Disagreeing with others about how to interpret a social interaction is a common occurrence. We often find ourselves offering divergent interpretations of others’ motives, intentions, beliefs, and emotions. Remarkably, philosophical accounts of how we understand others do not explain, or even attempt to explain such disagreements. I argue these disparities in social interpretation stem, in large part, from the effect of social categorization and our goals in social interactions, phenomena long studied by social psychologists. I argue we ought to expand (...) our accounts of how we understand others in order to accommodate these data and explain how such profound disagreements arise amongst informed, rational, well-meaning individuals. (shrink)

We have various strategies available to us for understanding another person’s state of mind. Cognitive empathy may be achieved by mental simulation, i.e. by imagining yourself in another’s situation and figuring out what you would think and feel in that situation. Alternatively, you could consider all the relevant information about the person’s situation and folk psychology and draw a sophisticated inference to the best explanation of that person’s perspective. In this chapter, I examine the conditions under which we are (...) likely to use these two familiar strategies for cognitive empathy and when they are likely to be effective. In addition, I discuss a third underexplored pattern of reasoning in understanding others. Self-serving goals, such as anxiety reduction, self-esteem, and confirmation of one’s worldview, distort cognitive empathy. I consider these different strategies in light of hybrid theories of cognitive empathy. (shrink)

In this chapter, I explore the importance of ethics education for senior military officers with responsibilities at the strategic level of government. One problem, as I see it, is that senior commanders might demand “ethics” from their soldiers but then they are themselves primarily informed by a “morally skeptical viewpoint” (in the form of political realism). I argue that ethics are more than a matter of personal behavior alone: the ethical position of an armed service is a matter of the (...) collective responsibility of the people who constitute it, and senior military officers, having authority to give the orders, have a particularly important role. First, I discuss the continued prominence of a neorealist mindset in strategic thinking. Neorealist theory assumes that ethics is a largely irrelevant concern for strategic decision-makers. In contrast, I argue that consideration of moral values should be at the center of strategic theory and practice. Then, in the second section, I explore the “professional ethics” of senior military officers in order to develop an appreciation of the shared values and expectations regarding required conduct within any professional military service. I argue that senior military leaders should be concerned with what it means to be “good” and that this concept of the good can, and should, be applied across cultures and national boundaries. In the final section, I examine armed conflict and briefly discuss the moral reasoning involved to justify deliberate killing and destruction. I conclude that senior military officers require an understanding of sound moral reasoning to implement effective strategy. (shrink)

How are biases encoded in our representations of social categories? Philosophical and empirical discussions of implicit bias overwhelmingly focus on salient or statistical associations between target features and representations of social categories. These are the sorts of associations probed by the Implicit Association Test and various priming tasks. In this paper, we argue that these discussions systematically overlook an alternative way in which biases are encoded, that is, in the dependency networks that are part of our representations of social categories. (...) Dependency networks encode information about how features in a conceptual representation depend on each other. This information determines the degree of centrality of a feature for a conceptual representation. Importantly, centrally encoded biases systematically disassociate from those encoded in salient-statistical associations. Furthermore, the degree of centrality of a feature determines its cross-contextual stability: in general, the more central a feature is for a concept, the more likely it is to survive into a wide array of cognitive tasks involving that concept. Accordingly, implicit biases that are encoded in the central features of concepts are predicted to be more resilient across different tasks and contexts. As a result, the distinction between centrally encoded and salient-statistical biases has important theoretical and practical implications. (shrink)

Cyberspace relies on information technologies to mediate relations between different people, across different communication networks and is reliant on the supporting technology. These interactions typically occur without physical proximity and those working depending on cybersystems must be able to trust the overall human–technical systems that support cyberspace. As such, detailed discussion of cybersecurity policy would be improved by including trust as a key value to help guide policy discussions. Moreover, effective cybersystems must have resilience designed into them. This paper (...) argues that trustworthy cybersystems are a key element to resilient systems, and thus are core to cybersecurity policy. The paper highlights the importance of trustworthiness for resilient cybersystems. The importance of trustworthiness is shown through a discussion of three events where trustworthiness was the target or casualty of cyberattacks: Stuxnet, hacking of communications and the Edward Snowden revelations. The impact of losing trust is highlighted, to underpin the argument that a resilient cybersystem ought to design in trustworthiness. The paper closes off by presenting a general set of policy implications arising from recognition of the interplay between trust, trustworthiness and resilience for effective cybersecurity. (shrink)

In this chapter, we examine the key moral issues for the intelligence community with regard to the use of robots for intelligence collection. First, we survey the diverse range of spy robots that currently exist or are emerging, and examine their value for national security. This includes describing a number of plausible scenarios in which they have been (or could be) used, including: surveillance, attack, sentry, information collection, delivery, extraction, detention, interrogation and as Trojan horses. Second, we examine several (...) areas in which spy robots present serious ethical and legal challenges. We conclude by examining some moral concerns with shifting from intelligence collection to action, as enabled by robotics technology. (shrink)

The role of the physiological processes involved in human vision escapes clarification in current literature. Many unanswered questions about vision include: 1) whether there is more to lateral inhibition than previously proposed, 2) the role of the discs in rods and cones, 3) how inverted images on the retina are converted to erect images for visual perception, 4) what portion of the image formed on the retina is actually processed in the brain, 5) the reason we have an after-image with (...) antagonistic colors, and 6) how we remember space. This theoretical article attempts to clarify some of the physiological processes involved with human vision. The global integration of visual information is conceptual; therefore, we include illustrations to present our theory. Universally, the eyeball is 2.4 cm and works together with membrane potential, correspondingly representing the retinal layers,photoreceptors, and cortex. Images formed within the photoreceptors must first be converted into chemical signals on the photoreceptors’ individual discs and the signals at each disc are transduced from light photons into electrical signals. We contend that the discs code the electrical signals into accurate distances and are shown in our figures. The pre-existing oscillations among the various cortices including the striate and parietal cortex,and the retina work in unison to create an infrastructure of visual space that functionally ‘‘places” the objects within this ‘‘neural” space. The horizontal layers integrate all discs accurately to create a retina that is pre-coded for distance. Our theory suggests image inversion never takes place on the retina,but rather images fall onto the retina as compressed and coiled, then amplified through lateral inhibition through intensification and amplification on the OFF-center cones. The intensified and amplified images are decompressed and expanded in the brain, which become the images we perceive as external vision. (shrink)

Information flow in a system is a core cybernetics concept. It has been used frequently in Sensory Psychology since 1951. There, Shannon Information Theory was used to calculate "information transmitted" in "absolute identification" experiments involving human subjects. Originally, in Shannon's "system", any symbol received ("outcome") is among the symbols sent ("events"). Not all symbols are received as transmitted, hence an indirect noise measure is calculated, "information transmitted", which requires knowing the confusion matrix, its columns (...) labeled by "event" and its rows labeled by "outcome". Each matrix entry is dependent upon the frequency with which a particular outcome corresponds to a particular event. However, for the sensory psychologist, stimulus intensities are "events"; the experimenter partitions the intensity continuum into ranges called "stimulus categories" and "response categories", such that each confusion-matrix entry represents the frequency with which a stimulus from a stimulus category falls within a particular response category. Of course, a stimulus evokes a sensation, and the subject's immediate memory of it is compared to the memories of sensations learned during practice, to make a categorization. Categorizing thus introduces "false noise", which is only removed if categorizations can be converted back to their hypothetical evoking stimuli. But sensations and categorizations are both statistically distributed, and the stimulus that corresponds to a given mean categorization cannot be known from only the latter; the relation of intensity to mean sensation, and of mean sensation to mean categorization, are needed. Neither, however, are presently knowable. This is a quandary, which arose because sensory psychologists ignored an ubiquitous component of Shannon's "system", the uninvolved observer, who calculates "information transmitted". Human sensory systems, however, are within de facto observers, making "false noise" inevitable. (shrink)

In this article, we explore the implications of opposing domains theory for developing ethical leaders. Opposing domains theory highlights a neurological tension between analytic reasoning and socioemotional reasoning. Specifically, when we engage in analytic reasoning, we suppress our ability to engage in socioemotional reasoning and vice versa. In this article, we bring together the domains of neuroscience, psychology, and ethics, to inform our theorizing around ethical leadership. We propose that a key issue for ethical leadership is achieving a healthy balance (...) between analytic reasoning and socioemotional reasoning. We argue that organizational culture often encourages too heavy a reliance on nonemotional forms of reasoning to arrive at moral judgments. As a result, leaders run the risk of suppressing their ability to pay attention to the human side of moral dilemmas and, in doing so, dehumanize colleagues, particularly subordinates, and clients. (shrink)

Semantic Information conveyed by daily language has been researched for many years; yet, we still need a practical formula to measure information of a simple sentence or prediction, such as “There will be heavy rain tomorrow”. For practical purpose, this paper introduces a new formula, Semantic Information Formula (SIF), which is based on L. A. Zadeh’s fuzzy set theory and P. Z. Wang’s random set falling shadow theory. It carries forward C. E. Shannon and K. Popper’s (...) thought. The fuzzy set’s probability defined by Zadeh is treated as the logical probability sought by Popper, and the membership grade is treated as the truth-value of a proposition and also as the posterior logical probability. The classical relative information formula (Information=log(Posterior probability / Prior probability) is revised into SIF by replacing the posterior probability with the membership grade and the prior probability with the fuzzy set’s probability. The SIF can be explained as “Information=Testing severity – Relative square deviation” and hence can be used as Popper's information criterion to test scientific theories or propositions. The information measure defined by the SIF also means the spared codeword length as the classical information measure. This paper introduces the set-Bayes’ formula which establishes the relationship between statistical probability and logical probability, derives Fuzzy Information Criterion (FIC) for the optimization of semantic channel, and discusses applications of SIF and FIC in areas such as linguistic communication, prediction, estimation, test, GPS, translation, and fuzzy reasoning. Particularly, through a detailed example of reasoning, it is proved that we can improve semantic channel with proper fuzziness to increase average semantic information to reach its upper limit: Shannon mutual information. (shrink)

A generalized information theory is proposed as a natural extension of Shannon's information theory. It proposes that information comes from forecasts. The more precise and the more unexpected a forecast is, the more information it conveys. If subjective forecast always conforms with objective facts then the generalized information measure will be equivalent to Shannon's information measure. The generalized communication model is consistent with K. R. Popper's model of knowledge evolution. The mathematical foundations (...) of the new information theory, the generalized communication model , information measures for semantic information and sensory information, and the coding meanings of generalized entropy and generalized mutual information are introduced. Assessments and optimizations of pattern recognition, predictions, and detection with the generalized information criterion are discussed. For economization of communication, a revised version of rate-distortion theory: rate-of-keeping-precision theory, which is a theory for datum compression and also a theory for matching an objective channels with the subjective understanding of information receivers, is proposed. Applications include stock market forecasting and video image presentation. (shrink)

In 1948, Claude Shannon introduced his version of a concept that was core to Norbert Wiener's cybernetics, namely, information theory. Shannon's formalisms include a physical framework, namely a general communication system having six unique elements. Under this framework, Shannon information theory offers two particularly useful statistics, channel capacity and information transmitted. Remarkably, hundreds of neuroscience laboratories subsequently reported such numbers. But how (and why) did neuroscientists adapt a communications-engineering framework? Surprisingly, the literature offers no (...) clear answers. To therefore first answer "how", 115 authoritative peer-reviewed papers, proceedings, books and book chapters were scrutinized for neuroscientists' characterizations of the elements of Shannon's general communication system. Evidently, many neuroscientists attempted no identification of the system's elements. Others identified only a few of Shannon's system's elements. Indeed, the available neuroscience interpretations show a stunning incoherence, both within and across studies. The interpretational gamut implies hundreds, perhaps thousands, of different possible neuronal versions of Shannon's general communication system. The obvious lack of a definitive, credible interpretation makes neuroscience calculations of channel capacity and information transmitted meaningless. To now answer why Shannon's system was ever adapted for neuroscience, three common features of the neuroscience literature were examined: ignorance of the role of the observer, the presumption of "decoding" of neuronal voltage-spike trains, and the pursuit of ingrained analogies such as information, computation, and machine. Each of these factors facilitated a plethora of interpretations of Shannon's system elements. Finally, let us not ignore the impact of these "informational misadventures" on society at large. It is the same impact as scientific fraud. (shrink)

Categorical logic has shown that modern logic is essentially the logic of subsets (or "subobjects"). Partitions are dual to subsets so there is a dual logic of partitions where a "distinction" [an ordered pair of distinct elements (u,u′) from the universe U ] is dual to an "element". An element being in a subset is analogous to a partition π on U making a distinction, i.e., if u and u′ were in different blocks of π. Subset logic leads to finite (...) probability theory by taking the (Laplacian) probability as the normalized size of each subset-event of a finite universe. The analogous step in the logic of partitions is to assign to a partition the number of distinctions made by a partition normalized by the total number of ordered pairs |U|² from the finite universe. That yields a notion of "logical entropy" for partitions and a "logical information theory." The logical theory directly counts the (normalized) number of distinctions in a partition while Shannon's theory gives the average number of binary partitions needed to make those same distinctions. Thus the logical theory is seen as providing a conceptual underpinning for Shannon's theory based on the logical notion of "distinctions.". (shrink)

Information is a central notion for cognitive sciences and neurosciences, but there is no agreement on what it means for a cognitive system to acquire information about its surroundings. In this paper, we approximate three influential views on information: the one at play in ecological psychology, which is sometimes called information for action; the notion of information as covariance as developed by some enactivists, and the idea of information as minimization of uncertainty as presented (...) by Shannon. Our main thesis is that information for action can be construed as covariant information, and that learning to perceive covariant information is a matter of minimizing uncertainty through skilled performance. We argue that the agent’s cognitive system conveys information for acting in an environment by minimizing uncertainty about how to achieve her intended goals in that environment. We conclude by reviewing empirical findings that support our view and by showing how direct learning, seen as instance of ecological rationality at work, is how mere possibilities for action are turned into embodied know-how. Finally, we indicate the affinity between direct learning and sense-making activity. (shrink)

The sunspot data seems to indicate that the Sun is likely to enter Maunder Minimum, then it will mean that low Sun activity may cause low temperature in Earth. If this happens then it will cause a phenomenon which is called by some climatology experts as “The Little Ice Age” for the next 20-30 years, starting from the next few years. Therefore, the Earth climate in the coming years tend to be cooler than before. This phenomenon then causes us to (...) ask: what can we do as human being in Earth to postpone or avoid the worsening situation in terms of Earth cooling temperature in the coming years? We think this is a more pressing problem for the real and present danger that we are facing in the Earth. What we are suggesting in this paper is that perhaps it is possible to model Sun-Earth interaction in terms of Shannon entropy. Since Shannon entropy can be expressed as bits of information, then it would mean that perhaps we can do something with Earth temperature by controlling the amount of information transfer and storage in the Earth. This proposal is somewhat in resemblance with message of a 2012 movie “A Thousand words” where we shall strive to love our neighbours and nature, instead of being absorbed in a culture of less-meaningful fast-talk (starred by Eddie Murphy). (shrink)

The logical basis for information theory is the newly developed logic of partitions that is dual to the usual Boolean logic of subsets. The key concept is a "distinction" of a partition, an ordered pair of elements in distinct blocks of the partition. The logical concept of entropy based on partition logic is the normalized counting measure of the set of distinctions of a partition on a finite set--just as the usual logical notion of probability based on the Boolean (...) logic of subsets is the normalized counting measure of the subsets (events). Thus logical entropy is a measure on the set of ordered pairs, and all the compound notions of entropy (join entropy, conditional entropy, and mutual information) arise in the usual way from the measure (e.g., the inclusion-exclusion principle)--just like the corresponding notions of probability. The usual Shannon entropy of a partition is developed by replacing the normalized count of distinctions (dits) by the average number of binary partitions (bits) necessary to make all the distinctions of the partition. (shrink)

In this short paper, we show that a popular view in information science, information-as-thing, fails to account for a common example of information that seems physical. We then demonstrate how the distinction between types and tokens, recently used to analyse Shannon information, can account for this same example by viewing information as abstract, and discuss existing definitions of information that are consistent with this approach. -/- Dans ce court article nous montrons qu'une vision (...) populaire en sciences de l'information, l'information en tant qu’une chose, échoue à rendre compte d'un exemple commun d'information qui semble physique. Nous démontrons ensuite comment la distinction type/token, utilisée récemment pour analyser l'information de Shannon, peut rendre compte de ce même exemple en considérant l'information comme abstraite, et nous discutons des définitions existantes de l’information qui sont compatibles avec cette approche. (shrink)

A recent study suggests that value and quantum states seem to be governed by the same underlying mechanisms. In our recent book titled "Better economics for the Earth: A lesson from quantum and information theories," specifically Chapter 5, we have proposed an informational entropy-based notion of value, drawing on the granular worldview and primary features of quantum mechanics, Shannon’s information theory, and the mindsponge theory. Specifically, the notion suggests that values are created through the interactions of (...) class='Hi'>information. But how do information and its interactions lead to values? This research note aims to contribute to the answer to this question. (shrink)

Scholarship on the politics of new media widely assumes that communication functions as a sufficient conceptual paradigm for critically assessing new media politics. This article argues that communication-centric analyses fail to engage the politics of information itself, limiting information only to its consequences for communication, and neglecting information as it reaches into our selves, lives, and actions beyond the confines of communication. Furthering recent new media historiography on the “information theory” of Shannon and Wiener, the (...) article reveals both the primacy of communication in midcentury information theory, and also a striking resonance between these postwar communication theories and Habermas’s more recent communicative theory of democracy. To achieve a critical perspective beyond communication, the article proposes a media genealogy of the politics of subjects as a methodology for developing an analysis of how information formats us as subjects of data. (shrink)

Logical information theory is the quantitative version of the logic of partitions just as logical probability theory is the quantitative version of the dual Boolean logic of subsets. The resulting notion of information is about distinctions, differences and distinguishability and is formalized using the distinctions of a partition. All the definitions of simple, joint, conditional and mutual entropy of Shannon information theory are derived by a uniform transformation from the corresponding definitions at the logical level. The (...) purpose of this paper is to give the direct generalization to quantum logical information theory that similarly focuses on the pairs of eigenstates distinguished by an observable, i.e., qudits of an observable. The fundamental theorem for quantum logical entropy and measurement establishes a direct quantitative connection between the increase in quantum logical entropy due to a projective measurement and the eigenstates that are distinguished by the measurement. Both the classical and quantum versions of logical entropy have simple interpretations as “two-draw” probabilities for distinctions. The conclusion is that quantum logical entropy is the simple and natural notion of information for quantum information theory focusing on the distinguishing of quantum states. (shrink)

There are (at least) three approaches to quantifying information. The first, algorithmic information or Kolmogorov complexity, takes events as strings and, given a universal Turing machine, quantifies the information content of a string as the length of the shortest program producing it [1]. The second, Shannon information, takes events as belonging to ensembles and quantifies the information resulting from observing the given event in terms of the number of alternate events that have been ruled (...) out [2]. The third, statistical learning theory, has introduced measures of capacity that control (in part) the expected risk of classifiers [3]. These capacities quantify the expectations regarding future data that learning algorithms embed into classifiers. Solomonoff and Hutter have applied algorithmic information to prove remarkable results on universal induction. Shannon information provides the mathematical foundation for communication and coding theory. However, both approaches have shortcomings. Algorithmic information is not computable, severely limiting its practical usefulness. Shannon information refers to ensembles rather than actual events: it makes no sense to compute the Shannon information of a single string – or rather, there are many answers to this question depending on how a related ensemble is constructed. Although there are asymptotic results linking algorithmic and Shannon information, it is unsatisfying that there is such a large gap – a difference in kind – between the two measures. This note describes a new method of quantifying information, effective information, that links algorithmic information to Shannon information, and also links both to capacities arising in statistical learning theory [4, 5]. After introducing the measure, we show that it provides a non-universal analog of Kolmogorov complexity. We then apply it to derive basic capacities in statistical learning theory: empirical VC-entropy and empirical Rademacher complexity. A nice byproduct of our approach is an interpretation of the explanatory power of a learning algorithm in terms of the number of hypotheses it falsifies [6], counted in two different ways for the two capacities. We also discuss how effective information relates to information gain, Shannon and mutual information. (shrink)

In Cybernetics (1961 Edition), Professor Norbert Wiener noted that “The role of information and the technique of measuring and transmitting information constitute a whole discipline for the engineer, for the neuroscientist, for the psychologist, and for the sociologist”. Sociology aside, the neuroscientists and the psychologists inferred “information transmitted” using the discrete summations from Shannon Information Theory. The present author has since scrutinized the psychologists’ approach in depth, and found it wrong. The neuroscientists’ approach is highly (...) related, but remains unexamined. Neuroscientists quantified “the ability of [physiological sensory] receptors (or other signal-processing elements) to transmit information about stimulus parameters”. Such parameters could vary along a single continuum (e.g., intensity), or along multiple dimensions that altogether provide a Gestalt – such as a face. Here, unprecedented scrutiny is given to how 23 neuroscience papers computed “information transmitted” in terms of stimulus parameters and the evoked neuronal spikes. The computations relied upon Shannon’s “confusion matrix”, which quantifies the fidelity of a “general communication system”. Shannon’s matrix is square, with the same labels for columns and for rows. Nonetheless, neuroscientists labelled the columns by “stimulus category” and the rows by “spike-count category”. The resulting “information transmitted” is spurious, unless the evoked spike-counts are worked backwards to infer the hypothetical evoking stimuli. The latter task is probabilistic and, regardless, requires that the confusion matrix be square. Was it? For these 23 significant papers, the answer is No. (shrink)

Purpose – For half a century, neuroscientists have used Shannon Information Theory to calculate “information transmitted,” a hypothetical measure of how well neurons “discriminate” amongst stimuli. Neuroscientists’ computations, however, fail to meet even the technical requirements for credibility. Ultimately, the reasons must be conceptual. That conclusion is confirmed here, with crucial implications for neuroscience. The paper aims to discuss these issues. Design/methodology/approach – Shannon Information Theory depends upon a physical model, Shannon’s “general communication system.” (...) Neuroscientists’ interpretation of that model is scrutinized here. Findings – In Shannon’s system, a recipient receives a message composed of symbols. The symbols received, the symbols sent, and their hypothetical occurrence probabilities altogether allow calculation of “information transmitted.” Significantly, Shannon’s system’s “reception” (decoding) side physically mirrors its “transmission” (encoding) side. However, neurons lack the “reception” side; neuroscientists nonetheless insisted that decoding must happen. They turned to Homunculus, an internal humanoid who infers stimuli from neuronal firing. However, Homunculus must contain a Homunculus, and so on ad infinitum – unless it is super-human. But any need for Homunculi, as in “theories of consciousness,” is obviated if consciousness proves to be “emergent.” Research limitations/implications – Neuroscientists’ “information transmitted” indicates, at best, how well neuroscientists themselves can use neuronal firing to discriminate amongst the stimuli given to the research animal. Originality/value – A long-overdue examination unmasks a hidden element in neuroscientists’ use of Shannon Information Theory, namely, Homunculus. Almost 50 years’ worth of computations are recognized as irrelevant, mandating fresh approaches to understanding “discriminability.”. (shrink)

Logical Probability (LP) is strictly distinguished from Statistical Probability (SP). To measure semantic information or confirm hypotheses, we need to use sampling distribution (conditional SP function) to test or confirm fuzzy truth function (conditional LP function). The Semantic Information Measure (SIM) proposed is compatible with Shannon’s information theory and Fisher’s likelihood method. It can ensure that the less the LP of a predicate is and the larger the true value of the proposition is, the more (...) class='Hi'>information there is. So the SIM can be used as Popper's information criterion for falsification or test. The SIM also allows us to optimize the true-value of counterexamples or degrees of disbelief in a hypothesis to get the optimized degree of belief, i. e. Degree of Confirmation (DOC). To explain confirmation, this paper 1) provides the calculation method of the DOC of universal hypotheses; 2) discusses how to resolve Raven Paradox with new DOC and its increment; 3) derives the DOC of rapid HIV tests: DOC of “+” =1-(1-specificity)/sensitivity, which is similar to Likelihood Ratio (=sensitivity/(1-specificity)) but has the upper limit 1; 4) discusses negative DOC for excessive affirmations, wrong hypotheses, or lies; and 5) discusses the DOC of general hypotheses with GPS as example. (shrink)

In this work, we propose a definition of logical consequence based on the relation between the quantity of information present in a particular set of formulae and a particular formula. As a starting point, we use Shannon‟s quantitative notion of information, founded on the concepts of logarithmic function and probability value. We first consider some of the basic elements of an axiomatic probability theory, and then construct a probabilistic semantics for languages of classical propositional logic. We define (...) the quantity of information for the formulae of these languages and introduce the concept of informational logical consequence, identifying some important results, among them: certain arguments that have traditionally been considered valid, such as modus ponens, are not valid from the informational perspective; the logic underlying informational logical consequence is not classical, and is at the least paraconsistent sensu lato; informational logical consequence is not a Tarskian logical consequence. (shrink)

Shannon’s information theory has been a popular component of first-order cybernetics. It quantifies information transmitted in terms of the number of times a sent symbol is received as itself, or as another possible symbol. Sent symbols were events and received symbols were outcomes. Garner and Hake reinterpreted Shannon, describing events and outcomes as categories of a stimulus attribute, so as to quantify the information transmitted in the psychologist’s category (or absolute judgment) experiment. There, categories are (...) represented by specific stimuli, and the human subject must assign those stimuli, singly and in random order, to the categories that they represent. Hundreds of computations ensued of information transmitted and its alleged asymptote, the sensory channel capacity. The present paper critically re-examines those estimates. It also reviews estimates of memory capacity from memory experiments. It concludes that absolute judgment is memory-limited and that channel capacities are actually memory capacities. In particular, there are factors that affect absolute judgment that are not explainable within Shannon’s theory, factors such as feedback, practice, motivation, and stimulus range, as well as the anchor effect, sequential dependences, the rise in information transmitted with the increase in number of stimulus dimensions, and the phenomena of masking and stimulus duration dependence. It is recommended that absolute judgments be abandoned, because there are already many direct estimates of memory capacity. (shrink)

The first use of the term "information" to describe the content of nervous impulse occurs 20 years prior to Shannon`s (1948) work, in Edgar Adrian`s The Basis of Sensation (1928). Although, at least throughout the 1920s and early 30s, the term "information" does not appear in Adrian`s scientific writings to describe the content of nervous impulse, the notion that the structure of nervous impulse constitutes a type of message subject to certain constraints plays an important role in (...) all of his writings throughout the period. The appearance of the concept of information in Adrian`s work raises at least two important questions: (i) what were the relevant factors that motivated Adrian`s use of the concept of information? (ii) What concept of information does Adrian appeal to, and how can it be situated in relation to contemporary philosophical accounts of the notion of information in biology? The first question involves an account of the application of communications technology in neurobiology as well as the historical and scientific background of Adrian`s major scientific achievement, which was the recording of the action potential of a single sensory neuron. The response to the second question involves an explication of Adrian`s concept of information and an evaluation of how it may be situated in relation to more contemporary philosophical explications of a semantic concept of information. I suggest that Adrian`s concept of information places limitations on the sorts of systems that are referred to as information carriers by causal and functional accounts of information. (shrink)

In spite of being ubiquitous in life sciences, the concept of information is harshly criticized. Uses of the concept other than those derived from Shannon's theory are denounced as pernicious metaphors. We perform a computational experiment to explore whether Shannon's information is adequate to describe the uses of said concept in commonplace scientific practice. Our results show that semantic sequences do not have unique complexity values different from the value of meaningless sequences. This result suggests that (...) quantitative theoretical frameworks do not account fully for the complex phenomenon that the term “information” refers to. We propose a restructuring of the concept into two related, but independent notions, and conclude that a complete theory of biological information must account completely not only for both notions, but also for the relationship between them. (shrink)

Rejection is an essential part of the scholarly publishing process, acting as a filter to distinguish between robust and less credible scientific works. This study examines the advantages and limitations of the rejection mechanism through the lens of Shannon’s information theory and the theory of granular interactions thinking. We argue that while rejection helps reduce entropy and increase the likelihood of disseminating useful knowledge, the process is not devoid of subjectivity. We propose two recommendations to improve the rejection (...) mechanism: providing more informative rejection letters to guide authors and encouraging humility among editors and reviewers to minimize bias against novel ideas. These steps could enhance the efficiency of knowledge production by ensuring that valuable scientific contributions are not overlooked. (shrink)

By bringing together Dretske’s theory of knowledge, Shannon’s theory of information, and the conceptual framework of statistical physics, this paper explores some of the meta-physical challenges posed by a naturalistic notion of semantical information. It is argued that Dretske’s theory cannot be said to be naturalistically grounded in the world described by classical physics and that Dretske information is not consistent with Shannon information. A possible route to reconciling Dretske’s insights with Shannon’s theory (...) is proposed. Along the way, an attempt is made to clarify several points of possible confusion about the relationships between Dretske information, Shannon information and statistical physics. (shrink)

Philosophers have relied on visual metaphors to analyse ideas and explain their theories at least since Plato. Descartes is famous for his system of axes, and Wittgenstein for his first design of truth table diagrams. Today, visualisation is a form of ‘computer-aided seeing’ information in data. Hence, information is the fundamental ‘currency’ exchanged through a visualisation pipeline. In this article, we examine the types of information that may occur at different stages of a general visualization pipeline. We (...) do so from a quantitative and a qualitative perspective. The quantitative analysis is developed on the basis of Shannon’s information theory. The qualitative analysis is developed on the basis of Floridi’s taxonomy in the philosophy of information. We then discuss in detail how the condition of the ‘data processing inequality’ can be broken in a visualisation pipeline. This theoretic finding underlines the usefulness and importance of visualisation in dealing with the increasing problem of data deluge. We show that the subject of visualisation should be studied using both qualitative and quantitative approaches, preferably in an interdisciplinary synergy between information theory and the philosophy of information. (shrink)

This insight to art came from chess composition concentrating art in a very dense form. To identify and mathematically assess the uniqueness is the key applicable to other areas eg. computer programming. Maximization of uniqueness is minimization of entropy that coincides as well as goes beyond Information Theory (Shannon, 1948). The reusage of logic as a universal principle to minimize entropy, requires simplified architecture and abstraction. Any structures (e.g. plugins) duplicating or dividing functionality increase entropy and so unreliability (...) (eg. British Airways IT system). The ideas here were verified by my chess compositions, art works and complex information system as an author of each co uk, and were presented at conferences in Santorini, Adelaide, Geneva, Daejon and virtually. (shrink)

Purpose – The purpose of this paper is to examine the popular “information transmitted” interpretation of absolute judgments, and to provide an alternative interpretation if one is needed. Design/methodology/approach – The psychologists Garner and Hake and their successors used Shannon’s Information Theory to quantify information transmitted in absolute judgments of sensory stimuli. Here, information theory is briefly reviewed, followed by a description of the absolute judgment experiment, and its information theory analysis. Empirical channel capacities (...) are scrutinized. A remarkable coincidence, the similarity of maximum information transmitted to human memory capacity, is described. Over 60 representative psychology papers on “information transmitted” are inspected for evidence of memory involvement in absolute judgment. Finally, memory is conceptually integrated into absolute judgment through a novel qualitative model that correctly predicts how judgments change with increase in the number of judged stimuli. Findings – Garner and Hake gave conflicting accounts of how absolute judgments represent information transmission. Further, “channel capacity” is an illusion caused by sampling bias and wishful thinking; information transmitted actually peaks and then declines, the peak coinciding with memory capacity. Absolute judgments themselves have numerous idiosyncracies that are incompatible with a Shannon general communication system but which clearly imply memory dependence. Research limitations/implications – Memory capacity limits the correctness of absolute judgments. Memory capacity is already well measured by other means, making redundant the informational analysis of absolute judgments. Originality/value – This paper presents a long-overdue comprehensive critical review of the established interpretation of absolute judgments in terms of “information transmitted”. An inevitable conclusion is reached: that published measurements of information transmitted actually measure memory capacity. A new, qualitative model is offered for the role of memory in absolute judgments. The model is well supported by recently revealed empirical properties of absolute judgments. (shrink)

The common use of Shannon’s information, specified for the needs of telecommunications, gives rise to many misunderstandings outside of this context. (e.g. in conceptions of such well-known theorists as C.F. Weizsäcker and J. A. Wheeler). This article shows that the terms of the general definition of information meet the structural information, and Shannon’s information is a special case of it. Similarly, complexity is misunderstood today as exemplified by the concepts of reputable computer scientists, such (...) as S. Lloyd, S. Wolfram and J. Schmidhuber. These theorists use an algorithmic definition of complexity and the so-called logical depth, neither of which meets the intuitive criterion of complexity. Hence, their misconceptions of beauty, art and the universe. It will be shown that the intuitive criterion is met by Abstract Complexity Definition. This definition also fulfils the criterion for a general complexity definition, as it defines complexity of the most general/abstract structure of our reality, that is, binary structure. It also explains such fundamental issues of information theory as the information–energy relationship and the value of information, which are still discussed and need to be clarified. (shrink)

This essay introduces the philosophy of legal information (PLI), which is a response to the radical changes brought about in philosophy by the information revolution. It reviews in some detail the work of Luciano Floridi, who is an influential advocate for an information turn in philosophy that he calls the philosophy of information (PI). Floridi proposes that philosophers investigate the conceptual nature of information as it currently exists across multiple disciplines. He shows how a focus (...) on the informational nature of traditional philosophical questions can be transformative for philosophy and for human self-understanding. The philosophy of legal information (PLI) proposed here views laws as a body of information that is stored, manipulated, and analyzed through multiple methods, including the computational methodologies. PLI offers resources for evaluating the ethical and political implications of legal infomatics (also known as "legal information systems"). -/- This essay introduces PLI. Parts I and II describe Floridi's philosophy of information. First, Part I introduces the transformation in the concept of information that occurred in the twentieth century through the work of Alan Turning and Claude Shannon. Part II describes Floridi's approaches to traditional questions in epistemology, ontology, and ethics. Part III applies PI to the analysis of legal positivism. It suggests that PLI is a viable project that has potential for transforming the understanding law in the information age. -/- . (shrink)

In this article, I outline the three main philosophical lessons that we may learn from Turing’s work, and how they lead to a new philosophy of information. After a brief introduction, I discuss his work on the method of levels of abstraction (LoA), and his insistence that questions could be meaningfully asked only by specifying the correct LoA. I then look at his second lesson, about the sort of philosophical questions that seem to be most pressing today. Finally, I (...) focus on the third lesson, concerning the new philosophical anthropology that owes so much to Turing’s work. I then show how the lessons are learned by the philosophy of information. In the conclusion, I draw a general synthesis of the points made, in view of the development of the philosophy of information itself as a continuation of Turing’s work. (shrink)

This is the Outline: 1. Introduction 2. L’information en première approche 3. L’information-Janus 4. À quoi sert l’information? 5. Les usages que l’on fit de la théorie de l’information 6. L’information dite de « second ordre » 7. Doit-on mettre l’information au service d’une vision théologique ? 7.1 Une incidence à partir de nos images de Dieu.

The main objective of this dissertation is to philosophically assess how the use of informational concepts in the field of classical thermostatistical physics has historically evolved from the late 1940s to the present day. I will first analyze in depth the main notions that form the conceptual basis on which 'informational physics' historically unfolded, encompassing (i) different entropy, probability and information notions, (ii) their multiple interpretative variations, and (iii) the formal, numerical and semantic-interpretative relationships among them. In the following, (...) I will assess the history of informational thermophysics during the second half of the twentieth century. Firstly, I analyse the intellectual factors that gave rise to this current in the late forties (i.e., popularization of Shannon's theory, interest in a naturalized epistemology of science, etc.), then study its consolidation in the Brillouinian and Jaynesian programs, and finally claim how Carnap (1977) and his disciples tried to criticize this tendency within the scientific community. Then, I evaluate how informational physics became a predominant intellectual current in the scientific community in the nineties, made possible by the convergence of Jaynesianism and Brillouinism in proposals such as that of Tribus and McIrvine (1971) or Bekenstein (1973) and the application of algorithmic information theory into the thermophysical domain. As a sign of its radicality at this historical stage, I explore the main proposals to include information as part of our physical reality, such as Wheeler’s (1990), Stonier’s (1990) or Landauer’s (1991), detailing the main philosophical arguments (e.g., Timpson, 2013; Lombardi et al. 2016a) against those inflationary attitudes towards information. Following this historical assessment, I systematically analyze whether the descriptive exploitation of informational concepts has historically contributed to providing us with knowledge of thermophysical reality via (i) explaining thermal processes such as equilibrium approximation, (ii) advantageously predicting thermal phenomena, or (iii) enabling understanding of thermal property such as thermodynamic entropy. I argue that these epistemic shortcomings would make it impossible to draw ontological conclusions in a justified way about the physical nature of information. In conclusion, I will argue that the historical exploitation of informational concepts has not contributed significantly to the epistemic progress of thermophysics. This would lead to characterize informational proposals as 'degenerate science' (à la Lakatos 1978a) regarding classical thermostatistical physics or as theoretically underdeveloped regarding the study of the cognitive dynamics of scientists in this physical domain. (shrink)

A new trend in deep learning, represented by Mutual Information Neural Estimation (MINE) and Information Noise Contrast Estimation (InfoNCE), is emerging. In this trend, similarity functions and Estimated Mutual Information (EMI) are used as learning and objective functions. Coincidentally, EMI is essentially the same as Semantic Mutual Information (SeMI) proposed by the author 30 years ago. This paper first reviews the evolutionary histories of semantic information measures and learning functions. Then, it briefly introduces the author’s (...) semantic information G theory with the rate-fidelity function R(G) (G denotes SeMI, and R(G) extends R(D)) and its applications to multi-label learning, the maximum Mutual Information (MI) classification, and mixture models. Then it discusses how we should understand the relationship between SeMI and Shannon’s MI, two generalized entropies (fuzzy entropy and coverage entropy), Autoencoders, Gibbs distributions, and partition functions from the perspective of the R(G) function or the G theory. An important conclusion is that mixture models and Restricted Boltzmann Machines converge because SeMI is maximized, and Shannon’s MI is minimized, making information efficiency G/R close to 1. A potential opportunity is to simplify deep learning by using Gaussian channel mixture models for pre-training deep neural networks’ latent layers without considering gradients. It also discusses how the SeMI measure is used as the reward function (reflecting purposiveness) for reinforcement learning. The G theory helps interpret deep learning but is far from enough. Combining semantic information theory and deep learning will accelerate their development. (shrink)

A Geometrical Approach to Quantum Information.

Purpose – The purpose of this paper is to ask whether a first-order-cybernetics concept, Shannon’s Information Theory, actually allows a far-reaching mathematics of perception allegedly derived from it, Norwich et al.’s “Entropy Theory of Perception”. Design/methodology/approach – All of The Entropy Theory, 35 years of publications, was scrutinized for its characterization of what underlies Shannon Information Theory: Shannon’s “general communication system”. There, “events” are passed by a “source” to a “transmitter”, thence through a “noisy channel” (...) to a “receiver”, that passes “outcomes” (received events) to a “destination”. Findings – In the entropy theory, “events” were sometimes interactions with the stimulus, but could be microscopic stimulus conditions. “Outcomes” often went unnamed; sometimes, the stimulus, or the interaction with it, or the resulting sensation, were “outcomes”. A “source” was often implied to be a “transmitter”, which frequently was a primary afferent neuron; elsewhere, the stimulus was the “transmitter” and perhaps also the “source”. “Channel” was rarely named; once, it was the whole eye; once, the incident photons; elsewhere, the primary or secondary afferent. “Receiver” was usually the sensory receptor, but could be an afferent. “Destination” went unmentioned. In sum, the entropy theory’s idea of Shannon’s “general communication system” was entirely ambiguous. Research limitations/implications – The ambiguities indicate that, contrary to claim, the entropy theory cannot be an “information theoretical description of the process of perception”. Originality/value – Scrutiny of the entropy theory’s use of information theory was overdue and reveals incompatibilities that force a reconsideration of information theory’s possible role in perception models. A second-order-cybernetics approach is suggested. (shrink)

Prominent views about representation share a premise: that mathematical communication theory is blind to representational content. Here I challenge that premise by rejecting two common misconceptions: that Claude Shannon said that the meanings of signals are irrelevant for communication theory (he didn't and they aren't), and that since correlational measures can't distinguish representations from natural signs, communication theory can't distinguish them either (the premise is true but the conclusion is false; no valid argument can link them).

Purpose – A key cybernetics concept, information transmitted in a system, was quantified by Shannon. It quickly gained prominence, inspiring a version by Harvard psychologists Garner and Hake for “absolute identification” experiments. There, human subjects “categorize” sensory stimuli, affording “information transmitted” in perception. The Garner-Hake formulation has been in continuous use for 62 years, exerting enormous influence. But some experienced theorists and reviewers have criticized it as uninformative. They could not explain why, and were ignored. Here, the (...) “why” is answered. The paper aims to discuss these issues. Design/methodology/approach – A key Shannon data-organizing tool is the confusion matrix. Its columns and rows are, respectively, labeled by “symbol sent” (event) and “symbol received” (outcome), such that matrix entries represent how often outcomes actually corresponded to events. Garner and Hake made their own version of the matrix, which deserves scrutiny, and is minutely examined here. Findings – The Garner-Hake confusion-matrix columns represent “stimulus categories”, ranges of some physical stimulus attribute (usually intensity), and its rows represent “response categories” of the subject’s identification of the attribute. The matrix entries thus show how often an identification empirically corresponds to an intensity, such that “outcomes” and “events” differ in kind (unlike Shannon’s). Obtaining a true “information transmitted” therefore requires stimulus categorizations to be converted to hypothetical evoking stimuli, achievable (in principle) by relating categorization to sensation to intensity. But those relations are actually unknown, perhaps unknowable. Originality/value – The author achieves an important understanding: why “absolute identification” experiments do not illuminate sensory processes. (shrink)

The problem of emergence in physical theories makes necessary to build a general theory of the relationships between the observed system and the observing system. It can be shown that there exists a correspondence between classical systems and computational dynamics according to the Shannon-Turing model. A classical system is an informational closed system with respect to the observer; this characterizes the emergent processes in classical physics as phenomenological emergence. In quantum systems, the analysis based on the computation theory fails. (...) It is here shown that a quantum system is an informational open system with respect to the observer and able to exhibit processes of observational, radical emergence. Finally, we take into consideration the role of computation in describing the physical world. (shrink)

Since 2019, I have been accompanying the development of the Mindsponge Theory (MT). In 2023, the book Mindsponge Theory was officially published, marking a significant milestone. During this period, I completed my doctoral research and explored new methods to enhance work efficiency. The journey was filled with challenges. Surprisingly, the difficulties arose from concepts that seemed small and basic, almost taken for granted as "already known." I was entrusted by my co-author—the father of the mindsponge mechanism—with the task of further (...) developing the theory. He was not satisfied with what seemed like the final form, encapsulating Mindsponge theory as an information mechanism of biological systems. This was a significant challenge because the next form could only be approached using scientific information from quantum physics, as predicted by Erwin Schrödinger. Indeed, our exploration traversed many realms of physics and information. This journey was greatly supported by books like Reality Is Not What It Seems by Rovelli and On the Origin of Time by Hertog, along with Shannon's principles of information theory. The more valuable information we gathered, the more we realized the vast contributions left by countless brilliant minds, highlighting our own limitations. I was driven by a seemingly simple yet profoundly challenging question: • What is value? What approach is needed to reach its deepest essence? (shrink)

Complexity is a relatively new field of study that is still heavily influenced by philosophy. However, with the advent of modern computing, it has become easier to conduct thorough investigations of complex systems using computational simulations. Despite significant progress, there remain certain characteristics of complex systems that are difficult to comprehend. To better understand these features, information can be applied using simple models of complex systems. The concepts of Shannon's information theory, Kolgomorov complexity, and logical depth are (...) helpful in this regard. Computational mathematical models based on recursive processes, recursive functions, and information in functions offer new insights into complexity by exploring its connection with irreversibility and the loss of information. Through these models, it is possible to see complexity as a transformation of input information through algorithmic computation, with time, logical depth, and information loss serving as key factors in understanding the process. (shrink)

Introduction & Objectives: Norwich’s Entropy Theory of Perception (1975 [1] -present) stands alone. It explains many firing-rate behaviors and psychophysical laws from bare theory. To do so, it demands a unique sort of interaction between receptor and brain, one that Norwich never substantiated. Can it now be confirmed, given the accumulation of empirical sensory neuroscience? Background: Norwich conjoined sensation and a mathematical model of communication, Shannon’s Information Theory, as follows: “In the entropic view of sensation, magnitude of sensation (...) is regarded as a measure of the entropy or uncertainty of the stimulus signal” [2]. “To be uncertain about the outcome of an event, one must first be aware of a set of alternative outcomes” [3]. “The entropy-establishing process begins with the generation of a [internal] sensory signal by the stimulus generator. This is followed by receipt of the [external] stimulus by the sensory receptor, transmission of action potentials by the sensory neurons, and finally recapture of the [response to the internal] signal by the generator” [4]. The latter “recapture” differentiates external from internal stimuli. The hypothetical “stimulus generators” are internal emitters, that generate photons in vision, audible sounds in audition (to Norwich, the spontaneous otoacoustic emissions [SOAEs]), “temperatures in excess of local skin temperature” in skin temperature sensation [4], etc. Method (1): Several decades of empirical sensory physiology literature was scrutinized for internal “stimulus generators”. Results (1): Spontaneous photopigment isomerization (“dark light”) does not involve visible light. SOAEs are electromechanical basilar-membrane artefacts that rarely produce audible tones. The skin’s temperature sensors do not raise skin temperature, etc. Method (2): The putative action of the brain-and-sensory-receptor loop was carefully reexamined. Results (2): The sensory receptor allegedly “perceives”, experiences “awareness”, possesses “memory”, and has a “mind”. But those traits describe the whole human. The receptor, thus anthropomorphized, must therefore contain its own perceptual loop, containing a receptor, containing a perceptual loop, etc. Summary & Conclusions: The Entropy Theory demands sensory awareness of alternatives, through an imagined brain-and-sensory-receptor loop containing internal “stimulus generators”. But (1) no internal “stimulus generators” seem to exist and (2) the loop would be the outermost of an infinite nesting of identical loops. (shrink)

Until the late 19th century scientists almost always assumed that the world could be described as a rule-based and hence deterministic system or as a set of such systems. The assumption is maintained in many 20th century theories although it has also been doubted because of the breakthrough of statistical theories in thermodynamics (Boltzmann and Gibbs) and other fields, unsolved questions in quantum mechanics as well as several theories forwarded within the social sciences. Until recently it has furthermore been assumed (...) that a rule-based and deterministic system was also predictable if only the rules were known, but this assumption has now been undermined by modern chaos-theory describing rule-based and deterministic, but unpredictable systems, while catastrophe-theory delivers a set of types describing various kinds of instability and conditions for the stability of a given system. Hence the main trait in the theoretical development in the 20th-century science can be described as a basic modification and limitation of some of the fundamental and strong assumptions forwarded in the previous epochs of modern science. Ironically, the very same process has been a process in which the human capacity to intervene in nature has expanded dramatically and mainly with the help of the very same theories, and not least because they allow nature to be described and made manipulable on a lower level and a more fine-grained scale. While the overall theoretical consistency between the various theories has gone, the reach of human intervention in nature has increased based on quite new dimensions whether in the area of physics (e.g.: energy technologies, chemical technologies, nanotechnologies etc.) or biology (ge¬netic manipulation) or in the area of psychology, sociology and culture (artificial simulations of mental processes, new means of communication, implying changes in the social infrastructure and cultural behaviour etc.). While some of these changes and new conditions can be reflected from within the conceptual framework of rule-based systems, albeit more complex than formerly recognized, others seem to give rise to the question of whether there are »systems« and relations between different systems in the world which are not rule-based? For instance, it seems to be obvious that the notion of instability represents a major conceptual break with former theories of rule-based systems, as the stability of the latter is an axiomatically given property implied in the very notion of rule-based systems, while instability can only be the result of external influence which should be explained as the result of another rule-based sy¬stem. While there are no difficulties implied concerning the stability of rule-based systems, the notion of unstable states of a system raises the question of how there can be a system at all if there are no invariant stabilising principles? This is the first question which I will address. And I shall do so by taking two examples of such systems as my point of departure. The first example will be the computer and the second will be ordinary language. In both cases I will argue that the stability of these systems (which are both defined by the existence/presence of human intentions) are provided by the help of - differently organised - redundancy functions which both allow the maintenance of systems in unstable macro-states, suspension of previous rules, underdetermination and overdetermination and generation/emergence/creation of new rules more or less independent of previous rules by the help of optional recursions to the permanently accessible underlying levels as for instance the level of binary representation in computers. Since the notion of redundancy is both controversial as such and often avoided, the concept is discussed (as defined in Claude Shannon's mathematical theory of information and in the semiotic framework of J. J. Greimas) and leading to a more general definition in which the redundancy functions serve to overcome noisy conditions, but at the cost of rule-based stability, determination, and predictability. A second question will be how the notion of rule-generating systems relates to the notion of anticipatory systems and it will be argued that rule-generating systems share some features with an¬ticipatory systems and that the former from a certain viewpoint can be seen as a subclass of the latter, although anticipative features are not necessarily a part of the definition of rule-generating systems. On the other hand, it will be discussed whether anticipatory systems which are not rule-generating systems can exist and it will be argued that the capacity to anticipate is strongly limited if it is not part of a rule-generating system. Therefore, it is concluded that the most powerful anticipatory systems need to be rule-generating systems. (shrink)

Đồng hành với lý thuyết MT từ 2019, tới 2023 cuốn sách Mindsponge Theory (MT) chính thức ra mắt, đánh dấu một chặng đường cá nhân tôi vừa hoàn thành các nghiên cứu tiến sỹ, vừa tìm tòi phát triển các phương pháp trợ giúp công việc hiệu quả hơn. Trên hành trình xuất hiện rất nhiều thách thức. Nhưng kỳ lạ ở chỗ khó khăn lại xuất hiện ở khái niệm tưởng như rất bé, rất cơ bản, thậm chí (...) gần như được mặc định là “đã biết”. Tôi được đồng tác giả—chính là người đã đưa ra cơ chế mindsponge—giao phó việc tiếp tục cùng phát triển ở tầng nấc sâu hơn nữa. Ông không hài lòng với nấc tưởng như đã tận cùng khi đóng gói MT là cơ chế thông tin của các hệ thống sinh học. Đây là điều rất thách thức, vì nấc tiếp theo chỉ có thể tiến hành dựa trên các thông tin khoa học từ vật lý hạt cơ bản, như dự báo của Erwin Schrödinger. Quả vậy, hành trình tìm tòi đã đi qua nhiều địa hạt của vật lý và thông tin. Hành trình này được sự hỗ trợ vô cùng hiệu quả của cuốn sách Reality is not what it seems của Rovelli cũng như On the origin of time của Hertog và bản chất thông tin của Shannon. Càng nhiều thông tin hay, những phát hiện lớn và tầng tầng lớp lớp những bộ óc thiên tài để lại cho nhân loại những kho báu kiến thức vô giá, một cá nhân lại càng cảm nhận sự nhỏ bé và hạn chế của bản thân. Tôi được thôi thúc với câu hỏi rất bé, cuối cùng hóa ra lại rất thách thức để trả lời: Vậy giá trị là gì? Cần cách tiếp cận nào để đến gần được bản chất sâu xa nhất của nó? (shrink)

In recent years noise seems to have become an interdisciplinary concept par excellence, apt to capturing important dynamics at work whether in technological, scientific, social, or aesthetic domains. But when economists, biologists, psychologists, and musicians speak of noise, are they really all referring to the same thing? In An Epistemology of Noise Cecile Malaspina takes this dispersion of the notion of noise as a starting point, and moreover accepts that, when removed from its mathematical formulation in information theory and (...) spread into diverse disciplines, noise takes on a metaphorical ambiguity. Yet rather than aiming to eliminate this ambiguity, Malaspina sets out to account for it. The key problem in An Epistemology of Noise is not to identify the legitimate usage of the concept of noise, but rather to examine what happens when noise moves between disciplines, and what the ‘noisiness’ of this movement tells us about the conditions for interdisciplinary knowledge. Noise here is both an object (or many objects) of inquiry and a condition for that inquiry, and presents us with the problem of how knowledge can find its ground in these ‘shifting sands’ (9). (shrink)