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Abstract

This paper investigates the relationship between reality and model, information and truth. It will argue that meaningful data need not be true in order to constitute information. Information to which truth-value cannot be ascribed, partially true information or even false information can lead to an interesting outcome such as technological innovation or scientific breakthrough. In the research process, during the transition between two theoretical frameworks, there is a dynamic mixture of old and new concepts in which truth is not well defined. Instead of veridicity, correctness of a model and its appropriateness within a context are commonly required. Despite empirical models being in general only truthlike, they are nevertheless capable of producing results from which conclusions can be drawn and adequate decisions made.
Abstract This paper investigates the relationship between reality and model,
information and truth. It will argue that meaningful data need not be true in
order to constitute information. Information to which truth-value cannot be
ascribed, partially true information or even false information can lead to an
interesting outcome such as technological innovation or scientific break-
through. In the research process, during the transition between two theoretical
frameworks, there is a dynamic mixture of old and new concepts in which
truth is not well defined. Instead of veridicity, correctness of a model and its
appropriateness within a context are commonly required. Despite empirical
models being in general only truthlike, they are nevertheless capable of
producing results from which conclusions can be drawn and adequate deci-
sions made.
Keywords Modeling Æ Information Æ Semantics Æ Validation and verification Æ
Veridicity Æ Truthlikeness
1 System modeling and simulation: validation and verification
A model is a simplified representation of a complex system or a process
developed for its understanding, control and prediction; it resembles the
target system in some respects while it differs in other respects that are not
considered essential. It follows that a model, which is valid for one objective,
may not be valid for another. Models are abstracted or constructed on the
G. Dodig-Crnkovic (&)
Department of Computer Science and Electronics,
Ma
¨
lardalen University, Vasteras, Sweden
e-mail: gordana.dodig-crnkovic@mdh.se
123
Mind & Society
DOI 10.1007/s11299-007-0035-5
ORIGINAL ARTICLE
Empirical modeling and information semantics
Gordana Dodig-Crnkovic
Received: 18 January 2006 / Accepted: 29 November 2006
Fondazione Rosselli 2007
grounds that they potentially satisfy important constraints of the target do-
main.
Model-based reasoning is essential for all sciences, particularly for the
empirical. It supports conceptual change and facilitates novel insights as
demonstrated in Magnani et al. (1999).
When discussing models, two concepts are fundamental: verification and
validation; where model verification is the confirmation that the model is
constructed as a model specification based on a problem formulation and
model validation is the demonstration that the model, within its domain of
applicability, is consistent with its objectives.
Consequently, the term ‘‘valid’’ refers to a model that adequately repre-
sents a target system in its domain of applicability. Determining whether or
not a model is an appropriate representation of reality, for a well-specified
goal, is the essence of model validation. The relationship between the model
and the physical reality is established by conducting empirical tests. Deter-
mining whether or not a model is an appropriate representation of reality, for
a well specified goal, is the essence of model validation, but there are other
significant factors to be considered such as the relevance of the goal itself,
Dodig-Crnkovic 2003
Simulation as a special case of modeling implies time-dependent goal-di-
rected experimentation with a dynamic model. Simulation can be used in
analysis, control, and design, Wildberger (2000). It is a tool, which facilitates
the gaining of insight, the testing of theories, experimentation with strategies
of control, and prediction of performance. In the concept of simulation as a
model-based computational activity, the emphasis is on the generation of
model behaviour. Simulation can be interpreted as model-based experimental
knowledge generation, O
¨
ren (2001), and can be combined with different types
of knowledge generation techniques such as optimization, statistical inference,
reasoning and hypothesis processing.
Questions of interest are to what degree the results of modeling and sim-
ulation can be trusted and can they be said to generate reliable information?
The former may be answered in a pragmatic way, by asking what would be the
alternative to using model-based reasoning, learning and prediction tech-
niques. In the case of weather forecasting, for example, we know that the
reliability of the prediction is not extremely high, but it is improving, and it
should be compared to a pure guess, which obviously is a less successful
prediction method. The output of a model for producing weather forecasts
may be seen as information that is probable but not certain (true), yet nec-
essary and useful.
2 Construction Process: data information knowledge
Data is generally considered to be a series of disconnected facts and
observations. These may be converted to information by analyzing,
cross-referring, selecting, sorting, summarizing, or in some way orga-
G. Dodig-Crnkovic
123
nizing the data. Patterns of information, in turn, can be worked up into a
coherent body of knowledge. Knowledge consists of an organized body
of information, such information patterns forming the basis of the kinds
of insights and judgments which we call wisdom.
The above conceptualization may be made concrete by a physical
analogy (Stonier 1983): consider spinning fleece into yarn, and then
weaving yarn into cloth. The fleece can be considered analogous to data,
the yarn to information and the cloth to knowledge. Cutting and sewing
the cloth into a useful garment is analogous to creating insight and
judgment (wisdom). This analogy emphasizes two important points: (1)
going from fleece to garment involves, at each step, an input of work, and
(2) at each step, this input of work leads to an increase in organization,
thereby producing a hierarchy of organization. Stonier (1997).
A model or simulation outcome depends essentially on the quality of the input
data such as correctness, reliability, sufficiency, relevance and alike. If we see
the world itself as informational, modeling implies selection and processing of
information of interest. It is the actual data representation of the information
at hand which makes possible an analysis of the relationship between changes
in the underlying physical process and the changes in the model (observational
information versus model-generated information). In order to study the
relationship between reality and model, we will first focus on the semantics of
information.
Floridi (2004c) suggests a list of the eighteen principal problems of phi-
losophy of information. Among those, the most fundamental is the question:
‘‘What is information?’’
Searching for the answer, Marijuan (2003) concluded that ‘‘Inconsistencies
and paradoxes in the conceptualization of information can be found through
numerous fields of natural, social and computer science.’
Or, as Floridi (2005) formulates it, ‘‘Information is such a powerful and
elusive concept that it can be associated with several explanations, depending
on the requirements and intentions.’’ See even the forthcoming Handbook on
the philosophy of information, (van Benthem J and Adriaans P eds, http://
www.illc.uva.nl/HPI/. In the same spirit, Capurro and Hjørland (2003) analyze
the term information, explaining its role as a constructive tool and its theory-
dependence as a typical interdisciplinary concept.
On the other hand, Capurro et al. (1999) discuss the possibility of a unified
theory of information (UTI), considering this in a cautiously affirmative way.
According to the authors, UTI is an expression of the metaphysical quest for a
unifying concept of the same fundamental nature as energy and matter. In the
unification approach, reality is an information-processing phenomenon. ‘‘We
would then say: whatever exists can be digitalized. Being is computation.’’
(ibid) In other words, at a fundamental level, information characterizes the
world itself, for it is through information we gain all our knowledge, and yet
Empirical modeling and information semantics
123
we are only beginning to understand its meaning. If information is to be
considered the primary stuff of the universe, as information physics and
paninformationalism suggest, it will provide a new basic unifying framework
for understanding and constructing reality. It is interesting to observe how
information can be understood in conjunction with its complementary con-
cept, computation, in a dual-aspect information-computation unified theory,
Dodig-Crnkovic (2006).
3 The Standard definition of information versus strongly semantic
information
A standard definition of information which is assumed to be declarative,
objective and semantic is given as data + meaning, (Floridi 2004a, b). In this
context Floridi refers to The Cambridge Dictionary of Philosophy definition
of information:
an objective (mind independent) entity. It can be generated or carried by
messages (words, sentences) or by other products of cognizers (inter-
preters). Information can be encoded and transmitted, but the infor-
mation would exist independently of its encoding or transmission.
It is instructive to compare the above formulation with the web http://
www.pespmc1.vub.ac.be/ASC/INFORMATION.html, Dictionary of Cyber-
netics and Systems, which offers the following definition of information:
that which reduces uncertainty (Claude Shannon); that which changes us.
(Gregory Bateson)
Literally that which forms within, but more adequately: the equivalent of
or the capacity of something to perform organizational work, the dif-
ference between two forms of organization or between two states of
uncertainty before and after a message has been received, but also the
degree to which one variable of a system depends on or is constrained by
another. E.g., the DNA carries genetic information inasmuch as it
organizes or controls the orderly growth of a living organism. A message
carries information inasmuch as it conveys something not already known.
In the background there is the most fundamental notion of information, as-
cribed to a number of authors; ‘‘a distinction that makes a difference’’,
MacKay (1969), or ‘‘a difference that makes a difference’’, Bateson (1973).
Floridi’s (2004a) Outline of a theory of strongly semantic information
contributes to the current debate by scrutinizing and revising the standard
definition of declarative, objective and semantic information (SDI). The main
thesis defended is that meaningful and well-formed data constitute informa-
tion only if they also qualify as contingently truthful. SDI is criticized for
providing insufficient conditions for the definition of information, because
G. Dodig-Crnkovic
123
truth-values do not supervene on information. Floridi argues strongly against
misinformation as a possible source of information or knowledge. As a rem-
edy, SDI is modified to include a condition about the truth of the data; so that
‘‘r is an instance of declarative objective and semantic information if and only
if:
1. r consists of n data (d), for n 1;
2. the data are well-formed (wfd);
3. the wfd are meaningful (mwfd = d);
4. the d are truthful.’’
Floridi’s concept of strongly semantic information from the outset encapsu-
lates truth and thus can avoid the Bar-Hillel paradox, Floridi (2004a).
It is important to remember that Floridi analyses only one specific type of
information, namely the alethic (pertaining to truth and falsehood) declara-
tive, objective and semantic information, which is supposed to have definite
truth-value. Non-declarative meanings of ‘‘information’’, e.g. referring to
graphics, music or information processing taking place in a biological cell or a
DNA molecule, such as defined in Marijua
´
n(2004) are not considered.
4 Information, truth and truthlikeness
...by natural selection our mind has adapted itself to the conditions of the
external world. It has adopted the geometry most advantageous to the
species or, in other words, the most convenient. Geometry is not true, it
is advantageous. Poincare
´
, science and method
Science is accepted as one of the principal sources of ‘‘truth’’ about the
physical world. It might be instructive to see the view of truth from the sci-
entific perspective. When do we expect to be able to label some information as
‘‘true’’? Is it possible for a theory, a model or a simulation to be ‘‘true’’? When
do we use the concept of truth and why is it important?
Popper was the first prominent realist philosopher and scientist to declare a
radical fallibilism about science (claim that accepted knowledge could be
wrong or flawed), while at the same time insisting on the epistemic superiority
of the scientific method. In his Logik der Forschung Popper argues that the
only kind of progress an inquiry can make consists in falsification of theories.
Popper was the first philosopher to question the idea that science is about
truth and instead to consider the problem of truthlikeness as an alternative.
Now the question is: can a succession of falsehoods constitute epistemic
progress? This would mean that if some false hypotheses are closer to the
truth than others, then the history of inquiry may be seen as steady progress
towards the goal of truth Oddie (2001).
While truth is the aim of inquiry, some falsehoods seem to realize this
aim better than others. Some truths better realize the aim than other
truths. And perhaps even some falsehoods realize the aim better than
Empirical modeling and information semantics
123
some truths do. The dichotomy of the class of propositions into truths
and falsehoods should thus be supplemented with a more fine-grained
ordering—one which classifies propositions according to their closeness
to the truth, their degree of truthlikeness or verisimilitude. The problem
of truthlikeness is to give an adequate account of the concept and to
explore its logical properties and its applications to epistemology and
methodology.
On those lines, (Kuipers 1987, 2000, 2002) developed a synthesis of a quali-
tative, structuralist theory of truth approximation:
In this theory, three concepts and two intuitions play a crucial role. The
concepts are confirmation, empirical progress, and (more) truthlikeness.
The first intuition, the success intuition, amounts to the claim that
empirical progress is, as a rule, functional for truth approximation, that
is, an empirically more successful theory is, as a rule, more truthlike or
closer to the truth, and vice versa. The second intuition, the I&C (ide-
alization and concretization) intuition, is a kind of specification of the
first.
According to Kuipers the truth approximation is a two-sided affair amounting
to achieving more true consequences and more correct models, which obvi-
ously belongs to scientific common sense.
The conclusion from the scientific methodology point of view is that, at
best, we can discuss truthlikeness, but not the truth of a theory. Like Poin-
care
´
’s geometry, models or theories are in the first place more or less correct
and advantageous tools of inquiry.
5 Correspondence (static) versus interactive (dynamic) models of information
Information is not a disembodied abstract entity; it is always tied to a
physical representation. It is represented by engraving on a stone tablet,
a spin, a charge, a hole in a punched card, a mark on paper, or some
other equivalent. This ties the handling of information to all the possi-
bilities and restrictions of our real physical world, its laws of physics, and
its storehouse of available parts. (Landauer 1996)
In the tradition of Western thought, since the ancient Greeks, information was
understood in conjunction with representation. As Zurek (1994) put it: ‘‘No
information without representation’’. In correspondence theory, mind is
simply carrying out passive input processing. The transformations from the
information in the world into information for the agent are supposed to be
causally related.
There are several versions of the correspondence (encoding-decoding)
models of representation of information, such as isomorphic correspondence,
as in the physical symbol system hypothesis (Newell, Vera and Simon); trained
correspondences, as in connectionist models (Rumelhart, McClelland); causal/
G. Dodig-Crnkovic
123
nomological (general physical/logical) relationships (Fodor) and representa-
tion as function (Godfrey-Smith, Millikan).
In the traditional correspondence framework, the information is caused by
some external past event. The problem of this view is to explain what exactly
produced the representation in the animal or machine.
Some state or event in a brain or machine that is in informational cor-
respondence with something in the world must in addition have content
about what that correspondence is with in order to function as a repre-
sentation for that system—in order to be a representation for that sys-
tem. Any such correspondence, for example with this desk, will also be in
correspondence (informational, and causal) with the activities the retina,
with the light processes, with the quantum processes in the surface of the
desk, with the desk last week, with the manufacture of the desk, with the
pumping of the oil out of which the desk was manufactured, with the
growth and decay of the plants that yielded the oil, with the fusion
processes in the sun that stimulated that growth, and so on all the way to
the beginning of time, not to mention all the unbounded branches of
such informational correspondences. Which one of these relationships is
supposed to be the representational one? There are attempts to answer
this question too (e.g., Smith 1987), but, again, none that work (Bickhard
and Terveen 1995). (Bickhard 2004)
This passage from Bickhard indicates the importance of intentionality in
forming representations. Informational content of the world is infinite, and
each object is a part of that all-encompassing network of causation and
physical interaction. The only way one can explain the fact that the agent
extracts (registers) some specific information from the world is the fact that it
acts in the world, pursuing different goals, the most basic one being that of
survival, and in that way an agent actively chooses particular information of
interest.
Pragmatic theory has developed during the last century as an alternative to
the correspondence model of representation. (Joas, Rosenthal, Bickhard).
Pragmatism suggests interaction as the most appropriate mechanism for
understanding information.
There are several important differences between the interactive model of
representation and standard correspondence models. Interactive explanation
is future-oriented; based on the fact that the agent is concerned with antici-
pated future potentialities of interaction. So the actions are oriented internally
to the system, which optimize its internal outcome, while the environment
constitutes resources for the agent. Correspondence with the environment is
only a part of the interactive relation: representation emerges in the antici-
patory interactive processes in (natural or artificial) agents, who are pursuing
their goals while communicating with the environment.
In the contemporary fields of artificial intelligence, cognition, cognitive
robotics, consciousness, language and interface design, interactive models are
becoming more and more prominent. This is in parallel with the new inter-
Empirical modeling and information semantics
123
active computing paradigm (Wegner, Goldin), and new approaches to logic
(dialogic logic, game-theoretic approaches to logic), see Dodig-Crnkovic
(2006).
6 Conclusion
There are two major approaches to the individuation of scientific theo-
ries that have been called syntactic and semantic. We prefer to call them
the linguistic and non-linguistic conceptions. On the linguistic view, also
known as the received view, theories are identified with (pieces of)
languages. On the non-linguistic view, theories are identified with
extralinguistic structures, known as models. We would like to distinguish
between strong and weak formulations of each approach. On the strong
version of the linguistic approach, theories are identified with certain
formal-syntactic calculi, whereas on a weaker reading, theories are
merely analyzed as collections of claims or propositions. Correspond-
ingly, the strong semantic approach identifies theories with families of
models, whereas on a weaker reading the semantic conception merely
shifts analytical focus, and the burden of representation, from language
to models. Hendry and Psillos (2004)
Here we can refer to Laudan’s Methodological Naturalism, in Psillos (1997)
formulation:
All normative claims are instrumental: methodological rules link up aims
with methods, which will bring them about, and recommend what action is
more likely to achieve one’s favoured aim.
The soundness of methodological rules depends on whether they lead to
successful action, and their justification is a function of their effectiveness
in bringing about their aims. A sound methodological rule represents our
‘‘best strategy’’ for reaching a certain aim.
In the actual process of discovery and in model building, information is the
fundamental entity. During the process, information is transformed and it
changes its place and physical form. Depending on context, it also changes its
meaning, (Barwise and Perry 1983).
When dealing with empirical information we meet the fact that the real
world never perfectly conforms to the ideal abstract structures of the model
(Plato’s stance). Ideal atoms might be represented by ideal spheres. Real atoms
have neither perfect shape nor sharp boundaries. In the physical world of
technological artefacts and empirical scientific research, situations are rare in
which models can be sharply divided into true and false. However, it is often
possible to conventionally set the limits for different outcomes that we can label
as ‘‘acceptable/non-acceptable’’ which in turn can be translated in terms of
‘‘true/false’’ if we agree to use the term truth in this very specific sense.
G. Dodig-Crnkovic
123
There are cases in the history of science in which false information/
knowledge (false for us here and now) has lead to the production of true
information/knowledge (true for us here and now). A classical example is
serendipity, making unexpected discoveries by accident. The pre-condition for
the discovery of new scientific ‘‘truths’ (where the term ‘‘true’’ is used in its
limited sense to mean ‘‘true to our best knowledge’’) is not that we start with a
critical mass of absolutely true information, but that in continuous interaction
(feedback coupling of learning process) with the empirical world we refine our
set of (partial) truths. With good reason, truth is not an operative term for
scientists.
Christopher Columbus had, for the most part, incorrect information about
his proposed journey to India. He never saw India, but he made a great
discovery. The ‘‘discovery’’ of America was not incidental; it was a result of a
combination of many favourable historical preconditions combined with both
true and false information about the state of affairs. Similar discoveries are
constant occurrences in science.
‘‘Yet libraries are full of ‘false knowledge’ ’’, as Floridi (2004d) rightly
points out. Nevertheless we need all that ‘‘false knowledge’’. Should we throw
away all books containing false information, and all newspapers containing
misinformation, what would be left? And what would our information and
knowledge about the real world look like?
In the standard (general) definition of semantic information commonly used
in empirical sciences, information is defined as meaningful data. Floridi in his
Theory of Strongly Semantic Information adds the requirement that standard
semantic information should also contain truth in order to avoid the logical
paradox of Bar-Hillel’s semantic theory. This paper argues that meaningful
data need not necessarily be true to constitute information. Partially true
information or even completely false information can lead to an outcome
adequate and relevant for inquiry. Instead of insisting on the veridicity of the
empirical model, we should focus on such basic criteria as the validity of the
model and its appropriateness within a given context. Models and theories are
seen as instruments of epistemic progress, which enable us to learn from the
interaction with the empirical world, changing the world, our understanding of
it and our epistemic tools in a dynamic process of meaning production.
Acknowledgments This article is a revised version of the paper presented at the International
Conference Model-Based Reasoning in Science and Engineering (MBR04), held at the University
of Pavia, Italy (16–18 December 2004) and chaired by Lorenzo Magnani.I wish to thank Lorenzo
Magnani for organizing MBR 2004, with great efficiency and unadulterated enthusiasm. I also
wish to thank Lorenzo Magnani for organizing E-CAP 2004, the conference that created so much
interest for the field of Computing and Philosophy.
References
Barwise J, Perry J (1983) Situations and attitudes. MIT, Cambridge
Bateson G (1973) Steps to an ecology of mind, Paladin. Frogmore, St Albans
Empirical modeling and information semantics
123
Capurro R, Hjørland B (2003) The concept of information In: Annual Review of Information
Science and Technology (ARIST) (ed) Blaise Cronin, Information Today, Medford
Capurro R, Fleissner P, Hofkirchner W (1999) Is a unified theory of information feasible? A
trialogue In: Hofkirchner W (ed) The quest for a unified theory of information. Proceedings of
the second international conference on the foundations of information science, Gordon &
Breach, New York
Dodig-Crnkovic G (2003) Shifting the paradigm of the philosophy of science: the philosophy of
information and a new renaissance, Mind Mach 13(4):521–536
Dodig-Crnkovic G (2006) Investigations into information semantics and ethics of computing,
Ma
¨
lardalen University Press, Sweden
Floridi L (2004a) Information. In: Floridi L (ed) The Blackwell guide to the philosophy of
computing and information. Blackwell, Oxford
Floridi L (2004b) Outline of a theory of strongly semantic information. Mind Mach 14(2):197–222
Floridi L (2004c) Open problems in the philosophy of information, metaphilosophy, vol 35 issue 4
Floridi L (2004d) Afterword—LIS as applied philosophy of information: a reappraisal. Libr
Trends 52(3)
Floridi L (2005) Is information meaningful data? Philos Phenomenol Res 70(2):351–370
Hendry RF, Psillos S (2004) How to do things with theories: an interactive view of language and
models in science. In: Paprzycka K, Przybysz P (eds) Idealization and concretization. Rodopi,
Amsterdam
Kuipers TAF (1987) ed What is closer-to-the-truth? A parade of approaches to truthlikeness,
Poznan Studies in the philosophy of the sciences and the humanities, vol 10. Rodopi,
Amsterdam
Kuipers TAF (2000) From instrumentalism to constructive realism: on some relations between
confirmation, empirical progress, and truth approximation. Kluwer, Dordrecht
Kuipers TAF (2002) Inductive aspects of confirmation, information, and content, to appear in the
Schilpp-volume The Philosophy of Jaakko Hintikka
MacKay DM (1969) Information, mechanism and meaning. MIT, Cambridge
Magnani L, Nersessian NJ, Thagard P (1999) (eds) Model-based reasoning. Scientific discovery.
Kluwer, New York
Marijuan PC (2003) Foundations of information science: selected papers from FIS 2002. Entropy
5:214–219
Marijua
´
n PC (2004) Information and life: towards a biological understanding of informational
phenomena, TripleC 2(1): 6–19, ISSN 1726-670X, http://www.tripleC.uti.at
Oddie G (2001) Truthlikeness, The Stanford Encyclopedia of Philosophy. In: Zalta EN (ed) http://
www.plato.stanford.edu/archives/fall2001/entries/truthlikeness/
Stonier T (1997) Information and meaning. An evolutionary perspective. Springer, Heidelberg
Wildberger AM (2000) AI & simulation. Simulation, 74
O
¨
ren TI (2001) Impact of data on simulation: from early practices to federated and agent-directed
simulations. In: A. Heemink et al (eds) Proceedings of EUROSIM 2001, Delft, 26–29 June
2001
Zurek WH (1994) Decoherence and the existential interpretation of quantum theory. In: Grass-
berger P, Nadal J-P (eds) From statistical physics to statistical inference and back. Plenum,
Dordrecht pp 341–350
G. Dodig-Crnkovic
123
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This work explores a new understanding of informational phenomena based on the molecular organization of life. One of the central ideas is that the interrelationship among the recently framed fields of genomics, proteomics, and signaling science (crucial elements of the bioinformatic whole enterprise) may provide fundamental aspects of a new information synthesis. Thus, the new knowledge gained on the functionality and "existential flow" of the phenotypic molecular elements (basically the production and degradation of constituent enzymes and proteins—the transient proteome of the cell), which is intimately coupled with the intrinsic dynamics of the "DNA world" and with the communicational events stemming from the cell environment, could represent a microcosm for the whole in-formation phenomena. The variant in-formation spelling emphasizes that the cellular coupling among constitutive (proteomic), generative (genomic), and communicational (signaling) information genera produces a differentiated mode of existence, the living state, which is always in the making, perpetually in formation. The in-formability of the living supports the emergence of a completely new realm of 'cognitive' autonomous causality —and implies, in other regards, the emergence of meaning and of agency, and the foundation upon which far more complex, organismic, neuronal, and social events have been evolutionarily deployed. There follows a fundamental break with respect to the mechanistic chains of causality (and explanation) afforded by the reductionist vision. There is also, in this biological approach to informational phenomena, a compelling need for the development of a new communication theory of non-conservative nature. New logical principles are discussed which could guide biological systems in their inner choices between information 'factories' and information 'garbage camps'. Finally, this bottom-up approach to the nature of information, molecular-biologically grounded as it is, does not militate against the top-down strategies. Conversely, it aims at a complementarity with germane conceptualizations that are currently being addressed in theoretical science, philosophy, neurosciences, and in social and technological disciplines.
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This work explores a new understanding of informational phenomena based on the molecular organization of life. One of the central ideas is that the interrelationship among the recently framed fields of genomics, proteomics, and signaling science (crucial elements of the bioinformatic whole enterprise) may provide fundamental aspects of a new information synthesis. Thus, the new knowledge gained on the functionality and “existential flow” of the phenotypic molecular elements (basically the production and degradation of constituent enzymes and proteins—the transient proteome of the cell), which is intimately coupled with the intrinsic dynamics of the “DNA world” and with the communicational events stemming from the cell environment, could represent a microcosm for the whole in-formation phenomena. The variant in-formation spelling emphasizes that the cellular coupling among constitutive (proteomic), generative (genomic), and communicational (signaling) information genera produces a differentiated mode of existence, the living state, which is always in the making, perpetually in formation. The in-formability of the living supports the emergence of a completely new realm of ‘cognitive’ autonomous causality —and implies, in other regards, the emergence of meaning and of agency, and the foundation upon which far more complex, organismic, neuronal, and social events have been evolutionarily deployed. There follows a fundamental break with respect to the mechanistic chains of causality (and explanation) afforded by the reductionist vision. There is also, in this biological approach to informational phenomena, a compelling need for the development of a new communication theory of non-conservative nature. New logical principles are discussed which could guide biological systems in their inner choices between information ‘factories’ and information ‘garbage camps’. Finally, this bottom-up approach to the nature of information, molecular-biologically grounded as it is, does not militate against the top-down strategies. Conversely, it aims at a complementarity with germane conceptualizations that are currently being addressed in theoretical science, philosophy, neurosciences, and in social and technological disciplines.
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The volume is based on the papers that were presented at the Interna­ tional Conference Model-Based Reasoning in Scientific Discovery (MBR'98), held at the Collegio Ghislieri, University of Pavia, Pavia, Italy, in December 1998. The papers explore how scientific thinking uses models and explanatory reasoning to produce creative changes in theories and concepts. The study of diagnostic, visual, spatial, analogical, and temporal rea­ soning has demonstrated that there are many ways of performing intelligent and creative reasoning that cannot be described with the help only of tradi­ tional notions of reasoning such as classical logic. Traditional accounts of scientific reasoning have restricted the notion of reasoning primarily to de­ ductive and inductive arguments. Understanding the contribution of model­ ing practices to discovery and conceptual change in science requires ex­ panding scientific reasoning to include complex forms of creative reasoning that are not always successful and can lead to incorrect solutions. The study of these heuristic ways of reasoning is situated at the crossroads of philoso­ phy, artificial intelligence, cognitive psychology, and logic; that is, at the heart of cognitive science. There are several key ingredients common to the various forms of model­ based reasoning to be considered in this book. The models are intended as in­ terpretations of target physical systems, processes, phenomena, or situations. The models are retrieved or constructed on the basis of potentially satisfying salient constraints of the target domain.
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The accompanying papers in the first issue of Entropy, volume 5, 2003 were presented at the electronic conference on Foundations of Information Science FIS 2002 (http://www.mdpi.net/fis2002/). The running title of this FIS e-conference was THE NATURE OF INFORMATION: CONCEPTIONS, MISCONCEPTIONS, AND PARADOXES. It was held on the Internet from 6 to 10 May 2002, and was followed by a series of discussions -structured as focused sessions- which took place in the net from 10 May 2002 until 31 January 2003 (more than 400 messages were exchanged, see: http://fis.iguw.tuwien.ac.at/mailings/). This Introduction will briefly survey the problems around the concept of information, will present the central ideas of the FIS initiative, and will contrast some of the basic differences between information and mechanics (reductionism).