Relevant bibliographies by topics / Human behavior

Contents

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

Academic literature on the topic 'Human behavior'

Author: Grafiati
Published: 4 June 2021
Last updated: 21 September 2024

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Journal articles on the topic "Human behavior"

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Tripati, Prof Rk, and Prof Deepak Sharma. "Aggressive Behavior At Home And Human Rights." American Journal of Social Science and Education Innovations 01, no. 03 (October 21, 2019): 11–15. http://dx.doi.org/10.37547/tajssei/volume01issue03-01.

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Nielsen, Fran. "Human behavior." StandardView 4, no. 1 (March 1996): 36–41. http://dx.doi.org/10.1145/230871.230878.

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Johnson, Thomas M. "Human Behavior." Journal of Nervous and Mental Disease 183, no. 6 (June 1995): 418. http://dx.doi.org/10.1097/00005053-199506000-00017.

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Gnanavel, Sundar. "Human Behavior." Indian Journal of Psychological Medicine 37, no. 4 (October 2015): 478–79. http://dx.doi.org/10.1177/0975156420150401.

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Heriyati, Pantri. "Analyzing Factors Affect Human Behavior During Covid-19 Pandemic." Journal of Advanced Research in Dynamical and Control Systems 12, SP8 (July 30, 2020): 73–80. http://dx.doi.org/10.5373/jardcs/v12sp8/20202503.

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D. Wilson, T. "Human Information Behavior." Informing Science: The International Journal of an Emerging Transdiscipline 3 (2000): 049–56. http://dx.doi.org/10.28945/576.

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Silverman, Hirsch Lazaar. "Human Behavior Integrated." Contemporary Psychology: A Journal of Reviews 43, no. 7 (July 1998): 501–2. http://dx.doi.org/10.1037/002579.

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Carr, Thomas H. "Understanding human behavior." Contemporary Psychology: A Journal of Reviews 31, no. 10 (October 1986): 818. http://dx.doi.org/10.1037/024209.

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Loehlin, J. C., L. Willerman, and J. M. Horn. "Human Behavior Genetics." Annual Review of Psychology 39, no. 1 (January 1988): 101–33. http://dx.doi.org/10.1146/annurev.ps.39.020188.000533.

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Einspieler, Christa, Peter B. Marschik, and Heinz F. R. Prechtl. "Human Motor Behavior." Zeitschrift für Psychologie / Journal of Psychology 216, no. 3 (January 2008): 147–53. http://dx.doi.org/10.1027/0044-3409.216.3.147.

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The spontaneous movements of the newborn infant have a long prenatal history. From 8 weeks postmenstrual age onward the fetus moves in distinct motor patterns. There is no period of amorphic and random movements. The patterns are easily recognizable, as all of them can be seen after birth. The human neonate demonstrates a continuum of motor patterns from prenatal to early postnatal life. Around the 3rd month a major transformation of motor and sensory patterns occurs. This makes the infant more fit to meet the requirements of the extra-uterine environment. The developmental course of spontaneous movements during the first 20 weeks postterm age shows the emergence and disappearance of various movement patterns. The so-called general movements deserve special interest as they are in their altered quality a most reliable indicator of brain (dys)function with a specific prediction of later developing cerebral palsy.
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Dissertations / Theses on the topic "Human behavior"

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Roberts, Michael E. "Human collective behavior." [Bloomington, Ind.] : Indiana Unversity, 2008. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3330786.

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Thesis (Ph.D.)--Indiana University, Depts. of Psychological and Brain Sciences and Cognitive Science, 2008.
Title from PDF t.p. (viewed on Jul 22, 2009). Source: Dissertation Abstracts International, Volume: 69-10, Section: B, page: 6448. Advisers: Robert L. Goldstone; Peter M. Todd.
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Koutentakis, Dimitrios. "Modeling human driving behavior." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/129895.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, February, 2020
Cataloged from student-submitted PDF of thesis.
Includes bibliographical references (pages 81-84).
The goal of this thesis paper is to explore models that can predict and anticipate driver behaviors on the road and give probabilities on future actions of neighboring vehicles, while being lightweight enough to be formally verifiable. This thesis starts with looking into related work and doing a short literature review on previous work on driver models. We then talk about the available datasets used to perform such work, different models used (from classic regressions to neural networks) and finally present my approach and my results.
by Dimitrios Koutentakis.
M. Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
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Kalwar, Santosh. "Human behavior on the Internet." Thesis, Lappeenranata University of Technology, 2009. http://www.kalwar.com.np.

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In this thesis, "Human behavior on the Internet", the human anxiety is conceptualized. The following questions have guided the writing of the thesis: How humans behave with the Internet technology? What goes in their mind? What kinds of behaviors are shown while using the Internet? What is the role of the content on the Internet and especially what are the types of anxiety behavior on the Internet? By conceptualization this thesis aims to provide a model for studying whether humans show signs of less or exacerbated anxiety while using the Internet.The empirical part of this thesis was built on new developed model and user study that utilizes that model. For the user study, the target users were divided into two groups based on their skill level. The user study used both qualitative and quantitative research methods. The qualitative research was conducted using interviews and observational analysis. The quantitative research was conducted in three iterations by using questionnaires and surveys.These results suggest that the significance of human on using technology would be integral part of such a study. The study also suggests that Internet has lulled humans with the sense of dependency to greater extent. In particular, the results identified seven main areas of human anxiety. These forms of anxiety require further studies to encompass human anxiety in more detail.
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Poletti, Piero. "Human Behavior in Epidemic Modelling." Doctoral thesis, Università degli studi di Trento, 2010. https://hdl.handle.net/11572/367834.

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Mathematical models represent a powerful tool for investigating the dynamics of human infection diseases, providing useful predictions about the spread of a disease and the effectiveness of possible control measures. One of the central aspects to understand the dynamics of human infection is the heterogeneity in behavioral patters adopted by the host population. Beyond control measures imposed by public authorities, human behavioral changes can be triggered by uncoordinated responses driven by the diffusion of fear in the general population or by the risk perception. In order to assess how and when behavioral changes can affect the spread of an epidemic, spontaneous social distancing - e.g. produced by avoiding crowded environments, using face masks or limiting travels - is investigated. Moreover, in order to assess whether vaccine preventable diseases can be eliminated through not compulsory vaccination programs, vaccination choices are investigated as well. The proposed models are based on an evolutionary game theory framework. Considering dynamical games allows explicitly modeling the coupled dynamics of disease transmission and human behavioral changes. Specifically, the information diffusion is modeled through an imitation process in which the convenience of different behaviors depends on the perceived risk of infection and vaccine side effects. The proposed models allow the investigation of the effects of misperception of risks induced by partial, delayed or incorrect information (either concerning the state of the epidemic or vaccine side effects) as well. The performed investigation highlights that a small reduction in the number of potentially infectious contacts in response to an epidemic and an initial misperception of the risk of infection can remarkably affect the spread of infection. On the other hand, the analysis of vaccination choices showed that concerns about proclaimed risks of vaccine side effects can result in widespread refusal of vaccination which in turn leads to drops in vaccine uptake and suboptimal vaccination coverage.
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Poletti, Piero. "Human Behavior in Epidemic Modelling." Doctoral thesis, University of Trento, 2010. http://eprints-phd.biblio.unitn.it/422/1/tesi.pdf.

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Mathematical models represent a powerful tool for investigating the dynamics of human infection diseases, providing useful predictions about the spread of a disease and the effectiveness of possible control measures. One of the central aspects to understand the dynamics of human infection is the heterogeneity in behavioral patters adopted by the host population. Beyond control measures imposed by public authorities, human behavioral changes can be triggered by uncoordinated responses driven by the diffusion of fear in the general population or by the risk perception. In order to assess how and when behavioral changes can affect the spread of an epidemic, spontaneous social distancing - e.g. produced by avoiding crowded environments, using face masks or limiting travels - is investigated. Moreover, in order to assess whether vaccine preventable diseases can be eliminated through not compulsory vaccination programs, vaccination choices are investigated as well. The proposed models are based on an evolutionary game theory framework. Considering dynamical games allows explicitly modeling the coupled dynamics of disease transmission and human behavioral changes. Specifically, the information diffusion is modeled through an imitation process in which the convenience of different behaviors depends on the perceived risk of infection and vaccine side effects. The proposed models allow the investigation of the effects of misperception of risks induced by partial, delayed or incorrect information (either concerning the state of the epidemic or vaccine side effects) as well. The performed investigation highlights that a small reduction in the number of potentially infectious contacts in response to an epidemic and an initial misperception of the risk of infection can remarkably affect the spread of infection. On the other hand, the analysis of vaccination choices showed that concerns about proclaimed risks of vaccine side effects can result in widespread refusal of vaccination which in turn leads to drops in vaccine uptake and suboptimal vaccination coverage.
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Wang, Wei. "Human Face and Behavior Analysis." Doctoral thesis, Università degli studi di Trento, 2018. https://hdl.handle.net/11572/367945.

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Human face and behavior analysis are very important research topics in the field of computer vision and they have broad applications in our everyday life. For instance, face alignment, face aging, face expression analysis and action recognition have been well studied and applied for security and entertainment. With these face analyzing techniques (e.g., face aging), we could enhance the performance of cross-age face verification system which now has been used for banks and electronic devices to recognize their clients. With the help of action recognition system, we could better summarize the user uploaded videos or generate logs for surveillance videos. This could help us retrieve the videos more accurately and easily. The dictionary learning and neural networks are powerful machine learning models for these research tasks. Initially, we focus on the multi-view action recognition task. First, a class-wise dictionary is pre-trained which encourages the sparse representations of the between-class videos from different views to lie close by. Next, we integrate the classifiers and the dictionary learning model into a unified model to learn the dictionary and classifiers jointly. For face alignment, we frame the standard cascaded face alignment problem as a recurrent process by using a recurrent neural network. Importantly, by combining a convolutional neural network with a recurrent one we alleviate hand-crafted features to learn task-specific features. For human face aging task, it takes as input a single image and automatically outputs a series of aged faces. Since human face aging is a smooth progression, it is more appropriate to age the face by going through smooth transitional states. In this way, the intermediate aged faces between the age groups can be generated. Towards this target, we employ a recurrent neural network. The hidden units in the RFA are connected autoregressively allowing the framework to age the person by referring to the previous aged faces. For smile video generation, one person may smile in different ways (e.g., closing/opening the eyes or mouth). This is a one-to-many image-to-video generation problem, and we introduce a deep neural architecture named conditional multi-mode network (CMM-Net) to approach it. A multi-mode recurrent generator is trained to induce diversity and generate K different sequences of video frames.
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Wang, Wei. "Human Face and Behavior Analysis." Doctoral thesis, University of Trento, 2018. http://eprints-phd.biblio.unitn.it/2925/1/phd_thesis.pdf.

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Human face and behavior analysis are very important research topics in the field of computer vision and they have broad applications in our everyday life. For instance, face alignment, face aging, face expression analysis and action recognition have been well studied and applied for security and entertainment. With these face analyzing techniques (e.g., face aging), we could enhance the performance of cross-age face verification system which now has been used for banks and electronic devices to recognize their clients. With the help of action recognition system, we could better summarize the user uploaded videos or generate logs for surveillance videos. This could help us retrieve the videos more accurately and easily. The dictionary learning and neural networks are powerful machine learning models for these research tasks. Initially, we focus on the multi-view action recognition task. First, a class-wise dictionary is pre-trained which encourages the sparse representations of the between-class videos from different views to lie close by. Next, we integrate the classifiers and the dictionary learning model into a unified model to learn the dictionary and classifiers jointly. For face alignment, we frame the standard cascaded face alignment problem as a recurrent process by using a recurrent neural network. Importantly, by combining a convolutional neural network with a recurrent one we alleviate hand-crafted features to learn task-specific features. For human face aging task, it takes as input a single image and automatically outputs a series of aged faces. Since human face aging is a smooth progression, it is more appropriate to age the face by going through smooth transitional states. In this way, the intermediate aged faces between the age groups can be generated. Towards this target, we employ a recurrent neural network. The hidden units in the RFA are connected autoregressively allowing the framework to age the person by referring to the previous aged faces. For smile video generation, one person may smile in different ways (e.g., closing/opening the eyes or mouth). This is a one-to-many image-to-video generation problem, and we introduce a deep neural architecture named conditional multi-mode network (CMM-Net) to approach it. A multi-mode recurrent generator is trained to induce diversity and generate K different sequences of video frames.
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Ellis, Jon E. Martin Michael W. "Human behavior representation of military teamwork." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2006. http://library.nps.navy.mil/uhtbin/hyperion/06Jun%5FEllis.pdf.

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Thesis (M.S. in Modeling, Virtual Environment and Simulation (MOVES))--Naval Postgraduate School, June 2006.
Thesis Advisor(s): Christian Darken and Jeffrey Crowson. "June 2006." Includes bibliographical references (p. 73-75). Also available in print.
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Wright, James Robert. "Modeling human behavior in strategic settings." Thesis, University of British Columbia, 2016. http://hdl.handle.net/2429/58840.

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Increasingly, electronic interactions between individuals are mediated by specialized algorithms. One might hope to optimize the relevant algorithms for various objectives. An aspect of online platforms that complicates such optimization is that the interactions are often strategic: many agents are involved, all with their own distinct goals and priorities, and the outcomes for each agent depend both on their own actions, and upon the actions of the other agents. My thesis is that human behavior can be predicted effectively in a wide range of strategic settings by a single model that synthesizes known deviations from economic rationality. In particular, I claim that such a model can predict human behavior better than the standard economic models. Economic mechanisms are currently designed under behavioral assumptions (i.e., full rationality) that are known to be unrealistic. A mechanism designed based on a more accurate model of behavior will be more able to achieve its goal. In the first part of the dissertation, we develop increasingly sophisticated data-driven models to predict human behavior in strategic settings. We begin by applying machine learning techniques to compare many existing models from behavioral game theory on a large body of experimental data. We then construct a new family of models called quantal cognitive hierarchy (QCH), which have even better predictive performance than the best of the existing models. We extend this model with a richer notion of nonstrategic behavior that takes into account features such as fairness, optimism, and pessimism, yielding further performance improvements. Finally, we perform some initial explorations into applying techniques from deep learning in order to automatically learn features of strategic settings that influence human behavior. A major motivation for modeling human strategic behavior is to improve the design of practical mechanisms for real-life settings. In the second part of the dissertation, we study an applied strategic setting (peer grading), beginning with an analysis of the question of how to optimally apply teaching assistant resources to incentivize students to grade each others' work accurately. We then report empirical results from using a variant of this system in a real-life undergraduate class.
Science, Faculty of
Computer Science, Department of
Graduate
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Khosla, Aditya. "Predicting human behavior using visual media." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/109001.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 161-173).
The ability to predict human behavior has applications in many domains ranging from advertising to education to medicine. In this thesis, I focus on the use of visual media such as images and videos to predict human behavior. Can we predict what images people remember or forget? Can we predict the type of images people will like? Can we use a photograph of someone to determine their state of mind? These are some of the questions I tackle in this thesis. Through my work, I demonstrate: (1) It is possible to predict with near human-level correlation, the probability with which people will remember images, (2) it is possible to predictably modify the extent to which a face photograph is remembered, (3) it is possible to predict, with a high correlation, the number of views an image will receive even before it is uploaded, (4) it is possible to accurately identify the gaze of people in images, both from the perspective of a device, and third-person. Further, I develop techniques to visualize and understand machine learning algorithms that could help humans better understand themselves through the analysis of algorithms capable of predicting behavior. Overall, I demonstrate that visual media is a rich resource for the prediction of human behavior.
by Aditya Khosla.
Ph. D.
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Books on the topic "Human behavior"

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1946-, Marsh Peter, ed. Human behavior. New York: Marshall Cavendish, 1990.

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Ann, Graham Jean, ed. Human behavior. New York: Marshall Cavendish, 1990.

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1948-, Beaumont J. Graham, ed. Human behavior. New York: Marshall Cavendish, 1990.

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1942-, Wilson Glenn, ed. Human behavior. New York: Marshall Cavendish, 1990.

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M, Wallace Louise, ed. Human behavior. New York: Marshall Cavendish, 1990.

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1946-, Marsh Peter, ed. Human behavior. New York: Marshall Cavendish, 1990.

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Rosalie, Burnett, ed. Human behavior. New York: Marshall Cavendish, 1990.

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M, Breakwell Glynis, ed. Human behavior. New York: Marshall Cavendish, 1990.

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Salah, Albert Ali, Theo Gevers, Nicu Sebe, and Alessandro Vinciarelli, eds. Human Behavior Understanding. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14715-9.

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Salah, Albert Ali, Javier Ruiz-del-Solar, Çetin Meriçli, and Pierre-Yves Oudeyer, eds. Human Behavior Understanding. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34014-7.

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Book chapters on the topic "Human behavior"

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Yılmaz, Ensar. "Human behavior." In Understanding Financial Crises, 169–93. Abingdon, Oxon; New York, NY: Routledge, 2020. —: Routledge, 2020. http://dx.doi.org/10.4324/9781003037828-8.

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Andersen, Michael Moesgaard, and Torben Pedersen. "Human behavior." In Data-Driven Innovation, 6–19. Abingdon, Oxon ; New York, NY : Routledge, 2021.: Routledge, 2021. http://dx.doi.org/10.4324/9781003041702-2.

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Colombo, Jorge A. "On the Human Dimension." In Dominance Behavior, 133–45. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97401-5_12.

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Sreedharan, Sarath, Anagha Kulkarni, and Subbarao Kambhampati. "Legible Behavior." In Explainable Human-AI Interaction, 47–57. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-03767-2_4.

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Figueroa, Adolfo. "Evolutionary Human Behavior." In Economics of the Anthropocene Age, 89–112. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62584-3_3.

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Papelis, Yiannis, and Poornima Madhavan. "Modeling Human Behavior." In Modeling and Simulation Fundamentals, 271–324. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470590621.ch9.

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Spehr, Jens. "Human Behavior Analysis." In Studies in Systems, Decision and Control, 135–59. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11325-8_7.

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Vogler, George P., and David W. Fulker. "Human Behavior Genetics." In Handbook of Multivariate Experimental Psychology, 475–503. Boston, MA: Springer US, 1988. http://dx.doi.org/10.1007/978-1-4613-0893-5_15.

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Schäfer, Achim Th. "Human Primitive Behavior." In Forensic Pathology Reviews, 189–204. Totowa, NJ: Humana Press, 2005. http://dx.doi.org/10.1385/1-59259-872-2:189.

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Koren, Herman. "Understanding Human Behavior." In Management and Supervision for Working Professionals, 129–52. 3rd ed. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9780367812928-6.

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Conference papers on the topic "Human behavior"

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Yao, Ting, and Jingen Liu. "Human Behavior Understanding." In MM '18: ACM Multimedia Conference. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3240508.3241474.

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Kronman, Linda, and Andreas Zingerle. "Suspicious Behavior." In NordiCHI '22: Nordic Human-Computer Interaction Conference. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3546155.3547288.

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Kupryanov, K., and M. G. Gorodnichev. "Recognition of Human Behavior." In 2021 Systems of Signals Generating and Processing in the Field of on Board Communications. IEEE, 2021. http://dx.doi.org/10.1109/ieeeconf51389.2021.9416044.

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Bullock, I. M., and A. M. Dollar. "Classifying human manipulation behavior." In 2011 IEEE 12th International Conference on Rehabilitation Robotics: Reaching Users & the Community (ICORR 2011). IEEE, 2011. http://dx.doi.org/10.1109/icorr.2011.5975408.

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Consolvo, Sunny. "Session details: Human behavior." In UbiComp '14: The 2014 ACM Conference on Ubiquitous Computing. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/3255111.

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Song, Yale, Louis-Philippe Morency, and Randall Davis. "Multimodal human behavior analysis." In the 14th ACM international conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2388676.2388684.

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Doki, Kae, Kohjiro Hashimoto, Shinji Doki, Shigeru Okuma, and Akihiro Torii. "Estimation of next human behavior and its timing for human behavior support." In Vision (ICARCV 2010). IEEE, 2010. http://dx.doi.org/10.1109/icarcv.2010.5707301.

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Rutjes, Heleen, Martijn C. Willemsen, and Wijnand A. IJsselsteijn. "Beyond Behavior." In CHI '19: CHI Conference on Human Factors in Computing Systems. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3290605.3300900.

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Jaouedi, Neziha, Noureddine Boujnah, Oumayma Htiwich, and Med Salim Bouhlel. "Human action recognition to human behavior analysis." In 2016 7th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT). IEEE, 2016. http://dx.doi.org/10.1109/setit.2016.7939877.

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Jaouedi, Neziha, Noureddine Boujnah, Oumayma Htiwich, and Med Salim Bouhlel. "Human action recognition to human behavior analysis." In 2017 International Conference on Information and Digital Technologies (IDT). IEEE, 2017. http://dx.doi.org/10.1109/dt.2017.8012111.

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Reports on the topic "Human behavior"

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Bernard, Michael Lewis, Dereck H. Hart, Stephen J. Verzi, Matthew R. Glickman, Paul R. Wolfenbarger, and Patrick Gordon Xavier. Simulating human behavior for national security human interactions. Office of Scientific and Technical Information (OSTI), January 2007. http://dx.doi.org/10.2172/900422.

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Gilbert, Stanley. Human Behavior in Home Fires. National Institute of Standards and Technology, November 2021. http://dx.doi.org/10.6028/nist.tn.2191.

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Chen, Yan, Peter Cramton, John List, and Axel Ockenfels. Market Design, Human Behavior, and Management. Cambridge, MA: National Bureau of Economic Research, March 2020. http://dx.doi.org/10.3386/w26873.

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Kuligowski, Erica D. Modeling human behavior during building fires. Gaithersburg, MD: National Bureau of Standards, 2008. http://dx.doi.org/10.6028/nist.tn.1619.

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Mendel, Aaron. Effects of Toxoplasmosis on Human Behavior. Ames (Iowa): Iowa State University, January 2020. http://dx.doi.org/10.31274/cc-20240624-605.

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Tammy Cloutier, Tammy Cloutier. How does human behavior affect endangered African painted dog behavior? Experiment, September 2016. http://dx.doi.org/10.18258/7907.

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Kuligowski, Erica D. The process of human behavior in fires. Gaithersburg, MD: National Bureau of Standards, 2009. http://dx.doi.org/10.6028/nist.tn.1632.

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Bier, Asmeret Brooke. Sensitivity analysis techniques for models of human behavior. Office of Scientific and Technical Information (OSTI), September 2010. http://dx.doi.org/10.2172/1008119.

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Kimagai, Toru, and Motoyuki Akamatsu. Human Driving Behavior Prediction Using Dynamic Bayesian Networks. Warrendale, PA: SAE International, May 2005. http://dx.doi.org/10.4271/2005-08-0305.

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Shiller, Robert. Human Behavior and the Efficiency of the Financial System. Cambridge, MA: National Bureau of Economic Research, January 1998. http://dx.doi.org/10.3386/w6375.

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