Bibliografie tematiche / Uncertainty (Information theory)

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Letteratura scientifica selezionata sul tema "Uncertainty (Information theory)"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 28 luglio 2024

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Articoli di riviste sul tema "Uncertainty (Information theory)"

Egghe, Leo. "Uncertainty and information: Foundations of generalized information theory." Journal of the American Society for Information Science and Technology 58, no. 5 (2007): 756–58. http://dx.doi.org/10.1002/asi.20519.

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Shokry, M., and Manar Omran. "The Information System by Uncertainty Theory." Journal of Engineering Research 3, no. 12 (December 1, 2019): 35–40. http://dx.doi.org/10.21608/erjeng.2019.125749.

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Tang, Yongchuan, Yong Chen, and Deyun Zhou. "Measuring Uncertainty in the Negation Evidence for Multi-Source Information Fusion." Entropy 24, no. 11 (November 2, 2022): 1596. http://dx.doi.org/10.3390/e24111596.

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Dempster–Shafer evidence theory is widely used in modeling and reasoning uncertain information in real applications. Recently, a new perspective of modeling uncertain information with the negation of evidence was proposed and has attracted a lot of attention. Both the basic probability assignment (BPA) and the negation of BPA in the evidence theory framework can model and reason uncertain information. However, how to address the uncertainty in the negation information modeled as the negation of BPA is still an open issue. Inspired by the uncertainty measures in Dempster–Shafer evidence theory, a method of measuring the uncertainty in the negation evidence is proposed. The belief entropy named Deng entropy, which has attracted a lot of attention among researchers, is adopted and improved for measuring the uncertainty of negation evidence. The proposed measure is defined based on the negation function of BPA and can quantify the uncertainty of the negation evidence. In addition, an improved method of multi-source information fusion considering uncertainty quantification in the negation evidence with the new measure is proposed. Experimental results on a numerical example and a fault diagnosis problem verify the rationality and effectiveness of the proposed method in measuring and fusing uncertain information.

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Yan, Xi-zu, and Zhong-min Song. "The portfolio models of contained grey profit under uncertainty." Grey Systems: Theory and Application 4, no. 3 (October 28, 2014): 487–94. http://dx.doi.org/10.1108/gs-09-2014-0035.

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Purpose – The purpose of this paper is to establish the portfolio models of contained grey profit under uncertainty, and the results are applied to solve uncertain investment problem. Design/methodology/approach – In investment problems, uncertainties may exist in model parameters and input data. For the investment problems contained grey profit and incomplete information about natural world state, according to the portfolio theory, the grey systems theory and the uncertainty decision theory, the paper puts forward portfolio models and the methods. Findings – Traditional uncertainty decision is researched for incomplete information about natural world state, in reality, investment problems are not only uncertain state information, but income are uncertain. Practical implications – Because the investment problems have been widely used in economic analysis, decision analysis and economic management, examples are provided at the end to verify its feasibility. Originality/value – The paper successfully combined the portfolio theory, the gray system theory and uncertainty decision theory and new uncertainty investment decision-making models and methods are presented.

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Yang, Bin, Dingyi Gan, Yongchuan Tang, and Yan Lei. "Incomplete Information Management Using an Improved Belief Entropy in Dempster-Shafer Evidence Theory." Entropy 22, no. 9 (September 7, 2020): 993. http://dx.doi.org/10.3390/e22090993.

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Quantifying uncertainty is a hot topic for uncertain information processing in the framework of evidence theory, but there is limited research on belief entropy in the open world assumption. In this paper, an uncertainty measurement method that is based on Deng entropy, named Open Deng entropy (ODE), is proposed. In the open world assumption, the frame of discernment (FOD) may be incomplete, and ODE can reasonably and effectively quantify uncertain incomplete information. On the basis of Deng entropy, the ODE adopts the mass value of the empty set, the cardinality of FOD, and the natural constant e to construct a new uncertainty factor for modeling the uncertainty in the FOD. Numerical example shows that, in the closed world assumption, ODE can be degenerated to Deng entropy. An ODE-based information fusion method for sensor data fusion is proposed in uncertain environments. By applying it to the sensor data fusion experiment, the rationality and effectiveness of ODE and its application in uncertain information fusion are verified.

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YOU, CUILIAN. "UNCERTAINTY EXTENSION THEOREM AND PRODUCT UNCERTAIN MEASURE." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 18, no. 02 (April 2010): 197–208. http://dx.doi.org/10.1142/s0218488510006489.

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The additivity axiom of classical measure theory has been challenged by many mathematicians. Different replacements of the additivity correspond with different theory. In uncertainty theory, the additivity is replaced with self-duality and countable subadditivity. Similar to classical measure theory, there are some properties studied in uncertainty theory. Given the measure of each singleton set, the measure can be fully and uniquely determined in the sense of the maximum uncertainty principle. Generally speaking, a product uncertain measure may be defined in many ways, in this paper, a kind of definition is proposed.

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Li, Xihua, Fuqiang Wang, and Xiaohong Chen. "Trapezoidal Intuitionistic Fuzzy Multiattribute Decision Making Method Based on Cumulative Prospect Theory and Dempster-Shafer Theory." Journal of Applied Mathematics 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/279138.

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With respect to decision making problems under uncertainty, a trapezoidal intuitionistic fuzzy multiattribute decision making method based on cumulative prospect theory and Dempster-Shafer theory is developed. The proposed method reflects behavioral characteristics of decision makers, information fuzziness under uncertainty, and uncertain attribute weight information. Firstly, distance measurement and comparison rule of trapezoidal intuitionistic fuzzy numbers are used to derive value function under trapezoidal intuitionistic fuzzy environment. Secondly, the value function and decision weight function are used to calculate prospect values of attributes for each alternative. Then considering uncertain attribute weight information, Dempster-Shafer theory is used to aggregate prospect values for each alternative, and overall prospect values are obtained and thus the alternatives are sorted consequently. Finally, an illustrative example shows the feasibility of the proposed method.

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Lee, Jejung, Abdallah Sayyed-Ahmad, and Dong-Hoon Sheen. "Basin model inversion using information theory and seismic data." GEOPHYSICS 72, no. 6 (November 2007): R99—R108. http://dx.doi.org/10.1190/1.2757738.

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We present a new approach to basin-model inversion in which uncertain parameters in a basin model are estimated using information theory and seismic data. We derive a probability function from information theory to quantify uncertainties in the estimated parameters in basin modeling. The derivation requires two constraints: a normalization and a misfit constraint. The misfit constraint uses seismic information by minimizing the difference between calculated seismograms from a basin simulator and observed seismograms. The information-theory approach emphasizes the relative difference between the so-called expected and calculated minima of the misfit function. The synthetic-model application shows that the greater the difference between the expected and calculated minima of the misfit function, the larger the uncertainty in parameter estimation. Uncertainty analysis provides secondary information on how accurately the inversion process is performed in basin modeling.

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Zhang, Shao Pu, and Tao Feng. "Uncertainty Measure Based on Evidence Theory." Applied Mechanics and Materials 329 (June 2013): 344–48. http://dx.doi.org/10.4028/www.scientific.net/amm.329.344.

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Evidence theory is an effective method to deal with uncertainty information. And uncertainty measure is to reflect the uncertainty of an information system. Thus we want to merge evidence theory with uncertainty method in order to measure the roughness of a rough approximation space. This paper discusses the information fusion and uncertainty measure based on rough set theory. First, we propose a new method of information fusion based on the Bayse function, and define a pair of belief function and plausibility function using the fused mass function in an information system. Then we construct entropy for every decision class to measure the roughness of every decision class, and entropy for decision information system to measure the consistence of decision table.

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He, Rongheng, Hui Li, Bo Zhang, and Mei Chen. "The multi-level warehouse layout problem with uncertain information: uncertainty theory method." International Journal of General Systems 49, no. 5 (June 22, 2020): 497–520. http://dx.doi.org/10.1080/03081079.2020.1778681.

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Tesi sul tema "Uncertainty (Information theory)"

De, Aguinaga José Guillermo. "Uncertainty Assessment of Hydrogeological Models Based on Information Theory." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2011. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-71814.

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There is a great deal of uncertainty in hydrogeological modeling. Overparametrized models increase uncertainty since the information of the observations is distributed through all of the parameters. The present study proposes a new option to reduce this uncertainty. A way to achieve this goal is to select a model which provides good performance with as few calibrated parameters as possible (parsimonious model) and to calibrate it using many sources of information. Akaike’s Information Criterion (AIC), proposed by Hirotugu Akaike in 1973, is a statistic-probabilistic criterion based on the Information Theory, which allows us to select a parsimonious model. AIC formulates the problem of parsimonious model selection as an optimization problem across a set of proposed conceptual models. The AIC assessment is relatively new in groundwater modeling and it presents a challenge to apply it with different sources of observations. In this dissertation, important findings in the application of AIC in hydrogeological modeling using different sources of observations are discussed. AIC is tested on ground-water models using three sets of synthetic data: hydraulic pressure, horizontal hydraulic conductivity, and tracer concentration. In the present study, the impact of the following factors is analyzed: number of observations, types of observations and order of calibrated parameters. These analyses reveal not only that the number of observations determine how complex a model can be but also that its diversity allows for further complexity in the parsimonious model. However, a truly parsimonious model was only achieved when the order of calibrated parameters was properly considered. This means that parameters which provide bigger improvements in model fit should be considered first. The approach to obtain a parsimonious model applying AIC with different types of information was successfully applied to an unbiased lysimeter model using two different types of real data: evapotranspiration and seepage water. With this additional independent model assessment it was possible to underpin the general validity of this AIC approach<br>Hydrogeologische Modellierung ist von erheblicher Unsicherheit geprägt. Überparametrisierte Modelle erhöhen die Unsicherheit, da gemessene Informationen auf alle Parameter verteilt sind. Die vorliegende Arbeit schlägt einen neuen Ansatz vor, um diese Unsicherheit zu reduzieren. Eine Möglichkeit, um dieses Ziel zu erreichen, besteht darin, ein Modell auszuwählen, das ein gutes Ergebnis mit möglichst wenigen Parametern liefert („parsimonious model“), und es zu kalibrieren, indem viele Informationsquellen genutzt werden. Das 1973 von Hirotugu Akaike vorgeschlagene Informationskriterium, bekannt als Akaike-Informationskriterium (engl. Akaike’s Information Criterion; AIC), ist ein statistisches Wahrscheinlichkeitskriterium basierend auf der Informationstheorie, welches die Auswahl eines Modells mit möglichst wenigen Parametern erlaubt. AIC formuliert das Problem der Entscheidung für ein gering parametrisiertes Modell als ein modellübergreifendes Optimierungsproblem. Die Anwendung von AIC in der Grundwassermodellierung ist relativ neu und stellt eine Herausforderung in der Anwendung verschiedener Messquellen dar. In der vorliegenden Dissertation werden maßgebliche Forschungsergebnisse in der Anwendung des AIC in hydrogeologischer Modellierung unter Anwendung unterschiedlicher Messquellen diskutiert. AIC wird an Grundwassermodellen getestet, bei denen drei synthetische Datensätze angewendet werden: Wasserstand, horizontale hydraulische Leitfähigkeit und Tracer-Konzentration. Die vorliegende Arbeit analysiert den Einfluss folgender Faktoren: Anzahl der Messungen, Arten der Messungen und Reihenfolge der kalibrierten Parameter. Diese Analysen machen nicht nur deutlich, dass die Anzahl der gemessenen Parameter die Komplexität eines Modells bestimmt, sondern auch, dass seine Diversität weitere Komplexität für gering parametrisierte Modelle erlaubt. Allerdings konnte ein solches Modell nur erreicht werden, wenn eine bestimmte Reihenfolge der kalibrierten Parameter berücksichtigt wurde. Folglich sollten zuerst jene Parameter in Betracht gezogen werden, die deutliche Verbesserungen in der Modellanpassung liefern. Der Ansatz, ein gering parametrisiertes Modell durch die Anwendung des AIC mit unterschiedlichen Informationsarten zu erhalten, wurde erfolgreich auf einen Lysimeterstandort übertragen. Dabei wurden zwei unterschiedliche reale Messwertarten genutzt: Evapotranspiration und Sickerwasser. Mit Hilfe dieser weiteren, unabhängigen Modellbewertung konnte die Gültigkeit dieses AIC-Ansatzes gezeigt werden

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Dalvi, Nilesh. "Managing uncertainty using probabilistic databases /." Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/6920.

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Aleem, I. "Information, uncertainty and rural credit markets in Pakistan." Thesis, University of Oxford, 1985. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.482927.

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Zhang, Yanyang. "Second-order effects on uncertainty analysis calculations." Master's thesis, Mississippi State : Mississippi State University, 2002. http://library.msstate.edu/etd/show.asp?etd=etd-10292002-122359.

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Noronha, Alston Marian Lee Jejung. "Information theory approach to quantifying parameter uncertainty in groundwater modeling." Diss., UMK access, 2005.

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Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2005.<br>"A thesis in civil engineering." Typescript. Advisor: Jejung Lee. Vita. Title from "catalog record" of the print edition Description based on contents viewed March 12, 2007. Includes bibliographical references (leaves 96-100). Online version of the print edition.

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Adams, Carl. "Dealing with uncertainty within information systems development : applying prospect theory." Thesis, University of Southampton, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.395995.

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Khiripet, Noppadon. "An architecture for intelligent time series prediction with causal information." Diss., Georgia Institute of Technology, 2001. http://hdl.handle.net/1853/13896.

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Lu, An. "Processing and management of uncertain information in vague databases /." View abstract or full-text, 2009. http://library.ust.hk/cgi/db/thesis.pl?CSED%202009%20LU.

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Windholz, Thomas. "Strategies for Handling Spatial Uncertainty due to Discretization." Fogler Library, University of Maine, 2001. http://www.library.umaine.edu/theses/pdf/Windholz.pdf.

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Cvijanovic, Zoran. "A computer laboratory for generalized information theory (COLGIT)." Diss., Online access via UMI:, 2007.

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Thesis (Ph. D.)--State University of New York at Binghamton, Department or Systems Science and Industrial Engineeering, Thomas J. Watson School of Engineering and Applied Science, 2007.<br>Includes bibliographical references.

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Libri sul tema "Uncertainty (Information theory)"

Theory of decision under uncertainty. New York: Cambridge University Press, 2009.

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1940-, Ronen Yigal, ed. Uncertainty analysis. Boca Raton, Fla: CRC Press, 1988.

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Liu, Baoding. Uncertainty Theory: A Branch of Mathematics for Modeling Human Uncertainty. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010.

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Klir, George J. Uncertainty and information: Foundations of generalized information theory. Hoboken, NJ: Wiley-Interscience, 2006.

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1955-, Wierman Mark J., ed. Uncertainty-based information: Elements of generalized information theory. New York: Physica-Verlag, 1998.

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Klir, George J. Uncertainty-based information elements of generalized information theory. Omaha: Creighton University, Center for Research in Fuzzy Mathematics and Computer Science, 1997.

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Katzan, Harry. Managing uncertainty: A pragmatic approach. New York: Van Nostrand Reinhold, 1992.

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Time, uncertainty, and information. Oxford, UK: B. Blackwell, 1989.

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1948-, Bouchon-Meunier B., Yager Ronald R. 1941-, and Zadeh Lotfi Asker, eds. Information, uncertainty, and fusion. Boston: Kluwer Academic Publishers, 2000.

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1948-, Bouchon-Meunier B., ed. Uncertainty and intelligent information systems. Singapore: World Scientific, 2008.

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Capitoli di libri sul tema "Uncertainty (Information theory)"

Sandri, Sandra, and Jacques Wainer. "A Fuzzy Extension to a Temporal Parsimonious Covering Theory." In Information, Uncertainty and Fusion, 147–58. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-5209-3_11.

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Lalmas, Mounia. "Information Retrieval and Dempster-Shafer’s Theory of Evidence." In Applications of Uncertainty Formalisms, 157–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/3-540-49426-x_8.

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Mueller, Dennis C. "Information and Uncertainty: Power, Profits and Morality." In Game Theory, Experience, Rationality, 349–57. Dordrecht: Springer Netherlands, 1998. http://dx.doi.org/10.1007/978-94-017-1654-3_28.

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Huibers, Theo, and Bernd Wondergem. "Towards an Axiomatic Aboutness Theory for Information Retrieval." In Information Retrieval: Uncertainty and Logics, 297–318. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5617-6_12.

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Lodwick, Weldon A., and Phantipa Thipwiwatpotjana. "Generalized Uncertainty Theory: A Language for Information Deficiency." In Flexible and Generalized Uncertainty Optimization, 37–69. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51107-8_2.

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Lodwick, Weldon A., and Luiz L. Salles-Neto. "Generalized Uncertainty Theory: A Language for Information Deficiency." In Flexible and Generalized Uncertainty Optimization, 37–72. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-61180-4_2.

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Gilbert, Hugo. "Sequential Decision Making Under Uncertainty Using Ordinal Preferential Information." In Algorithmic Decision Theory, 573–77. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23114-3_36.

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Dinh, Hai Q. "Some Aspects of Information Theory in Gambling and Economics." In Uncertainty Analysis in Econometrics with Applications, 61–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35443-4_5.

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Solaiman, Basel, and Éloi Bossé. "The Interrelated Uncertainty Modeling Theories." In Possibility Theory for the Design of Information Fusion Systems, 137–64. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32853-5_5.

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Quaeghebeur, Erik. "Introduction to the Theory of Imprecise Probability." In Uncertainty in Engineering, 37–50. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-83640-5_3.

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AbstractThe theory of imprecise probability is a generalization of classical ‘precise’ probability theory that allows modeling imprecision and indecision. This is a practical advantage in situations where a unique precise uncertainty model cannot be justified. This arises, for example, when there is a relatively small amount of data available to learn the uncertainty model or when the model’s structure cannot be defined uniquely. The tools the theory provides make it possible to draw conclusions and make decisions that correctly reflect the limited information or knowledge available for the uncertainty modeling task. This extra expressivity however often implies a higher computational burden. The goal of this chapter is to primarily give you the necessary knowledge to be able to read literature that makes use of the theory of imprecise probability. A secondary goal is to provide the insight needed to use imprecise probabilities in your own research. To achieve the goals, we present the essential concepts and techniques from the theory, as well as give a less in-depth overview of the various specific uncertainty models used. Throughout, examples are used to make things concrete. We build on the assumed basic knowledge of classical probability theory.

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Atti di convegni sul tema "Uncertainty (Information theory)"

Liu Zhiyong, Wang Qingyin, and Li Qi. "Universal information of uncertainty systematic theory." In 2007 IEEE International Conference on Grey Systems and Intelligent Services. IEEE, 2007. http://dx.doi.org/10.1109/gsis.2007.4443324.

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Capellari, Giovanni, Eleni Chatzi, and Stefano Mariani. "PARAMETER IDENTIFIABILITY THROUGH INFORMATION THEORY." In 1st International Conference on Uncertainty Quantification in Computational Sciences and Engineering. Athens: Institute of Structural Analysis and Antiseismic Research School of Civil Engineering National Technical University of Athens (NTUA) Greece, 2017. http://dx.doi.org/10.7712/120217.5376.17179.

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Sudan, Madhu. "Communication amid uncertainty." In 2012 IEEE Information Theory Workshop (ITW 2012). IEEE, 2012. http://dx.doi.org/10.1109/itw.2012.6404647.

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Javidi, Tara, Yonatan Kaspi, and Himanshu Tyagi. "Gaussian estimation under attack uncertainty." In 2015 IEEE Information Theory Workshop (ITW). IEEE, 2015. http://dx.doi.org/10.1109/itw.2015.7133120.

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Sundaresan, Rajesh. "Guessing Under Source Uncertainty With Side Information." In 2006 IEEE International Symposium on Information Theory. IEEE, 2006. http://dx.doi.org/10.1109/isit.2006.262026.

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Steiner, Avi, and Shlomo Shamai Shitz. "Broadcast Approach under Information Bottleneck Capacity Uncertainty." In 2020 Information Theory and Applications Workshop (ITA). IEEE, 2020. http://dx.doi.org/10.1109/ita50056.2020.9244931.

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Pantelidou, Anna, and Anthony Ephremides. "A cross-layer view of wireless multicasting under uncertainty." In 2009 IEEE Information Theory Workshop on Networking and Information Theory (ITW). IEEE, 2009. http://dx.doi.org/10.1109/itwnit.2009.5158552.

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Veresnikov, G. S., L. A. Pankova, V. A. Pronina, and E. A. Trahtengerts. "Using Uncertainty Theory in Optimal Robust Design with Uncertain Parameters." In 2017 IEEE 11th International Conference on Application of Information and Communication Technologies (AICT). IEEE, 2017. http://dx.doi.org/10.1109/icaict.2017.8687062.

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Che, Pak Hou, Mayank Bakshi, Chung Chan, and Sidharth Jaggi. "Reliable deniable communication with channel uncertainty." In 2014 IEEE Information Theory Workshop (ITW). IEEE, 2014. http://dx.doi.org/10.1109/itw.2014.6970786.

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Hachem, Jad, I.-Hsiang Wang, Christina Fragouli, and Suhas Diggavi. "Coding with encoding uncertainty." In 2013 IEEE International Symposium on Information Theory (ISIT). IEEE, 2013. http://dx.doi.org/10.1109/isit.2013.6620231.

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Rapporti di organizzazioni sul tema "Uncertainty (Information theory)"

Sentz, Kari. Uncertainty-Based Information Theory for Heterogenous Data Fusion. Office of Scientific and Technical Information (OSTI), May 2024. http://dx.doi.org/10.2172/2350592.

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J. Lucero, F. Hemez, T. Ross, K.Kline, J.Hundhausen, and T. Tippetts. Uncertainty Quantification of Composite Laminate Damage with the Generalized Information Theory. Office of Scientific and Technical Information (OSTI), May 2006. http://dx.doi.org/10.2172/884685.

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Zio, Enrico, and Nicola Pedroni. Literature review of methods for representing uncertainty. Fondation pour une culture de sécurité industrielle, December 2013. http://dx.doi.org/10.57071/124ure.

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This document provides a critical review of different frameworks for uncertainty analysis, in a risk analysis context: classical probabilistic analysis, imprecise probability (interval analysis), probability bound analysis, evidence theory, and possibility theory. The driver of the critical analysis is the decision-making process and the need to feed it with representative information derived from the risk assessment, to robustly support the decision. Technical details of the different frameworks are exposed only to the extent necessary to analyze and judge how these contribute to the communication of risk and the representation of the associated uncertainties to decision-makers, in the typical settings of high-consequence risk analysis of complex systems with limited knowledge on their behaviour.

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Zilberman, David, Amir Heiman, and B. McWilliams. Economics of Marketing and Diffusion of Agricultural Inputs. United States Department of Agriculture, November 2003. http://dx.doi.org/10.32747/2003.7586469.bard.

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Specific Research Objective. Develop a theory of technology adoption to analyze the role of promotional tools such as advertising, product sampling, demonstrations, money back guarantees and warranties in inducing technological change. Use this theory to develop criteria for assessing the optimal use of marketing activities in launching new agricultural input technologies. Apply the model to analyze existing patterns of marketing budget allocation among promotional tools for various agricultural input industries in the United States and Israel. Background to the Topic. Marketing tools (money-back guarantees [MBG] demonstration, free sampling and advertising) are used extensively to induce the adoption of agricultural inputs, but there is little understanding of their impacts on the diffusion of new technologies. The agricultural economic literature on technology adoption ignores marketing efforts by the private sector, which may result in misleading extension and technology transfer policies. There is a need to integrate marketing and economic approaches in analyzing technology adoption, especially in the area of agricultural inputs. Major Conclusion. Marketing tools play an important role in reducing uncertainties about product performance. They assist potential buyers to learn both about objective features, about a product, and about product fit to the buyer's need. Tools, such as MBGs and demonstration, provide different information about product fit but also require different degrees of cost for the consumer. In some situations they can be complimentary and optimal strategy combines the use of both. In other situations there will be substitution. Sampling is used to reduce the uncertainty about non-durable goods. An optimal level of informational tools declines throughout the life of a product but stays positive at a steady state. Implications. Recognizing the heterogeneity of consumers and the sources of their uncertainty about new technologies is crucial to develop a marketing strategy that will enhance the adoption of innovation. When fit uncertainty is high, allowing an MBG option, as well as a demonstration, may be an optimal strategy to enhance adoption.

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Golan, Amos. Information-Theoretic Modeling and Inference: Theory and Practice. Instats Inc., 2023. http://dx.doi.org/10.61700/f5yykdaqso4bf469.

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Info-metrics is a framework for rational inference based on insufficient and imperfect information. This workshop explores the use and application of Information-Theoretic Modeling and Inference in a variety of scientific fields, emphasizing the use of info-metrics for solving complex, uncertain problems that traditional methods struggle with, and includes practical sessions with software experiments. Aimed at a wide range of researchers, the workshop offers deep theoretical insights and practical skills, enhancing participants' research capabilities and competitiveness in the research landscape.

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Marold, Juliane, Ruth Wagner, Markus Schöbel, and Dietrich Manzey. Decision-making in groups under uncertainty. Fondation pour une culture de sécurité industrielle, February 2012. http://dx.doi.org/10.57071/361udm.

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The authors have studied daily decision-making processes in groups under uncertainty, with an exploratory field study in the medical domain. The work follows the tradition of naturalistic decision-making (NDM) research. It aims to understand how groups in this high reliability context conceptualize and internalize uncertainties, and how they handle them in order to achieve effective decision-making in their everyday activities. Analysis of the survey data shows that uncertainty is thought of in terms of issues and sources (as identified by previous research), but also (possibly a domain-specific observation) as a lack of personal knowledge or skill. Uncertainty is accompanied by emotions of fear and shame. It arises during the diagnostic process, the treatment process and the outcome of medical decision making. The most frequently cited sources of uncertainty are partly lacking information and inadequate understanding owing to instability of information. Descriptions of typical group decisions reveal that the individual himself is a source of uncertainty when a lack of knowledge, skills and expertise is perceived. The group can serve as a source of uncertainty if divergent opinions in the decision making group exist. Three different situations of group decisions are identified: Interdisciplinary regular meetings (e.g. tumor conferences), formal ward meetings and ad hoc consultations. In all healthcare units concerned by the study, only little use of structured decision making procedures and processes is reported. Strategies used to handle uncertainty include attempts to reduce uncertainty by collecting additional information, delaying action until more information is available or by soliciting advice from other physicians. The factors which ultimately determine group decisions are hierarchy (the opinion of more senior medical staff carries more weight than that of junior staff), patients’ interest and professional competence. Important attributes of poor group decisions are the absence of consensus and the use of hierarchy as the predominant decision criterion. On the other hand, decisions judged to be effective are marked by a sufficient information base, a positive discussion culture and consensus. The authors identify four possible obstacles to effective decision making: a steep hierarchy gradient, a poor discussion culture, a strong need for consensus, and insufficient structure and guidance of group decision making processes. A number of intervention techniques which have been shown in other industries to be effective in improving some of these obstacles are presented.

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Kucherova, Hanna, Anastasiia Didenko, Olena Kravets, Yuliia Honcharenko, and Aleksandr Uchitel. Scenario forecasting information transparency of subjects' under uncertainty and development of the knowledge economy. [б. в.], October 2020. http://dx.doi.org/10.31812/123456789/4469.

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Topicality of modeling information transparency is determined by the influence it has on the effectiveness of management decisions made by an economic entity in the context of uncertainty and information asymmetry. It has been found that information transparency is a poorly structured category which acts as a qualitative characteristic of information and at certain levels forms an additional spectrum of properties of the information that has been adequately perceived or processed. As a result of structuring knowledge about the factor environment, a fuzzy cognitive model of information transparency was constructed in the form of a weighted digraph. Structural analysis and scenario forecasting of optimal alternatives of the fuzzy cognitive model made it possible to evaluate the classes of factors, identify their limited relations, establish the centrality of the roles of information transparency and information and communication security in the system built and evaluate their importance when modeling the situation self-development. Information visibility, reliability and availability have been found to have the strongest impact on the system. Taking into account different initial weights of the key factors — information transparency and information and communication security — the study substantiates the strategic ways for economic entities to achieve their goals in the context of uncertainty and information asymmetry, which allows us to use this approach as a tool for strategic management in the information environment.

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Soloviev, V. N., and Y. V. Romanenko. Economic analog of Heisenberg uncertainly principle and financial crisis. ESC "IASA" NTUU "Igor Sikorsky Kyiv Polytechnic Institute", May 2017. http://dx.doi.org/10.31812/0564/2463.

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The Heisenberg uncertainty principle is one of the cornerstones of quantum mechanics. The modern version of the uncertainty principle, deals not with the precision of a measurement and the disturbance it introduces, but with the intrinsic uncertainty any quantum state must possess, regardless of what measurement is performed. Recently, the study of uncertainty relations in general has been a topic of growing interest, specifically in the setting of quantum information and quantum cryptography, where it is fundamental to the security of certain protocols. The aim of this study is to analyze the concepts and fundamental physical constants in terms of achievements of modern theoretical physics, they search for adequate and useful analogues in the socio-economic phenomena and processes, and their possible use in early warning of adverse crisis in financial markets. The instability of global financial systems depending on ordinary and natural disturbances in modern markets and highly undesirable financial crises are the evidence of methodological crisis in modelling, predicting and interpretation of current socio-economic conditions.

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Schultz, Martin, Leslie Campbell, Ramsay Bell, and Phillip Sauser. A study of phased-array ultrasonic testing (PAUT) for detecting, sizing, and characterizing flaws in the welds of existing hydraulic steel structures (HSS). Engineer Research and Development Center (U.S.), July 2024. http://dx.doi.org/10.21079/11681/48750.

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Hydraulic steel structures (HSS) are components of navigation, flood control, and hydropower projects that control or regulate the flow of water. Damage accumulates in HSS as they are operated over time, and they must be inspected periodically. This is often accomplished using nondestructive testing (NDT) techniques. If damage is detected, the structure’s fitness for continued service must be evaluated, which requires information on the location and size of discontinuities. This information can be obtained using ultrasonic testing (UT) techniques. However, there is limited information on the reliability of UT techniques with respect to detecting, sizing, and characterizing flaws in HSS. This study addresses this gap. Round-robin experiments were carried out using phased-array ultrasonic testing (PAUT) to scan weld specimens representing a variety of HSS geometries. The results of the round-robin experiments were analyzed to estimate the probability of detection (POD) and to assess the influence of factors potentially affecting POD. Uncertainty in estimates of flaw length and height were described, and partial safety factors were derived for use in fitness-for-service analyses. These results demonstrate the importance of the technician as a factor influencing the reliability of NDT techniques applied to HSS.

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