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Adapted and extended from an address to the Military Applications Society of INFORMS delivered in Monterey CA on 27 March 2012.

Neurons in the orbitofrontal cortex (OFC) fire in anticipation of and during rewards. Such firing has been suggested to encode reward predictions and to account in some way for the role of this area in adaptive behavior and learning. However, it has also been reported that neural activity in OFC reflects reward prediction errors, which might drive learning directly. Here we tested this question by analyzing the firing of OFC neurons recorded in an odor discrimination task in which rats were trained to sample odor cues and respond left or right on each trial for reward. Neurons were recorded across blocks of trials in which we switched either the number or the flavor of the reward delivered in each well. Previously we have described how neurons in this dataset fired to the predictive cues (Stalnaker et al., 2014); here we focused on the firing in anticipation of and just after delivery of each drop of reward, looking specifically for differences in firing based on whether the reward number or flavor was unexpected or expected. Unlike dopamine neurons recorded in this setting, which exhibited phasic error-like responses after surprising changes in either reward number or reward flavor (Takahashi et al., 2017), OFC neurons showed no such error correlates and instead fired in a way that reflected reward predictions. Copyright 2018 ; This work was supported by funding from NIDA . The opinions expressed in this article are the authors' own and do not reflect the view of the National Institutes of Health, the Department of Health and Human Services, or the United States government. ; https://dx.doi.org/10.1016/j.nlm.2018.01.013

The predictive power of experts, operations analysis, and the value of information are interwoven subjects that are hard to winnow down to an essence. Prediction is a big subject, so I have limited this article to what I know best: the operational and tactical domains of conventional warfare.

The article of record as published may be found at https://digital-commons.usnwc.edu/nwc-review/vol73/iss1/8 ; The predictive power of experts, operations analysis, and the value of information are interwoven subjects that are hard to winnow down to an essence. Prediction is a big subject, so I have limited this article to what I know best: the operational and tactical domains of conventional warfare. First you will read three examples of limitations of predictions when they are formed on the basis of information alone. Next I will demonstrate that even a modest amount of quantitative analysis, even with incomplete information, can help a decision maker execute a military campaign without making explicit predictions about the coming battles or operations. Analysts cannot eliminate wartime surprises, but they can help to avoid the worst mistakes and steer military leaders toward better decisions. I will conclude by advocating what is too rarely done: the comparison of quantitative campaign analysis done before a war with what actually transpired in the war, to show that useful—even critically important—advice can be formulated very quickly to help decision makers. On one hand, intense thinking about the war is necessary; on the other hand, expert judgment alone should be augmented with simple, transparent, timely—even if incomplete—quantitative analysis.

Prediction markets sind generell bekannt als ein Sondertyp der Kapitalmärkte, an welchen der Wert der gehandelten Vermögenswerte durch den Ausgang von unsicheren, zukünftigen Ereignissen bedingt wird. Die Masterarbeit hat sich zum Ziel gesetzt, die berichtenswertesten Anwendungsbefunde in Bezug auf Prediction markets systematisch vorzustellen. Um dies zu verwirklichen wurde folgende Forschungsfrage formuliert: Was sind die berichtenswertesten Anwendungsbereiche für Prediction Markets und welche Verhaltensaspekte der Menschen zeigen sich bei der Verwendung derartiger Prognosemechanismen? Die Masterarbeit untersucht und stellt methodologisch zusammen sowohl die neuesten akademischen Ansätze, als auch die praktischen Implementationen von Prediction Markets mit dem Ziel, diesem wenig systematisierten Wissenschaftsfeld eine gewisse Kontinuität zu verschaffen. Politik, Sport und zahlreiche Anwendungen innerhalb verschiedenen Unternehmen stellen den Kern dieser Masterarbeit dar und liefern folgende Befunde: Im Bereich der Vorhersage politischer Ereignisse liefern Prediction Markets regelmäßig bessere Leistungen als Umfragen und Experten. Im Sportbereich sind Prediction Markets in der Lage, die allgemeine Auffassung über die Prognosefähigkeit der Wettquoten zu widerlegen. Anwendungsbeispiele aus der Praxis liefern glaubwürdige Belege dafür, dass Prediction Markets erfolgreich verstreute Information aggregieren können. Favorite longshot bias, reverse favorite longshot bias, herding bias und optimistic bias sind Verhaltensmuster, die durchgehend festgestellt werden können. Auf der negativen Seite sind das Nichtvorhandensein eines geeigneten Benchmark-Mechanismus, ein experimenteller Charakter und das Fehlen rechtlicher Rahmenbedingungen hervorzuheben. Die fehlende Übereinstimmung über theoretische Grundlagen, weitere konkrete Anwendungen und allgemeine Popularisierung der Thematik drängen sich schließlich als forschungsbedürftige Themen auf. ; Prediction markets are generally being referred to as a special case of asset markets where the value of the traded asset is contingent upon the outcome of some uncertain event at or before some pre-specified point in time. The master thesis aims at systematically presenting the newsworthiest application findings related to prediction markets. In order to succeed in such an endeavor, the following research question finds itself at the heart of the thesis: What are the newsworthiest prediction markets application fields, and which aspects of human behavior become apparent with the use of such forecasting mechanisms? The thesis compiles and methodically examines both the latest academic as well as practical efforts related to the application fields of prediction markets, with the aim of adding continuity to this still somewhat disorganized scientific field. Application fields of politics, sports and various types of corporate applications represent the core of the paper and provide the following findings: the field of politics shows that prediction markets are able to outperform polls and experts on a consistent basis. In the field of sports, prediction market studies refute the general belief that betting lines represent predictions. Numerous corporate applications provide substantial evidence for successful aggregation of scattered information and the use of collective wisdom. Behavioral patterns in the form of the favorite-longshot bias, reverse favorite-longshot bias, herding bias, and optimistic bias are consistently being detected throughout the different application fields. The frequent absence of an appropriate benchmark mechanism, an experimental character, and a deficiency of a legal framework come into the forefront on the negative side. To conclude, the thesis identifies the following topics as requiring further research: fundamental theoretical postulates, further practical implementations, and the popularization of the field. ; Miroslav Tepavac ; Zusammenfassungen in Deutsch und Englisch ; Karl-Franzens-Universität Graz, Masterarbeit, 2018 ; (VLID)2945882

Present work uses four separate methods to arrive at the prediction value by taking the average of the results of these four. Here, the calculations are based on past 32 year history of rainfall in Telangana. The four methods are: (1) The Root Mean Square (RMS) values, (2) the Artificial Neural Network (ANN) method, (3) The Fast Fourier Transform (FFT) method, and the Time Series method. Out of these the first and the last methods involve linear regression hence the results obtained exhibit a linear curve. Here, the prediction can be made about 8 months in advance to give sufficient time for planning to the farmers or hydro-electric power generators, or the governments at different levels.

:- Education loans help students to cover the cost of tuition, books and supplies, and living expenses while in the process of pursuing a degree. Education loans are granted by private banks and by government organizations. This paper isan analysis on student loan data for the interest free education loans granted to students as per the standards and rules of Goa Education Development Corporation(GEDC). The dataset is prepared complying the standards of criteria mentionedby organization. The accuracy of prediction is compared using models like Support Vector Machine(SVM), Random forest(RF), Logistic regression(LR), Decision tree classifier and XG-boost.

Prediction markets - markets used to forecast future events - have been used to accurately forecast the outcome of political contests, sporting events, and, occasionally, economic outcomes. This chapter summarizes the latest research on prediction markets in order to further their utilization by economic forecasters. We show that prediction markets have a number of attractive features: they quickly incorporate new information, are largely efficient, and impervious to manipulation. Moreover, markets generally exhibit lower statistical errors than professional forecasters and polls. Finally, we show how markets can be used to both uncover the economic model behind forecasts, as well as test existing economic models.

Prediction markets - markets used to forecast future events - have been used to accurately forecast the outcome of political contests, sporting events, and, occasionally, economic outcomes. This chapter summarizes the latest research on prediction markets in order to further their utilization by economic forecasters. We show that prediction markets have a number of attractive features: they quickly incorporate new information, are largely efficient, and impervious to manipulation. Moreover, markets generally exhibit lower statistical errors than professional forecasters and polls. Finally, we show how markets can be used to both uncover the economic model behind forecasts, as well as test existing economic models.

When a person moves through an environment he/she will decide where to travel based on their interpretation of the surroundings. This becomes important in search and rescue scenarios and military operations when a person's route is of interest. One solution to predict this route is to model the way people travel. This paper documents the process of developing a prototype path prediction tool using the Python scripting language and ArcMap tools. The general model approach was to create a simulation based on a least cost path analysis restricted by viewshed analysis. While the concept is straightforward, creating the program proved complex due to the management of vector and raster data, and accounting for numerous application possibilities and variable combinations. The result was a durable simulation capable of incorporating a directional bias, observer height, travel speed, and the ability to accommodate a level of randomness.

International audience ; IMproving PRedictions and management of hydrological EXtremes (IMPREX) was a European Union Horizon 2020 project that ran from September 2015 to September 2019. IMPREX aimed to improve society's ability to anticipate and respond to future extreme hydrological events in Europe across a variety of uses in the water-related sectors (flood forecasting, drought risk assessment, agriculture, navigation, hydropower and water supply utilities). Through the engagement with stakeholders and continuous feedback between model outputs and water applications, progress was achieved in better understanding the way hydrological predictions can be useful to (and operationally incorporated into) problem-solving in the water sector. The work and discussions carried out during the project nurtured further reflections toward a common vision for hydrological prediction. In this article, we summarized the main findings of the IMPREX project within a broader overview of hydrological prediction, providing a vision for improving such predictions. In so doing, we first presented a synopsis of hydrological and weather forecasting, with a focus on medium-range to seasonal scales of prediction for increased preparedness. Second, the lessons learned from IMPREX were discussed. The key findings were the gaps highlighted in the global observing system of the hydrological cycle, the degree of accuracy of hydrological models and the techniques of post-processing to correct biases, the origin of seasonal hydrological skill in Europe and user requirements of hydrometeorological forecasts to ensure their appropriate use in decision-making models and practices. Last, a vision for how to improve these forecast systems/products in the future was expounded, including advancing numerical weather and hydrological models, improved earth monitoring and more frequent interaction between forecasters and users to tailor the forecasts to applications. We conclude that if these improvements can be implemented in the coming years, earth ...

International audience ; IMproving PRedictions and management of hydrological EXtremes (IMPREX) was a European Union Horizon 2020 project that ran from September 2015 to September 2019. IMPREX aimed to improve society's ability to anticipate and respond to future extreme hydrological events in Europe across a variety of uses in the water-related sectors (flood forecasting, drought risk assessment, agriculture, navigation, hydropower and water supply utilities). Through the engagement with stakeholders and continuous feedback between model outputs and water applications, progress was achieved in better understanding the way hydrological predictions can be useful to (and operationally incorporated into) problem-solving in the water sector. The work and discussions carried out during the project nurtured further reflections toward a common vision for hydrological prediction. In this article, we summarized the main findings of the IMPREX project within a broader overview of hydrological prediction, providing a vision for improving such predictions. In so doing, we first presented a synopsis of hydrological and weather forecasting, with a focus on medium-range to seasonal scales of prediction for increased preparedness. Second, the lessons learned from IMPREX were discussed. The key findings were the gaps highlighted in the global observing system of the hydrological cycle, the degree of accuracy of hydrological models and the techniques of post-processing to correct biases, the origin of seasonal hydrological skill in Europe and user requirements of hydrometeorological forecasts to ensure their appropriate use in decision-making models and practices. Last, a vision for how to improve these forecast systems/products in the future was expounded, including advancing numerical weather and hydrological models, improved earth monitoring and more frequent interaction between forecasters and users to tailor the forecasts to applications. We conclude that if these improvements can be implemented in the coming years, earth system and hydrological modelling will become more skillful, thus leading to socioeconomic benefits for the citizens of Europe and beyond.

The main purpose of this paper is to review the literature on the empirical methodologies utilized in bankruptcy prediction and the potential predictors of organization failure by emphasis in Small and medium enterprises (SMEs). In developing countries, small-scale businesses are the most important source of new employment opportunities. Governments throughout the world attempt to promote economic progress by focusing on small-scale enterprises. Despite the fact that SMEs play an increasingly important role in providing new products and employment opportunities, SMEs in Thailand have encountered many difficulties, especially financing. SMEs frequently lack access to institutional credit, causing them to encounter high financing costs and facing failure. The economic, financial, and social losses resulting from these failures are significant. Thus, it is valuable to try to develop methods to predict such failures. However, there are only very few studies dealing with failure prediction methods for SMEs compared to those that focus on listed companies context. The studies examined SMEs failure or survival such as Keasey and Watson (1987), Laitinen (1992), Wagner (1994), Huyghebaert and Gaeremynck (2000), Watson (2003), Bilderbeek and Pompe (2005), April (2005), Altman and Sabato (2007) and Fantazzini and Figini (2009b). It is important to note that the studies mentioned earlier were not conducted for the case of Thailand.

Note: This is a background paper raising the problem of central government's obligations in the event that advice should be received by an appropriate Minister that reputable scientists believe there should be consideration of the consequences for public policy of earthquake prediction. It is not intended that it should be presented at the Seminar but the author believes that these general reflections may be of some interest to the participants.

Scientists' responsibility to inform the public about their results may conflict with their responsibility not to cause social disturbance by the communication of these results. A study of the well-known Brady-Spence and Then Browning earthquake predictions illustrates this conflict in the publication of scientifically unwarranted predictions. Furthermore a public policy that considers public sensitivity caused by such publications as an opportunity to promote public awareness is ethically problematic from (i) a refined consequentialist point of view that any means cannot be justified by any ends, and (ii) a rights view according to which individuals should never be treated as a mere means to ends. The Parkfield experiment, the so-called paradigm case of cooperation between natural and social scientists and the political authorities in hazard management and risk communication, is also open to similar ethical criticism. For the people in the Parkfield area were not informed that the whole experiment was based on a contested seismological paradigm.