Bacteria-Oil Microaggregates Are an Important Mechanism for Hydrocarbon Degradation in the Marine Water Column

Vast quantities of oil-associated marine snow (MOS) formed in the water column as part of the natural biological response to the Deepwater Horizon drilling accident. Despite the scale of the event, uncertainty remains about the mechanisms controlling MOS formation and its impact on the environment.

A global-scale evaluation of extreme event uncertainty in the <i>eartH2Observe</i> project

Knowledge of how uncertainty propagates through a hydrological land surface modelling sequence is of crucial importance in the identification and characterisation of system weaknesses in the prediction of droughts and floods at global scale. We evaluated the performance of five state-of-the-art global hydrological and land surface models in the context of modelling extreme conditions (drought and flood). Uncertainty was apportioned between the model used (model skill) and also the satellite-based precipitation products used to drive the simulations (forcing data variability) for extreme values of precipitation, surface runoff and evaporation. We found in general that model simulations acted to augment uncertainty rather than reduce it. In percentage terms, the increase in uncertainty was most often less than the magnitude of the input data uncertainty, but of comparable magnitude in many environments. Uncertainty in predictions of evapotranspiration lows (drought) in dry environments was especially high, indicating that these circumstances are a weak point in current modelling system approaches. We also found that high data and model uncertainty points for both ET lows and runoff lows were disproportionately concentrated in the equatorial and southern tropics. Our results are important for highlighting the relative robustness of satellite products in the context of land surface simulations of extreme events and identifying areas where improvements may be made in the consistency of simulation models.

Chasing Private Information

Using over 5,000 trades unequivocally based on nonpublic information about firm fundamentals, we find that asymmetric information proxies display abnormal values on days with informed trading. Volatility and volume are abnormally high, whereas illiquidity is low, in equity and option markets. Daily returns reflect the sign of private signals, but bid-ask spreads are lower when informed investors trade. Market makers’ learning under event uncertainty and limit orders help explain these findings. The cross-section of information duration indicates that traders select days with high uninformed volume. Evidence from the U.S. SEC Whistleblower Reward Program and the FINRA involvement addresses selection concerns. Received January 11, 2017; editorial decision December 17, 2018 by Editor Andrew Karolyi. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

Attribute Selection Based on Constraint Gain and Depth Optimal for a Decision Tree

Uncertainty evaluation based on statistical probabilistic information entropy is a commonly used mechanism for a heuristic method construction of decision tree learning. The entropy kernel potentially links its deviation and decision tree classification performance. This paper presents a decision tree learning algorithm based on constrained gain and depth induction optimization. Firstly, the calculation and analysis of single- and multi-value event uncertainty distributions of information entropy is followed by an enhanced property of single-value event entropy kernel and multi-value event entropy peaks as well as a reciprocal relationship between peak location and the number of possible events. Secondly, this study proposed an estimated method for information entropy whose entropy kernel is replaced with a peak-shift sine function to establish a decision tree learning (CGDT) algorithm on the basis of constraint gain. Finally, by combining branch convergence and fan-out indices under an inductive depth of a decision tree, we built a constraint gained and depth inductive improved decision tree (CGDIDT) learning algorithm. Results show the benefits of the CGDT and CGDIDT algorithms.

Communicating Uncertainty During Public Health Emergency Events: A Systematic Review

To answer the question, What are the best ways to communicate uncertainties to public audiences, at-risk communities, and stakeholders during public health emergency events? we conducted a systematic review of published studies, grey literature, and media reports in English and other United Nations (UN) languages: Arabic, Chinese, French, Russian, and Spanish. Almost 11,500 titles and abstracts were scanned of which 46 data-based primary studies were selected, which were classified into four methodological streams: Quantitative-comparison groups; Quantitative-descriptive survey; Qualitative; and Mixed-method and case-study. Study characteristics (study method, country, emergency type, emergency phase, at-risk population) and study findings (in narrative form) were extracted from individual studies. The findings were synthesized within methodological streams and evaluated for certainty and confidence. These within-method findings were next synthesized across methodological streams to develop an overarching synthesis of findings. The findings showed that country coverage focused on high and middle-income countries in Asia, Europe, North America, and Oceania, and the event most covered was infectious disease followed by flood and earthquake. The findings also showed that uncertainty during public health emergency events is a multi-faceted concept with multiple components (e.g., event occurrence, personal and family safety, recovery efforts). There is universal agreement, with some exceptions, that communication to the public should include explicit information about event uncertainties, and this information must be consistent and presented in an easy to understand format. Additionally, uncertainty related to events requires a distinction between uncertainty information and uncertainty experience. At-risk populations experience event uncertainty in the context of many other uncertainties they are already experiencing in their lives due to poverty. Experts, policymakers, healthcare workers, and other stakeholders experience event uncertainty and misunderstand some uncertainty information (e.g., event probabilities) similar to the public. Media professionals provide event coverage under conditions of contradictory and inconsistent event information that can heighten uncertainty experience for all.

Estimating a Structural Model of Herd Behavior in Financial Markets

We develop a new methodology to estimate herd behavior in financial markets. We build a model of informational herding that can be estimated with financial transaction data. In the model, rational herding arises because of information-event uncertainty. We estimate the model using data on a NYSE stock (Ashland Inc.) during 1995. Herding occurs often and is particularly pervasive on some days. On average, the proportion of herd buyers is 2 percent; that of herd sellers is 4 percent. Herding also causes important informational inefficiencies in the market, amounting, on average, to 4 percent of the asset's expected value. (JEL C58, D82, D83, G12, G14)

Efficiency of Price Discovery during Nighttime Trading Session : Evidence from KOSPI200 Global Futures

Trading of KOSPI 200 futures on CME Globex platform, which was launched in November 2009, starts at 18:00 and closes at 05:00 in the next morning. This paper examines how price of KOSPI200 Global futures is discovered during nighttime trading session by using tick data. The overall results of this study can be summarized as follows; First, we find that the weighted price contribution (WPC) exhibits asymmetric ‘W’-shaped curve during session. This finding is interpreted as that information is consequently transmitted from Globex and NYSE with ‘U’-shaped curve of intradaily price discovery to KOSPI 200 Global futures. Meanwhile, the weighted volume contribution (WVC) also shows ‘W’-shaped curve but weighted price contribution per volume contribution (WPCV) indicates asymmetric ‘U’-shaped curve. This finding that a trade is more (less) informative when trading intensity is higher (lower) provides evidence of partially supporting the “Event Uncertainty Hypothesis” over “Hot Potato Hypothesis”. Second, the price change of closing to opening time significantly contributes to price change during the close-to-close time span. This result explains information during regular daytime trading of KOSPI200 futures is efficiently incorporated in opening price of nighttime session. Third, nighttime traders of KOSPI200 futures recognize volatility of US stock market as more valuable information than the price of futures on CME Globex.