No Arabic abstract
Any theory amenable to scientific inquiry must have testable consequences. This minimal criterion is uniquely challenging for the study of consciousness, as we do not know if it is possible to confirm via observation from the outside whether or not a physical system knows what it feels like to have an inside - a challenge referred to as the hard problem of consciousness. To arrive at a theory of consciousness, the hard problem has motivated the development of phenomenological approaches that adopt assumptions of what properties consciousness has based on first-hand experience and, from these, derive the physical processes that give rise to these properties. A leading theory adopting this approach is Integrated Information Theory (IIT), which assumes our subjective experience is a unified whole, subsequently yielding a requirement for physical feedback as a necessary condition for consciousness. Here, we develop a mathematical framework to assess the validity of this assumption by testing it in the context of isomorphic physical systems with and without feedback. The isomorphism allows us to isolate changes in $Phi$ without affecting the size or functionality of the original system. Indeed, we show that the only mathematical difference between a conscious system with $Phi>0$ and an isomorphic philosophical zombies with $Phi=0$ is a permutation of the binary labels used to internally represent functional states. This implies $Phi$ is sensitive to functionally arbitrary aspects of a particular labeling scheme, with no clear justification in terms of phenomenological differences. In light of this, we argue any quantitative theory of consciousness, including IIT, should be invariant under isomorphisms if it is to avoid the existence of isomorphic philosophical zombies and the epistemological problems they pose.
In an effort to develop the foundations for a non-stochastic theory of information, the notion of $delta$-mutual information between uncertain variables is introduced as a generalization of Nairs non-stochastic information functional. Several properties of this new quantity are illustrated, and used to prove a channel coding theorem in a non-stochastic setting. Namely, it is shown that the largest $delta$-mutual information between received and transmitted codewords over $epsilon$-noise channels equals the $(epsilon, delta)$-capacity. This notion of capacity generalizes the Kolmogorov $epsilon$-capacity to packing sets of overlap at most $delta$, and is a variation of a previous definition proposed by one of the authors. Results are then extended to more general noise models, and to non-stochastic, memoryless, stationary channels. Finally, sufficient conditions are established for the factorization of the $delta$-mutual information and to obtain a single letter capacity expression. Compared to previous non-stochastic approaches, the presented theory admits the possibility of decoding errors as in Shannons probabilistic setting, while retaining a worst-case, non-stochastic character.
Despite of the known gap from the Shannons capacity, several standards are still employing QAM or star shape constellations, mainly due to the existing low complexity detectors. In this paper, we investigate the low complexity detection for a family of QAM isomorphic constellations. These constellations are known to perform very close to the peak-power limited capacity, outperforming the DVB-S2X standard constellations. The proposed strategy is to first remap the received signals to the QAM constellation using the existing isomorphism and then break the log likelihood ratio computations to two one dimensional PAM constellations. Gains larger than 0.6 dB with respect to QAM can be obtained over the peak power limited channels without any increase in detection complexity. Our scheme also provides a systematic way to design constellations with low complexity one dimensional detectors. Several open problems are discussed at the end of the paper.
Although the gulf between the theory and practice in Information Systems is much lamented, few researchers have offered a way forward except through a number of (failed) attempts to develop a single systematic theory for Information Systems. In this paper, we encourage researchers to re-examine the practical consequences of their theoretical arguments. By examining these arguments we may be able to form a number of more rigorous theories of Information Systems, allowing us to draw theory and practice together without undertaking yet another attempt at the holy grail of a single unified systematic theory of Information Systems.
The 10th Asia-Europe workshop in Concepts in Information Theory and Communications AEW10 was held in Boppard, Germany on June 21-23, 2017. It is based on a longstanding cooperation between Asian and European scientists. The first workshop was held in Eindhoven, the Netherlands in 1989. The idea of the workshop is threefold: 1) to improve the communication between the scientist in the different parts of the world; 2) to exchange knowledge and ideas; and 3) to pay a tribute to a well respected and special scientist.
We offer a new approach to the information decomposition problem in information theory: given a target random variable co-distributed with multiple source variables, how can we decompose the mutual information into a sum of non-negative terms that quantify the contributions of each random variable, not only individually but also in combination? We derive our composition from cooperative game theory. It can be seen as assigning a fair share of the mutual information to each combination of the source variables. Our decomposition is based on a different lattice from the usual partial information decomposition (PID) approach, and as a consequence our decomposition has a smaller number of terms: it has analogs of the synergy and unique information terms, but lacks terms corresponding to redundancy. Because of this, it is able to obey equivalents of the axioms known as local positivity and identity, which cannot be simultaneously satisfied by a PID measure.