Relevant bibliographies by topics / Granger causality

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Granger causality'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Granger causality"

1

Seth, Anil. "Granger causality." Scholarpedia 2, no. 7 (2007): 1667. http://dx.doi.org/10.4249/scholarpedia.1667.

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Friston, Karl J., André M. Bastos, Ashwini Oswal, Bernadette van Wijk, Craig Richter, and Vladimir Litvak. "Granger causality revisited." NeuroImage 101 (November 2014): 796–808. http://dx.doi.org/10.1016/j.neuroimage.2014.06.062.

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Malekpour, Sheida, and William A. Sethares. "Conditional Granger causality and partitioned Granger causality: differences and similarities." Biological Cybernetics 109, no. 6 (2015): 627–37. http://dx.doi.org/10.1007/s00422-015-0665-3.

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He, Zonglu, and Koichi Maekawa. "On spurious Granger causality." Economics Letters 73, no. 3 (2001): 307–13. http://dx.doi.org/10.1016/s0165-1765(01)00498-0.

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Al-Sadoon, Majid M. "Testing subspace Granger causality." Econometrics and Statistics 9 (January 2019): 42–61. http://dx.doi.org/10.1016/j.ecosta.2017.08.003.

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Chao, John, Valentina Corradi, and Norman R. Swanson. "OUT-OF-SAMPLE TESTS FOR GRANGER CAUSALITY." Macroeconomic Dynamics 5, no. 4 (2001): 598–620. http://dx.doi.org/10.1017/s1365100501023070.

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Clive W.J. Granger has summarized his personal viewpoint on testing for causality in numerous articles over the past 30 years and has outlined what he considers to be a useful operational version of his original definition of Granger causality, which he notes is partially alluded to in the Ph.D. dissertation of Norbert Wiener. This operational version of Granger causality is based on a comparison of the one-step-ahead predictive ability of competing models. However, Granger concludes his discussion by noting that it is common practice to test for Granger causality using in-sample F-tests. The
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Ahmadi, Salman, Girish N. Nair, and Erik Weyer. "Granger causality from quantized measurements." Automatica 142 (August 2022): 110371. http://dx.doi.org/10.1016/j.automatica.2022.110371.

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Elsner, James B. "Granger causality and Atlantic hurricanes." Tellus A: Dynamic Meteorology and Oceanography 59, no. 4 (2007): 476–85. http://dx.doi.org/10.1111/j.1600-0870.2007.00244.x.

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Tastan, Hüseyin. "Testing for Spectral Granger Causality." Stata Journal: Promoting communications on statistics and Stata 15, no. 4 (2015): 1157–66. http://dx.doi.org/10.1177/1536867x1501500411.

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Angelini, Leonardo, Mario Pellicoro, and Sebastiano Stramaglia. "Granger causality for circular variables." Physics Letters A 373, no. 29 (2009): 2467–70. http://dx.doi.org/10.1016/j.physleta.2009.05.009.

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Dissertations / Theses on the topic "Granger causality"

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AZEVEDO, RONALDO. "GRANGER CAUSALITY IN TIME SERIES." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1991. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=8782@1.

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REDE FERROVIÁRIA FEDERAL SA<br>Neste trabalho fazemos uma revisita à causalidade no sentido de Granger aplicada às Séries Temporais bivariadas no domínio do tempo e da freqüência. Um programa computacional foi escrito usando a linguagem Pascal para, testando casos reais e simulados, construir modelos de causalidade/feedback, que são então analisados no ambiente espectral, com ênfase maior à discussão da coerência e da fase de causalidade.<br>In this work causality in the sense defined by Granger is revisited. Applications to bivariante temporal systems in time domain and
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Zou, Cunlu. "Applications of Granger causality to biological data." Thesis, University of Warwick, 2010. http://wrap.warwick.ac.uk/35694/.

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In computational biology, one often faces the problem of deriving the causal relationship among different elements such as genes, proteins, metabolites, neurons and so on, based upon multi-dimensional temporal data. In literature, there are several well-established reverse-engineering approaches to explore causal relationships in a dynamic network, such as ordinary differential equations (ODE), Bayesian networks, information theory and Granger Causality. To apply the four different approaches to the same problem, a key issue is to choose which approach is used to tackle the data, in particular
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Stokes, Patrick A. "Fundamental problems in Granger causality analysis of neuroscience data." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/97828.

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Thesis: Ph. D., Harvard-MIT Program in Health Sciences and Technology, 2015.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 111-115).<br>Granger causality methods analyze the flow of information between time series. The Geweke measure of Granger causality (GG-causality) has been widely applied in neuroscience because its frequency-domain and conditional forms appear well-suited to highly-multivariate oscillatory data. In this work, I analyze the statistical and structural properties of GG-causality in the context of neuroscience data analysis. 1. I analy
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Nasseef, Md Taufiq. "Measuring directed functional connectivity in mouse fMRI networks using Granger Causality." Doctoral thesis, Università degli studi di Trento, 2015. https://hdl.handle.net/11572/368149.

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Resting-state functional magnetic resonance imaging (rsfMRI) of the mouse brain has revealed the presence of robust functional connectivity networks, including an antero-posterior system reminiscent of the human default network (DMN) and correlations between anterior insular and cingulate cortices recapitulating features of the human “salience network†. However, rsfMRI networks are typically identified using symmetric measurements of correlation that do not provide a description of directional information flow within individual network nodes. Recent progress has allowed the measure of dire
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Nasseef, Md Taufiq. "Measuring directed functional connectivity in mouse fMRI networks using Granger Causality." Doctoral thesis, University of Trento, 2015. http://eprints-phd.biblio.unitn.it/1582/1/Taufiq_thesis_CiMeC_UniTN.pdf.

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Resting-state functional magnetic resonance imaging (rsfMRI) of the mouse brain has revealed the presence of robust functional connectivity networks, including an antero-posterior system reminiscent of the human default network (DMN) and correlations between anterior insular and cingulate cortices recapitulating features of the human “salience network”. However, rsfMRI networks are typically identified using symmetric measurements of correlation that do not provide a description of directional information flow within individual network nodes. Recent progress has allowed the measure of directed
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Salata, Andrea <1989&gt. "Differenze topologiche nei Granger Causality Networks: Esempi dal mercato dei CDS." Master's Degree Thesis, Università Ca' Foscari Venezia, 2015. http://hdl.handle.net/10579/5899.

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La tesi affronta sotto molteplici punti di vista il tema dei network costruiti sulla base di relazioni di causalità di Granger, applicate ai credit default swaps scambiati nel mercato tra banche e assicurazioni e stati. Affronta quindi i temi di cos'è la causalità di Granger e come sia possibile dedurne il network partendo dai dati dei CDS, per farne infine anche un confronto con il network ottenuto utilizzando i dati del fair value CDS per poter individuare le differenze topologiche tra i due metodi di indagine del network.
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Mbeleke, Paul Wuakoh. "The monetary sector in Cameroon money demand and causality analysis." Thesis, University of Salford, 1997. http://usir.salford.ac.uk/26806/.

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This thesis investigates the monetary sector in Cameroon within an open economy framework. Two main hypotheses: money demand and Granger-causality are investigated. The data used are found to be non-stationary. Consequently, the money demand relationship is tested for the null hypothesis that it is spurious or not co-integrated. This is rejected in all the models put forward. The models are estimated and found to exhibit elasticities that are not unusual. Price homogeneity is found to be data incompatible. Income elasticities are generally found to be significantly less than unity suggesting e
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Guo, Yuanxiang. "Chinese wheat price analysis - with application of cointegration and Granger causality test." Thesis, Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/52978.

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Traditional demonstration of price fluctuation in the wheat market, by the theory of supply and demand is not comprehensive enough. With limited understanding of macroeconomic effects on the wheat market, accurate prediction of wheat price is impossible. Given the Chinese self—sustainable food policy, grain imports is a sensitive topic which may incur fierce argument. In this paper, however, I emphasize effect of exchange rate on nominal wheat price. By application of the cointegration theory, CPI shows slight negative correlation with nominal wheat price, yet GDP and population move in the sa
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SUN, FEI. "Analysis to China's Urban and Rural CPI Data." Thesis, Uppsala universitet, Statistiska institutionen, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-175796.

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Murakami, Patricia Nagami. "Causalidade Granger em medidas de risco." Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-14072011-221932/.

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Esse trabalho apresenta um estudo da causalidade de Granger em Risco bivariado aplicado a séries temporais financeiras. Os eventos de risco, no caso de séries financeiras, estão relacionados com a avaliação do Valor em Risco das posições em ativos. Para isso, os modelos CaViaR, que fazem parte do grupo de modelos de Regressão Quantílica, foram utilizado para identificação desses eventos. Foram expostos os conceitos principais envolvidos da modelagem, assim como as definições necessárias para entendê-las. Através da análise da causalide de Granger em risco entre duas séries, podemos investigar
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Books on the topic "Granger causality"

1

Yi, Wen. Granger causality and equilibrium business cycle theory. Federal Reserve Bank of St. Louis, 2005.

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Reuveny, Rafael. International trade, political conflict/cooperation, and Granger causality. Indiana University, Graduate School of Business, 1994.

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Bianchi, Marco. Granger causality in the presence of structural changes. Bank of England, 1995.

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Reuveny, Rafael. Disaggregated bilateral trade political conflict/cooperation and Granger causality. Indiana University, Graduate School of Business, 1996.

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Halbert, White, Engle R. F, and Granger, C. W. J. 1934-, eds. Cointegration, causality, and forecasting: A festschrift in honour of Clive W.J. Granger. Oxford University Press, 1999.

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Lyons, Susanna E. Cointegration and Granger-causality testing of hypotheses on supply-leading and demand-following finance. Cardiff Business School, Financial and Banking Economics Research Group, 1993.

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Spanos, Aris. On the statistical implications of modelling the error term: Common factors, Granger-causality and unit roots. Birkbeck College, Dept. of Economics, 1987.

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Abhyankar, Abhay. Linear and nonlinear Granger causality: Evidence from the FT-SE 100 stock index futures and cash markets. University of Stirling, Dept. of Accountancy and Finance, 1994.

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Philonenko, Alexis. Kant et l'île de la vérité: Néant, chose en soi, sujet transcendantal, causalité, grandeur intensive, temps, substance. Librarie philosophique J. Vrin, 2011.

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(Editor), Robert F. Engle, and Halbert White (Editor), eds. Cointegration, Causality, and Forecasting: A Festschrift in Honour of Clive W.J. Granger. Oxford University Press, USA, 1999.

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Book chapters on the topic "Granger causality"

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Kirchgässner, Gebhard, Jürgen Wolters, and Uwe Hassler. "Granger Causality." In Introduction to Modern Time Series Analysis. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33436-8_3.

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Kirchgässner, Gebhard, and Jürgen Wolters. "Granger Causality." In Introduction to Modern Time Series Analysis. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73291-4_3.

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Deistler, Manfred, and Wolfgang Scherrer. "Granger Causality." In Time Series Models. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13213-1_9.

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Kuersteiner, G. M. "Granger–Sims Causality." In The New Palgrave Dictionary of Economics. Palgrave Macmillan UK, 2008. http://dx.doi.org/10.1057/978-1-349-95121-5_2095-1.

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Kuersteiner, G. M. "Granger–Sims Causality." In The New Palgrave Dictionary of Economics. Palgrave Macmillan UK, 2018. http://dx.doi.org/10.1057/978-1-349-95189-5_2095.

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Kuersteiner, G. M. "Granger-Sims causality." In Macroeconometrics and Time Series Analysis. Palgrave Macmillan UK, 2010. http://dx.doi.org/10.1057/9780230280830_14.

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Michaelides, Panayotis G. "The Granger Causality Equation." In 21 Equations that Shaped the World Economy. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-76140-9_19.

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Koller, Ingrid, Claus H. Carstensen, Wolfgang Wiedermann, and Alexander von Eye. "Granger Meets Rasch: Investigating Granger Causation with Multidimensional Longitudinal Item Response Models." In Statistics and Causality. John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781118947074.ch10.

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Guo, Shuixia, Christophe Ladroue, and Jianfeng Feng. "Granger Causality: Theory and Applications." In Frontiers in Computational and Systems Biology. Springer London, 2010. http://dx.doi.org/10.1007/978-1-84996-196-7_5.

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Li, Xiang, Kaiming Li, Lei Guo, Chulwoo Lim, and Tianming Liu. "Fiber-Centered Granger Causality Analysis." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23629-7_31.

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Conference papers on the topic "Granger causality"

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Zoroddu, Lucas, Pierre Humbert, and Laurent Oudre. "Using Graph Prior to Learn Network Granger Causality." In 2024 32nd European Signal Processing Conference (EUSIPCO). IEEE, 2024. http://dx.doi.org/10.23919/eusipco63174.2024.10715186.

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Prasad, Rajesh, and Ajisola Ayotola Segun. "Forecasting Tax Revenue with Machine Learning and Granger Causality." In 2025 International Conference on Intelligent Control, Computing and Communications (IC3). IEEE, 2025. https://doi.org/10.1109/ic363308.2025.10956548.

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Barac, Sven, Karlo Kovačić, and Dubravko Sabolić. "An Example of Spurious Granger Causality in Electricity Market Data." In 2024 IEEE PES Innovative Smart Grid Technologies Europe (ISGT EUROPE). IEEE, 2024. https://doi.org/10.1109/isgteurope62998.2024.10863511.

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Park, Il, and Jose C. Principe. "Correntropy based Granger causality." In ICASSP 2008 - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/icassp.2008.4518432.

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Jia, Xinxin, Sanqing Hu, Jianhai Zhang, and Wanzeng Kong. "Blockwise Granger causality and blockwise new causality." In 2015 Seventh International Conference on Advanced Computational Intelligence (ICACI). IEEE, 2015. http://dx.doi.org/10.1109/icaci.2015.7184744.

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Chvostekova, Martina. "Granger Causality Inference and Time Reversal." In 2019 12th International Conference on Measurement. IEEE, 2019. http://dx.doi.org/10.23919/measurement47340.2019.8779895.

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Gautama, T., and M. M. Van Hulle. "Surrogate-based test for Granger causality." In 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718). IEEE, 2003. http://dx.doi.org/10.1109/nnsp.2003.1318079.

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Stramaglia, S., L. Angelini, J. M. Cortes, and D. Marinazzo. "Synergy, redundancy and unnormalized Granger causality." In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2015. http://dx.doi.org/10.1109/embc.2015.7319280.

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Stramaglia, S., L. Angelini, M. Pellicoro, and D. Marinazzo. "Nonlinear granger causality for brain connectivity." In 2011 IEEE International Symposium on Medical Measurements and Applications (MeMeA). IEEE, 2011. http://dx.doi.org/10.1109/memea.2011.5966694.

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Amblard, P. O., R. Vincent, O. J. J. Michel, and C. Richard. "Kernelizing Geweke's measures of granger causality." In 2012 IEEE International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2012. http://dx.doi.org/10.1109/mlsp.2012.6349710.

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Reports on the topic "Granger causality"

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Wen, Yi. Granger Causality and Equilibrium Business Cycle Theory. Federal Reserve Bank of St. Louis, 2005. http://dx.doi.org/10.20955/wp.2005.038.

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Buiter, Willem. Granger-Causality and Policy Ineffectiveness: A Rejoinder. National Bureau of Economic Research, 1986. http://dx.doi.org/10.3386/t0061.

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Hafer, R. W., and Scott E. Hein. Federal Government Debt and Inflation: Evidence from Granger Causality Tests. Federal Reserve Bank of St. Louis, 1986. http://dx.doi.org/10.20955/wp.1986.003.

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Mehbub Anwar, Ahm, Nourah Al-Hosain, and Yagyavalk Bhatt. Analyzing the Interplay of Urbanization, Economic Development, and Seaborne Trade A Case of Saudi Arabia. King Abdullah Petroleum Studies and Research Center, 2024. https://doi.org/10.30573/ks--2024-dp62.

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Urbanization is widely recognized as a critical factor influencing economic growth and global trade, yet there is ongoing debate about whether it drives these outcomes or is a consequence of them. To address this, it is essential to determine whether urbanization spurs economic development and trade, or if these processes influence urbanization, or if the relationship is one of mutual causality. This study investigates the interplay between urbanization, economic development, and trade in both the short and the long term. Using data from Saudi Arabia spanning from 1991 to 2022, the research em
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Perdigão, Rui A. P., and Julia Hall. Spatiotemporal Causality and Predictability Beyond Recurrence Collapse in Complex Coevolutionary Systems. Meteoceanics, 2020. http://dx.doi.org/10.46337/201111.

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Causality and Predictability of Complex Systems pose fundamental challenges even under well-defined structural stochastic-dynamic conditions where the laws of motion and system symmetries are known. However, the edifice of complexity can be profoundly transformed by structural-functional coevolution and non-recurrent elusive mechanisms changing the very same invariants of motion that had been taken for granted. This leads to recurrence collapse and memory loss, precluding the ability of traditional stochastic-dynamic and information-theoretic metrics to provide reliable information about the n
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Mbaye, Safiétou, Rémi Kouabenan, and Philippe Sarnin. L'explication naïve et la perception des risques comme des voies pour améliorer les pratiques de REX: des études dans l'industrie chimique et l'industrie nucléaire. Fondation pour une culture de sécurité industrielle, 2009. http://dx.doi.org/10.57071/311rex.

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L’analyse de l’accident soulève de nombreux enjeux dont le poids préfigure également des biais possibles dans l’explication causale et laisse apparaître que l’explication de l’accident peut difficilement être neutre. En l’occurrence, les difficultés rencontrées dans la conduite du REX relèvent en grande partie de conflits qui surviennent lors des analyses d’accidents. Ce document présente quatre études de terrain qui visaient à comprendre l’origine des conflits entre les acteurs du REX lors des analyses d’accidents et à mieux appréhender le rôle de la perception des risques et du climat de séc
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