningful and well-formed data qualify as information, no matter
whether they represent or convey a truth or a falsehood or have no alethic
value at all.
It follows that
FI) false information (including contradictions), i.e. misinformation, is a
genuine type of DOS information, not pseudo-information;
TA) tautologies qualify as information; and
TI) “it is true that σwhere σ is a variable that can be replaced by any
instance of genuine DOS information, is not a redundant expression; for
360 LUCIANO FLORIDI
example, “it is true” in the conjunction “‘the earth is round’ qualifies as
information and it is true” cannot be eliminated without semantic loss.9
None of these consequences seems ultimately defensible, and their rejection
forces a revision of AN and hence of SDI. For the sake of simplicity, in the
rest of this article only the rejection of FI will be pursued, following two
strategies. The first consist in showing that none of the main reasons that
could be adduced for interpreting false information as a type of information is
convincing. This strategy is pursued in section four. The second strategy con-
sists in showing that there are compelling reasons to treat false and tautologi-
cal information as pseudo-information. This is argued in section five. Regard-
ing TA, this is commonly assumed to be false in the philosophical literature
on semantic information (see Floridi 2003c), but it is also crucially connected
to the interpretation of mathematical and analytic truths, so a satisfactory
discussion of its negation cannot be pursued here but must be left to another
paper. Further arguments against AN could also be formulated on the basis of
the literature on deflationary theories of truth and hence a criticism of TI.
These arguments are not going to be rehearsed here because the development
of this strategy, which has interesting consequences for the deflationary theo-
ries themselves, deserves an independent analysis that lies beyond the scope
of this paper. I shall return to the issue in the conclusion, but only to clarify
what may be expected from this line of reasoning.
4. Nine bad reasons to think that false information
is a type of semantic information
Linguistically, the expression “false information” is common and perfectly
acceptable. What is meant by it is often less clear, though. The American
legislation on food disparagement provides an enlightening example.
Food disparagement is legally defined in the US as the wilful or malicious
dissemination to the public, in any manner, of false information that a per-
ishable food product or commodity is not safe for human consumption.
“False information” is then defined, rather vaguely, as
“information not based on reasonable and reliable scientific inquiry, facts, or data” (Ohio
legislation, http://www.ohiocitizen.org/campaigns/pesticides/veglibel.html);
“information that is not based on verifiable fact or on reliable scientific data or evid ence
(Vermont legislation, http://www.leg.state.vt.us/docs/2000/bills/intro/h-190.htm);
9Note that the conjunction of FI and TI presupposes two theses that are usually uncontro-
versial: (i) that information is strictly connected with, and can be discussed in terms of
alethic concepts; and (ii) that any theory of truth should treat alethic values or concepts
symmetrically.
IS SEMANTIC INFORMATION MEANINGFUL DATA? 361
“information which is not based on reliable, scientific facts and reliable scientific data which
the disseminator knows or should have known to be false” (Arkansas legislation,
http://www.arkleg.state.ar.us/ftproot/bills/1999/htm/hb1938.htm).
In each case, false information is defined in the same way in which one could
define a rotten apple, i.e. as if it were a “bad” type of information, vitiated by
some shortcoming. Why? Suppose that there are going to be exactly two
guests for dinner tonight, one of whom is in fact vegetarian. This is our
situation S. Let the false information about S be FI = “(A) there will be
exactly three guests for dinner tonight and (B) one of them is vegetarian”.
One may wish to argue that FI is not mere pseudo-information, but a certain
type of information that happens to be false, for a number of reasons, yet
even the most convincing ones are not convincing enough. Let us see why:
FI.1) FI can include genuine information.
Objection: this merely shows that FI is a compound in which only the true
component B qualifies as information.
FI.2) FI can entail genuine information.
Objection: even if one correctly infers only some semantically relevant and
true information TI from FI, e.g. that “there will be more than one guest”,
what now counts as information is the inferred true consequence TI, not FI.
Besides, ex falso quod libet sequitur, so any contradiction would count as
information.
FI.3) FI can still be genuinely informative, if only indirectly.
Objection: this is vague, but it can be reduced to t he precise concept of non-
primary information µ discussed in section two. For example, FI may be
coupled to some true, metainformation M that the source of FI is not fully
reliable. What now counts as information is the true M, not the false FI.
FI.4) FI can support decision-making processes.
Objection: one could certainly cook enough food on the basis of FI but this
is only accidental. The actual situation S may be represented by a wedding
dinner for a hundred people. That is why FI fails to qualify as information.
However, FI.4 clarifies that, if FI is embedded in a context in which there is
enough genuine metainformation about its margins of error, then FI can be
epistemically preferable to, because more useful than, both a false FI1, e.g.
“there will be only one guest for dinner”, and a true but too vacuous FI2, e.g.
“there will be less than a thousand guests for dinner”. What this shows is not
(i) that false information is an alethically qualified type of genuine informa-
362 LUCIANO FLORIDI
tion, but that (ii) false information can still be pragmatically interesting (in
the technical sense of the expression, see section two), because sources of
information are usually supposed to be truth-oriented or truth-tracking by
default (i.e. if they are mistaken, they are initially supposed to be so only
accidentally and minimally), and that (iii) logically, an analysis of the infor-
mation content of σ must take into account the level of approximation of σ
to its reference, both when σ is true and when it is false.
FI.5) FI is meaningful and has the same logical structure as genuine informa-
tion.
Objection: this is simply misleading. Consider the following FI: “One day
we shall discover the biggest of all natural numbers”. Being necessarily false,
this can hardly qualify as genuine but false information. It can only provide
some genuine, non-primary information µ, e.g. about the mathematical
naivety of the source. In the same sense in which hieroglyphics could qualify
as information even when they were not yet interpretable, vice versa, an
infon σ does not qualify as information just because it is interpretable. This
point is further discussed in section five.
FI.6) FI could have been genuine information had the relevant situation been
different. Perhaps the difficulty seen in FI.5 is caused by the necessary false-
hood of the example discussed. Meaningful and well-formed data that are only
contingently false represent a different case and could still qualify as a type of
information. It only happens that there will be fewer guests than predicted by
FI.
Objection: this only shows that we are ready to treat FI as quasi-information
in a hypothetical-counterfactual sense, which is just to say that, if S had been
different then FI would have been true and hence it would have qualified as
information. Since S is not, FI does not. FI need not necessarily be pseudo-
information. It may be so contingently. This point too is further discussed in
section five.
FI.7) If FI does not count as information, what is it? Assuming that p is
false “if S only thinks he or she has information that p, then what does S
really have? Another cognitive category beyond information or knowledge
would be necessary to answer this question. But another cognitive category is
not required because we already have language that covers the situation: S
only thinks he or she has knowledge that p, and actually has only informa-
tion that p.” (Colburn [2000], 468).
Objection: first, a new cognitive category could be invented, if required; sec-
ondly, there is actually another cognitive category, that of well-formed and
IS SEMANTIC INFORMATION MEANINGFUL DATA? 363
meaningful data, which, when false, constitute misinformation, not a type of
information. Third, the difference between being informed that p and knowing
that p is that, in the latter case, S is supposed to be able to provide, among
other things, a reasonable and appropriate (possibly non-Gettierisable)
account of why p is the case. The student Q who can recall and state a
mathematical theorem p but has no understanding of p or can provide no fur-
ther justification for p, can be said to be at most informed that p without hav-
ing knowledge that p. But if the mathematical theorem is not correct (if p is
false), it must be concluded that Q is misinformed (i.e. not informed) that p
(Q does not have any information about the theorem). It is perfectly possible,
but strikes one as imprecise and conceptually unsatisfactory, to reply that Q
is informed that p (Q does have some information about the theorem) and p is
false.
FI.8) We constantly speak of FI. Rejecting FI as information means denying
the obvious fact that there is plenty of information in the world that is not
true.
Objection: insofar as DOC information is concerned, this is a non sequitur.
Denying that FI counts as a kind of information is not equivalent to denying
that FI is a common phenomenon; it is equivalent to denying that a false
policeman, who can perfectly well exist, counts as a kind of policeman at all.
We shall see this better in the next section. Here it is sufficient to acknowl-
edge that ordinary uses of technical words may be too generic and idiosyn-
cratic, if not incorrect, to provide conceptual guidelines.
FI.9) “‘x misinforms y that p’ entails that ¬ p but ‘x informs y that pdoes
not entail that p [and since] … we may be expected to be justified in extend-
ing many of our conclusions about ‘inform’ to conclusions about ‘informa-
tion’ [it follows that]… informing does not require truth, and information
need not be true; bu t misinforming requires falsehood, and misinformation
must be false.” (Fox [1983], 160-1, 189, 193).
Objection: the principle of “exportation” (from information as process to
information as content) is more than questionable, but suppose it is accepted;
misinforming becomes now a way of informing and misinformation a type of
information. All this is as odd as considering lying a way of telling the truth
about something else and a contingent falsehood a type of truth on a different
topic. The interpretation becomes perfectly justified, however, if informing/
information is used to mean, more generically, communicating/communica-
tion, since the latter does not entail any particular truth value. But then com-
pare the difference between: (a) “Q is told that pand (b) “Q is informed that
p”, where in both cases p is a contradiction. (a) does not have to entail that p
364 LUCIANO FLORIDI
is true and hence it is perfectly acceptable, but (b) is more ambiguous. It can
be read as meaning just “Q was made to believe that p”, for example, in
which case information is treated as synonymous with (a form of) communi-
cation (this includes teaching, indoctrination, brain-washing etc.), as pre-
sumed by FI.9. But more likely, one would rephrase it and say that (b) means
“Q is misinformed that p precisely because p is necessarily false, thus
implying that it makes little sense to interpret (b) as meaning “S has the
information that p” because a contradiction can hardly qualify as information
(more on this in section five) and being informed, strictly speaking, entails
truth.
In conclusion, there seem to be no good reason to treat false information
as a type of information. This negative line of reasoning, however, may still
be unconvincing. We need more “constructive” arguments showing that false
information is pseudo-information. This is the task of the next section.
5. Two good reasons to believe that false information
is pseudo-information
The first positive argument is a test based on a conceptual clarification. The
clarification is this. The confusion about the nature of false information
seems to be generated by a misleading analogy. The most typical cases of
misinformation are false propositions and incorrect data. Now a false proposi-
tion is still a proposition, even if it is further qualified as not being true. The
same holds true for incorrect data. Likewise, one may think that misinforma-
tion is still a type of information, although it happens not to be true. The
logical confusion here is between attributive and predicative uses of “false”.
The distinction was already known to medieval logicians, was revived by
Geach (1956) and requires a further refinement before being applied as a test
to argue that “false information” is pseudo-information.
Take two adjectives like “male” and “good”. A male constable is a person
who is both male and employed as a policeman. A good constable, however,
is not a good person who is also employed as a member of the police force,
but rather a person who performs all the duties of a constable well. “Male” is
being used as a predicative adjective, whereas “good” modifies “constable”
and is being used as an attributive adjective. On this distinction one can
build the following test: if an adjective in a compound is attributive, the lat-
ter cannot be split up. This property of indivisibility means that we cannot
safely predicate of an attributively-modified x what we predicate of an x. So
far Geach. We now need to introduce two further refinements. Pace Geach, at
least some adjectives can be used attributively or predicatively depending on
the context, rather than necessarily being classified as either attributive or
predicative intrinsically. Secondly, the attributive use can be either positive
or negative. Positive attributively-used adjectives further qualify their refer-
IS SEMANTIC INFORMATION MEANINGFUL DATA? 365
ence x as y. “Good constable” is a clear example. Negative, attributively-used
adjectives negate one or more of the qualities necessary for x to be x. They
can be treated as logically equivalent to “not”. For example, a false constable
(attributive use) is clearly not a specific type of constable, but not a constable
at all (negative use), although the person pretending to be a constable may
successfully perform all the duties of a genuine constable (this further
explains FI.4 above). The same holds true for other examples such as “forged
banknote”, “counterfeit signature”, “false alarm” and so on. They are all
instances of a correct answer “no, it is a F(x)” to the type-question “is this a
genuine x?”.
Let us now return to the problem raised by the analogy between a false
proposition and false information. When we say that p, e.g. “the earth has
two moons”, is a false proposition, we are using “false” predicatively. The
test is that the compound can be split into “p is a proposition” and “p is a
contingent falsehood” without any semantic loss or confusion. On the con-
trary, when we describe p as false information, we are using “false” attribu-
tively, to negate the fact that p qualifies as information at all. Why? Because
“false information” does not pass the test. As in the case of the false consta-
ble, the compound cannot be correctly split: it is not the case, and hence it
would be a mistake or an act of misinformation to assert, that p constitutes
information about the number of natural satellites orbiting around the earth
and is also a falsehood. Compare this case to the one in which we qualify σ
as digital information, which obviously splits into “σ is information” and “σ
is digital”. If false information were a genuine type of information it should
pass the splitting test. It does not, so it is not.
The second argument is semantic and more technical but its gist can be
outlined in rather simple terms. If false information does not count as seman-
tic junk but as a type of information, it becomes difficult to make sense of
the ordinary phenomenon of semantic erosion. Operators like “not” lose their
semantic power to corrupt information, information becomes semantically
indestructible and the informative content of a repository can decrease only by
physical and syntactical manipulation of data. This is utterly implausible,
even if not logically impossible. We know that the cloth of semantic infor-
mation can be, and indeed is, often undone by equally semantic means. When
false information is treated as semantic information, what may be under dis-
cussion is only a purely quantitative or syntactic concept of information, that
is meaningful and well-formed data, not DOS information.
6. The standard definition of information revised
Well-formed and meaningful data may be of poor quality. Data that are incor-
rect (somehow vitiated by errors or inconsistencies), imprecise (understanding
precision as a measure of the repeatability of the collected data) or inaccurate
366 LUCIANO FLORIDI
(accuracy refers to how close the average data value is to the “true” value) are
still data and they are often recoverable, but, if they are not truthful, they can
only constitute misinformation (let me repeat: they can be informative, but
only indirectly or derivatively, for example they can be informative about the
unreliability of a source, but this is not the issue here). We have seen that
misinformation (false information) has turned out to be not a type of infor-
mation but rather pseudo-information. This is the Dretske-Grice approach
(other philosophers who accept a truth-based definition of semantic informa-
tion are Barwise and Seligman [1997] and Graham [1999]):
[…] false information and mis-information are not kinds of information—any more than decoy
ducks and rubber ducks are kinds of ducks. Dretske (1981), 45.
False information is not an inferior kind of information; it just is not information. Grice (1989),
371.
Like “truth” in the expression “theory of truth”, “information” can be used as
a synecdoche to refer both to “information” and tomisinformation”. “False
information” is like “false evidence”: it is not an oxymoron, but a way of
specifying that the contents in question do not conform to the situation they
purport to map. This is why, strictly speaking, to exchange (receive, sell,
buy, etc.) false DOS information about x, e.g. the number of moons orbiting
around the earth, is to exchange (receive, sell, buy, etc.) no DOS information
at all about x, only meaningful and well-formed data, that is semantic con-
tent. Since syntactical well-formedness and meaningfulness are necessary but
insufficient conditions for information, it follows that SDI needs to be modi-
fied, to include a fourth condition about the positive alethic nature of the data
in question:
RSDI) σ is an instance of DOS information if and only if:
1. σ consists of n data (d), for n 1;
2. the data are well-formed (wfd);
3. the wfd are meaningful (mwfd = δ);
4. the δ are truthful.
“Truthful” is used here as synonymous for “true”, to mean “representing or
conveying true contents about the referred situation or topic”. It is preferable
to speak of “truthful data” rather than “true data” because the data in question
may not be linguistic (a map, for example, is truthful rather than true) and
because we have seen that “true data” may give rise to a confusion, as if one
IS SEMANTIC INFORMATION MEANINGFUL DATA? 367
were stressing the genuine nature of the data in question, not their positive
alethic value.10
7. Conclusion: summary of results and future developments
Whales are not fish just because one may conceptually think so or say so.
Likewise, we ordinarily speak of false information when what we mean is
misinformation, i.e. no information at all. The goal of this paper has been to
clarify the confusion. The standard definition of DOS information (SDI) pro-
vides necessary but insufficient conditions for the qualification of data as
information. The definition has been modified to take into account the fact
that information encapsulates truthfulness. The new version of the definition
(RSDI) now describes DOS information as well-formed, meaningful and
truthful data. In the course of the analysis, the paper has provided an explana-
tion and refinement of the three necessary conditions established by SDI; an
analysis of the concept of data; a clarification of four popular interpretations
of SDI; and a revision of some basic principles and requirements that under-
pin any theory of semantic information. It may be useful to list now at least
five interesting areas of application of RSDI:
1) the critique of the deflationary theories of truth (DTT). From RSDI, it
follows that one could accept deflationary arguments as perfectly correct while
rejecting the explanatory adequacy of DTT. “It is true that” in “it is true that
σ” is redundant because there cannot be semantic information that is not true,
but DTT could mistake this linguistic or conceptual redundancy for unquali-
fied dispensability. “It is true that” is redundant precisely because, strictly
speaking, information is not a truth-bearer but already encapsulates truth as
truthfulness. Thus, DTT may be satisfactory as theories of truth-ascriptions
while being inadequate as theories of truthfulness;
2) the analysis of the informative nature of mathematical and analytic propo-
sitions;
3) the debate on the informative nature of sentences containing “empty”
names, such as “Sherlock Holmes is a bachelor” or “Unicorns do not exist” (a
true negative existential);11
4) a revision of the analysis of the standard definition of knowledge as justi-
fied and true belief in light of a “continuum” hypothesis that knowledge
encapsulates truth because it encapsulates DOS information;
10 I am grateful to Timothy Colburn and Philipp Keller for having pointed out this oth er
possible source of confusion.
11 I am grateful to a PPR referee for having called my attention to the parallelism between
the analysis of false information and the debate on the semantics of empty names.
368 LUCIANO FLORIDI
5) the development of a quantitative theory of semantic information based on
truth-values and degrees of discrepancy of σ with respect to a given situation
rather than probability distributions (Bar-Hillel and Carnap [1953], Floridi
[2003c]).
The development of these lines of research has been left to a second stage of
this work.12
12 I wish to thank Frederick R. Adams, Ian C. Dengler, Roger Brownsword, Timothy Col-
burn, James Fetzer, Ken Herold, Bernard Katz, Philipp Keller, Janet D. Sisson, and J. L.
Speranza for their valuable suggestions on previous drafts of this paper. A more polished
version was used by Anthonie W. M. Meijers and his students in a series of lectures about
the philosophical aspects of information at the Delft University of Technology, The
Netherlands, and I am very grateful to him and to the students who attended the lectures
for having shared with me their detailed comments. Finally, I am in debt to a PPR referee
for his very useful comments on the last draft. Any remaining mistake is only mine.
IS SEMANTIC INFORMATION MEANINGFUL DATA? 369
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