Coding against a Limited-view Adversary: The Effect of Causality and Feedback

We consider the problem of communication over a multi-path network in the presence of a causal adversary. The limited-view causal adversary is able to eavesdrop on a subset of links and also jam on a potentially overlapping subset of links based on the current and past information. To ensure that the communication takes place reliably and secretly, resilient network codes with necessary redundancy are needed. We study two adversarial models - additive and overwrite jamming and we optionally assume passive feedback from decoder to encoder, i.e., the encoder sees everything that the decoder sees. The problem assumes transmissions are in the large alphabet regime. For both jamming models, we find the capacity under four scenarios - reliability without feedback, reliability and secrecy without feedback, reliability with passive feedback, reliability and secrecy with passive feedback. We observe that, in comparison to the non-causal setting, the capacity with a causal adversary is strictly increased for a wide variety of parameter settings and present our intuition through several examples.Comment: 15 page

What Makes Autocracies’ Soft Power Strategies Special? Evidence from Russia and China

The paper problematizes the national soft power strategies of authoritarian states arguing that many of their features stem from those countries’ political regime. In particular, the author focuses on such features as actors involved in soft power policies, the public media’s international and domestic rhetoric, the presence or absence of ideological commitments, strategies’ proactiveness/reactiveness as well as their long- and short-termness. The author presents his argumentation in a fashion similar to what is called theory-building process tracing: first, he shows causal links between an autocratic political regime and each of those features, and then illustrates them with relevant examples taken from case studies and media publications on the soft power strategies of contemporary Russia and China

Extracting causation from millennial-scale climate fluctuations in the last 800 kyr

The detection of cause-effect relationships from the analysis of paleoclimatic records is a crucial step to disentangle the main mechanisms at work in the climate system. Here, we show that the approach based on the generalized Fluctuation-Dissipation Relation, complemented by the analysis of the Transfer Entropy, allows the causal links to be identified between temperature, CO2 concentration and astronomical forcing during the glacial cycles of the last 800 kyr based on Antarctic ice core records. When considering the whole spectrum of time scales, the results of the analysis suggest that temperature drives CO2 concentration, or that are both driven by the common astronomical forcing. However, considering only millennial-scale fluctuations, the results reveal the presence of more complex causal links, indicating that CO2 variations contribute to driving the changes of temperature on such time scales. The results also evidence a slow temporal variability in the strength of the millennial-scale causal links between temperature and CO2 concentration.Comment: 13 pages, 5 figures + Supplemental material (10 pages, 7 figures

A CAUSAL ANALYSIS OF THE DEFENCE-GROWTH RELATIONSHIPS: EVIDENCE FROM THE BALKANS

The causal relationships between military burden and economic growth have attracted considerable interest of academics, scholars and practitioners during the last three decades. This survey is hoping to contribute to the existing pool of literature by investigating the causal links between defence spending and economic growth for three developing Balkan countries (Bulgaria, Romania and Albania) and their mature counterpart in the Balkan Peninsula (Greece) during the period 1988-2009. Empirical results imply that there are no bilateral links between the tested variables for any of the tested countries. However, findings indicate the presence of one-way causal links running from military expenditures to GDP only for Bulgaria and Albania, implying the significant impact of defence burden on growth for these countries. On the other hand, empirical results for Greece and Romania suggest that defence spending and GDP growth are independent, which favours neutrality hypothesis. Nevertheless, it should be mentioned that we would expect to find significant links especially in the case of Greece, due to the fact that the country presents the highest defence expenditures in the Balkan region for the last fifteen years. These contradictory results could be due to different levels of maturity between the tested countries but it could also be attributed to temporary changes of accounting practises (i.e. recording expenses when military material was ordered rather than received, as evidenced in the case of Greece in the late 1990’s by government officials). These accounting changes could be the obstacle in some cases (e.g. Greece) to provide empirical evidence of the links between defence burden and economic growth

Responding to global challenges in food, energy, environment and water: Risks and options assessment for decision-Making

We analyse the threats of global environmental change, as they relate to food security. First, we review three discourses: (i) ‘sustainable intensification’, or the increase of food supplies without compromising food producing inputs, such as soils and water; (ii) the ‘nexus’ that seeks to understand links across food, energy, environment and water systems; and (iii) ‘resilience thinking’ that focuses on how to ensure the critical capacities of food, energy and water systems are maintained in the presence of uncertainties and threats. Second, we build on these discourses to present the causal, risks and options assessment for decision-making process to improve decision-making in the presence of risks. The process provides a structured, but flexible, approach that moves from problem diagnosis to better risk-based decision-making and outcomes by responding to causal risks within and across food, energy, environment and water systems

Understanding causation via correlations and linear response theory

In spite of the (correct) common-wisdom statement correlation does not imply causation, a proper employ of time correlations and of fluctuation-response theory allows to understand the causal relations between the variables of a multi-dimensional linear Markov process. It is shown that the fluctuation-response formalism can be used both to find the direct causal links between the variables of a system and to introduce a degree of causation, cumulative in time, whose physical interpretation is straightforward. Although for generic non-linear dynamics there is no simple exact relationship between correlations and response functions, the described protocol can still give a useful proxy also in presence of weak nonlinear terms

Synergy and redundancy in the Granger causal analysis of dynamical networks

We analyze by means of Granger causality the effect of synergy and redundancy in the inference (from time series data) of the information flow between subsystems of a complex network. Whilst we show that fully conditioned Granger causality is not affected by synergy, the pairwise analysis fails to put in evidence synergetic effects. In cases when the number of samples is low, thus making the fully conditioned approach unfeasible, we show that partially conditioned Granger causality is an effective approach if the set of conditioning variables is properly chosen. We consider here two different strategies (based either on informational content for the candidate driver or on selecting the variables with highest pairwise influences) for partially conditioned Granger causality and show that depending on the data structure either one or the other might be valid. On the other hand, we observe that fully conditioned approaches do not work well in presence of redundancy, thus suggesting the strategy of separating the pairwise links in two subsets: those corresponding to indirect connections of the fully conditioned Granger causality (which should thus be excluded) and links that can be ascribed to redundancy effects and, together with the results from the fully connected approach, provide a better description of the causality pattern in presence of redundancy. We finally apply these methods to two different real datasets. First, analyzing electrophysiological data from an epileptic brain, we show that synergetic effects are dominant just before seizure occurrences. Second, our analysis applied to gene expression time series from HeLa culture shows that the underlying regulatory networks are characterized by both redundancy and synergy

Beyond Covariation: Cues to Causal Structure

Causal induction has two components: learning about the structure of causal models and learning about causal strength and other quantitative parameters. This chapter argues for several interconnected theses. First, people represent causal knowledge qualitatively, in terms of causal structure; quantitative knowledge is derivative. Second, people use a variety of cues to infer causal structure aside from statistical data (e.g. temporal order, intervention, coherence with prior knowledge). Third, once a structural model is hypothesized, subsequent statistical data are used to confirm, refute, or elaborate the model. Fourth, people are limited in the number and complexity of causal models that they can hold in mind to test, but they can separately learn and then integrate simple models, and revise models by adding and removing single links. Finally, current computational models of learning need further development before they can be applied to human learning