Groups, information theory, and Einstein's likelihood principle

We propose a unifying picture where the notion of generalized entropy is related to information theory by means of a group-theoretical approach. The group structure comes from the requirement that an entropy be well defined with respect to the composition of independent systems, in the context of a recently proposed generalization of the Shannon-Khinchin axioms. We associate to each member of a large class of entropies a generalized information measure, satisfying the additivity property on a set of independent systems as a consequence of the underlying group law. At the same time, we also show that Einstein's likelihood function naturally emerges as a byproduct of our informational interpretation of (generally nonadditive) entropies. These results confirm the adequacy of composable entropies both in physical and social science contexts.

Emotional Theory of Rationality

In recent decades, it has been definitely established the existence of a close relationship between the emotional phenomena and rational processes, but we still do not have a unified definition, or effective models to describe any of them well. To advance our understanding of the mechanisms governing the behavior of living beings we must integrate multiple theories, experiments and models from both fields. In this paper we propose a new theoretical framework that allows integrating and understanding, from a functional point of view, the emotion-cognition duality. Our reasoning, based on evolutionary principles, add to the definition and understanding of emotion, justifying its origin, explaining its mission and dynamics, and linking it to higher cognitive processes, mainly with attention, cognition, decision-making and consciousness. According to our theory, emotions are the mechanism for brain function optimization, besides being the contingency and stimuli prioritization system. As a result of this approach, we have developed a dynamic systems-level model capable of providing plausible explanations for some psychological and behavioral phenomena, and establish a new framework for scientific definition of some fundamental psychological terms.

Perturbative Ward identities for Yang-Mills field theory stochastically quantized

%'e compute the divergent part of the three-point vertex function of the non-Abelian Yang-Mills gauge field theory within the stochastic quantization approach to the one-loop order. This calculation allows us to find four renormalization constants which, together with the four previously obtained, verify, to the calculated order, some Ward identities.