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Journal articles on the topic 'Causal fields'

Author: Grafiati

Published: 4 May 2024

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1

FABBRI, LUCA. "CAUSAL PROPAGATION FOR ELKO FIELDS." Modern Physics Letters A 25, no. 03 (January 30, 2010): 151–57. http://dx.doi.org/10.1142/s0217732310032408.

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We shall consider the general problem of causal propagation for spinor fields, focus attention in particular on the case constituted by ELKO fields and will show that the problem of causal propagation for ELKO fields is always solvable.

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2

Holland, P. R. "Causal interpretation of Fermi fields." Physics Letters A 128, no. 1-2 (March 1988): 9–18. http://dx.doi.org/10.1016/0375-9601(88)91033-x.

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3

Sverdlov, Roman. "Bosonic Fields in Causal Set Theory." International Journal of Theoretical Physics 60, no. 4 (March 31, 2021): 1481–506. http://dx.doi.org/10.1007/s10773-021-04772-6.

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4

Ambjorn, Jan, Zbigniew Drogosz, Jakub Gizbert-Studnicki, Andrzej Görlich, Jerzy Jurkiewicz, and Dániel Németh. "Scalar fields in causal dynamical triangulations." Classical and Quantum Gravity 38, no. 19 (September 16, 2021): 195030. http://dx.doi.org/10.1088/1361-6382/ac2135.

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5

Pham, Viet Son. "Lévy-driven causal CARMA random fields." Stochastic Processes and their Applications 130, no. 12 (December 2020): 7547–74. http://dx.doi.org/10.1016/j.spa.2020.08.006.

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6

Pinotsis, D. A., R. J. Moran, and K. J. Friston. "Dynamic causal modeling with neural fields." NeuroImage 59, no. 2 (January 2012): 1261–74. http://dx.doi.org/10.1016/j.neuroimage.2011.08.020.

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7

Fabbri, L. "Errata: "CAUSAL PROPAGATION FOR ELKO FIELDS"." Modern Physics Letters A 25, no. 15 (May 20, 2010): 1295. http://dx.doi.org/10.1142/s0217732310033463.

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8

Bergqvist, Göran, and José M. M. Senovilla. "On the causal propagation of fields." Classical and Quantum Gravity 16, no. 10 (August 27, 1999): L55—L61. http://dx.doi.org/10.1088/0264-9381/16/10/101.

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9

Yousefi, S., and N. Kehtarnavaz. "Generating symmetric causal Markov random fields." Electronics Letters 47, no. 22 (2011): 1224. http://dx.doi.org/10.1049/el.2011.1364.

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10

Dappiaggi, Claudio, and Felix Finster. "Linearized fields for causal variational principles: existence theory and causal structure." Methods and Applications of Analysis 27, no. 1 (2020): 1–56. http://dx.doi.org/10.4310/maa.2020.v27.n1.a1.

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11

Hicks, Raymond, and Dustin Tingley. "Causal Mediation Analysis." Stata Journal: Promoting communications on statistics and Stata 11, no. 4 (December 2011): 605–19. http://dx.doi.org/10.1177/1536867x1201100407.

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Estimating the mechanisms that connect explanatory variables with the explained variable, also known as “mediation analysis,” is central to a variety of social-science fields, especially psychology, and increasingly to fields like epidemiology. Recent work on the statistical methodology behind mediation analysis points to limitations in earlier methods. We implement in Stata computational approaches based on recent developments in the statistical methodology of mediation analysis. In particular, we provide functions for the correct calculation of causal mediation effects using several different types of parametric models, as well as the calculation of sensitivity analyses for violations to the key identifying assumption required for interpreting mediation results causally.

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12

Wang, Aiguo, Li Liu, Jiaoyun Yang, and Lian Li. "Causality fields in nonlinear causal effect analysis." Frontiers of Information Technology & Electronic Engineering 23, no. 8 (August 2022): 1277–86. http://dx.doi.org/10.1631/fitee.2200165.

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13

Kämpke, T. "Inferencing the graphs of causal Markov fields." Mathematical and Computer Modelling 25, no. 3 (February 1997): 1–22. http://dx.doi.org/10.1016/s0895-7177(97)00011-3.

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14

Saller, Heinrich. "Realizations of causal manifolds by quantum fields." International Journal of Theoretical Physics 36, no. 12 (December 1997): 2783–826. http://dx.doi.org/10.1007/bf02435710.

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15

Galea, Sandro, and Miguel A. Hernán. "Win-Win: Reconciling Social Epidemiology and Causal Inference." American Journal of Epidemiology 189, no. 3 (October 3, 2019): 167–70. http://dx.doi.org/10.1093/aje/kwz158.

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Abstract Social epidemiology is concerned with the health effects of forces that are “above the skin.” Although causal inference should be a key goal for social epidemiology, social epidemiology and quantitative causal inference have been seemingly at odds over the years. This does not have to be the case and, in fact, both fields stand to gain through a closer engagement of social epidemiology with formal causal inference approaches. We discuss the misconceptions that have led to an uneasy relationship between these 2 fields, propose a way forward that illustrates how the 2 areas can come together to inform causal questions, and discuss the implications of this approach. We argue that quantitative causal inference in social epidemiology is an opportunity to do better science that matters, a win-win for both fields.

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16

Wawrzycki, J. "Bogoliubov’s causal perturbative QED and white noise. Interacting fields." Theoretical and Mathematical Physics 211, no. 3 (June 2022): 775–816. http://dx.doi.org/10.1134/s0040577922060034.

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17

Lindeberg, Tony. "Time-Causal and Time-Recursive Spatio-Temporal Receptive Fields." Journal of Mathematical Imaging and Vision 55, no. 1 (December 7, 2015): 50–88. http://dx.doi.org/10.1007/s10851-015-0613-9.

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18

Frances, Charles. "Causal conformal vector fields, and singularities of twistor spinors." Annals of Global Analysis and Geometry 32, no. 3 (February 24, 2007): 277–95. http://dx.doi.org/10.1007/s10455-007-9060-1.

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19

Brandt, Howard E. "Lorentz-invariant quantum fields in the space-time tangent bundle." International Journal of Mathematics and Mathematical Sciences 2003, no. 24 (2003): 1529–46. http://dx.doi.org/10.1155/s0161171203109076.

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A maximal-acceleration invariant quantum field is defined on the space-time tangent bundle with vanishing eigenvalue when acted on by the Laplace-Beltrami operator of the bundle, and the case is addressed in which the space-time is Minkowskian, and the field is Lorentz invariant. In this case, the field is shown to be automatically regularized at the Planck scale, and particle spectra are cut off at extremely high energies. The microcausality is addressed by calculating the appropriate field commutators; and it is shown that provided the adjoint field is consistently generalized, the necessary commutators are vanishing and the field is microcausal, but that there are Planck-scale modifications of the boundary of the causal domain that are significant for extremely large relative four-velocities between the separated space-time points. For vanishing relative four-velocity, the causal domain is canonical. The geometry of the causal domain indicates that near the Planck scale, causal connectivity may occur between spacelike separated points, and also at larger scales for extremely large relative four-velocities.

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20

Belenchia, Alessio, Dionigi M. T. Benincasa, Marco Letizia, and Stefano Liberati. "On the entanglement entropy of quantum fields in causal sets." Classical and Quantum Gravity 35, no. 7 (February 28, 2018): 074002. http://dx.doi.org/10.1088/1361-6382/aaae27.

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21

Schrader, Robert. "Finite propagation speed and causal free quantum fields on networks." Journal of Physics A: Mathematical and Theoretical 42, no. 49 (November 20, 2009): 495401. http://dx.doi.org/10.1088/1751-8113/42/49/495401.

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22

SENOVILLA, J. M. M. "CAUSAL TENSORS AND RAINICH'S CONDITIONS." International Journal of Modern Physics A 17, no. 20 (August 10, 2002): 2775. http://dx.doi.org/10.1142/s0217751x02012028.

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The mathematical properties of causal tensors,1 which are the tensors satisfying the dominant property (generalization of the dominant energy condition), were presented. The whole class of the so-called superenergy tensors2, including in particular the Bel-Robinson tensor, are causal tensors. Conversely, the superenergy tensors are the basic building blocks of all causal tensors1. Actually, the superenergy tensors of simple forms are intimately related to conformally Lorentz transformations as they leave the null cone invariant.1 This provides, in particular, a wide generalization of the algebraic Rainich conditions1 in arbitrary dimension and for many different fields. See 1,2 and references therein for details.

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23

Saigo, Hayato. "Quantum Fields as Category Algebras." Symmetry 13, no. 9 (September 17, 2021): 1727. http://dx.doi.org/10.3390/sym13091727.

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In the present paper, we propose a new approach to quantum fields in terms of category algebras and states on categories. We define quantum fields and their states as category algebras and states on causal categories with partial involution structures. By utilizing category algebras and states on categories instead of simply considering categories, we can directly integrate relativity as a category theoretic structure and quantumness as a noncommutative probabilistic structure. Conceptual relationships with conventional approaches to quantum fields, including Algebraic Quantum Field Theory (AQFT) and Topological Quantum Field Theory (TQFT), are also be discussed.

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24

Dütsch, Michael. "Slavnov–Taylor Identities from the Causal Point of View." International Journal of Modern Physics A 12, no. 18 (July 20, 1997): 3205–48. http://dx.doi.org/10.1142/s0217751x97001699.

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We continue the investigation of quantized Yang–Mills theories coupled to matter fields in the framework of causal perturbation theory which goes back to Epstein and Glaser. In this approach gauge invariance is expressed by a simple commutator relation for the S matrix and the corresponding gauge transformations are simple transformations of the free fields only. In spite of this simplicity, gauge invariance implies the usual Slavnov–Taylor identities. The main purpose of this paper is to prove the latter statement. Since the Slavnov–Taylor identities are formulated in terms of Green functions, we investigate the agreement of two perturbative definitions of Green functions, namely Epstein and Glaser's definition with the Gell-Mann–Low series.

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25

Ton, Jean-François, Dino Sejdinovic, and Kenji Fukumizu. "Meta Learning for Causal Direction." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 11 (May 18, 2021): 9897–905. http://dx.doi.org/10.1609/aaai.v35i11.17189.

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The inaccessibility of controlled randomized trials due to inherent constraints in many fields of science has been a fundamental issue in causal inference. In this paper, we focus on distinguishing the cause from effect in the bivariate setting under limited observational data. Based on recent developments in meta learning as well as in causal inference, we introduce a novel generative model that allows distinguishing cause and effect in the small data setting. Using a learnt task variable that contains distributional information of each dataset, we propose an end-to-end algorithm that makes use of similar training datasets at test time. We demonstrate our method on various synthetic as well as real-world data and show that it is able to maintain high accuracy in detecting directions across varying dataset sizes.

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26

Wawrzycki, J. "Erratum to: Bogoliubov’s causal perturbative QED and white noise. Interacting fields." Theoretical and Mathematical Physics 212, no. 3 (September 2022): 1312–14. http://dx.doi.org/10.1134/s0040577922090124.

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27

Sverdlov, Roman, and Luca Bombelli. "Dynamics for causal sets with matter fields: a Lagrangian-based approach." Journal of Physics: Conference Series 174 (June 1, 2009): 012019. http://dx.doi.org/10.1088/1742-6596/174/1/012019.

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28

Buchholz, Detlev, and Stephen J. Summers. "String- and brane-localized causal fields in a strongly nonlocal model." Journal of Physics A: Mathematical and Theoretical 40, no. 9 (February 14, 2007): 2147–63. http://dx.doi.org/10.1088/1751-8113/40/9/019.

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29

Qiu, Jiangnan, Liwei Xu, Jie Zhai, and Ling Luo. "Extracting Causal Relations from Emergency Cases Based on Conditional Random Fields." Procedia Computer Science 112 (2017): 1623–32. http://dx.doi.org/10.1016/j.procs.2017.08.252.

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30

Stavraki, G. L. "Discrete operator fields as carriers of causal spacetime structure. Internal symmetry." Theoretical and Mathematical Physics 84, no. 3 (September 1990): 911–20. http://dx.doi.org/10.1007/bf01017349.

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31

Marsan, David, Daniel Schertzer, and Shaun Lovejoy. "Causal space-time multifractal processes: Predictability and forecasting of rain fields." Journal of Geophysical Research: Atmospheres 101, no. D21 (November 1, 1996): 26333–46. http://dx.doi.org/10.1029/96jd01840.

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32

Saller, H. "Causal order and quantization. Quantized fields with and without particle interpretation." Il Nuovo Cimento A 95, no. 4 (October 1986): 358–83. http://dx.doi.org/10.1007/bf02906451.

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33

Plimak, L. I., and S. Stenholm. "Causal signal transmission by quantum fields. III: Coherent response of fermions." Annals of Physics 324, no. 3 (March 2009): 600–636. http://dx.doi.org/10.1016/j.aop.2008.08.006.

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34

Fewster, Christopher J., and Rainer Verch. "Quantum Fields and Local Measurements." Communications in Mathematical Physics 378, no. 2 (July 27, 2020): 851–89. http://dx.doi.org/10.1007/s00220-020-03800-6.

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Abstract The process of quantum measurement is considered in the algebraic framework of quantum field theory on curved spacetimes. Measurements are carried out on one quantum field theory, the “system”, using another, the “probe”. The measurement process involves a dynamical coupling of “system” and “probe” within a bounded spacetime region. The resulting “coupled theory” determines a scattering map on the uncoupled combination of the “system” and “probe” by reference to natural “in” and “out” spacetime regions. No specific interaction is assumed and all constructions are local and covariant. Given any initial state of the probe in the “in” region, the scattering map determines a completely positive map from “probe” observables in the “out” region to “induced system observables”, thus providing a measurement scheme for the latter. It is shown that the induced system observables may be localized in the causal hull of the interaction coupling region and are typically less sharp than the probe observable, but more sharp than the actual measurement on the coupled theory. Post-selected states conditioned on measurement outcomes are obtained using Davies–Lewis instruments that depend on the initial probe state. Composite measurements involving causally ordered coupling regions are also considered. Provided that the scattering map obeys a causal factorization property, the causally ordered composition of the individual instruments coincides with the composite instrument; in particular, the instruments may be combined in either order if the coupling regions are causally disjoint. This is the central consistency property of the proposed framework. The general concepts and results are illustrated by an example in which both “system” and “probe” are quantized linear scalar fields, coupled by a quadratic interaction term with compact spacetime support. System observables induced by simple probe observables are calculated exactly, for sufficiently weak coupling, and compared with first order perturbation theory.

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35

BARCI, D. G., C. G. BOLLINI, L. E. OXMAN, and M. ROCCA. "HIGHER ORDER EQUATIONS AND CONSTITUENT FIELDS." International Journal of Modern Physics A 09, no. 23 (September 20, 1994): 4169–83. http://dx.doi.org/10.1142/s0217751x94001692.

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We consider a simple wave equation of fourth degree in the D'Alembertian operator. It contains the main ingredients of a general Lorentz-invariant higher order equation, namely, a normal bradyonic sector, a tachyonic state and a pair of complex conjugate modes. The propagators are respectively the Feynman causal function and three Wheeler-Green functions (half-advanced and half-retarded). The latter are Lorentz-invariant and consistent with the elimination of tachyons and complex modes from free asymptotic states. We also verify the absence of absorptive parts from convolutions involving Wheeler propagators.

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36

Curiel, Erik, Felix Finster, and José M. Isidro. "Two-dimensional area and matter flux in the theory of causal fermion systems." International Journal of Modern Physics D 29, no. 15 (October 1, 2020): 2050098. http://dx.doi.org/10.1142/s0218271820500984.

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The notions of two-dimensional area, Killing fields and matter flux are introduced in the setting of causal fermion systems. It is shown that for critical points of the causal action, the area change of two-dimensional surfaces under a Killing flow in null directions is proportional to the matter flux through these surfaces. This relation generalizes an equation in classical general relativity due to Ted Jacobson setting of causal fermion systems.

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37

Paunković, Nikola, and Marko Vojinović. "Causal orders, quantum circuits and spacetime: distinguishing between definite and superposed causal orders." Quantum 4 (May 28, 2020): 275. http://dx.doi.org/10.22331/q-2020-05-28-275.

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We study the notion of causal orders for the cases of (classical and quantum) circuits and spacetime events. We show that every circuit can be immersed into a classical spacetime, preserving the compatibility between the two causal structures. Using the process matrix formalism, we analyse the realisations of the quantum switch using 4 and 3 spacetime events in classical spacetimes with fixed causal orders, and the realisation of a gravitational switch with only 2 spacetime events that features superpositions of different gravitational field configurations and their respective causal orders. We show that the current quantum switch experimental implementations do not feature superpositions of causal orders between spacetime events, and that these superpositions can only occur in the context of superposed gravitational fields. We also discuss a recently introduced operational notion of an event, which does allow for superpositions of respective causal orders in flat spacetime quantum switch implementations. We construct two observables that can distinguish between the quantum switch realisations in classical spacetimes, and gravitational switch implementations in superposed spacetimes. Finally, we discuss our results in the light of the modern relational approach to physics.

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38

Tan, Fiona Anting. "To Know the Causes of Things: Text Mining for Causal Relations." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (March 24, 2024): 23425–26. http://dx.doi.org/10.1609/aaai.v38i21.30413.

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Causality expresses the relation between two arguments, one of which represents the cause and the other the effect (or consequence). Causal text mining refers to the extraction and usage of causal information from text. Given an input sequence, we are interested to know if and where causal information occurs. My research is focused on the end-to-end challenges of causal text mining. This involves extracting, representing, and applying causal knowledge from unstructured text. The corresponding research questions are: (1) How to extract causal information from unstructured text effectively? (2) How to represent extracted causal relationships in a graph that is interpretable and useful for some application? (3) How can we capitalize on extracted causal knowledge for downstream tasks? What tasks or fields will benefit from such knowledge? In this paper, I outline past and on-going works, and highlight future research challenges.

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39

BOLLINI, C. G., and L. E. OXMAN. "UNITARITY AND COMPLEX MASS FIELDS." International Journal of Modern Physics A 08, no. 18 (July 20, 1993): 3185–98. http://dx.doi.org/10.1142/s0217751x93001272.

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We consider a field obeying a simple higher order equation with a real mass and two complex conjugate mass parameters. The evaluation of vacuum expectation values leads to the propagators, which are (resp.) a Feynman causal function and two complex conjugate Wheeler–Green functions (half retarded plus half advanced). By means of the computation of convolutions, we are able to show that the total self-energy has an absorptive part which is only due to the real mass. In this way it is shown that this diagram is compatible with unitarity and the elimination of free complex-mass asymptotic states from the set of external legs of the S-matrix. It is also shown that the complex masses act as regulators of ultraviolet divergences.

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40

Bodory, Hugo, Hannah Busshoff, and Michael Lechner. "High Resolution Treatment Effects Estimation: Uncovering Effect Heterogeneities with the Modified Causal Forest." Entropy 24, no. 8 (July 28, 2022): 1039. http://dx.doi.org/10.3390/e24081039.

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There is great demand for inferring causal effect heterogeneity and for open-source statistical software, which is readily available for practitioners. The mcf package is an open-source Python package that implements Modified Causal Forest (mcf), a causal machine learner. We replicate three well-known studies in the fields of epidemiology, medicine, and labor economics to demonstrate that our mcf package produces aggregate treatment effects, which align with previous results, and in addition, provides novel insights on causal effect heterogeneity. For all resolutions of treatment effects estimation, which can be identified, the mcf package provides inference. We conclude that the mcf constitutes a practical and extensive tool for a modern causal heterogeneous effects analysis.

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41

Ohta, Nobuyoshi. "Causal fields and spin-statistics connection for massless particles in higher dimensions." Physical Review D 31, no. 2 (January 15, 1985): 442–45. http://dx.doi.org/10.1103/physrevd.31.442.

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42

Peel, Tyler R., Ziad M. Hafed, Suryadeep Dash, Stephen G. Lomber, and Brian D. Corneil. "A Causal Role for the Cortical Frontal Eye Fields in Microsaccade Deployment." PLOS Biology 14, no. 8 (August 10, 2016): e1002531. http://dx.doi.org/10.1371/journal.pbio.1002531.

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43

Fabbri, Luca. "Non-causal Propagation for Higher-Order Interactions of Torsion with Spinor Fields." International Journal of Theoretical Physics 57, no. 6 (February 20, 2018): 1683–90. http://dx.doi.org/10.1007/s10773-018-3694-6.

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44

Plimak, L. I., and S. Stenholm. "Causal signal transmission by quantum fields. I: Response of the harmonic oscillator." Annals of Physics 323, no. 8 (August 2008): 1963–88. http://dx.doi.org/10.1016/j.aop.2007.11.013.

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45

Hu, Yaowei, Yongkai Wu, Lu Zhang, and Xintao Wu. "A Generative Adversarial Framework for Bounding Confounded Causal Effects." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 13 (May 18, 2021): 12104–12. http://dx.doi.org/10.1609/aaai.v35i13.17437.

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Causal inference from observational data is receiving wide applications in many fields. However, unidentifiable situations, where causal effects cannot be uniquely computed from observational data, pose critical barriers to applying causal inference to complicated real applications. In this paper, we develop a bounding method for estimating the average causal effect (ACE) under unidentifiable situations due to hidden confounding based on Pearl's structural causal model. We propose to parameterize the unknown exogenous random variables and structural equations of a causal model using neural networks and implicit generative models. Then, using an adversarial learning framework, we search the parameter space to explicitly traverse causal models that agree with the given observational distribution, and find those that minimize or maximize the ACE to obtain its lower and upper bounds. The proposed method does not make assumption about the type of structural equations and variables. Experiments using both synthetic and real-world datasets are conducted.

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46

FABBRI, LUCA. "THE SPIN-TORSION COUPLING AND CAUSALITY FOR THE STANDARD MODEL." Modern Physics Letters A 26, no. 27 (September 7, 2011): 2091–100. http://dx.doi.org/10.1142/s0217732311036498.

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47

Jensen, David D. "Improving Causal Inference by Increasing Model Expressiveness." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 17 (May 18, 2021): 15053–57. http://dx.doi.org/10.1609/aaai.v35i17.17767.

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The ability to learn and reason with causal knowledge is a key aspect of intelligent behavior. In contrast to mere statistical association, knowledge of causation enables reasoning about the effects of actions. Causal reasoning is vital for autonomous agents and for a range of applications in science, medicine, business, and government. However, current methods for causal inference are hobbled because they use relatively inexpressive models. Surprisingly, current causal models eschew nearly every major representational innovation common in a range of other fields both inside and outside of computer science, including representation of objects, relationships, time, space, and hierarchy. Even more surprisingly, a range of recent research provides strong evidence that more expressive representations make possible causal inferences that are otherwise impossible and remove key biases that would otherwise afflict more naive inferences. New research on causal inference should target increases in expressiveness to improve accuracy and effectiveness.

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48

Wahl, Jonas, Urmi Ninad, and Jakob Runge. "Vector Causal Inference between Two Groups of Variables." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 10 (June 26, 2023): 12305–12. http://dx.doi.org/10.1609/aaai.v37i10.26450.

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Methods to identify cause-effect relationships currently mostly assume the variables to be scalar random variables. However, in many fields the objects of interest are vectors or groups of scalar variables. We present a new constraint-based non-parametric approach for inferring the causal relationship between two vector-valued random variables from observational data. Our method employs sparsity estimates of directed and undirected graphs and is based on two new principles for groupwise causal reasoning that we justify theoretically in Pearl's graphical model-based causality framework. Our theoretical considerations are complemented by two new causal discovery algorithms for causal interactions between two random vectors which find the correct causal direction reliably in simulations even if interactions are nonlinear. We evaluate our methods empirically and compare them to other state-of-the-art techniques.

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49

Berra-Montiel, Jasel, Alberto Molgado, and César D. Palacios-García. "Causal Poisson bracket via deformation quantization." International Journal of Geometric Methods in Modern Physics 13, no. 07 (July 25, 2016): 1650104. http://dx.doi.org/10.1142/s0219887816501048.

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Starting with the well-defined product of quantum fields at two spacetime points, we explore an associated Poisson structure for classical field theories within the deformation quantization formalism. We realize that the induced star-product is naturally related to the standard Moyal product through an appropriate causal Green’s functions connecting points in the space of classical solutions to the equations of motion. Our results resemble the Peierls–DeWitt bracket that has been analyzed in the multisymplectic context. Once our star-product is defined, we are able to apply the Wigner–Weyl map in order to introduce a generalized version of Wick’s theorem. Finally, we include some examples to explicitly test our method: the real scalar field, the bosonic string and a physically motivated nonlinear particle model. For the field theoretic models, we have encountered causal generalizations of the creation/annihilation relations, and also a causal generalization of the Virasoro algebra for the bosonic string. For the nonlinear particle case, we use the approximate solution in terms of the Green’s function, in order to construct a well-behaved causal bracket.

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50

Jordan, Elizabeth, and Amy Javernick-Will. "Determining Causal Factors of Community Recovery." International Journal of Mass Emergencies & Disasters 32, no. 3 (November 2014): 405–27. http://dx.doi.org/10.1177/028072701403200301.

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Abstract:

As the number and economic impact of disasters rise annually, additional information is required to understand the recovery process. Clearly, any theory of disaster recovery will require the identification of factors that cause vulnerability and resilience, as these can enable or impede successful recovery. The authors performed a content analysis of journal articles from 2000 to 2010 in four disaster-focused journals to identify the factors posited to influence vulnerability and resilience from multiple disciplinary perspectives. Factors were identified in the areas of infrastructural, social, economic, institutional and post-disaster recovery strategy. A panel of experts then validated these factors through a multi-round Delphi survey by rating the level of importance of each factor and providing reasoning on their ratings. All causal factors identified received median ratings of at least important, but not all came to consensus. This paper synthesizes findings related to resilience and vulnerability from many researchers in a variety of fields. This multi-disciplinary perspective may help propel future research to consider the interactions between these multiple factors and empirically examine the link between these factors and community recovery.

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