Information 2011, 2, 327-359; doi:10.3390/info2020327
Floridi’s “Open Problems in Philosophy of Information”,
Ten Years Later
Gordana Dodig Crnkovic
* and Wolfgang Hofkirchner
Mälardalen University, School of Innovation, Design and Engineering, Box 883, SE-72123
University of Technology, Karlsplatz 13, 1040 Wien, Austria;
* Author to whom correspondence should be addressed; E-Mail: email@example.com.
Received: 7 April 2011; in revised form: 2 May 2011 / Accepted: 12 May 2011 /
Published: 23 May 2011
Abstract: In his article Open Problems in the Philosophy of Information  Luciano
Floridi presented a Philosophy of Information research program in the form of eighteen
open problems, covering the following fundamental areas: Information definition,
information semantics, intelligence/cognition, informational universe/nature and
values/ethics. We revisit Floridi’s program, highlighting some of the major advances,
commenting on unsolved problems and rendering the new landscape of the Philosophy of
Information (PI) emerging at present. As we analyze the progress of PI we try to situate
Floridi’s program in the context of scientific and technological development that have been
made last ten years. We emphasize that Philosophy of Information is a huge and vibrant
research field, with its origins dating before Open Problems, and its domains extending
even outside their scope. In this paper, we have been able only to sketch some of the
developments during the past ten years. Our hope is that, even if fragmentary, this review
may serve as a contribution to the effort of understanding the present state of the art and
the paths of development of Philosophy of Information as seen through the lens of
Keywords: Philosophy of Information; information definition; information semantics;
intelligence/cognition; informational universe; Information Ethics; unified theories of
Information 2011, 2
In his programmatic paper Open Problems in the Philosophy of Information  based on the
Herbert A. Simon Lecture in Computing and Philosophy given at Carnegie Mellon University in 2001,
Luciano Floridi lists five of the most interesting areas with eighteen fundamental questions for the field
he named Philosophy of Information. The Open Problems program includes many already existing
topics to which researchers had been contributing even before 2001, but it is also asking new questions
or putting existing ones into a new context, aiming at organizing them into a coherent system.
The aim of the present paper is to address Floridi’s program from a 10-years distance. What have
we learned? What do we expect to learn in the future? The reader interested in the history of
Philosophy of Information will find more background in the van Benthem and Adriaans’ Philosophy of
Information  handbook, which describes contributions by Charles Sanders Peirce, Norbert Wiener,
Alan Turing, William Ross Ashby, Claude Shannon, Warren Weaver, Gregory Bateson, Fred Dretske,
Jon Barwise, John R. Perry, Brian Cantwell Smith, Rafael Capurro and others. We want to focus on
some of the recent developments we find worth bringing into the context of Floridi’s paper.
We can trace the origins of the program back to 1999 when Floridi’s book Philosophy and
Computing: An Introduction  appeared, immediately followed by the first shift towards an
information-centric framework in the article Information Ethics: On the Philosophical Foundations of
Computer Ethics, . The development from the first, more concrete technology- and practice-based
approach towards the abstract information-centric account is evident in the coming decade which will
result in numbers of articles developing several strands of the program declared in Metaphilosophy in
2004. Floridi has significantly contributed to the development of Information Ethics, Semantic Theory
of Information, Logic of Information and Informational Universe/Nature (Informational Structural
Realism)—to name the most important moves ahead. This article is based on the following works of
In 2008 Floridi edited the book Philosophy of Computing and Information-5 Questions  with
contributions by Boden, Braitenberg, Cantwell-Smith, Chaitin, Dennett, Devlin, Dretske, Dreyfus,
Floridi, Hoare, McCarthy, Searle, Sloman, Suppes, van Benthem, Winograd and Wolfram. The last of
five questions each of the distinguished interviewees answered was: “What are the most important
open problems concerning computation and/or information and what are the prospects for progress?”
Among answers there are suggestions of the need for synthetic approach to Cognitive Science
(including symbolic, connectionist, situated, dynamical, and homeostatic)—“because all of them (and
probably more) will be needed to emulate the rich space of possible minds” (Boden); emphasis on the
importance of complexity (Braitenberg); use of computers serving as “laboratories of middling
complexity” “in terms of which to explore issues of intentionality, embodiment, and semantics.”
(Smith); Mathematics, Biology and Metabiology (Chaitin); solid theory of semantic information
(Dennett); better understanding of natural language (Devlin); better understanding of the concept of
information (Dretske); learning and relevance in embodied AI (Dreyfus); further development of the PI
as Philosophia Prima (Floridi); error-free software (Hoare); experimental philosophy in a computing
lab (McCarthy); move from computational Cognitive Science to Cognitive Neuroscience (Searle);
“Understanding the variety of types of virtual machines and the variety of ways in which virtual
machines can be implemented or realized in physical machines or other virtual machines” (Sloman);
Information 2011, 2
understanding how large collections of synchronized neurons are computing, with all relevant physics
and chemistry (Suppes); the interplay between statics and dynamics, information and process
(van Benthem); the decoding of thought (Winograd); mining the computational universe for new ideas
in science and philosophy (Wolfram).
The state of the art of the research field is reflected in the special issue of the journal The
Information Society titled “The Philosophy of Information, its Nature and Future Developments”,
published in 2009 and edited by Luciano Floridi , which addresses: Floridi’s Philosophy of
Information and Information Ethics (Ess); the Philosophy of Information culture (Briggle and
Mitcham); epistemic values and information management (Fallis and Whitcomb); information and
knowledge in information systems (Willcocks and Whitley), starting with Floridi’s introduction: “The
Information Society and its Philosophy”.
The recent (April 2010) special issue of Metaphilosophy, , the same journal that published
Floridi’s program in 2004, was devoted to the theme “Luciano Floridi and the Philosophy of
Information” (PI) and guest edited by Patrick Allo. It is addressing issues of knowledge (Roush
and Hendricks), agency (Bringsjord), semantic information (Scarantino and Piccinini; Adams),
methodology (Colburn and Shute), metaphysics (Bueno) and ethics (Volkman) with an epilogue by
Bynum on the philosophy in the information age. It gives a good state of the art insight into the
development of PI.
“Luciano Floridi's Philosophy of Technology: Critical Reflections” is a topic of a special issue of
Knowledge, Technology & Policy , published in June 2010, guest edited by Hilmi Demir. It
contains several articles on PI, addressing informational realism (Gillies), contradictory information
(Allo), epistemology of AI (Ganascia), perceptual evidence and information (Piazza), ethics of
democratic access to information (da Silva), logic of ethical information (Brenner), the demise of
ethics (Byron), information as ontological pluralism (Durante), a critique of Information Ethics
(Doyle), pre-cognitive semantic information (Vakarelov), an argument that typ-ken (an amalgam of
type and token) drives infosphere (Gunji et al.). The special issue ends with Floridi’s responses to each
of the articles.
Floridi’s newly published book, The Philosophy of Information  shows the up-to-date state of
his view of the subject. It presents his contributions to the research field and contains his widely known
work which confirms the relevance of our account when it comes to Floridi’s main contributions.
Besides Floridi, a number of researchers have contributed, directly or indirectly to the advancement
of the field and offered interesting solutions and insights into the nature of information, its dynamics
and its cognitive aspects. In what follows we will try to list some of those contributions. As we analyze
the present state of the art of Philosophy of Information we try to situate the PI program in the context
of scientific and technological developments that have been made over the past ten years and see their
impact on the directions of PI research.
2. Open Problems Revisited
Floridi’s Open Problems cover a huge ground with five areas: Information definition, information
semantics, intelligence/cognition, informational universe/nature and values/ethics. The task of
assessment in one article of the progress achieved in one decade seems overwhelming. Nevertheless,
Information 2011, 2
let us make an attempt to re-examine the program and see what the listed questions look like today,
without any pretense as to the completeness of the account. Even if fragmentary, this review may serve
as a contribution to the effort of understanding the present state of the art and the paths of
development. We will find many novel ideas and suggested answers to the problems arisen in the
course of the development of Philosophy of Information. In order to elucidate the results of the
progress made, we will present different and sometimes opposing views, hoping to shed more light on
various aspects of the development and the future prospects.
2.1. Information Definition
2.1.1. What is Information?
One of the most significant events since 2004 was the publishing of the Philosophy of Information,
Handbook of the Philosophy of Science . The Part B of the book, titled Philosophy of Information:
Concepts and History, includes essays on Epistemology and Information (Dretske), Information in
Natural Language (Kamp and Stokhof), Trends in Philosophy of Information (Floridi) and Learning
and the Cooperative Computational Universe (Adriaans). From that part we can gain the insight in
various facets of the concept, providing supporting evidence that nowadays concepts of information
present a complex body of knowledge that accommodates different views of information through fields
of natural, social and computer science. Or, as Floridi  formulates it, “Information is such a
powerful and elusive concept that it can be associated with several explanations, depending on the
requirements and intentions.”
The discussion of the concept of information was shortly after Floridi’s program declaration in the
Herbert Simon Lecture in 2001 a subject of a lively discussion, and van Benthem and Adriaans 
point to a special issue of the Journal of Logic, Language and Information , edited by van
Benthem and van Rooij, and dedicated to the study of different facets of information. At the same time
Capurro and Hjørland  analyze the term “information” as a typical interdisciplinary concept, its
role as a constructive tool and its theory-dependence. They review significant contributions to the
theory of information over the past quarter of century from physicists, biologists, systems theorists,
philosophers and library and information scientists. The concept of information as it appears in
different domains is fluid, and changes its nature as it is used for special purposes in various theoretical
and practical settings. As a result, an intricate network of interrelated concepts has developed in
accordance with its uses in various contexts. In Wittgenstein’s philosophy of language, this situation is
described as family resemblance, applied to the condition in which some concepts within a concept
family share some resemblances, while other concepts share others. “The view epitomized by
Wittgenstein’s Philosophical Investigations is that meaning, grammar and syntactic rules emerge from
the collective practices through the situated, changing, meaningful use of language of communities of
users (Gooding, 2004b)” .
Information can be understood as range of possibilities (the opposite of uncertainty); as correlation
(and thus structure), and information can be viewed as code, as in DNA, according to van Bentham
and Martinez in  (p. 218). Furthermore, information can be seen as dynamic rather than static; it can
be considered as something that is transmitted and received, it can be looked upon as something that is
Information 2011, 2
processed, or it can be conceived as something that is produced, created, constructed . It can be
seen as objective or as subjective. It can be seen as thing, as property or as relation. It can be seen from
the perspective of formal theories or from the perspective of informal theories  (p. 253). It can be
seen as syntactic, as semantic or as pragmatic phenomenon, and it can be seen as manifesting itself
throughout every realm of our natural and social world.
In this context it is important to mention the contribution of the FIS (Foundations of Information
Science) network that “from its very beginnings in early 90’s” presented “an attempt to rescue the
information concept out from its classical controversies and use it as a central scientific tool, so as to
serve as a basis for a new, fundamental disciplinary development—Information Science.” by
Among initiatives with the aim to work towards a modern concept of information, a workshop
entitled Information Theory and Practice took place in 2007 at Duino, focusing on the difference
between syntactic (Shannon) and semantic information.
In 2008, a project was started in León, Spain, aiming at the illumination of the concept of
information. Its working principle resembles the mosaic window of the Cathedral of León. That’s why
it is named “BITrum” (after the Latin “vitrum”) .
“Towards a New Science of Information” was the motto of the Fourth International FIS Conference
held in Beijing in 2010. The proceedings of the conference will be published in a special issue of the
journal triple-c. The topics addressed include: Informatics at multiple scales (Kirby et al.), information
in scientific use (Collier), information in reality, logic and metaphysics (Brenner), reductionist,
projectivist, disjunctivist, and integrativist thinking about information (Hofkirchner), the identity of
objects (Hewitt), autopoiesis, observation and informatics (Hashimoto), the relationship between
autopoiesis and biosemiotics (Nishida), the informational essence; information cognition; information
sciences (Kun Wu), social information (Cai), philosophy of information in China (Tianqi Wu), method
of inquiry (Schroeder), life informatics (Gao), information needs and signaling resources of
mycobacterium (Navarro and Marijuan), information and cognition (Díaz Nafría, Pérez-Montoro),
Science of Information (Doucette), abduction (Kamiura), and many more.
In , an essentially new approach (called parametric definition) is proposed by Burgin in order to
solve the problem with the definition of information.
Besides already mentioned information types, additional distinction ought to be made between the
symbolic and sub-symbolic information, as well as conscious and sub-conscious information , seen
from a cognizing agent’s perspective. The world modeled as informational structure with
computational dynamics, presents “proto-information” (Dodig Crnkovic) for an agent  and it
affects an agent’s own physical structures, as not all of functions of our body are accessible for our
conscious mind. This process of information communication between an agent and the rest of the
world goes directly, subconsciously, sub-symbolically or via semiosis—sense-making information
processing. In this approach, information undergoing restructuring from proto-information in the world
to meaningful information in an agent on several levels of organization is modeled as purely natural
phenomenon. Cognitive functions of an agent, even though implemented in informational structures,
are not identical with structures themselves but present their dynamics that is computational processes.
The quest for a general concept of information that goes beyond family resemblances is still there as
can be testified by several publications during the last decade e.g., by Lyre , von Baeyer ,
Information 2011, 2
Roederer , Seife , Dodig-Crnkovic , Muller , Brier , Kauffman et al. ,
Hofkirchner , Burgin , Davies and Gregersen , and Dodig-Crnkovic and Burgin . It
seems legitimate to put the heuristic questions accordingly, ‘Can the static and the dynamic aspect of
information be integrated when considering the static as result, and starting point, of the dynamic
aspect? Can the objective and the subjective aspect be integrated when attributing degrees of
subjectivity to objects? Or perhaps the degree of objectivity to subjects, as some others would
propose? Can the thing, property and relation aspects be integrated when elaborating on
transformations between them? Can the formal and the informal aspect be integrated when postulating
an underlying common nature parts of which are formalizable while other parts are not? This is similar
to Ludwig von Bertalanffy’s idea concerning the use of mathematical tools in his General System
Theory, . Can the syntactic, semantic and pragmatic aspects be integrated when based upon a
unifying semiotic theory? Can the specific aspects be integrated when resorting to evolutionary theory
and identifying each information manifestation on a specific level of evolution?’
One of the explicitly dedicated approaches towards unity in diversity is that which is connected to
the term “Unified Theory of Information” (UTI). While the question of whether or not a UTI is
feasible was answered in a controversial way by Capurro, Fleissner and Hofkirchner , Fleissner
and Hofkirchner tried to lay the foundations for a project of unification reconciling legitimate claims of
existing information concepts underlying science and technology with those characteristic of social
sciences, humanities, and arts [53,54]. They have been doing so by resorting to complex systems
theory. In what follows we will return to various programs of unification and elucidate similarities and
differences in their approaches.
2.1.2. What Is the Dynamics of Information?
Floridi  gives the following explanation:
By “dynamics of information” the definition refers to:
(i) the constitution and modeling 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 utilization and possible
(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.
The reader interested in the development of the field of Dynamic of Information prior to Open
Problems, such as seminal work by Dretske  and Barwise and Seligman  is referred to the
Philosophy of Information handbook , as well as  or . Abramsky’s chapter in the same
Handbook connects information, process and games (representing the rules or logic) in the promising
novel attempt to develop a “fully-fledged dynamical theory”.
Information 2011, 2
Van Benthem’s new book Logical Dynamics of Information and Interaction,  models
information dynamics within a framework of logic developed as a theory of information-driven
rational agency and intelligent interaction between information-processing agents. Van Benthem
connects logic, philosophy, computer science, linguistics and game theory in a unified mathematical
theory which provides dynamic logics for inference, observation and communication, with update of
knowledge and revision of beliefs, changing of preferences and goals, group action and strategic
interaction in games. Van Benthem’s framework includes all three senses of dynamics of information
on the level of human agency. From the modeling point of view nothing prevents to apply Benthem’s
approach to a network of simpler agents. The book includes chapters on logical dynamics, agency, and
intelligent interaction; epistemic logic and semantic information; dynamic logic of public observation;
multi-agent dynamic-epistemic logic; dynamics of inference and awareness; preference statics and
dynamics; decisions, actions, and games; processes over time; epistemic group structure and collective
agency; computation as conversation and rational dynamics in game theory. Van Benthem explores
consequences of the ‘dynamic stance’ for logic as well as for cognitive science in a way which
smoothly connects to the program of Philosophy of Information. 
On a different level of abstraction, yet another answer to the question of information dynamics is
given by Mark Burgin in his article Information Dynamics in a Categorical Setting which presents
“a mathematical stratum of the general theory of information based on category theory. Abstract
categories allow us to develop flexible models for information and its flow, as well as for computers,
networks and computation. There are two types of representation of information dynamics in
categories: the categorical representation and functorial representation. Properties of these types of
representations are studied. (…) Obtained results facilitate building a common framework for
information and computation. Now category theory is also used as unifying framework for physics,
biology, topology, and logic, as well as for the whole mathematics. This provides a base for analyzing
physical and information systems and processes by means of categorical structures and methods” .
Similarly built on dual-aspect foundations is info-computationalism, ICON of Dodig Crnkovic [44,59-63].
It presupposes a hierarchy of levels, starting from the basic proto-information as a stuff of the universe and
building a number of levels of organization in an evolutionary way, through computational processes. It
relates to Floridi’s program for PI [1,6,8,9,17,19,20,24,26,27], combining it with the pancomputational stance
(Zuse, Fredkin, Wolfram, Chaitin, Lloyd) which takes the universe to be a computer. With the universe
represented as a network of computing processes at different scales or levels of granularity, ICON sees
information as a result of (natural) computation . Adopting Floridi’s informationalism, (Informational
Structural Realism)  which argues for the entire existing physical universe being an informational
structure, natural computation can be seen as a process governing the dynamics of information. In the
ICON—a synthesis of informationalism and computationalism, information and computation are two
mutually defining ideas .
On the level of the basic mechanism, communication is a special type of computation. Bohan
Broderick  compares notions of computation and communication and arrives at the conclusion that
they are not conceptually different. He shows how they may be distinguished if computation is limited
to a process within a system and communication is an interaction between a system and its
environment. Burgin  puts it in the following way:
Information 2011, 2
“It is necessary to remark that there is an ongoing synthesis of computation and communication
into a unified process of information processing. Practical and theoretical advances are aimed at this
synthesis and also use it as a tool for further development. Thus, we use the word computation in the
sense of information processing as a whole. Better theoretical understanding of computers, networks,
and other information-processing systems will allow us to develop such systems to a higher level.”
Close to info-computationalism (ICON) is the view that conceives informational dynamics as
processes of self-organization. Whenever self-organizing systems in their behavior relate to the
environment, they create information, that is, they rather generate information than process it and are
thus information-generating systems . This concept might be called “emergent information”. The
difference to info-computationalism lies in the dynamics that is assumed as background. While
info-computationalism regards any natural process that can be described by a definable model as
computation, which is equal to information processing, (so that e.g., the emission of a radioactive
particle creates information) in the “emergent information” approach only self-organization processes
are deemed to produce information.
The triple-c model developed in the context of emergent information finds information generation
in a series of orderly concatenated different manifestations: First comes cognition (the first “c”) which
refers to the information generation of a self-organizing system vis-á-vis its environment that is
unspecified; the coupling of cognitive processes of at least two self-organizing systems yields then
communication (the second “c”); and sustainable communicative processes lead to cooperation
(the third “c”) of co-systems for the sake of a commonly established meta- or suprasystem of which
the co-systems are elements . In a less-than-strict-deterministic way cooperation feeds back to
communication as communication does to cognition. That’s the basic dynamics of emergent information.
In the ICON scheme, the recurrent theme is information/computation as the underlying
structure/process. Information is fundamental as a basis for all knowledge and its processing
characterizes all our cognitive functions. In a wider sense of proto-information it represents every
physical/material phenomenon .
2.1.3. Is a Grand Unified Theory of Information (GUTI) Possible?
There are several approaches that make such a claim.
Among the prominent groups working on unification, the Unified Theory of Information (UTI)
Research Group—Association for the Advancement of Information Sciences can be mentioned.
UTI Research Group “aims at the advancement of reflection and discourse in academia and society
about the role of information, communication, media, technology, and culture in society. It works for
building a better understanding and for dialogue in information science, communication and media
studies, and science and technology studies (STS). It is interested in advancing critical ideas, approaches,
methods, and research that are needed for establishing a global sustainable information society.”
Hofkirchner’s UTI is about self-organizing systems (from the most primitive physical system to
the social systems) that for themselves (in the case of cognition) or in interaction with other
self-organizing systems (in the case of communication) or as part of higher-level self-organizing
systems (in the case of cooperation) generate information and make use of it. And it is about artificial
Information 2011, 2
devices like the Turing machine computers that contribute to information generation not by organizing
themselves (there is no self in the machine) but by being instrumental to the overarching social
The info-computational framework, ICON of Dodig Crnkovic , characterized by two basic
ontological principles: Information (structure) and computation (process), provides a unifying
generative scheme for the range of phenomena from inanimate physical objects to cells, organisms,
cognizing systems and ecologies offering a new conceptualization of the nature of structures and
dynamics of informational phenomena. We will come back to this approach in the discussion of
informational universe/nature . While UTI and ICON have different starting points—UTI in
humanities (which makes it have a strong socio-political focus) and ICON in natural sciences and
computing (which makes it primarily interested in structures and processes at different levels of
abstraction), they nevertheless converge towards compatible views.
According to the current idea of computationalism (natural computationalism,
pancomputationalism), not only machines are capable of computing, but any dynamic behavior of
physical systems can be interpreted as computation, including the dynamics of biological systems.
See  on self-organizing self-star (self-* models. Here self-* stands for self-organization,
self-configuration, self-optimization, self-healing, self-protection, self-explanation, and
self/context-awareness—applied to information-processing systems. Scheutz in  argues that this
new kind of computationalism applied to the theory of mind is able to explain the nature of
intentionality and the origin of language. The length of this article does not permit an extensive review
of Scheutz argument, but in short, the main difference between the new idea of computation (like in
ICON as well) is that nature itself computes, so whatever processes are going on in our brains, they
represent information processing, which is a general form of computation. It is not the same view as in
old computationalism where the brain was supposed to be equivalent to Turing Machine. And it is
definitely not the claim that the brain is the mind. The distinction must be made between the structure
(brain) and the process (mind).
Kampis in his book Self-Modifying Systems in Biology and Cognitive Science: A New Framework
for Dynamics, Information, and Complexity describes the computational nature of those systems 
that today are part of the new organic computing field. Self-modification is one of already mentioned
self-* properties of living systems.
It is important to recognize the paradigm shift in the thinking about structures and functions of
living organisms that traditionally were considered to form a domain qualitatively different from
technological artifacts such as computers and robots. The difference between the present-day
computing and the Turing-type model of computation lies in the role of context of a given system. The
Turing machine is context-independent, and computes a function in isolation from the outer world.
However, self-organizing organisms are essentially open and coupled to the environment .
The Turing Machine model is not the most expressive model for the type of processes going on in
living organisms . Expressing biology in informational terms leads to increased understanding of
structures in the living world as scale-independent networks. Interactions within those networks are
essential for the formation and maintenance of biological structures on different levels of organization.
Burgin  in his new book, Theory of Information. Fundamentality, Diversity and Unification,
offers an approach to unification based on a synthesis of concepts of information describing processes
Information 2011, 2
in nature, technology, and society, with the main insights from information theory. He calls his
approach a General Theory of Information, explaining :
“The general theory of information is a synthetic approach, which organizes and encompasses all
main directions in information theory. It is developed on three levels: conceptual, methodological and
theoretical. On the conceptual level, the concept of information is purified and information operations
are separated and described. On the methodological level, it is formulated as system of principles,
explaining what information is and how to measure information. On the theoretical level,
mathematical models of information are constructed and studied.”
Besides General Theory of Information, Burgin’s book addresses Statistical Information Theory,
Semantic Information Theory, Algorithmic Information Theory, Pragmatic Information Theory and
Dynamics of Information.
Though, prima facie, Brier’s Cybersemiotics does not appear to be a theory of information—in
particular, if you consider the subtitle of his book from 2008 which runs “Why information is not
enough!”—it is, among others, an attempt to find common grounds of information processes, at least,
in the living world. In a recent description Brier writes  (pp. 1902-1903):
“The integrative transdisciplinary synthesis of Cybersemiotics starts by accepting two major, but not
fully explanatory, and very different transdisciplinary paradigms: 1. The second order cybernetic and
autopoietic approach united in Luhmann’s triple autopoietic system theory of social communication;
2. The Peircean phaneroscopic, triadic, pragmaticistic, evolutionary, semiotic approach to meaning,
which has led to modern biosemiotics, based in a phenomenological intersubjective world of partly
self-organizing triadic sign processes in an experiental meaningful world. The two are integrated by
inserting the modern development of information theory and self-organizing emergent chemico-biological
phenomena as an aspect of a general semiotic evolution in the Peircean framework.”
Biosemiotics, biology interpreted as sign systems study (notably Barbieri’s code semiotics) is one
of the necessary links in the chain of hierarchies of meaning production. On the level of an organism,
Menant in  (p. 255) defines meaning as a consequence of the constraint to the living entity: “stay
alive”. Such constraint that is to be locally satisfied by the organism goes with the process of
interpretation, or meaning generation, that links the living entity to its environment.
Like the UTI and ICON approaches, Brier’s Cybersemiotics is critical of mechanicism because it
either neglects meaning and related phenomena or is reductionistic and levels them down. It is
important to keep in mind that mechanistic approaches have been criticized even by informationalists.
But unlike UTI and ICON Brier associates the mechanistic approach with the term “information”,
because Shannon and Weaver, Wiener and Schrödinger’s definition that in his view is prototypical for
the mechanistic approach is widely accepted in natural and technical sciences  (p. 1914). Despite
his scepticism towards informational approaches (based on Shannon), Brier construes an ontological
hierarchy (“heterarchy”) of different levels across which information processes and meaning can
develop  (p. 381): “Across levels, various forms of causation … are more or less explicit
(manifest). This leads to more or less explicit manifestations of information and semiotic meaning at
the various levels of the world of energy and matter.” Brier argues that the foundation in system theory
is not enough to explain living systems’ cognitive abilities, in particular, meaning, and that Peircean
Information 2011, 2
semiotics is necessary. Thus very much like UTI—that represents a perspective-shifting methodology
that allows for both third-person and first-person experience [73,74]—but in contradistinction to
ICON—that accepts only third-person methodology—and in contradistinction to other biosemiotic
threads such as Barbieri’s idea of copymakers and codemakers, Cybersemiotics includes a
The impact of those new theories on the development of Philosophy of Information will be visible
in the years to come.
2.2. Information Semantics
2.2.1. The Data Grounding Problem: How Can Data Acquire Their Meaning?
Floridi, who together with Taddeo  contributed to the research on the data grounding problem,
explains the situation in the following way: “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 ). 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.” Trends in Philosophy of Information, in .
The data grounding problem can be related to the two kinds of information, symbolic (language)
and sub-symbolic (signals) and the world as proto-information, [44,60,61,63]. Within pragmatic
tradition, meaning is the result of use, or more generally, meaning is generated through the interaction
of an agent with the world, including other agents . An agent is defined in a generic sense: An
entity is an “agent” if it has some degree of autonomy, that is, if it is distinguishable from its
environment by some kind of spatial, temporal, or functional attribute and it is able to engage in tasks
in an environment without direct external control. That is the definition originating in Agent Based
Modeling which makes it possible to model computationally a range of agents, from viruses to
Data semantics (as especially evident in computer science and cognitive informatics) is therefore
defined by the use of the data. Symbols are grounded in sub-symbolic information through the
interactions of an agent. Symbols here are defined in the sense of Harnad (1991) who uses the Chinese
Room Argument (Searle 1980) to introduce the Symbol Grounding Problem.
This is in line with the praxical solution proposed by Taddeo and Floridi  in form of
Action-based Semantics with the simple basic idea that initially, the meanings of the symbols
generated by an agent are the internal states of the agent which are directly correlated with the
On the fundamental level, quantum-informational universe performs computation on its own,
Lloyd , Vedral . Symbols appear on a much higher level of organization, and always in
relation with living organisms/cognizing agents. Symbols represent something for a living organism;
they have a function as carriers of meaning. See Menant’s article in  (p. 255).
As already pointed out, there are two different types of computation and both are implemented in a
physical substrate: Sub-symbolic and symbolic computation. Douglas Hofstadter has addressed the
question of symbols formed by other symbols or sub-symbols in his book Gödel, Escher, Bach: An
Information 2011, 2
Eternal Golden Braid from 1979. Interesting to notice is that in the fields of Artificial Intelligence and
Cognitive Science similar suggestions for the symbol grounding problem solutions are proposed by
number of researchers, from Harnad  to Ziemke . Smolensky and Legendre present a way of
integration of connectionist (‘neural’) and symbolic computation, addressing computational, linguistic,
and philosophical issues in .
John Mingers who had developed a theory about data, information and meaning in the 90ies ,
advised in 2001  to give greater consideration to neurophysiological processes in living systems
when it comes to meaning and puts emphasis on embodied cognition drawing on the concept of
autopoiesis (self-organization) by Maturana and Varela. He states that it is the readiness of the nervous
system that determines the response that is triggered by some external event. It’s the body that
unconsciously presents our conscious mind with preconfigured meanings.
Søren Brier in his The Cybersemiotic Framework as a Means to Conceptualize the Difference
between Computing and Semiosis in  (p. 178) offers a critical view which he also defends in his
book Cybersemiotics. Why Information Is Not Enough!, in which he argues that first-person semiosis
cannot be captured by info-computational models alone. Semiosis is a sign process which
includes production of meaning, and computation is assumed to be adequately modeled by Turing
machine. However, recent developments in the fields of cognitive computing and cognitive
informatics involve much more complex info-computational architectures, as discussed by Müller and
Dodig-Crnkovic . Computation is not based on Turing machine model, but is taken to be Natural
computation, which encompasses all physical, chemical, biological and psychological as well as social
processes, on different levels of organization of informational structures. Technically, the concept of
virtual machine is used from theory of computation as discussed by Aaron Sloman in (17); virtual
machines that can be implemented or realized in physical machines or other virtual machines.
2.2.2. Truth Problem: How Can Meaningful Data Acquire Their Truth Value?
2.2.3. Informational Semantic Problem: Can Information Theory Explain Meaning?
We discuss the above two problems together, as they are connected. Based on scientific tradition,
information semantics can be related with system modeling  and model validity. Truth might be
ascribed to meaningful data organized into information in the sense of “correct well-formed
information” within a coherent theoretical framework, implying that the data are correctly obtained,
transmitted and stored, that they have not been corrupted in communication or storage or used
inappropriately. Such correct data might be called “true data” but that is not the usual terminology in
sciences and technology.
As knowledge is constructed from information, in order to provide a guarantee for knowledge to be
true, Floridi proposes a new concept of Strongly Semantic Information , which requires
information to be true and not only well formed and meaningful data. Adriaans  presents an
interesting critique, claiming that Floridi’s theory of semantic information as well-formed, meaningful,
and truthful data is “more or less orthogonal to the standard entropy-based notions of information
known from physics, information theory, and computer science that all define the amount of
information in a certain system as a scalar value without any direct semantic implication.” Even
Information 2011, 2
Scarantino and Piccinini in their article Information without Truth for the special issue of
Metaphilosophy  remind that “the main notions of information used in cognitive science and
computer science allow A to have information about the obtaining of p even when p is false.”
Adriaans defends the position that “the formal treatment of the notion of information as a general
theory of entropy is one of the fundamental achievements of modern science that in itself is a rich
source for new philosophical reflection. This makes information theory a competitor of classical
epistemology rather than a servant.” Chaitin in  argues for the similar position.
According to Adriaans, information theories belong to two programs, empirical/Humean school and
transcendental/Kantian school. Floridi’s Strongly Semantic Information belongs to the transcendental
program. Empirical approaches (such as those proposed by Shannon, Gibbs and Kolmogorov) present
mathematical tools for selection of “the right model given a set of observations”. While classical
epistemology studies truth and justification, theory of information is based on model selection and
probability. Floridi’s philosophy, according to Adriaans analysis, incorporates selected notions from
information theory into a classical research framework, while “information theory as a philosophical
research program in the current historical situation seems much more fruitful and promising than
This sounds like a convincing diagnosis. What Floridi’s program finally aims at is to provide the
basis for understanding of knowledge, truth and justification in terms of information (and as can be
added from an info-computationalist stance, necessarily also in terms of its complementary notion of
computation). At some point all high level concepts (truth, justification) will be required to be
translated into low level (info-computational level); in much the same way as symbolic and
subsymbolic cognition must be connected in order to be able to reconstruct the mechanisms that
On the other hand, Sequoiah-Grayson  defends Floridi’s theory of Strongly Semantic
Information “against recent independent objections from Fetzer and Dodig-Crnkovic. It is argued
that Fetzer and Dodig-Crnkovic’s objections result from an adherence to a redundant practice of
analysis. (..) It is demonstrated that Fetzer and Dodig-Crnkovic fail to acknowledge that Floridi’s
theory of strongly semantic information captures one of our deepest and most compelling intuitions
regarding informativeness as a basic notion.”
Nevertheless, even so, for Dodig-Crnkovic it seems reasonable to consequently rely on the
fundamental framework of theory of information instead of a mix of classical epistemological and new
2.2.4. Informational Truth Theory: Can a Theory of Information Explain Truth?
Theory of information can explain truth as info-computational phenomenon, even though truth is not
absolute, but represents our best present knowledge, within a given framework, as Adriaans suggests:
“Based on contributions of philosophers like Popper, Kuhn, Feyerabend, Lakatosh, Stegmuller, and
Sneed in the middle of the twentieth century the common view among scientist is that scientific theories
never can claim to be true definitively. What we can only do is try to find and select the best theory
that fits the data so far. When new data are gathered, the current theory is either corroborated or,
when the data are in conflict with the theory it has to be revised. The best we can reach in science is
Information 2011, 2
provisional plausibility. This is effectively the position of mitigated skepticism that is defended by
Hume. This methodological position fits perfectly with the recent insights in philosophy of information,
notably the theory of general induction that has been initiated by Solomonoff and his theory of
algorithmic probability which is a cornerstone of modern information theory.”
Naturalized epistemology  and  describes the evolution of increasingly complex cognitive
capacities in organisms as a result of interactive information processes where information is more
concerned with meaning for an agent than with truth, as meaning is directly related to agency. Agents
are different entities on different levels of organization, characterized by well-defined identity and
mode of autonomous action—as we already mentioned in the generic definition of an agent, as used in
Agent Based Modeling theory. Knowledge is typically distributed in a system of agents in a
community of practice (interacting network of agents). Agency in the natural world is typically based
on incomplete knowledge, where probabilities govern actions. Being internalized by an agent, data
becomes information, in the context of an agent’s experiences, habits and preferences. All is
implemented in the agent’s bodily structures (including brain, for those agents who possess it) and
determines its possible interactions with the world. Adaptive structures of agents in networks act as
memory of the past development, and represent their learning history. As Salthe  puts it:
“A species’ storage of historically acquired information is held in the genomes of the cells of its parts,
as well as in material configurations in cell structures. At its own scale each species is unique; while
at their scales, its parts (e.g., organisms) differentiate increasingly as they recover from perturbations
during development, becoming ever more intensively unique.” This makes the relationship between
information and meaning natural . Meaning governs an intelligent agent’s behavior, based upon
data structured into information and further structured into knowledge that in interaction with the
world results in agency. Truth as a control mechanism is arrived at first in the interaction, based on
propositional knowledge, between several agents (inter-subjective consensus about knowledge) or in
the relationship between different pieces of propositional knowledge that an agent possess and can
reason about. In the sense of Chaitin’s “truth islands” , some well-defined parts of reality can be
organized and systematized in such a way that truth may be well-defined within those sets, via
inter-agent communication. For an agent, meaning is a more fundamental phenomenon than truth, and
both must be possible to express in terms of models :
“Within the context of information theory, the problem of founding knowledge as true justified belief
is replaced by the problem of selecting the optimal model that fits the observations.”
From the everyday experience we know that we act based on knowledge we judge as plausible and
which may be true or not. The underlying fundamental debate about certainty and probability
is discussed by Fallis  in the analysis of probabilistic proofs and the epistemic goals
As uses for information can be many, in different contexts and for different agents, Allo in 
addresses the problem of formalizing semantic information with logical pluralism taken into account.
Benthem’s view is that logical pluralism is one of several ways of broadening the understanding of
logic and its development, .
Information 2011, 2
2.3.1. Descartes’ Problem: Can Cognition Be Fully Analyzed in Terms of Information Processing at
Some Level of Abstraction?
An example is Wang’s Cognitive Informatics [92-96] which shows even how this can be done in
practice at some level(s) of abstraction. According to its founder, Yingxu Wang, :
“Cognitive Informatics (CI) is a transdisciplinary enquiry of cognitive and information sciences
that investigates the internal information processing mechanisms and processes of the brain and
natural intelligence, and their engineering applications via an interdisciplinary approach.”
This transdisciplinary research builds on the results from computer science, computer/software
engineering, systems science, cybernetics, cognitive science, knowledge engineering, and neuropsychology,
among others. Applications of CI include cognitive computing, knowledge engineering, and software
engineering. The theoretical framework of CI links the information-matter-energy model, the layered
reference model of the brain, the object-attribute-relation model of information representation in the
brain, natural intelligence, autonomic computing, neural informatics, human perception processes, the
cognitive processes of formal inferences, and the formal knowledge systems. In order to provide
coherent formal framework for CI, new descriptive mathematical formalisms of Concept Algebra,
Real-Time Process Algebra and System Algebra have been developed. From Wang’s work as well as
van Benthem’s  it is evident that adopting information as a new fundamental principle calls for a
change in formal approaches in logic, mathematics, model-building and understanding of their
On the basic level, according to the triple-C UTI model of Hofkirchner, cognition is a manifestation
of information, that is, cognitive processes are those types of information processes that perform the
function of relating of a self-organizing system to some event or entity in its environment. When that
system enters such a relation, it generates information. It is important not to forget that “cognition” in
this context is not only meant for human systems but for all living systems and material systems as
long as they self-organize. The model concedes cognizability to non-human systems too, albeit in
different degrees according to the evolutionary stage they represent. In terms of complexity,
“cognizability” refers just to the dimension of solitary systems, that is, individual phases of
metasystem transitions or elementary levels of suprasystem hierarchies.
The point, however, is that cognition in UTI is an emergent process, a less-than-deterministic
process the outcome of which is the generation of information that cannot be reduced to some
perturbation of the system or some input in the system or some algorithmic information processing
inside the system because it constitutes a leap in quality. Thus Turing machine computation is not able
to provide a model of natural or human information generation .
On the other hand, info-computational approach, ICON, analyzes cognition in terms of information
structures and computational processes. Cognition is understood as self-organized hierarchy of
information processing levels in a cognizing agent, in agreement with Maturana and Varela’s view of
life as cognition [98,99]. The lowest level of organization of reality is “proto-information”, the term
used by Dodig Crnkovic  to denote the physical world as information. Naturalized epistemology
Information 2011, 2
argues that all cognition is embodied and all mental activity arises as an emergent phenomenon from
an agent’s interaction with the environment, including self-reflection and interactions with other
agents. Unlike UTI, info-computationalism does not ascribe cognition to any non-living entities, not
even in case of self-organizing systems (such as for example tornados). It should be pointed out that in
ICON computation in general does not need to be deterministic, as in nature there are indeterministic
processes as well, and they also present computation in a computational universe. Concerning
continuum vs. discrete debate, it is evident that those two complementary modes of description are
used on different levels of organisation, so they both are part of ICON.
2.3.2. Dennett’s Reengineering Problem: Can Natural Intelligence Be Fully Analyzed in Terms of
Information Processing at Some Level of Abstraction?
Even here, the natural intelligence is based on a complex hierarchy of levels of information
processing architectures. Intelligence is closely related to cognition. As Maturana and Varela [98,99]
argue; for a living system, to live is to cognize, and cognitive domain is the domain of states in
self-organization (autopoiesis). Wang  defines abstract intelligence in the following way:
“In the broad sense, abstract intelligence is any human or system ability that autonomously
transfers the forms of abstract information between data, information, knowledge, and behaviors in the
brain or systems.”
In the field of AI, behaviors are important, so the chain data-information-knowledge (“information”
here used in a restricted sense) ends with behavior and not with wisdom as was earlier proposed by
Stonier . Wisdom in a sense of Stonier may be interpreted as a state of information that allows for
successful behavior of the human system.
One of the fundaments of intelligence is logic. As we are learning about intelligence, natural and
artificial, we also learn about logic. Here is van Benthem’s description of the state of the art :
“Since the 1930s, modern core logic has been about at least two topics: valid inference, yes—but on
a par with that, definability, language and expressive power. In fact, many of the deep results in logic
are about the latter, rather than the former aspect: linked with Model Theory, not Proof Theory. And
to me, that definability aspect has always been about describing the world, and once we can do that,
communicating to others what we know about it. In fact, there is even a third pillar of the field, if we
also count computation and Recursion Theory.”
This new emerging broader understanding of logic with “scope and agenda beyond classical
foundational issues” will also contribute to the future developments in AI (Artificial Intelligence), IA
(Intelligence Augmentation), Cognitive Informatics and Cognitive Computing. Anyway, from the
position of UTI, doubts have to be raised whether the new scope and agenda will further these
developments or rather restrict them as far as emergence in natural intelligence is concerned.
One of the important advancements in understanding of intelligence, knowledge generation and
modeling is the development of generative multi-agent models. Generative models are generalizations
of cellular automata to encompass agents with different individual characteristics and types of
interactions, asynchronously communicating in a general topology. Those kinds of models, (which
Wolfram  rightly characterized as a “new kind of science”) have developmental properties very
Information 2011, 2
useful in modeling of life phenomena. As living systems exhibit self-similar network structures from
the molecular level to the level of ecology, agent modeling is the most general framework for such
systems. Interesting to notice is the difference between the structural description and the dynamic
description as in (multi) agent models. Even very simple structures in a course of temporal
development through interactions can develop surprisingly complex patterns, and even lead
computationally to randomness . Among important insights learned from generative models and
simulations in general are scale-independent network phenomena in living systems, directly connected
to information communication among network nodes.
2.3.3. Turing’s Problem: Can Natural Intelligence Be Fully and Satisfactorily Implemented
The answer to this question depends on what is meant by “natural intelligence” and “fully and
satisfactorily”. If we consider a dolphin as possessing natural intelligence, which features shall we be
able to reproduce in order to claim that dolphin intelligence has been implemented fully and
satisfactorily? The development of AI seems to suggest that we will quite soon be able to
reproduce the intelligent behavior of some simple living organisms. Projects like Blue Brain
http://bluebrain.epfl.ch  are designed specifically to simulate natural intelligence, by reverse
engineering mammalian brain, first in a rat and then in a human. (This also relates to the previous
question about Dennett’s reengineering problem.) The biologically accurate model of the cortex, the
“grey matter” of the brain that first appeared in mammals during evolution, responsible for mental
capacities such as thinking, anticipation, etc., has a fundamentally simple repetitive structure of
neocortical column found in all mammals. The difference between rat brain and human brain is
supposed to be basically just in the volume of cortex. The Blue Brain simulation re-creates this
fundamental microcircuit of the neocortical column “down to the level of biologically accurate
individual neurons”. In 2007 the Blue Brain project announced plans to model the entire human brain
within the next 10 years. From the claim that the difference in the intelligence in mammalians is
proportional to the volume of the cortex, one can conclude that continued increasing of the cortex of a
simulated brain will result in increasing intelligence.
There is another approach, taken by Boahen at Stanford and Meier at Heidelberg, in the FACETS
project (Fast Analog Computing with Emergent Transient States), which instead of simulating,
emulates neurons, building “a brain on a silicon chip” in the form of hardware.
However, Wang  would not agree with those optimistic expectations concerning non-biological
intelligence, and he presents a theorem (without proof) stating:
“The law of compatible intelligent capability states that artificial intelligence (AI) is always a
subset of the natural intelligence (NI), that is: AI
Taking the basic assumptions of complexity in the perspective of UTI  seriously, Wang’s
theorem would be supported: Natural intelligence crucially relies on emergent information, on
processes that show emergence which, according to UTI, in principle cannot be incorporated in
Information 2011, 2
However, one can wonder what will happen with natural intelligence augmented by increasingly
advanced AI or in general with extended mind . Cognitive computing devices can exceed specific
human cognitive capabilities (such as logical problem solving, pattern-recognition, search, and
memory). Already now there are software systems which exceed any single human’s comprehension,
and which augment our cognition by performing cognitive tasks for us.
It is evident that not all researchers would agree on the claim that human intelligence is the limit,
and indeed from the perspective of info-computationalism, there is no fundamental reason not to
exceed human level intelligence by use of cognitive machines. It will be interesting to follow the
development of mentioned simulation/emulation projects aiming at (at least) human level AI.
2.3.4. The MIB (Mind-Information-Body) Problem: Can an Informational Approach Solve the
Cognitive Informatics postulates two different essences: Matter-energy and information: “The
Information-Matter-Energy-Intelligence (IME-I) model states that the natural world (NW) which forms
the context of human and machine intelligence is a dual: one facet of it is the physical world (PW), and
the other is the abstract world (AW), where intelligence (I) plays a central role in the transformation
between information (I), matter (M), and energy (E)” .
The above is the classical mind-body dualism but without mystical problem of connecting the
physical with the intelligence/mind. It seems evident from AI that some rudimentary intelligence can
be programmed into a physical medium, and if even higher level intelligence will be possible to
implement non-biologically in the near future, is a question that will be resolved empirically.
Within the info-computational framework ICON of Dodig-Crnkovic, information and matter/energy
are represented by information and computation. Computation presents implementation of physical
laws on an informational structure . Instead of describing the world in terms of matter/energy
(where energy stands for equivalent of matter) and information (which corresponds to a structuralist
view of the world as consisting of stuff that changes patterns), the info-computationalist approach,
makes the distinction between structure (information) and a process (computation). The mind/body
problem is solved in a simple way. Mind is a process, information processing, and body is a structure
(proto-information). Thus, mind is a process of natural computation that results from dynamical
re-configuration/re-structuring of the information in the brain, au fait with the rest of the body which
connects it with the physical world. The structure and the process are inseparably interwoven by
physical laws .
In the emergentist UTI frame mind is an emergent evolutionary level of information
manifestation . Human mind is inextricably bound to the corresponding physical stratum (human
body, human brain) brought about by evolution.
2.3.5. The Informational Circle: If Information Cannot Be Transcended but Can Only Be Checked
against Further Information—If It Is Information All the Way up and All the Way Down—What Does
This Tell Us about Our Knowledge of the World?
If we adopt Stonier’s  view that information is structured data, and that knowledge is
structured information, we may say that information is a building block in more complex structures,
Information 2011, 2
but the structure is what makes the difference. Informational Structural Realism, ISR is developed by
Floridi . If we want to understand the behavior of a living organism, we must know those structural
relationships, both upwards and downwards in the complexity hierarchy.
Wang  argues for adding the behavior to Stoniers hierarchy:
“A key in the study of natural and artificial intelligence is the relationships between information,
knowledge, and behavior. Therefore, the nature of intelligence is an ability to know and to do,
posessed by both human brains and man-made systems. In this view, the major objectives of cognitive,
software, and intelligence sciences are to answer:
− How the three forms of cognitive entities, i.e., information, knowledge, and behavior, are
transformed in the brain or a system?
− What is the driving force to enable these transmissions? ”
The transformation from information (in the broader sense as used in our context) of one kind,
level, or quality to a higher information kind, level, or quality cannot be sought in a mechanistic
process, since in a mechanistic process nothing new can emerge as the result is fully derivable from,
and thus reducible to, the initial conditions and the mechanism in operation. Emergent information
would point to self-organization as driving force. Humans do not only produce mechanistic systems as
Turing-machine computers, but also build self-organizing systems as any kind of social systems.
The “information cannot be transcended”—situation reminds of pre-Socratic natural philosophy in
which only one basic cosmological principle, quintessential substance, was sought after, with
prominent representatives like Anaximander who advocated apeiron as the beginning or ultimate
reality from which everything existent can be derived, and the atomist school who postulated atoms as
indivisible basic elements of matter.
The philosophical study of the nature of information and its relationships to intelligence leads
directly to biology, (among others molecular biology, developmental biology, computational biology,
bioinformatics, neurobiology, ethology, evolutionary biology, biotechnology, biochemistry and
biophysics, genetics, genomics, structural biology, systems biology) and other life sciences (such as
cognitive and computational neurosciences, ecology, neuroinformatics) and similar research providing
new insights from the study of living things into processes of cognition and intelligence. This process
of philosophical meta-analysis must be informed by results from current research and must be
accurately updated. The progress of life sciences at the moment is such that no single human can have
complete insight into any broader field but his/her narrow field of specialization, which makes
transdisciplinary collaboration increasingly important.
That information is always audited by information only is supported by the interminable cascade of
building one metalevel after the other, viewed from the angle of “emergent information”. Here
information is the self-organized relating of a system to another event or entity and every system that
organizes itself is free to position itself vis-à-vis its environment, to establish a new level and thus to
add another metalevel to whatever level there exists so far. An idea like this need not end up in radical
constructivism, though. The system-made construction of a metalevel is always bound to the activity
of a situated, embodied real-world system that engages with its environment and is capable of
renewing its engagement according to the feedback it is exposed to from the environment.
Information 2011, 2
2.3.6. The Information Continuum Conjecture: Does Knowledge Encapsulate Truth Because It
Encapsulates Semantic Information? Should Epistemology Be Based on a Theory of Information?
If information is meant as strongly semantic information  then the answer should be yes, as the
knowledge properly constructed from strongly semantic information, should encapsulate truth.
However, concept of truth may not exist when information is used in a broad context, in which reality
is an informational structure .
Even in the case of “information in the wild” (e.g., biological information for which truth is not
well defined) it is good to base epistemology on a theory of information, as already pointed out by
Adriaans, so as to get phenomenologically informed, naturalized epistemology.
Chaitin in  formulates Epistemology as Information Theory.
Epistemology as a part of philosophy deals with human cognition on the highest level of
abstraction. On the other hand, human cognition seems to be a special case of cognition that shows
evolutionary stages, it is a late product of biotic evolution on earth. From a theoretical view of
information in the broad sense (among others the UTI, the ICON and, to a certain extent,
Cybersemiotics) cognition can be seen as a manifestation of information, and cognitive processes in
human and prehuman systems as information (generation) processes. Philosophy of Information deals
with information on the highest level of abstraction. Thus it should include evolutionary thinking and it
is obvious that, given that assumption, there is a continuum and epistemology can be based upon
Philosophy of Information (in analogy to looking upon cognition as information process) as was
initiated in Evolutionary Epistemology by Erhard Oeser .
2.3.7. The Semantic View of Science: Is Science Reducible to Information Modeling?
The answer depends on how we understand modeling. Information modeling is at the very heart of
every empirical science. Theoretical physics, for example, uses the results of empirical models for
building layers of theory upon empirical informational structures, originating in object-level
information modeling. New scientific knowledge is obtained not only from empirical data but also
from relating to existing theories. One can also view all theoretical work as a kind of modeling. In that
case the answer would be yes, scientific knowledge is a result of information modeling even though
many scientific practices do not have character of modeling—e.g., observations and measurements are
fundamental interactions of intelligent agents with the environment, so they per se are not modeling,
even though they are theory laden. At present we are only in the beginning of the development of
automated discovery, automated knowledge mining, automated theorem proving, and similar
techniques based on the idea that science might be reducible to information modeling.
As already mentioned, in order to be able to provide relevant discourse, Philosophy of Information
must be informed by life sciences as well as material sciences. Discussing Informational Structural
Realism leads to the discussion of different levels of organization of the physical world—from
material systems like elementary particles, atoms, molecules, planets, planetary systems, galaxies and
universe[s] to living systems like biomolecules, cells, organisms, ecosystems, to human societies which
are the result of the natural process of self-organization, present at different spatial and temporal scales.
Information 2011, 2
Exactly that connection to the up to date research is offered in the book Information and
computation  which examines questions of knowledge (Brier), information dynamics (Burgin),
mathematics as biological process (Chaitin), measurement and irreversibility (Collier), the
computational content of supervenience (Cooper), mechanicist vs. info-computational world systems
(Dodig-Crnkovic and Müller), computing and self-organization (Hofkirchner), information and
computation in physics as explanation of cognitive paradigms (Kreinovich, Araiza), bodies informed
and transformed (MacLennan), an evolutionary approach to computation, information, meaning and
representation (Menant), interior grounding, reflection, and self-consciousness (Minsky), biological
computing (Riofrio), super-recursive features of natural evolvability (Roglic), a modeling view of
computing (Shagrir), information, for an organism or intelligent machine (Sloman), inconsistent
information as a natural phenomenon (de Vey Mestdagh and Hoepman), and the algorithmic nature of
the world (Zenil, Delahaye).
2.4. Informational Universe/Nature
2.4.1. Wiener’s Problem: Is Information an Independent Ontological Category, Different from the
Physical/Material and the Mental?
This is a question about metaphysics, and the Philosophy of Information builds on metaphysical
naturalism. In order to put this view into context, it is instructive to look at the critique of present day
metaphysics in the book Everything Must Go: Metaphysics Naturalized by Ladyman, Ross, Spurrett,
and Collier , who propose a general “philosophy of nature” based on “ontic structural realism”.
The universe is “nothing but processes in structural patterns all the way down” (p. 228) “From the
metaphysical point of view, what exist are just real patterns” (p. 121). Understanding patterns as
information, one may infer that information is a fundamental ontological category. The ontology is
scale-relative. What we know about the universe is what we get from sciences, as “special sciences
track real patterns” (p. 242). “Our realism consists in our claim that successful scientific practice
warrants networks of mappings as identified above between the formal and the material” (p. 121).
This points back to the previous question about the information modeling in science. The authors
provide convincing critique against traditional analytic metaphysicians who are “still talking as if the
world is individual items in causal relations, rather than processes in structural patterns all the way
down”. The book defines verification in terms of information transfer, (p. 307-310) and adopts
Salmon’s process theory of causality in form of “information carrying”. Even though the focus of the
book is to argue for naturalized metaphysics, mainly through philosophy of physics, it is compatible
with the metaphysical claims of Philosophy of Information.
Information may be considered the most fundamental physical structure, as in Floridi’s
Informational Structural Realism . It is in permanent flow, in a process of transformation, as
known from physics. Von Baeyer  suggests that information is to replace matter/energy as the
primary constitutive principle of the universe. It will provide a new basic unifying framework for
describing and predicting reality in the twenty-first century. In the similar vein, Wang  postulates a
dual-aspect reality with matter-energy and information as its basic principles:
Information 2011, 2
“Information is recognized as the third essence of the natural world supplementing to matter and
energy (Wang, 2003b), because the primary function of the human brain is information processing.”
Structures are outcomes and mediums of processes. If these processes are processes of
self-organization in which systems relate to events or entities in the environment, the emerging
relations are structures that are essentially informational. The “emergent information” view (UTI) is in
that respect “emergentist monism”, as Peacocke in  names it, applied to information it means
information is something that emerges from matter and energy. If it emerges, then matter and energy
provide the necessary condition for information to come about but not a sufficient condition. That is,
without matter or energy no information. In that sense, the emergentist information concept is
materialistic. But it is neither materialistic in the mechanicist, reductionist sense nor idealistic,
according to (UTI).
At the fundamental level, information can be said to characterize the world itself, for it is through
information we gain all our knowledge—and yet we are only beginning to understand its meaning .
Among the unifying strategies statistical models presented in Adriaans  should be mentioned.
In the ICON dual-aspect theory of the physical universe, one sees information as a structure of the
material world, while computation is its time-dependent evolution, the implementation of physical
laws. Through biological evolution, self-organization by natural computation leads to increasingly
more powerful cognitive agents. Life is cognition according to Maturana and Varela [98,99]
and it produces intelligence, based on information processing. In that way, fundamental level
proto-information is identical with the physical structure, while mind is a process that appears as a
product of evolution in complex biological structures. Within the ICON framework  information is
an independent ontological category, its basic structures are the fabric of the physical world and mental
phenomena are natural computational processes in highly complex biological informational structures.
Physicists Zeilinger  and Vedral  suggest seeing reality and information as one.
Researchers such as Chiribella (Perimeter Institute for Theoretical Physics) are working on the
development of the mathematics of quantum theory completely reconstructed from a set of principles
about information processing. “The key principle in our reconstruction of quantum theory is the
‘purification principle’, stating that every mixed state of a system A can be obtained as the marginal
state of some pure state of a joint system AB. In other words, the principle requires that the
ignorance about a part, be always compatible with the maximal knowledge of a whole.” Chiribella, A
Seminar at Computing Laboratory Oxford, 18th January 2011
Biologists too, such as Kurakin  add to this information-based view of the Universe/Nature:
“When reconceptualized in equivalent terms of self-organizing adaptive networks of
energy/matter/information exchanges, complex systems of different scales appear to exhibit universal
scale-invariant patterns in their organization and dynamics, suggesting the self-similarity of
spatiotemporal scales and fractal organization of the living matter continuum.”
From the fields of physics and biology new insights essential for the Informational Universe/Nature
may be expected in the years to come. A forthcoming issue of the journal Information is dedicated to
matter/energy and information and will try to elucidate those fundamental relationships.
Information 2011, 2
2.4.2. The Problem of Localization: Could Information Be Neither Here (Intelligence) Nor There
(Natural World) but on the Threshold, as a Special Relation or Interface between the World and Its
Intelligent Inhabitants (Constructionism)?
In the ICON framework  there is no Cartesian divide between body and mind. The Naturalized
epistemology approach , conceptualizes information as both here (intelligence) and there (world)
and on the threshold, as information constitutes the basic existence. Its structural changes are the
results of computational processes. We have a long way to go in learning how exactly those
computational processes are to be understood and simulated on different levels of organization of
informational structures, but the first step is to establish the basic conceptual framework which
smoothly connects natural world with intelligence .
On the other hand, in the Hofkirchner’s UTI view, information is neither outside in the first
(natural) world nor in a Platonist third world waiting for being collected, detected and received by
intelligent beings nor is it something constructed by intelligent beings and residing in their second
world only. In UTI, information is overarching and comprising complex material systems and their
environment, connecting them by the establishment of relations between them that become manifest in
a change of the structure, or the state, or the behavior of the systems. As such, information is part of
the natural world but it is bound, exclusively, to the existence and activity of self-organizing systems
and cannot exist without, or external to, that. Information as objective relation between a “subjective”
system and its environment can become itself a trigger for another information process in which
another “subjective” system relates itself to that very information via another “subjective” change in its
own structure, state, or behavior. The term “subjective” is used here to characterize the spontaneity of
complex systems when self-organizing and is meant to come in degrees according to evolutionary
stages complex systems represent . “Subjectiveness” is what is modeledmodelled by means of
2.4.3. The “It from Bit” Hypothesis: Is the Universe Essentially Made of Informational Stuff, with
Natural Processes, including Causation, as Special Cases of Information Dynamics?
The development in this direction can be seen in Floridi who argues for Informational Structural
Realism, ISR. The fundamental claim of info-computationalism (ICON) which builds on ISR and
natural computationalism, is that the universe is essentially made of informational stuff, and that
computation may be seen as implementation of physical laws, that governs information dynamics.
In his “Universe from Bit” Davies  (pp. 65-91), argues for a shift in the sequence of
mathematics, physics and information: “The traditional relationship between mathematics, physics,
and information may be expressed symbolically as follows: Mathematics → Physics → Information”.
“The variant I wish to explore here is to place information at the base of the explanatory scheme, thus:
Information → Laws of Physics → Matter” (p. 75). The rationale is that “the laws of physics are
informational statements” (p. 75). Though this might seem an ontological flaw, Davies can state at the
same time that “the laws of physics are inherent in and emergent with the universe, not transcendent
of it” (p. 83) because he postulates “a self-consistent loop: The laws of physics determine what can be
computed, which in turn determines the informational basis of those same laws of physics” (p. 87).
Information 2011, 2
The thesis that the laws of physics evolve because of, and together with, self-organizational
capabilities of the systems inhabiting the universe is in compliance with the view that the laws of
physics are not a given to the universe but completely of this world—a view that is shared by the
emergentist variety of information concepts (UTI) which would still stick to the opposite of the saying
“It from Bit”: “Bit from It”.
2.5.1. Are Computing Ethics Issues Unique or Are They Simply Moral Issues that Happen to Involve
ICT? What Kind of Ethics Is CE? What Is the Contribution of CE to the Ethical Discourse?
It is interesting to follow the evolution of this question which firstly concerned the existing
Computer Ethics, then Computing Ethics and finally Information Ethics, defining increasingly more
abstract subject. Floridi’s focus, initially on computing  shifted gradually towards information as the
most abstract and fundamental principle and he developed Information Ethics (IE) as a part of his
Philosophy of Information (PI). In what follows we comment on the developments within Information
Ethics, leaving Computer Ethics/Computing Ethics as a historical origin.
Froehlich  introduces the history of Information Ethics by the claim: “Information ethics has
grown over the years as a discipline in library and information science, but the field or the phrase has
evolved and been embraced by many other disciplines.” Froelich mentions contributions by Capurro
(1988), who in 1999 founded the International Center for Information Ethics, Severson (1997),
Johnson (1985), Sullivan (1996), Spinello (2003) and number of others.
Starting from paper on Information Ethics , Floridi’s research in IE [5,7,12,13,16,23,25] has
attracted considerable interest in the research community and it was presented and reviewed with
several occasions in special issues of journals.
Two issues of APA’s Newsletter have discussed Floridi’s work. In the fall of 2007, Floridi
published an article in the newsletter titled Understanding Information Ethics . In the next issue of
the newsletter  a number of commentaries were published by prominent ethicists as a response to
Floridi’s article addressing topics such as IE as macroethics (Vaccaro), re-ontological revolution
(Sullins), discursive explorations in IE (Buchanan), and problems of infosphere (Chopra). The
discussion continues in the next issue  of the APA newsletter, with articles on metaphysical
foundation for IE (Bynum), good and evil in IE (Barker), moral status of informational objects
(Howlett Spence). The debate concludes by Floridi’s Understanding Information Ethics: Replies to
Comments, published in 2009 .
A special issue of the journal Ethics and Information Technology in 2008, edited by Charles
Ess  titled “Luciano Floridi’s Philosophy of Information and Information Ethics: Critical
Reflections and the State of the Art”, witness about the vitality of the PI and IE research program.
Dodig Crnkovic  addresses several critical views from  caused by the discussion of the role
which IE plays in relation to other ethical theories. It is argued that IE is a fundamental level approach
which, as an instrument of enquiry, can be used for specific purposes, and not as a replacement for all
existing tools of ethical analysis. Understanding of the proper application is essential, for otherwise it
would be like using a microscope to observe astronomical objects and concluding that it does not work.
Information 2011, 2
Floridi’s IE focuses on the fundamentally informational character of reality  and our
interactions with it. According to Floridi, ICTs create our new informational habitat which is an
abstract equivalent of an eco-system. As moral judgments vitally depend on the information about the
present state and what is understood to be a desirable state of affairs, the macro-ethical behavior
of networks of agents depends on mechanisms of information processing and communication.
Information streams in the Infosphere can both enrich and pollute the informational environment for an
agent. Those informational processes are essential in the analysis of the behavior of networks of
agents, biological and artificial.
Classical ethics approaches typically look at individual (e.g., Virtue Ethics) or a group behavior
(e.g., the Ethics of Rights) while IE provides a framework for an agent-based approach. It is important
to notice that Floridi’s Philosophy of Information with Information Ethics is a research program and
not a single theory. As a macro-ethics, applicable to networks of communicating agents and at the
same time giving a fundamental-level view of information patterns and processes IE can help identify
general mechanisms and understand their workings. The insight into the underlying informational
machinery helps to improve our analysis of ICT-supported systems. It is now possible to study the
effects of different sorts of information communication, and their influence on informational networks,
including the role of misinformation, disinformation, censorship (lack of information) and similar.
There are many parallels between IE and environmental ethics, of which IE is a generalization, where
the infosphere may be understood as our new cognitive environment. However, there is an important
sense in which they differ. Environmental ethics is placed on the same “macroscopic” and everyday-life
level of description, while IE is on a more abstract level of information structures and processes.
IE is likely to continue developing as one of the tools of investigation which will help improve
understanding of ethical aspects of life in an increasingly densely populated infosphere. We are far
from being able to reconstruct/generate/simulate the structure and behavior of an intelligent agent or a
network of agents starting from proto-information as the stuff of the universe.
IE is not a machine for production of the ultimate ethical advice,  but a promising analytical
instrument especially suitable for ethical analysis of techno-social systems with mixture of humans and
artificial intelligent agents. With the development of agent models we may expect numerable new
applications of IE. Floridi’s comments  on the articles in  can be summarized as:
“There are, however, ‘correct accounts’ that may complement and reinforce each other, like stones
in an arch” .
IE is far from a closed chapter in the history of ethics. Contrariwise, it is of great interest for many
researchers today, and its development can be expected to contribute elucidation of number of central
issues, particularly related to the systems of biological and artificial agents.
IE is manifestly a research field in its own right. It is not only an extension of classical ethical
issues to the realm of the infosphere today enhanced by ICTs. The contents of the extension cannot be
reduced to traditional ethics, that is, it is not possible to deduce findings in or for IE from the body of
traditional ethics plus the premises that represent the conditions of the existence of ICTs.
In Hofkirchner’s UTI framework, ICTs raised new issues in which philosophy has to reconcile the
particular with the universal. However, it is in contest whether or not artificial devices can be regarded
as informational agents that are patients, . Capurro, together with a number of other ethicists,
Information 2011, 2
argues that ICTs acquire their meaning by the very act of being instrumental in human
self-organization. And this meaning is related to the purpose for which ICTs are made and to the
purpose for which they are used and to the good or evil that is associated with their non-intended
consequences in social, biotic, or physical subsystems. Without their embeddedness in human
self-organization, in the suprasystem of societies, they would be meaningless. And therefore it is of
utmost importance to consciously and cautiously integrate ICTs in the bigger picture. However, we need
not postulate intrinsic values of all the entities to be morally guided to respect them,  (p. 188).
Self-organizing systems, including humans, are both agents and patients in varying degrees and are
evaluated in informational settings by each other .
In summary, Information Ethics has undoubtedly established itself as a unique research field. In the
future we expect IE to elaborate relationships with other ethical theories, and demonstrate applications
in different contexts, especially when it comes to the blend of natural and artificial agents in
techno-social informational environments far from traditional classical ethical scenarios, .
“In retrospect, all revolutions seem inevitable. Beforehand, all revolutions seem impossible.”
McFaul M., US National Security Council, NY Times June 21, 2009.
In hindsight we can conclude that Floridi’s program identified information-level approaches as the
most important developments in a variety of research fields. Philosophy of Information can be seen as
encompassing both Natural and Human Philosophy (Bacon’s distinction), based on the results of the
advances in the information studies and the development of computing technologies and theories. It
thus provides an overarching view, bridging the traditional division between the knowledge of
non-living and living world . Among central problems of Floridi’s program are the informational
universe, intelligence/cognition and nature of knowledge in relation to information.
Successively, in the past decade, the information processing paradigm of cognition has gained
dominance because of its ability to provide suitable framework for the interdisciplinary
communication and learning about intelligent mind and agency. Despite of all impressive progress
made in recent years, human mind is still to a high extent poorly understood. For the study of the
nature of information it is essential to understand information processes and structures in the brain and
nervous system. As “nothing makes sense in biology except in the light of evolution”  (p. 449), the
problem of informational reconstruction of human mind is also necessarily done in the light of evolution.
In years to come we expect to see new answers to Floridi’s questions about intelligence/cognition
and information semantics to emerge from both empirical and theoretical work in neuroscience,
cognitive informatics, natural computing/organic computing, generative modeling, bioinformatics,
intelligent systems, robotics, and more.
The development of Philosophy of Information, PI can be seen as proceeding simultaneously at two
levels, through the:
1. Externally driven philosophical reflection on the advances of the underlying research fields
providing the best current knowledge of the informational nature of the world
2. Internally driven restructuring of the PI itself as a philosophical discipline with ability to adapt to
the rapidly changing world of its subject matter.
Information 2011, 2
What can we expect from the Philosophy of Information in the future?
Adriaans  adds to the list of remaining open problems of PI: The nature of various probability
distributions that dominate logical, physical, biological end cultural domains; the interaction between
information and computation; the approximation of various compression measures and the study of
cognition and learning as data compression.
Van Benthem in the Philosophy of Information handbook proposes the following additions to the
list of open problems: Visual and other information carriers beyond language; information and context
as well as information, interaction and games  (p. 274).
We would suggest including the Complexity as one of the focus research areas where information is
the basic concept and where essential improvement of our current understanding of systemic behavior
of information can be expected. Improved understanding of information structures and dynamics in
networks is important for broad range of phenomena from networks of neurons to social phenomena.
Significant insight from biology  that
“diverse complex adaptive systems, such as proteins, cells, organisms, organizations, societies and
ecosystems, all together constitute one developing, multiscale continuum-economy composed of
interacting and interdependent adaptive organizational forms that co-exist and co-evolve at different
spatiotemporal scales, forming a nested set of interdependent organizational hierarchies.”
points towards a natural cohesive mechanisms between different levels of abstraction and should be
taken into account.
As a part of the study of the mutual relationship of information and computation, it is vital to look
at the agency/behavior/computational aspects of information. This is a domain of pragmatics of
information which together with syntactic aspects of information deserves special focus in the future
development of PI.
Interesting to notice is the phenomenon Philosophy of Information has in common with several
current research programs: The dynamical equilibrating of the building on a moving ground. It is
developing a framework based on the best current knowledge from several fundamental disciplines, all
of which are simultaneously undergoing paradigm shifts—from logics (changing ideas of truth, formal
system, proof, identity, contradiction, temporal and dynamic logic), computing (generalized idea of
computing, natural computing, organic computing), cognitive science (new insights into mechanisms
of human mind and its relationships to body and environment), neuroscience (basic level
understanding of neural information processing), biology/bioinformatics (computational models of
basic biological processes), physics (info-computational foundations of physics), sociology (generative
models of agent networks) and semiotics (meaning production in succession of levels of organization
in living agents) to the changing understanding of what indeed science is and what it should be in
It should be pointed out that Philosophy of Information is a vast field and in this paper we have
been able only to briefly sketch some of the developments during the past ten years. Our work can only
be a very modest contribution, to paraphrase Abramsky: Not least for the reason that the research field
we are attempting to overview does not exist yet in a fully realized form,  (p. 486). Given the context
of major paradigm shifts we are experiencing, Philosophy of Information should not be envisaged as
an automaton producing timeless correct statements about the world, built on the ground of basic
Information 2011, 2
theories in the process of transition. PI can instead provide a valuable cognitive tool for understanding
of informational phenomena on a more general level which we can consult instead of depending on
notoriously unreliable collective intuitions. As an integrative framework PI is expected even in the
future to provide dynamic epistemic support for number of research communities connected by the
fundamental idea of information.
The authors would like to acknowledge the many valuable suggestions made by Mark Burgin on
previous versions of the manuscript, and we also thank Luciano Floridi for helpful remarks on Ethics.
Finally, we would like to thank two anonymous reviewers for their insightful comments.
1. Floridi, L. Open problems in the Philosophy of Information. Metaphilosophy 2004, 35, 554-582.
2. van Benthem, J.; Adriaans, P. Philosophy of Information; North Holland: Amsterdam, The
3. Floridi, L. Philosophy and Computing: An Introduction; Taylor & Francis, Inc.: Philadelphia,
PA, USA, 1999; pp. 1-256.
4. Floridi, L. Information ethics: On the philosophical foundation of computer ethics. Ethics Inform.
Technol. 1998, 1, 33-52.
5. Floridi, L.; Sanders, J. Artificial evil and the foundation of computer ethics. Ethics Inform.
Technol. 2001, 3, 55-66.
6. Floridi, L. What is the Philosophy of Information? Metaphilosophy 2002, 33, 123-145.
7. Floridi, L.; Sanders, J. Mapping the foundationalist debate in computer ethics. Ethics Inform.
Technol. 2002, 4, 1-9.
8. Floridi, L. Two approaches to the Philosophy of Information. Mind Mach. 2003, 13, 459-469.
9. Floridi, L. Blackwell Guide to the Philosophy of Computing and Information; John Wiley and
Sons Ltd.: Oxford, UK, 2003; pp. 1-392.
10. Floridi, L. Informational Realism. In Selected Papers from Conference on Computers and
philosophy; Australian Computer Society, Inc.: Darlinghurst, Australia, 2003; Volume 37, pp. 7-12.
11. Floridi, L. Outline of a theory of strongly semantic information. Mind Mach. 2004, 14, 197-221.
12. Floridi, L.; Sanders, J. On the Morality of Artificial Agents. Mind Mach. 2004, 14, 349-379.
13. Floridi, L. Information ethics, its nature and scope. Comput. Soc. 2006, 36, 21-36.
14. Floridi, L. Understanding Information Ethics. APA Newslett. Philos. Comput. 2007, 7, 3-10.
15. Taddeo, M.; Floridi, L. A Praxical solution of the symbol grounding problem. Mind Mach. 2007,
16. Floridi, L. Information ethics: A reappraisal. Ethics Inform. Technol. 2008, 10, 189-204.
17. Floridi, L. Philosophy of Computing and Information: 5 Questions; Automatic Press: Birkerød,
Denmark, 2008; pp. 1-204.
18. Floridi, L. The method of levels of abstraction. Mind Mach. 2008, 18, 303-329.
19. Floridi, L. Information ethics: A reappraisal. Ethics Inform. Technol. 2008, 10, 189-204.
20. Floridi, L. A defence of informational structural realism. Synthese 2008, 161, 219-253.
Information 2011, 2
21. Floridi, L. The information society and its philosophy: Introduction to the special issue on “the
Philosophy of Information, its Nature, and future developments”. Inform. Soc. 2009, 25, 153-158.
22. Floridi, L. Understanding information ethics: Replies to comments. APA Newslett. Philos.
Comput. 2009, 8, 4-11.
23. Turilli, M.; Floridi, L. The ethics of information transparency. Ethics Inform. Technol. 2009, 11,
24. Floridi, L. The Philosophy of Information as a Conceptual Framework. Knowl. Technol. Policy
2010, 23, 253-281.
25. Floridi, L. The Cambridge Handbook of Information and Computer Ethics; Cambridge
University Press: Cambridge, UK, 2010; pp. 1-344.
26. Floridi, L. Information: A Very Short Introduction; Oxford University Press: Oxford, UK, 2010;
27. Floridi, L. The Philosophy of Information; Oxford University Press: Oxford, UK, 2011; pp. 1-432.
28. Allo, P. Putting information first: Luciano Floridi and the philosophy of information.
Metaphilosophy 2010, 41, 247-253.
29. Demir, H. The fourth revolution: Philosophical foundations and technological implications.
Knowl. Technol. Policy. 2010, 23, 1-6.
30. van Benthem, J.; van Rooy, R. Connecting the different faces of information. J. Logic Lang.
Inform. 2003, 12, 375-379.
31. Hjørland, B. The concept of information. Annu. Rev. Inform. Sci. Tech. 2003, 37, 343-411.
32. Addis, T.; Visscher, B.F.; Billinge, D.; Gooding, D. Socially Sensitive Computing: A Necessary
Paradigm Shift for Computer Science. In The Grand Challenge of Non-Classical Computation;
CPHC: Newcastle, UK, 2005; pp. 1-19.
33. Luhn, G. Towards an Ontology of Information and succeeding Fundamentals in Computing
Science. Triple-C 2011, 9, (in print).
34. Sommaruga, G. One or many concepts of information? Lect. Notes Comput. Sci. 2009, 5363,
35. Marijuán, P.C.; Lin, S.K. Papers from the foundations of information science 2002 (FIS 2002).
Entropy 2003, 5, 1-2.
36. Díaz Nafria, M.J.; Salto Alemany, F. What is really information? An interdisciplinary approach.
Triple C 2009, 7, i-vi.
37. Burgin, M. Theory of Information: Fundamentality, Diversity and Unification; World Scientific:
Singapore, 2010; pp. 1-400.
38. Hofstadter, D. Metamagical Themas: Questing for the Essence of Mind and Pattern; Basic
Books: New York, NY, USA, 1985; pp. 1-880.
39. Dodig Crnkovic, G. The Cybersemiotics and Info-computationalist research programmes as
platforms for knowledge production in organisms and machines. Entropy 2010, 12, 4878-4901.
40. Lyre, H. Informationstheorie: Eine Philosophisch-Naturwissenschaftliche Einführung; Wilhelm
Fink Verlag: Munich, Germany, 2002; pp. 1-279.
41. von Baeyer, H. Information: The New Language of Science; Harvard University Press:
Cambridge, MA, USA, 2004.
42. Roederer, J. Information and Its Role in Nature; Springer: Berlin, Germany, 2005.
Information 2011, 2
43. Seife, C. Decoding the Universe: How the New Science of Information is Explaining Everything
in the Cosmos, from Our Brains to Black Holes; Viking: New York, NY, USA, 2006.
44. Dodig Crnkovic, G. Investigations into Information Semantics and Ethics of Computing;
Mälardalen University Press: Västerås, Sweden, 2006; pp. 1-133.
45. Muller, S. Asymmetry the Foundation of Information; Springer: New York, NY, USA, 2007.
46. Brier, S. Cybersemiotics: Why Information Is Not Enough! University of Toronto Press: Toronto,
Canada, 2008; pp. 1-544.
47. Kauffman, S.; Logan, R.; Este, R.; Goebel, R.; Hobill, D.; Shmulevich, I. Propagating
organization: An enquiry. Biol. Philos. 2008, 23, 27-45.
48. Hofkirchner, W. A unified theory of information: An outline. Bitrumagora 2010, 64.
49. Davies, P.; Gregersen, N.H. Information and the Nature of Reality from Physics to Metaphysics;
Cambridge University Press: Cambridge, UK, 2010.
50. Dodig Crnkovic, G.; Burgin, M. Information and Computation; World Scientific: Singapore,
2010; pp. 1-350.
51. Schafranek, M. General System Theory. In Handbook of the Philosophy of Science, 10th ed.;
Hooker, C., Gabbay, D.M., Thagard, P., Woods, J., Eds.; Elsevier: North Holland, The
52. Capurro, R.; Fleissner, P.; Hofkirchner, W. Is a unified theory of information feasible? A
trialogue. In The Quest for a Unified Theory of Information; Hofkirchner, W. (ed.); Gordon and
Breach: Amsterdam, The Netherlands, 1999, 9-30.
53. Fleissner, P.; Hofkirchner, W. Emergent information. Towards a unified information theory.
BioSystems 1996, 38, 243-248.
54. Fleissner, P.; Hofkirchner, W. Actio non est Reactio: An extension of the concept of causality
towards Phenomena of Information. In The Quest for a Unified Theory of Information;
Hofkirchner, W. (ed.); Gordon and Breach: Amsterdam, The Netherlands, 1999, 197-214.
55. Dretske, F. Knowledge and the Flow of Information; Cambridge University Press: New York,
NY, USA, 1999; pp. 1-288.
56. Barwise, J.; Seligman, J. Information Flow: the Logic of Distributed Systems; Cambridge
University Press: Cambridge, UK, 1997.
57. van Benthem, J. Logical Dynamics of Information and Interaction; Cambridge University Press:
Cambridge, UK, 2011; pp. 1-384.
58. van Benthem, J. Logic and the dynamics of information. Mind Mach. 2003, 13, 503-519.
59. Dodig Crnkovic, G.; Stuart, S. Computation, Information, Cognition: The Nexus and the Liminal;
Cambridge Scholars Publishing: Newcastle, UK, 2007; pp. 1-380.
60. Dodig Crnkovic, G. Information and Computation Nets: Investigations into Info-Computational
World; Vdm Verlag: Saarbrucken, Germany, 2009; pp. 1-96.
61. Dodig Crnkovic, G.; Mueller, V. A Dialogue Concerning Two World Systems: Info-Computational
vs. Mechanistic. In Information and Computation; Dodig Crnkovic, G., Burgin, M., Eds.; World
Scientific Publishing Co. Inc.: Singapore, 2009; pp. 149-184.
62. Dodig Crnkovic, G. Biological Information and Natural Computation in Thinking Machines and
the Philosophy of Computer Science: Concepts and Principles; Vallverdú, J., Ed.; Information
Science Reference: Hershey, PA, USA, 2010.
Information 2011, 2
63. Dodig Crnkovic, G. The Cybersemiotics and Info-computationalist research programmes as
platforms for knowledge production in organisms and machines. Entropy 2010, 12, 878-901.
64. Dodig Crnkovic, G. Info-Computational Philosophy of Nature: An Informational Universe with
Computational Dynamics. In Festschrift for Søren Brier; Sørensen, B., Cobley, P.; Thellefsen, T.
Eds.; CBS University Press: New York, NY, USA, 2011 (in review).
65. Bohan Broderick, P. On Communication and Computation. Mind Mach. 2004, 14, 1-19.
66. Burgin, M. Super-Recursive Algorithms Monographs; Springer-Verlag New York Inc.: New
York, NY, USA, 2004; pp. 1-320.
67. Babaoǧlu, O. Self-Star Properties in Complex Information Systems Conceptual and Practical
Foundations; Springer: New York, NY, USA, 2005.
68. Hofkirchner, W. Twenty Questions about a Unified Theory of Information: A Short Exploration
into Information from a Complex Systems View; Emergent Publications: Litchfield Park, AZ,
69. Scheutz, M. Computationalism New Directions; MIT Press: Cambridge, MA, USA, 2002;
70. Kampis, G. Self-modifying Systems in Biology and Cognitive Science: A New Framework for
Dynamics, Information, and Complexity, 1st ed.; Pergamon Press: Amsterdam, The Netherlands,
1991; pp. 1-564.
71. Dodig Crnkovic, G. Significance of models of computation from turing model to natural
computation. Mind. Mach. 2011, 21, 301-322.
72. Brier, S. Cybersemiotics: An evolutionary world view going beyond entropy and information
into the question of meaning. Entropy 2010, 12, 1902-1920.
73. Hofkirchner, W. Emergent Information: An Outline Unified Theory of Information Framework;
World Scientific Publishing Co.: Hackensack, NJ, USA, 2011.
74. Hofkirchner, W. Four ways of thinking in information. Triple-C 2011, 9, (in press).
75. Lloyd, S. Programming the Universe:
A Quantum Computer Scientist Takes on the Cosmos,
1st ed., Knopf: New York, NY, USA, 2006.
76. Vedral, V. Decoding Reality: The Universe as Quantum Information; Oxford University Press:
Oxford, UK, 2010; pp. 1-240.
77. Harnad, S. The symbol grounding problem. Physica D 1991, 42, 335-346.
78. Ziemke, T. Rethinking grounding. In Does Representation Need Reality? Proceedings of the
International Conference 'New Trends in Cognitive Science’' (NTCS'97), Vienna, Austria, May
79. Smolensky, P.; Legendre, G. The Harmonic Mind: From Neural Computation to Optimality-
Theoretic Grammar; MIT Press: Cambridge, MA, USA, 2006; pp. 1-904.
80. Mingers, J. Information and meaning: Foundations for an intersubjective account. Inform. Syst. J.
1995, 5, 285-306.
81. Mingers, J. Embodying information systems: The contribution of phenomenology.
Inform. Organ 2001, 11, 103-128.
82. Dodig Crnkovic, G. Empirical modeling and information semantics. Mind Soc. 2008, 7, 157-166.
83. Adriaans, P. A critical analysis of floridi’s theory of semantic information. Knowl. Technol.
Policy 2010, 23, 41-56.
Information 2011, 2
84. Chaitin, G. Epistemology as Information Theory: From Leibniz to Ω. In Computation,
Information, Cognition–The Nexus and the Liminal; Dodig Crnkovic, G., Ed.; Cambridge
Scholars Publishing: Newcastle, UK, 2007; pp. 2-17.
85. Sequoiah-Grayson, S. The Metaphilosophy of Information. Mind. Mach. 2007, 17, 331-344.
86. Dodig Crnkovic, G. Epistemology naturalized: The Info-Computationalist approach. APA
Newslett. Philos. Comput. 2007, 6, 9-14.
87. Salthe, S. Development (and evolution) of the universe. Found. Sci. 2010, 15, 357-367.
88. Chaitin, G. Dijon Lecture. 2003. Available online: http://www.cs.auckland.ac.nz/CDMTCS/
chaitin (accessed on 11 May 2011).
89. Fallis, D. What do mathematicians want? Probabilistic proofs and the epistemic goals of
mathematicians. Logique Anal. 2002, 45, 373-388.
90. Allo, P. Logical pluralism and semantic information. J. Philos. Logic 2007, 36, 659-694.
91. van Benthem, J. Logical pluralism meets logical dynamics? Aust. J. Logic 2008, 6, 182–209.
92. Wang, Y. On Abstract intelligence: Toward a unifying theory of natural, artificial, machinable,
and computational intelligence. Int. J. Software Sci. Comput. Intell. 2009, 1, 1-17.
93. Wang, Y. Transactions on Computational Science V; Gavrilova, M.L., Tan, C.J.K., Wang, Y.,
Chan, K.C.C., Eds.; Springer: Berlin, Germany 2009; pp. 1-19.
94. Wang, Y. Toward a formal knowledge system theory and its cognitive informatics foundations.
Trans. Comput. Sci. 2009, 5, 1-19.
95. Wang, Y. On contemporary denotational mathematics for computational intelligence. Trans.
Comput. Sci. 2008, 2, 6-29.
96. Wang, Y.; Kinsner, W.; Zhang, D. Contemporary cybernetics and its facets of cognitive
informatics and computational intelligence. IEEE Trans. Syst. Man Cybern. B 2009, 39, 823-833.
97. Wang, Y. The theoretical framework of cognitive informatics. Int. J. Cognit. Inform. Nat. Intell.
2007, 1, 1-27.
98. Maturana, H. Biology of Cognition; Defense Technical Information Center: Ft. Belvoir, VA,
99. Maturana, H.; Varela, F. Autopoiesis and Cognition: The Realization of the Living, 1st ed.;
D. Reidel Publishing Co.: Dordrecht, The Netherlands, 1980.
100. Wang, Y. On abstract intelligence: toward a unifying theory of natural, artificial, machinable,
and computational intelligence. Int. J. Software Sci. Comput. Intell. 2009, 1, 1-17.
101. Stonier, T. Information and Meaning: An Evolutionary Perspective; Springer, New York, NY,
102. Wolfram, S. A New Kind of Science; Wolfram Media: Champaign, IL, USA, 2002.
103. Zenil, H. Randomness through Computation: Some Answers, More Questions; World Scientific:
104. Markram, H. The blue brain project. Nat. Rev. Neurosci. 2006, 7, 153-160.
105. Hofkirchner, W. Projekt Eine Welt: Kognition-Kommunikation-Kooperation: Versuch u
Selbstorganisation der Informationsgesellschaft; Lit: Munster, Germany, 2002.
106. Clark, A.; Chalmers, D. The extended mind. Analysis 1998, 58, 7-19.
107. Oeser, E. Wissenschaft und Information: Systematische Grundlagen einer Theorie der
Wissenschaftsentwicklung; Oldenbourg: Wien, Austria, 1976.
Information 2011, 2
108. Ladyman, J.; Ross, D.; Spurrett, D.; Collier, J. Everything Must Go: Metaphysics Naturalised;
Clarendon Press: Oxford, UK, 2007; pp. 1-368.
109. Zeilinger, A. The message of the quantum. Nature 2005, 438, 743.
110. Kurakin, A. Scale-free flow of life: On the biology, economics, and physics of the cell. Theor.
Biol. Med. Model. 2009, 6, 6.
111. Froehlich, T. A Brief History of Information Ethics; Facultat de Biblioteconomia i
Documentació: Universitat de Barcelona, Spain, 2004.
112. Boltuc, P. APA Newsletters; Springer: New York, NY, USA, 2008; Volume 07, Number 2.
113. Boltuc, P. APA Newsletters; Springer: New York, NY, USA, 2008; Volume 08, Number 1.
114. Ess, C. Luciano Floridi’s philosophy of information and information ethics: Critical reflections
and the state of the art. Ethics Inform. Technol. 2008, 10, 89-96.
115. Dodig Crnkovic, G. Floridi’s Information Ethics as Macro-Ethics and Info-Computational
Agent-Based Models. In Luciano Floridi`s Philosophy of Technology: Critical Reflections—
Philosophy and Engineering Series; Demir, H., Ed.; Springer: Berlin, Germany, 2011.
116. Capurro, R. Towards a Comparative Theory of Agents. In Contributions to Angeletics; Rafael,
C., John, H., Eds.; Fink Verlag: Munich, Germany, 2011.
117. Hofkirchner, W. How to design the infosphere: The fourth revolution, the management of the life
cycle of information, and information ethics as a macroethics. Knowl. Technol. Policy 2010, 23,
118. Dobzhansky, T. Biology, molecular and organismic. Am. Zool. 1964, 4, 443-452.
© 2011 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article
distributed under the terms and conditions of the Creative Commons Attribution license