Indexical Realism by Inter-Agentic Reference

I happen to believe that though human experiences are to be characterized as pluralistic they are all rooted in the one reality. I would assume the thesis of pluralism but how could I maintain my belief in the realism? There are various discussions in favor of realism but they appear to stay within a particular paradigm so to be called “internal realism”. In this paper I would try to justify my belief in the reality by discussing a special use of indexicals. I will argue for my indexical realism by advancing the thesis that indexicals can be used as an inter-agentic referential term. Three arguments for the thesis will be presented. The first argument derives from a revision of Kaplan-Kvart’s notion of exportation. Their notions of exportation of singular terms can be analyzed as intra-agentic exportation in the context of a single speaker and theirs may be revised so as to be an inter-agentic exportation in the context of two speakers who use the same indexicals. The second is an argument from the notion of causation which is specifically characterized in the context of inter-theoretic reference. I will argue that any two theories may each say “this” in order to refer what is beyond its own theory. Two theories address themselves to ‘this’ same thing though what ‘this’ represents in each theory turn out to be different objects all together. The third argument is an argument which is based on a possibility of natural reference. Reference is used to be taken mostly as a 3-place predicate: Abe refers an object oi with an expression ej. The traditional notion of reference is constructive and anthropocentric. But I would argue that natural reference is a reference that we humans come to recognize among denumerably many objects in natural states: at a moment mi in a natural state there is a referential relation among objects o1, o2, o3, . . , oj, o j+1, . . which interact to each other as agents of information processors. Natural reference is an original reference which is naturally given and to which humans are passive as we derivatively refer it by using ‘this’

Nonlocal quantum information transfer without superluminal signalling and communication

It is a frequent assumption that - via superluminal information transfers - superluminal signals capable of enabling communication are necessarily exchanged in any quantum theory that posits hidden superluminal influences. However, does the presence of hidden superluminal influences automatically imply superluminal signalling and communication? The non-signalling theorem mediates the apparent conflict between quantum mechanics and the theory of special relativity. However, as a 'no-go' theorem there exist two opposing interpretations of the non-signalling constraint: foundational and operational. Concerning Bell's theorem, we argue that Bell employed both interpretations at different times. Bell finally pursued an explicitly operational position on non-signalling which is often associated with ontological quantum theory, e.g., de Broglie-Bohm theory. This position we refer to as "effective non-signalling". By contrast, associated with orthodox quantum mechanics is the foundational position referred to here as "axiomatic non-signalling". In search of a decisive communication-theoretic criterion for differentiating between "axiomatic" and "effective" non-signalling, we employ the operational framework offered by Shannon's mathematical theory of communication. We find that an effective non-signalling theorem represents two sub-theorems, which we call (1) non-transfer-control (NTC) theorem, and (2) non-signification-control (NSC) theorem. Employing NTC and NSC theorems, we report that effective, instead of axiomatic, non-signalling is entirely sufficient for prohibiting nonlocal communication. An effective non-signalling theorem allows for nonlocal quantum information transfer yet - at the same time - effectively denies superluminal signalling and communication.Comment: 21 pages, 5 figures; The article is published with open acces in Foundations of Physics (2016

Control-theoretic Approach to Communication with Feedback: Fundamental Limits and Code Design

Feedback communication is studied from a control-theoretic perspective, mapping the communication problem to a control problem in which the control signal is received through the same noisy channel as in the communication problem, and the (nonlinear and time-varying) dynamics of the system determine a subclass of encoders available at the transmitter. The MMSE capacity is defined to be the supremum exponential decay rate of the mean square decoding error. This is upper bounded by the information-theoretic feedback capacity, which is the supremum of the achievable rates. A sufficient condition is provided under which the upper bound holds with equality. For the special class of stationary Gaussian channels, a simple application of Bode's integral formula shows that the feedback capacity, recently characterized by Kim, is equal to the maximum instability that can be tolerated by the controller under a given power constraint. Finally, the control mapping is generalized to the N-sender AWGN multiple access channel. It is shown that Kramer's code for this channel, which is known to be sum rate optimal in the class of generalized linear feedback codes, can be obtained by solving a linear quadratic Gaussian control problem.Comment: Submitted to IEEE Transactions on Automatic Contro

Information theoretic approach to interactive learning

The principles of statistical mechanics and information theory play an important role in learning and have inspired both theory and the design of numerous machine learning algorithms. The new aspect in this paper is a focus on integrating feedback from the learner. A quantitative approach to interactive learning and adaptive behavior is proposed, integrating model- and decision-making into one theoretical framework. This paper follows simple principles by requiring that the observer's world model and action policy should result in maximal predictive power at minimal complexity. Classes of optimal action policies and of optimal models are derived from an objective function that reflects this trade-off between prediction and complexity. The resulting optimal models then summarize, at different levels of abstraction, the process's causal organization in the presence of the learner's actions. A fundamental consequence of the proposed principle is that the learner's optimal action policies balance exploration and control as an emerging property. Interestingly, the explorative component is present in the absence of policy randomness, i.e. in the optimal deterministic behavior. This is a direct result of requiring maximal predictive power in the presence of feedback.Comment: 6 page

A Learning Theoretic Approach to Energy Harvesting Communication System Optimization

A point-to-point wireless communication system in which the transmitter is equipped with an energy harvesting device and a rechargeable battery, is studied. Both the energy and the data arrivals at the transmitter are modeled as Markov processes. Delay-limited communication is considered assuming that the underlying channel is block fading with memory, and the instantaneous channel state information is available at both the transmitter and the receiver. The expected total transmitted data during the transmitter's activation time is maximized under three different sets of assumptions regarding the information available at the transmitter about the underlying stochastic processes. A learning theoretic approach is introduced, which does not assume any a priori information on the Markov processes governing the communication system. In addition, online and offline optimization problems are studied for the same setting. Full statistical knowledge and causal information on the realizations of the underlying stochastic processes are assumed in the online optimization problem, while the offline optimization problem assumes non-causal knowledge of the realizations in advance. Comparing the optimal solutions in all three frameworks, the performance loss due to the lack of the transmitter's information regarding the behaviors of the underlying Markov processes is quantified

Mutual Information and Minimum Mean-square Error in Gaussian Channels

This paper deals with arbitrarily distributed finite-power input signals observed through an additive Gaussian noise channel. It shows a new formula that connects the input-output mutual information and the minimum mean-square error (MMSE) achievable by optimal estimation of the input given the output. That is, the derivative of the mutual information (nats) with respect to the signal-to-noise ratio (SNR) is equal to half the MMSE, regardless of the input statistics. This relationship holds for both scalar and vector signals, as well as for discrete-time and continuous-time noncausal MMSE estimation. This fundamental information-theoretic result has an unexpected consequence in continuous-time nonlinear estimation: For any input signal with finite power, the causal filtering MMSE achieved at SNR is equal to the average value of the noncausal smoothing MMSE achieved with a channel whose signal-to-noise ratio is chosen uniformly distributed between 0 and SNR