Department of Sociology
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Computational Modeling of Complex Socio-Technical Systems
Computational Modeling of Complex Socio‐Technical Systems 08‐810, 08‐621 Syllabus Fall 2016 12 Course Units Prof. Kathleen M. Carley E‐Mail: [email protected] Phone: x8‐6016 Office: Wean 5130 Office Hours: by appointment T.A.: Geoffrey Morgan Lectures: Monday, Wednesday 3:30 PM – 5:20 PM **There will be no class scheduled for Labor Day September 5th **There will be no classes scheduled on Wednesday November 23rd for the Thanksgiving Holiday **Recitation sections will be organized as needed for specific technologies – construct, system dynamics, and abms – times TBA and this area optional All course information is available on‐line via CMU Blackboard: http://www.cmu.edu/blackboard For a taste of what the course can be like: A fun little explainer and tutorial on Schelling's Dynamic Segregation Model, with a tie in for the need for awareness about diversity. http://ncase.me/polygons/ DESCRIPTION: We live and work in complex adaptive and evolving socio‐technical systems. These systems may be complex for a variety of reasons. For example, they may be complex because there is a need to coordinate many groups, because humans are interacting with technology, because there are non‐ routine or very knowledge intensive tasks. At the heart of this complexity is a set of adaptive agents who are connected or linked to other agents forming a network and who are constrained or enabled by the world they inhabit. Computational modeling can be used to help analyze, reason about, predict the behavior of, and possibly control such complex systems of "networked" agents. -
Dynamic Social Network Analysis: Present Roots and Future Fruits
Dynamic Social Network Analysis: Present Roots and Future Fruits Ms. Nancy K Hayden Project Leader Defense Threat Reduction Agency Advanced Systems and Concepts Office Stephen P. Borgatti, Ronald L. Breiger, Peter Brooks, George B. Davis, David S. Dornisch, Jeffrey Johnson, Mark Mizruchi, Elizabeth Warner July 2009 DEFENSE THREAT REDUCTION AGENCY •ADVANCED SYSTEMS AND CONCEPTS OFFICE REPORT NUMBER ASCO 2009 009 The mission of the Defense Threat Reduction Agency (DTRA) is to safeguard America and its allies from weapons of mass destruction (chemical, biological, radiological, nuclear, and high explosives) by providing capabilities to reduce, eliminate, and counter the threat, and mitigate its effects. The Advanced Systems and Concepts Office (ASCO) supports this mission by providing long-term rolling horizon perspectives to help DTRA leadership identify, plan, and persuasively communicate what is needed in the near term to achieve the longer-term goals inherent in the agency’s mission. ASCO also emphasizes the identification, integration, and further development of leading strategic thinking and analysis on the most intractable problems related to combating weapons of mass destruction. For further information on this project, or on ASCO’s broader research program, please contact: Defense Threat Reduction Agency Advanced Systems and Concepts Office 8725 John J. Kingman Road Ft. Belvoir, VA 22060-6201 [email protected] Or, visit our website: http://www.dtra.mil/asco/ascoweb/index.htm Dynamic Social Network Analysis: Present Roots and Future Fruits Ms. Nancy K. Hayden Project Leader Defense Threat Reduction Agency Advanced Systems and Concepts Office and Stephen P. Borgatti, Ronald L. Breiger, Peter Brooks, George B. Davis, David S. -
Jump Bifurcation and Hysteresis in an Infinite-Dimensional Dynamical System of Coupled Spins*
SIAM J. APPL. MATH. (C) 1990 Society for Industrial and Applied Mathematics Vol. 50, No. 1, pp. 108-124, February 1990 008 JUMP BIFURCATION AND HYSTERESIS IN AN INFINITE-DIMENSIONAL DYNAMICAL SYSTEM OF COUPLED SPINS* RENATO E. MIROLLOt AND STEVEN H. STROGATZ: Abstract. This paper studies an infinite-dimensional dynamical system that models a collection of coupled spins in a random magnetic field. A state of the system is given by a self-map of the unit circle. Critical states for the dynamical system correspond to equilibrium configurations of the spins. Exact solutions are obtained for all the critical states and their stability is characterized by analyzing the second variation of the system's potential function at those states. It is proven rigorously that the system exhibits a jump bifurcation and hysteresis as the coupling between spins is varied. Key words, bifurcation, dynamical system, phase transition, spin system AMS(MOS) subject classifications. 58F14, 82A57, 34F05 1. Introduction. There is currently a great deal of interest in the collective behavior of dynamical systems with many interacting subunits [2]. Such systems are very difficult to analyze rigorously unless some simplifying assumptions are made. "Phase models," in which each subunit is characterized by a single phase angle Oi, provide an analytically tractable class of many-body systems. Phase models have been used in a wide variety of scientific contexts, including charge-density wave transport [8], [9], [23], [24], magnetic spin systems [3], [26], oscillating chemical reactions 15], 18], [28], networks of biological oscillators [4], [7], [14], [27], [28], and arrays of Josephson junctions [11], [25]. -
Learning Within and Among Organizations
LEARNING WITHIN AND AMONG ORGANIZATIONS Kathleen M. Carley Social and Decision Sciences and H. J. Heinz III School of Policy and Management Carnegie Mellon University ACKNOWLEDGMENT This work was supported in part by the Office of Naval Research (ONR), United States Navy Grant No. N00014-97-1-0037 and by the National Science Foundation under Grant No. IRI9633 662. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Office of Naval Research, the National Science Foundation, or the U.S. government. Reference: Kathleen M. Carley, 1999, “Learning Within and Among Organizations.” Ch. 1 in Philip Anderson, Joel Baum and Anne Miner (Eds.) Advances in Strategic Management: Population-Level Learning and Industry Change, Vol. 16. Elsevier Science Ltd. Pp. 33-56. - 1 - ABSTRACT Change is readily seen both within organizations and within populations of organizations. Such change has been characterized as organization or population level learning or evolution. Underlying such change, is change at the individual human and social network level. Herein, it is asked, how does the way in which individuals learn and the way in which networks evolve reflect itself in organizational and population level learning. What changes should emerge at the organization and population level due to learning, information diffusion, and network change at the individual level? Herein it is argued that organization and population level phenomena, such as performance improvements, mis-learning, and shakeouts emerge from the on-going processes of change at the individual level. Looking at learning and information diffusion enables the organizational theorist to link micro and macro level organizational phenomena. -
Intra-Organizational Complexity and Computation
Intra-organizational Complexity and Computation Kathleen M. Carley Social and Decision Sciences H. J. Heinz III School of Policy and Management Engineering and Public Policy Carnegie Mellon University December 2000 Running Head: Intra-organizational Complexity and Computation Direct all correspondence to: Prof. Kathleen M. Carley Dept. of Social and Decision Sciences Carnegie Mellon University Pittsburgh, PA 15213 Email: [email protected] Tel: 1-412-268-3225 Fax: 1-412-268-6938 This work was supported in part by the Office of Naval Research (ONR), United States Navy Grant No. N00014-97-1-0037 and by the National Science Foundation under Grant No. IRI9633 662. The views and conclusions contained in this document are those of the author and should not be interpreted as representing the official policies, either expressed or implied, of the Office of Naval Research, the National Science Foundation, or the U.S. government. The author thanks the following people for their comments on this and related works: Carter Butts, Ju-Sung Lee, Bill McKelvey, Benoit Morel and Ranga Ramanujam. To be published in Companion to Organizations, Joel C. Baum (Ed.), Oxford UK: Blackwell, July 2001. Intra-organizational Complexity and Computation Organizations are complex systems. They are also information processing systems comprised of a large number of agents such as human beings. Combining these perspectives and recognizing the essential non-linear dynamics that are at work leads to the standard non-linear multi-agent system conclusions such as: history matters, organizational behavior and form is path dependent, complex behavior emerges from individual interaction, and change is inevitable. -
Dynamic Network Analysis (Spring 2020) Course 17-801, 17‐685, 19‐640 Instructor Prof
Dynamic Network Analysis (Spring 2020) Course 17-801, 17-685, 19-640 Instructor Prof. Kathleen M Carley Phone: x86016 Office: Wean 5130, ISR E‐Mail: [email protected] Office Hours: by appointment; contact Sienna Watkins for scheduling [email protected] Teaching Assistant Mihovil Bartulovic, [email protected], Wean 4211 Introduction Who knows who? Who knows what? Who communicates with whom? Who is influential? How do ideas, diseases, and technologies propagate through groups? How do social media, social, knowledge, and technology networks differ? How do these networks evolve? How do network constrain and enable behavior? How can a network be compromised or made resilient? What are network cascades? Such questions can be addressed using Network Science. Network Science, a.k.a. social network analysis, link analysis, geo‐network analysis, and dynamic network analysis is a fast‐growing interdisciplinary field aimed at understanding simple & high dimensional networks, from both a static and a dynamic perspective. Across an unlimited application space, graph theoretic, statistical, & simulation methodologies are used to reason about complex systems as networks. An interdisciplinary perspective on network science is provided, with an emphasis on high‐dimensional dynamic data. The fundamentals of network science, methods, theories, metrics & confidence estimation, constraints on data collection & bias, and key research findings & challenges are examined. Illustrative networks discussed include social media based (e.g., twitter), disaster response, organizational, semantic, political elite, crises, terror, & P2P networks. Critical procedures covered include: basic centralities and metrics, group and community detection, link inference, network change detection, comparative analytics, and big data techniques. Applications from business, science, art, medicine, forensics, social media & numerous other areas are explored. -
Question-And-Answer-Physics-Today
Questions and answers with Steven Strogatz An applied mathematician notes that for many physics topics, including general relativity and climate science, the whole is often not equal to the sum of the parts. By Jeremy N. A. Matthews April 2015 Bookends: Questions and answers with Steven Strogatz Questions and answers with Peter Pesic Questions and answers with Marcelo Gleiser Questions and answers with Amit Hagar Questions and answers with A. Douglas Stone Steven Strogatz is the Jacob Schurman Professor of Applied Mathematics at Cornell University in Ithaca, New York. He conducts research on dynamical systems in physics, biology, and social science. His 1998 Nature article “Collective dynamics of ‘small-world’ networks” is considered a seminal contribution to complexity research. Strogatz is also an active communicator of mathematics and related topics to the public. He frequently appears on public radio and writes for The New Yorker and other publications. A 2015 recipient of the Lewis Thomas Prize for Writing About Science, he has written books that include Sync: How Order Emerges from Chaos in the Universe, Nature, and Daily Life (Hyperion, 2003) and The Joy of x: A Guided Tour of Math, from One to Infinity (Houghton Mifflin Harcourt, 2012). Strogatz recently published the second edition of a text he wrote more than two decades ago. The new edition of Nonlinear Dynamics and Chaos: With Applications to Physics, Biology, Chemistry, and Engineering (Westview Press, 2015) contains new exercises on such topics as protein dynamics and superconductivity and covers applications in such emerging fields as sociophysics and systems biology. Physics Today reviewed the book in the April issue. -
THE NETWORK CITY Paul Craven Barry Wellman Research Paper No. 59 Centre for Urban and Community Studies Department of Sociology
THE NETWORK CITY Paul Craven Barry Wellman Research Paper No. 59 Centre for Urban and Community Studies and Department of Sociology University of Toronto July, 1973 To appear in a special issue on "The Community" of Sociological Inquiry. ABSTRACT The network approach to urban studies can be differentiated from other approaches by its insistence on the primacy of structures of interpersonal linkages, rather than the classification of social units according to their individual characteristics. Network analysis is an approach, leading to the formulation of particular kinds of questions, such as "who is linked to whom?" and "what is the structure and content of their relational network;" at the same time it is a methodology for their investigation. Following a discussion of network analytic methods, several of the key issues in urban studies are investigated from this perspective. Interpersonal ties in the city, migration, resource allocation, neighbor hood and connnunity are examined in terms of the network structures and processes that order and integrat~ urban activities. These structures and processes reveal U1emselves to be ever more complex and extensive at each level of the investigation. A view of the city itself as a network of networks is proposed. It is the organization of urban life by networks that makes the scale and diversity of the city a source of strength rather than chaos, while it is precisely that scale and diversity which maKes the complex and widely-ramified network structures possible. The flexibility inherent in network structures can accommodate a variety of situations, while variations in the content and intensity of network linkages allows for the co-ordination and integration of widely different people and activities. -
Damien Fair, Never at Rest
Spectrum | Autism Research News https://www.spectrumnews.org PROFILES Rising Star: Damien Fair, never at rest BY SARAH DEWEERDT 9 JANUARY 2019 One day last April, Damien Fair chuckled along with the members of his 30-person lab at their monthly lunch meeting as he attempted to maneuver a bowl of soup, a plate of salad and a pair of crutches all at the same time. The associate professor of behavioral neuroscience at Oregon Health and Science University in Portland had broken his ankle in a city-league basketball game the evening before. Fair waved away offers of help and soon got down to business. “All right, teach us something,” he told the lab members who were presenting their work that day. He mostly let the presenters, a graduate student and a research associate, hold the floor. Fair is tall and trim, with a salt-and- pepper goatee and expressive eyebrows. He laughed appreciatively at the visual puns in their PowerPoint presentation — for example, a photograph of a toddler in a wetsuit, a play on the name ‘FreeSurfer,’ software for analyzing brain scans. “Damien never sweats anything — officially,” says Nico Dosenbach, assistant professor of pediatric neurology at Washington University in St. Louis, Missouri. Underneath his cool exterior, though, Fair is intense and driven, says Dosenbach, who has been Fair’s friend since the two were in graduate school. And Fair is not intimidated by conventional notions of status or rank: He doesn’t hesitate to ask other scientists for their data or time — or to offer his in return. These traits have made Fair one of the most productive and sought-after collaborators in the field of brain imaging. -
Damon M. Centola
12 March 2021 Damon M. Centola Annenberg School for Communication Ph/Office: 215-898-7041 University of Pennsylvania Fax/Office: 215-898-2024 3620 Walnut Street Email: [email protected] Philadelphia, PA 19104 Website: https://ndg.asc.upenn.edu/ Education Ph.D. Sociology, Cornell University, 2006 MA Sociology, Cornell University, 2004 BA Philosophy/Logic, Marlboro College, 1997, summa cum laude Appointments 2019- Professor of Communication, Annenberg School for Communication Professor of Engineering (secondary), School of Engineering and Applied Sciences Professor of Sociology (secondary), School of Arts and Sciences University of Pennsylvania 2019- Senior Fellow, Penn LDI Center for Health Incentives and Behavioral Economics 2019- Faculty, Penn Population Studies Center 2015- Faculty, Penn Master of Behavioral and Decision Sciences Program 2013- Faculty, Penn Warren Center for Network and Data Sciences 2013- Director, Network Dynamics Group 2013-19 Associate Professor, Annenberg School for Communication & School of Engineering and Applied Sciences, University of Pennsylvania 2008-13 Assistant Professor, MIT Sloan School of Management 2006-08 Robert Wood Johnson Scholar in Health Policy, Harvard University 2001-02 Visiting Scholar, The Brookings Institution Research Computational Social Science, Social Epidemiology, Social Networks, Internet Experiments, Collective Action and Social Movements, Innovation Diffusion, Sustainability Policy, Cultural Evolution, Agent Based Modeling Awards and Fellowships 2019 Harrison White Outstanding -
Anti-Conformism and Social Networks Alexis Poindron
Anti-conformism and Social Networks Alexis Poindron To cite this version: Alexis Poindron. Anti-conformism and Social Networks. Economics and Finance. 2016. dumas- 02102411 HAL Id: dumas-02102411 https://dumas.ccsd.cnrs.fr/dumas-02102411 Submitted on 18 Apr 2019 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Anti-conformism and Social Networks Alexis Poindron Université Paris I Sorbonne, UFR de sciences économiques 02 Master 2 Économie Théorique et Empirique 2015-2016 Mémoire présenté et soutenu par Alexis Poindron le 17/05/2016 Sous la direction de Michel Grabisch et Agnieszka Rusinowska May 17, 2016 L’Université de paris I Panthéon Sorbonne n’entend donner aucune approbation, ni désappro- bation aux opinions émises dans ce mémoire ; elle doivent être considérées comme propres à leur auteur. 1 Contents 1 Introduction . .3 1.1 Literature review and context of this master thesis . .3 1.2 Some notations and definitions . .5 2 Generalized OWA . .6 2.1 GOWA: definitions and exemples . .6 2.2 Discussion on GOWA . .8 3 Terminal states and classes with GOWA: homogeneous framework . .9 3.1 Preliminary results . .9 3.2 List of possible terminal classes and unicity . -
10-5-19 Workshop Announcement V2
Cornell University Department of Mathematics K-12 Education and Outreach MATH 5080 – Mathematics for Secondary School Teachers October 5, 2019 u 9:00 am – 2:30 pm (lunch provided) u 406 Malott Hall 8:45 – 9:00 am Bagels & Juice (provided) 9:00 – 9:20 am Introductions 9:20 – 10:20 am Business Math for Entrepreneurs Lee Kaltman, M.A.T. (New Roots Charter School) I will describe the course called Business Math for Entrepreneurs, share some student work, and explain how I use real-world experiences to engage and motivate students to want to learn more. 10:30 – 11:30 am The Beauty of Calculus Steven Strogatz, Ph.D. (Cornell University, Mathematics) Based on my new book, Infinite Powers: How Calculus Reveals the Secrets of the Universe, I will present a broad look at the story of calculus, focusing on the connections between calculus and the laws of nature. 11:30 – 12:00 pm Lunch (provided) 12:00 – 12:50 pm Teaching Statistics in the Age of Data Science Michael Nussbaum, Ph.D. (Cornell University, Mathematics) I will try to summarize my 20 years of experience teaching statistics at Cornell, focusing in particular on the basic undergraduate course, which is a slightly more advanced version of AP Statistics. This course always includes a computer component, but new challenges have arisen with the advent of “Data Science,” which promises to revolutionize both the practice and the teaching of statistics, and applied mathematics in general, by total integration with computing. I will conclude with a demonstration of how some of the new CS-inspired courses are taught at Cornell, with on-screen computing using software like R or Python.