Dynamics of Information Diffusion and Social Sensing

Statistical inference using social sensors is an area that has witnessed remarkable progress and is relevant in applications including localizing events for targeted advertising, marketing, localization of natural disasters and predicting sentiment of investors in financial markets. This chapter presents a tutorial description of four important aspects of sensing-based information diffusion in social networks from a communications/signal processing perspective. First, diffusion models for information exchange in large scale social networks together with social sensing via social media networks such as Twitter is considered. Second, Bayesian social learning models and risk averse social learning is considered with applications in finance and online reputation systems. Third, the principle of revealed preferences arising in micro-economics theory is used to parse datasets to determine if social sensors are utility maximizers and then determine their utility functions. Finally, the interaction of social sensors with YouTube channel owners is studied using time series analysis methods. All four topics are explained in the context of actual experimental datasets from health networks, social media and psychological experiments. Also, algorithms are given that exploit the above models to infer underlying events based on social sensing. The overview, insights, models and algorithms presented in this chapter stem from recent developments in network science, economics and signal processing. At a deeper level, this chapter considers mean field dynamics of networks, risk averse Bayesian social learning filtering and quickest change detection, data incest in decision making over a directed acyclic graph of social sensors, inverse optimization problems for utility function estimation (revealed preferences) and statistical modeling of interacting social sensors in YouTube social networks.Comment: arXiv admin note: text overlap with arXiv:1405.112

The Unregulables? The Perilous Confluence of Hedge Funds and Credit Derivatives

This Note examines credit derivatives, hedge funds, and the increase in systemic risk that results from the combination of the two. The issues considered include what method of regulation--entity, transaction, or self-regulation--provides the form and amount of disclosure that best addresses the risk that the markets as a whole will be affected by a financial shock. Emphasizing the role of traders and efficient capital markets, this Note proposes that a system of disclosure for derivatives similar to the Trade Reporting and Compliance Engine, or TRACE, system for corporate bonds would prevent rapid repricings that have the potential to shock the financial system

The Unregulables? The Perilous Confluence of Hedge Funds and Credit Derivatives

This Note examines credit derivatives, hedge funds, and the increase in systemic risk that results from the combination of the two. The issues considered include what method of regulation--entity, transaction, or self-regulation--provides the form and amount of disclosure that best addresses the risk that the markets as a whole will be affected by a financial shock. Emphasizing the role of traders and efficient capital markets, this Note proposes that a system of disclosure for derivatives similar to the Trade Reporting and Compliance Engine, or TRACE, system for corporate bonds would prevent rapid repricings that have the potential to shock the financial system

Spatio-temporal correlation of extreme climate indices and river flood discharges

The occurrence of floods is strongly related to specific climatic conditions that favor extreme precipitation events. Although the impact of precipitation and temperature patterns on river flows is a well discussed topic in hydrology, few studies have focused on the rainfall and temperature extremes in their relation with peak discharges. This work presents a comparative analysis of Climate Change Indices (ETCCDI) annual time series, calculated using the NorthWestern Italy Optimal Interpolation (NWIOI) dataset, and annual maximum flows in the Piedmont Region. The Spearman’s rank correlation was used to determine which indices are temporally correlated with peak discharges, allowing to hypothesize the main physical processes involved in the production of floods. The correlation hypothesis was verified with the Spearman’s rank correlation test, considering a Student’s t-distribution with a 5% significance level. Moreover, the influence of climate variability on the tendency of annual maximum discharges was examined by correlating trends of climate indices with trends of the discharge series. These were calculated using the Theil-Sen slope estimator and tested with the Mann-Kendall test at the 5% significance level. The results highlight that while extreme precipitation indices are highly correlated with extreme discharges at the annual timescale, the interannual changes of extreme discharges may be better explained by the interannual changes of the total annual precipitation. This suggests that projections of the annual precipitation may be used as covariates for non-stationary flood frequency analysis

Life in the Time of a Pandemic

It has been confirmed that the number of cases and the death toll of COVID-19 are continuing to rise in many countries around the globe. Governments around the world have been struggling with containing and reducing the socioeconomic impacts of COVID-19; however, their respective responses have not been consistent. Aggressive measures imposed by some governments have resulted in a complete lockdown that has disrupted all facets of life and poses massive health, social, and financial impacts. Other countries, however, are taking a more wait-and-see approach in an attempt to maintain business as usual. Collectively, these challenges reflect a super wicked problem that places immense pressure on economies and societies and requires the strategic management of health systems to avoid overwhelming them—this has been linked to the public mantra of ‘flattening the curve’, which acknowledges that while the pandemic cannot be stopped, its impact can be regulated so that the number of cases at any given time is not beyond the capacity of the health system. Dynamic simulation modelling is a framework that facilitates the understanding/exploring of complex problems, of searching for and finding the best option(s) from all practical solutions where time dynamics are essential. The papers in this book provide research insights into this super wicked problem and case studies exploring the interactions between social, economic, environmental, and health factors through the use of a systems approach