Information Quality in Latin American Digital Native Media: Analysis Based on Structured Dimensions and Indicators

The current communicative ecosystem has profoundly transformed journalistic work and the media, generating with great eagerness the emergence of digital native media that do not follow the logic of their conventional peers. Although the advent of these media is not entirely negative, as they create multiple voices that contribute to pluralism, their quality has undoubtedly been questioned on several academic fronts. This work analyzes the most important Latin American digital native media by number of accesses (traffic), using a taxonomy of evaluation of dimensions of the informative quality, in which aspects such as informative sources, uses of international news agencies, correction of contents and factuality levels, ideological plurality in their opinion contents, among others, are taken into consideration. Of the emerging results, the ‘use of statistical indicators’ was the least rated (32.5%), mainly due to a lack of data journalism in the media studied. It is also worth noting that the indicator ‘comments and monitoring’ obtained the second-lowest rating, indicating an absence of conversation between the media and its audience through the comments section of each content

Rhetoric in legislative bargaining with asymmetric information

We analyze a three-player legislative bargaining game over an ideological and a distributive decision. Legislators are privately informed about their ideological intensities, i.e., the weight placed on the ideological decision relative to the weight placed on the distributive decision. Communication takes place before a proposal is offered and majority rule voting determines the outcome. We show that it is not possible for all legislators to communicate informatively. In particular, the legislator who is ideologically more distant from the proposer cannot communicate informatively, but the closer legislator may communicate whether he would \compromise "or flight" on ideology. Surprisingly, the proposer may be worse off when bargaining with two legislators (under majority rule) than with one (who has veto power), because competition between the legislators may result in less information conveyed in equilibrium. Despite separable preferences, the proposer is always better off making proposals for the two dimensions together

Complexity over Uncertainty in Generalized Representational\ud Information Theory (GRIT): A Structure-Sensitive General\ud Theory of Information

What is information? Although researchers have used the construct of information liberally to refer to pertinent forms of domain-specific knowledge, relatively few have attempted to generalize and standardize the construct. Shannon and Weaver(1949)offered the best known attempt at a quantitative generalization in terms of the number of discriminable symbols required to communicate the state of an uncertain event. This idea, although useful, does not capture the role that structural context and complexity play in the process of understanding an event as being informative. In what follows, we discuss the limitations and futility of any generalization (and particularly, Shannon’s) that is not based on the way that agents extract patterns from their environment. More specifically, we shall argue that agent concept acquisition, and not the communication of\ud states of uncertainty, lie at the heart of generalized information, and that the best way of characterizing information is via the relative gain or loss in concept complexity that is experienced when a set of known entities (regardless of their nature or domain of origin) changes. We show that Representational Information Theory perfectly captures this crucial aspect of information and conclude with the first generalization of Representational Information Theory (RIT) to continuous domains

The equivalence of information-theoretic and likelihood-based methods for neural dimensionality reduction

Stimulus dimensionality-reduction methods in neuroscience seek to identify a low-dimensional space of stimulus features that affect a neuron's probability of spiking. One popular method, known as maximally informative dimensions (MID), uses an information-theoretic quantity known as "single-spike information" to identify this space. Here we examine MID from a model-based perspective. We show that MID is a maximum-likelihood estimator for the parameters of a linear-nonlinear-Poisson (LNP) model, and that the empirical single-spike information corresponds to the normalized log-likelihood under a Poisson model. This equivalence implies that MID does not necessarily find maximally informative stimulus dimensions when spiking is not well described as Poisson. We provide several examples to illustrate this shortcoming, and derive a lower bound on the information lost when spiking is Bernoulli in discrete time bins. To overcome this limitation, we introduce model-based dimensionality reduction methods for neurons with non-Poisson firing statistics, and show that they can be framed equivalently in likelihood-based or information-theoretic terms. Finally, we show how to overcome practical limitations on the number of stimulus dimensions that MID can estimate by constraining the form of the non-parametric nonlinearity in an LNP model. We illustrate these methods with simulations and data from primate visual cortex