Deterministic chaos theory and forecasting in Social Sciences. Contribution to the discussion

Forecasting social phenomena may be hampered in many ways. This is because in nature of these phenomena lies strong and multilateral connection with other social phenomena; but not only – also physical and biological (natural) ones. The content of this publication constitutes presentation of chosen problems of forecasting in social sciences. The attention in the article was focused among others on deterministic chaos theory, on the attempt of its implementation to phenomena from the scope (or from borderline) of social sciences: economy, logistics, science about safety etc. Moreover, one of the threads of ponderation was the attempt to consider whether it’s possible to create so-called final theory. The aim of the publication is to signalize possibilities of taking advantage of seemingly exotic for “political scientists” methodology of modeling and explaining phenomena, having its source in exact sciences (in chaos theory) to study social phenomena and processes

Malware in the Future? Forecasting of Analyst Detection of Cyber Events

There have been extensive efforts in government, academia, and industry to anticipate, forecast, and mitigate cyber attacks. A common approach is time-series forecasting of cyber attacks based on data from network telescopes, honeypots, and automated intrusion detection/prevention systems. This research has uncovered key insights such as systematicity in cyber attacks. Here, we propose an alternate perspective of this problem by performing forecasting of attacks that are analyst-detected and -verified occurrences of malware. We call these instances of malware cyber event data. Specifically, our dataset was analyst-detected incidents from a large operational Computer Security Service Provider (CSSP) for the U.S. Department of Defense, which rarely relies only on automated systems. Our data set consists of weekly counts of cyber events over approximately seven years. Since all cyber events were validated by analysts, our dataset is unlikely to have false positives which are often endemic in other sources of data. Further, the higher-quality data could be used for a number for resource allocation, estimation of security resources, and the development of effective risk-management strategies. We used a Bayesian State Space Model for forecasting and found that events one week ahead could be predicted. To quantify bursts, we used a Markov model. Our findings of systematicity in analyst-detected cyber attacks are consistent with previous work using other sources. The advanced information provided by a forecast may help with threat awareness by providing a probable value and range for future cyber events one week ahead. Other potential applications for cyber event forecasting include proactive allocation of resources and capabilities for cyber defense (e.g., analyst staffing and sensor configuration) in CSSPs. Enhanced threat awareness may improve cybersecurity.Comment: Revised version resubmitted to journa

On Shelf Availability: A Literature Review & Conceptual Framework

On-Shelf Availability (OSA) is a key performance indicator for the retail industry, greatly impacting profit and customer loyalty. Strong competition in the industry causes retailers and suppliers to put heavy emphasis on improving performance in an effort to satisfy consumers and keep them coming back to their store or product. Over 40 years of research has been done on OSA and its complement, out-of stock (OOS), however very little progress has been made in improving performance in these areas, leading to the belief that gaps in extant research exist. In order to solve the OOS problem, the key drivers of OOS events must first be identified and then addressed. This paper focuses on identifying the drivers of poor OSA performance through a three step process. First, a comprehensive literature review was performed to identify the drivers of OOS addressed in existing literature. Second, interviews with industry professionals revealed potential drivers of poor OSA performance that have been explored at an industry level. Finally, the two lists were examined against each other and the potential drivers identified in the interviews that had yet to be researched were highlighted. This paper gives strategic direction for future research to help solve the OOS dilemma facing manufacturers and retailers today

Point process modeling of wildfire hazard in Los Angeles County, California

The Burning Index (BI) produced daily by the United States government's National Fire Danger Rating System is commonly used in forecasting the hazard of wildfire activity in the United States. However, recent evaluations have shown the BI to be less effective at predicting wildfires in Los Angeles County, compared to simple point process models incorporating similar meteorological information. Here, we explore the forecasting power of a suite of more complex point process models that use seasonal wildfire trends, daily and lagged weather variables, and historical spatial burn patterns as covariates, and that interpolate the records from different weather stations. Results are compared with models using only the BI. The performance of each model is compared by Akaike Information Criterion (AIC), as well as by the power in predicting wildfires in the historical data set and residual analysis. We find that multiplicative models that directly use weather variables offer substantial improvement in fit compared to models using only the BI, and, in particular, models where a distinct spatial bandwidth parameter is estimated for each weather station appear to offer substantially improved fit.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS401 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Some economic benefits of a synchronous earth observatory satellite

An analysis was made of the economic benefits which might be derived from reduced forecasting errors made possible by data obtained from a synchronous satellite system which can collect earth observation and meteorological data continuously and on demand. User costs directly associated with achieving benefits are included. In the analysis, benefits were evaluated which might be obtained as a result of improved thunderstorm forecasting, frost warning, and grain harvest forecasting capabilities. The anticipated system capabilities were used to arrive at realistic estimates of system performance on which to base the benefit analysis. Emphasis was placed on the benefits which result from system forecasting accuracies. Benefits from improved thunderstorm forecasts are indicated for the construction, air transportation, and agricultural industries. The effects of improved frost warning capability on the citrus crop are determined. The benefits from improved grain forecasting capability are evaluated in terms of both U.S. benefits resulting from domestic grain distribution and U.S. benefits from international grain distribution

PROCESS SIMULATION IN SUPPLY CHAIN USING LOGWARE SOFTWARE

The authors present basis of simulation usage in managerial decisionsupport focusing on the supply chain processes. In the beginning the need for simulationis presented, then advantages and disadvantages of simulation experiments and thesimulation tools juxtaposition. Finally the chances of supply chain process simulationusing Logware software are presented.simulation, supply chain

Community Detection and Growth Potential Prediction from Patent Citation Networks

The scoring of patents is useful for technology management analysis. Therefore, a necessity of developing citation network clustering and prediction of future citations for practical patent scoring arises. In this paper, we propose a community detection method using the Node2vec. And in order to analyze growth potential we compare three ''time series analysis methods'', the Long Short-Term Memory (LSTM), ARIMA model, and Hawkes Process. The results of our experiments, we could find common technical points from those clusters by Node2vec. Furthermore, we found that the prediction accuracy of the ARIMA model was higher than that of other models.Comment: arXiv admin note: text overlap with arXiv:1607.00653 by other author