Floridi's Open Problems in Philosophy of Information, Ten Years After

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Floridi's Open Problems in Philosophy of Information, Ten Years After

Authors: Gordana Dodig Crnkovic and Wolfgang Hofkirchner;
E-Mails: gordana.dodig-crnkovic@mdh.se; wolfgang.hofkirchner@sbg.ac.at

Abstract: In his article Open Problems in the Philosophy of Information (Metaphilosophy 2004, 35 (4)), based on the Herbert A. Simon Lecture in Computing and Philosophy given at Carnegie Mellon University in 2001, Luciano Floridi presented a Philosophy of Information research programme 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 programme in order to see what happened since then, highlighting some of the major advances, commenting on unsolved problems and rendering the new landscape of the Philosophy of Information emerging at present.


In his groundbreaking paper Open Problems in the Philosophy of Information Floridi [2004b] lists the five most interesting areas of research for the nascent field of Philosophy of Information, containing eighteen fundamental questions. The aim of present paper is to address Floridi’s programme from a 10-years distance. What have we learned? What do we expect to learn in the future?

We can trace the origins of the programme back to 1999 when Floridi’s book Philosophy and Computing: An Introduction [Floridi 1999a] appeared, immediately followed by the first shift towards information-centric framework in the article Information Ethics: On the Philosophical Foundations of Computer Ethics, [Floridi 1999b]. 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 programme 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.

Together with 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.

In 2008 Floridi edited the book Philosophy of Computing and Information - 5 Questions [Floridi 2008] 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?”

The special issue “The Philosophy of Information, its Nature and Future Developments,” of The Information Society: An International Journal, 25(3) published in 2009, and edited by Luciano Floridi, 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 special issue of Metaphilosophy, [Allo, 2010], the same journal that published Floridi’s program in 2004, was devoted to Luciano Floridi and the philosophy of information (PI) 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 good state of the art insights into the development of PI.

Luciano Floridi's Philosophy of Technology: Critical Reflections is presented in a special issue of Knowledge, Technology & Policy, Volume 23, Numbers 1-2 / June 2010, guest edited by Demir Hilmi [Demir 2010]. 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 comments.

Floridi’s newly published book [2011,] shows the present state of his view of the Philosophy of Information, and confirms relevance of our present analysis of the state of the art. Its content witness about Floridi’s substantial contributions and contains his widely known work within the PI.

As we analyze the present state of the art of Philosophy of Information we try to situate the PI programme in the context of scientific and technological development that have been made last ten years and see their impact on the directions of PI research.

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, let us make an attempt to re-examine the program and see how the listed questions look like today, without any pretense of 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.

I) Information definition

  1. What is Information?

One of the most significant events since 2004 was the publishing of the Handbook on the Philosophy of Information, [van Benthem and Adriaans, 2008]. The Part B of the handbook, entitled Philosophy of Information: Concepts and History, include 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 2005] formulates it, “Information is such a powerful and elusive concept that it can be associated with several explanations, depending on the requirements and intentions.”

The discussion of the concept of information was shortly after Floridi’s programme declaration in the Herbert Simon Lecture in 2001 a subject of a lively discussion, and [van Benthem and Adriaans, 2008] point to a special issue of the Journal of Logic, Language and Information (Volume 12 No 4 2003), [van Benthem and van Rooij, 2003], dedicated to the study of different facets of information. At the same time Capurro and Hjørland [2003] 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. 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. Wittgenstein compares it to a rope which is not made of continuous strands, but many shorter strands bound together, no one running the entire length of the rope. There is no universal concept of information, but rather concepts held together like families or ropes. “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).” [Addis, et al., 2005]

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, [van Bentham and Martinez in HPI, 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 processed, or it can be conceived as something that is produced, created, constructed [Luhn 2011]. 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 [Sommaruga 2009, p. 253]. It can be seen as syntactical, as semantic or as pragmatic phenomenon. And it can be seen as manifesting itself throughout every realm of our natural and social world.

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. Lyre 2002, von Baeyer 2003, Roederer 2005, Seife 2006, Muller 2007, Kauffman et al. 2008, Brier 2008, Hofkirchner 2009, Davies and Gregersen 2010, Dodig-Crnkovic and Burgin 2011]. 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? 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 [see Hofkirchner and Schafranek 2011]. Can the syntactical, 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 1999], 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 (Fleissner and Hofkirchner 1996 and 1997). They have been doing so by resorting to complex systems theory.

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.” [Marijuan 2010] http://fis.icts.sbg.ac.at/c_1.html

Among initiatives with the aim to work towards a modern concept of information, a workshop entitled Information Theory and Practice has taken place in 2007, http://www.isomorph.it/science/duino2007 at Duino Castle, focusing on the difference between syntactic and semantic information.

In 2008, a project was started in León, Spain, aiming at the illumination of the concept of information. Its working principle is the mosaic window of the Cathedral of León. That’s why it is named “BITrum” (after the Latin “vitrum”) [Díaz Nafría and Salto Alemany 2009].

“Towards a New Science of Information” was the motto of the Fourth International FIS Conference held in Beijing in 2010, see http://www.sciforum.net/conf/fis2010. The proceedings of the conference will be published in a special issue of the journal triple-c.

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 [Hofstadter, 1985], seen from a cognizing agent’s perspective. The world modeled as informational structure with computational dynamics, presents proto-information for an agent [Dodig Crnkovic Entropy 2010] 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 physical 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.

  1. What is the dynamics of information?

[Floridi, 2008c] 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 disappearance; and

iii) computation, both in the Turing-machine sense of algorithmic processing, and in the wider sense of information processing. This is a crucial specification. Although a very old concept, information has finally acquired the nature of a primary phenomenon only thanks to the sciences and technologies of computation and ICT (Information and Communication Technologies). Computation has therefore attracted much philosophical attention in recent years. “

Van Benthem’s new book Logical Dynamics of Information and Interaction, [2011] provides answers to the question of 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 is connecting 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. 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 programme of Philosophy of Information, building its necessary logical basis.

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.” [Dodig Crnkovic and Burgin 2010]

Similarly built on dual-aspect foundations is info-computationalism, ICON [Dodig-Crnkovic 2006 - 2010]. It relates to Floridi’s program for PI, taking the pancomputational stance as a point of departure. With the universe represented as a network of computing processes at different scales or levels of granularity, information is a result of (natural) computation. Adopting informationalism, (Informational Structural Realism) which argues for the entire existing physical universe being an informational structure, [Floridi 2008], natural computation can be seen as a process governing the dynamics of information. In a synthesis of Informationalism and Computationalism, information and computation are two complementary and mutually defining ideas. [Dodig Crnkovic 2010]

Communication is a special type of computation. Bohan Broderick [2004] 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 [2005] puts it in the following way:

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 [Hofkirchner 2010]. 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, 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 [Hofkirchner 2010]. 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 [Dodig-Crnkovic, 2010 Entropy], the recurrent theme is the information/computation as the underlying structure/process. Information is fundamental as a basis for all knowledge and it’s processing characterizes all our cognitive functions. In a wider sense of protoinformation it represents every physical/material phenomenon.

  1. 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, Unified Theory of Information (UTI) Research Group - Association for the Advancement of Information Sciences can be mentioned. http://uti.at/projects.html

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.” UTI Research Group publishes the tripleC journal supporting transdisciplinary research on information, communication, media, technology, and culture. http://www.triple-c.at/index.php/tripleC

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 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 self-organization.

One of the unified theories, the info-computational framework, ICON [Dodig-Crnkovic 2011] is characterized by two basic ontological principles: information (structure) and computation (process). ICON provides a unifying generative scheme useful for the conceptualization of the range of phenomena from inanimate physical objects to cells, organisms, cognizing systems and ecologies offering 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.

According to the current idea of computationalism (pancomputationalism, natural computationalism), 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 [Babaoglu, et al. 2005] on self-organizing self-star/self- models – 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 [2002] argues for this new kind of computationalism applied in the computational theory of mind explaining the nature of intentionality and the origin of language.

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 [Kampis, 1991] that today are part of the new organic computing field. http://www.organic-computing.org/

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 computers. The difference between the present-day computing and 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. [Dodig-Crnkovic, Entropy]

The Turing Machine model is not the most expressive model for the type of processes going on in living organisms. [Dodig-Crnkovic, M&M] Understanding biology in informational terms leads to increased understanding of all 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 [2010], 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 in nature, technology, and society, with the main insights from information theory. He calls his approach a “General Information Theory” [Burgin 2011]:

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.

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 he writes (Brier 2010, 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.

Like UTI and ICON approaches Brier’s Cybersemiotics is critical of mechanicism that either neglects meaning and related phenomena or is reductionistic and levels them down. However, Brier connects the mechanistic approach to the term “information”, because Shannon and Weaver and Wiener and Schrödinger’s definition that in his view is prototypical for the mechanistic approach is widely accepted in natural and technical sciences (Brier 2011, p. 1914). Despite that he construes an ontological hierarchy (“heterarchy”) of different levels across which information processes and meaning can develop (Brier 2008, 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.”

Impact of those new theories on the development of Philosophy of Information will be visible in the years to come.

II) Information Semantics

  1. The data grounding problem: How can data acquire their meaning?

Floridi, who together with Taddeo [Taddeo & Floridi, 2005 & 2007] 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 [2005]). In more metaphysical terms, this is the problem of the semanticisation of being and it is further connected with the problem of whether information can be naturalised.” [Floridi, 2008c]

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, [Dodig-Crnkovic 2010, 2009]. 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. [Dodig-Crnkovic, 2010; Dodig-Crnkovic and Müller 2010] 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.

This is in line with the praxical solution proposed by Taddeo and Floridi [2007] 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 agents actions.

On the fundamental level, quantum-informational universe performs computation on its own, Lloyd [2006], Vedral [2010]. 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 [Dodig-Crnkovic and Burgin, 2010]).

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 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 [1990] to Ziemke [1999]. Smolensky presents a way of integration of connectionist ('neural') and symbolic computation, addressing computational, linguistic, and philosophical issues in [Smolensky, P. and Legendre, G. 2006].

Søren Brier in his The Cybersemiotic Framework as a Means to Conceptualize the Difference between Computing and Semiosis in [Dodig-Crnkovic and Stuart, 2007] offers a critical view which he also defends in his book Cybersemiotics. Why Information Is Not Enough!, [2008] 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.

  1. Truth problem: How can meaningful data acquire their truth value?

  2. Informational semantic problem: Can information theory explain meaning?

Based on scientific tradition, information semantics can be related with system modeling [Dodig-Crnkovic 2005] and model validity [Dodig-Crnkovic 2008]. 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 [Floridi 2004c], which requires information to be true and not only well formed and meaningful data. Adriaans [2010] 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 Scarantino and Piccinini in their article Information Without Truth for the special issue of Metaphilosophy [Allo 2010] 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 [Dodig-Crnkovic and Stuart] 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 classical epistemology.”

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 I would add, 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 cognition and subsymbolic cognition must be connected in order to be able to reconstruct the mechanisms that produce meaning.

On the other hand, Sequoiah-Grayson [2007] 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, I would agree with Adriaans line of reasoning about the necessity of consequently relying on the fundamental framework of theory of information instead of a mix of classical epistemological and new information-theoretical concepts.

  1. 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 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 [Dodig-Crnkovic, 2007], and [Dodig-Crnkovic 2006] 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. 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 in agents models govern actions. Being internalized by an agent, data become information, in the context of the agent’s experiences, habits and preferences. All of it is implemented in an agent’s bodily structures (including brain where applicable) and determines its interactions with the world. Adaptive structures of agents in networks act as memory of the past development, and represent their learning history. This makes the relationship between information and meaning natural. Meaning governs an intelligent agent’s behavior, based on data structured to information and further structured to knowledge that in interaction with the world results in agency. Truth 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” [Chaitin 2003] 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 more fundamental phenomenon than truth, and both must be possible to express in info-computational terms

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.” Adriaans [2010]

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 [2002], in the analysis of probabilistic proofs and the epistemic goals of mathematicians.

As uses for information can be many, in different contexts and for different agents, Patrick Allo in [Dodig Crnkovic and Stuart 2007] 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. [Benthem 2008]

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