Podcasts about computational social science

Prof. James Evans, a University of Chicago sociologist and data scientist, believes we're training AI to think too much like humans—and it's holding science back.In this episode, Evans shares how our current models risk narrowing scientific exploration rather than expanding it, and explains why he's pushing for AIs that think differently from us—what he calls “cognitive aliens.” Could these “alien minds” help us unlock hidden breakthroughs? And what would it take to build them?

Are your choices really your own — or are they quietly shaped by the people around you? Nicholas Christakis joins Igor and Charles to reveal the hidden power of social networks, from the surprising spread of kindness and cooperation to the ripple effects that shape our health, decisions, and even our wisdom. Igor uncovers the invisible social forces influencing our daily lives, Nicholas shares how our deep-rooted instincts for love, friendship, and teaching have shaped human civilization, and Charles considers how tapping into these instincts could help us build stronger, wiser communities. Welcome to Episode 64. Special Guest: Nicholas Christakis.

President Donald Trump has been using the phrase “common sense” a lot. But it turns out that this is nothing new for politicians. This hour, we look at how common sense is used in politics. Plus, is there really such a thing as common sense? We dig into what it means and if it’s possible to teach it to artificial intelligence. GUESTS: Sophia Rosenfeld: Walter H. Annenberg Professor of History and Chair of the Department of History at the University of Pennsylvania; she is the author of multiple books, including Common Sense: A Political History and her new book, The Age of Choice: A History of Freedom in Modern Life Mark Whiting: Research fellow at the Computational Social Science lab at the University of Pennsylvania and chief technology officer of the startup Pareto.AI; you can find the common sense survey here Mayank Kejriwal: Research professor and principal scientist at the University of Southern California The Colin McEnroe Show is available as a podcast on Apple Podcasts, Spotify, Amazon Music, TuneIn, Listen Notes, or wherever you get your podcasts. Subscribe and never miss an episode! Subscribe to The Noseletter, an email compendium of merriment, secrets, and ancient wisdom brought to you by The Colin McEnroe Show. Join the conversation on Facebook and Twitter. Colin McEnroe, Angelica Gajewski, and Dylan Reyes contributed to this show.Support the show: http://www.wnpr.org/donateSee omnystudio.com/listener for privacy information.

It is a feature of many human activities - sports, cooking, music, interpersonal relations - that being able to do them well doesn't necessarily mean you can accurately describe how to do them well. Science is no different. Many successful scientists are not very good at explaining what goes into successful scientific practice. To understand that, it's necessary to study science in a scientific fashion. What kinds of scientists, in what kinds of collaborations, using what kinds of techniques, do well? I talk with James Evans, an expert on collective intelligence and the construction of knowledge, about how science really works.Blog post with transcript: https://www.preposterousuniverse.com/podcast/2025/02/10/304-james-evans-on-innovation-consolidation-and-the-science-of-science/Support Mindscape on Patreon.James Evans received his Ph.D. in Sociology from Stanford University. He is currently the Max Palevsky Professor of History and Civilizations, Director of Knowledge Lab, and Faculty Director of Computational Social Science at the University of Chicago; External Professor at the Santa Fe Institute; External Faculty at the Complexity Science Hub, Vienna; and Visiting Faculty Researcher at Google.Knowledge Lab web siteUniversity of Chicago web pageGoogle scholar publicationsSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Welcome to episode #961 of Six Pixels of Separation - The ThinkersOne Podcast. Sandra Matz is one of those rare individuals who sits at the intersection of academic rigor and cultural relevance. As a computational social scientist with a background in psychology and computer science, Sandra studies human behavior by uncovering the hidden relationships between our digital lives and our psychology. Her goal is to make data relatable, and help individuals and businesses make better and more ethical decisions. As the David W. Zalaznick Associate Professor of Business at Columbia Business School, Sandra has dedicated her career to understanding the hidden connections between human behavior and the data trails we leave behind. Over the last 10 years, she has published over 50 academic papers in the world's leading peer review journals. In her new book, Mindmasters - The Data-Driven Science Of Predicting And Changing Human Behavior, Sandra dives into how big data is not just a tool for understanding us but also for influencing our decisions - sometimes in ways that are empowering, other times in ways that are downright chilling. As someone who has always been fascinated by the promise and perils of technology, this conversation hit close to home. Sandra's perspective is nuanced: she's as much a champion of the transformative potential of algorithms in areas like mental health and financial well-being as she is a critic of their misuse for manipulation. Our conversation ranges from her conflicted feelings about the power of psychological targeting to her hope that these tools can help individuals lead happier, more balanced lives. What struck me most was her candor about the fine line between helpful nudges and invasive manipulation. Sandra is not just theorizing about these issues; she's actively shaping the conversation around them. If you're grappling with questions about the role of AI and algorithms in our lives - whether as a force for good or something we need to be deeply wary of - this episode will give you plenty to think about. Enjoy the conversation... Running time: 1:04:51. Hello from beautiful Montreal. Listen and subscribe over at Apple Podcasts. Listen and subscribe over at Spotify. Please visit and leave comments on the blog - Six Pixels of Separation. Feel free to connect to me directly on Facebook here: Mitch Joel on Facebook. Check out ThinkersOne. or you can connect on LinkedIn. ...or on Twitter. Here is my conversation with Sandra Matz. Mindmasters - The Data-Driven Science Of Predicting And Changing Human Behavior. Follow Sandra on LinkedIn. This week's music: David Usher 'St. Lawrence River'. Chapters: (00:00) - Introduction to Computational Social Science. (03:00) - The Conflict of Technology and Psychology. (06:13) - Understanding Psychological Targeting. (08:58) - The Intimacy Economy vs. The Attention Economy. (11:52) - The Dangers of Data Privacy. (15:09) - The Impact of Google Searches on Personal Life. (17:56) - Mass Surveillance and Data Collection. (20:57) - The Role of Regulation in Data Privacy. (24:07) - The Algorithmic Accountability. (26:49) - Synthetic Data and Its Implications. (30:09) - The Future of AI and Human Creativity. (33:01) - The Role of Algorithms in Society. (36:08) - The Importance of Perspective in AI. (41:59) - The Challenge of Transparency in Algorithms. (44:46) - Grassroots Movements and Algorithm Accountability. (47:46) - The Future of AI and Human Interaction. (51:05) - Conclusion and Reflections on Technology.

David Stillwell is Professor of Computational Social Science at Judge Business School, University of Cambridge. Prof. Stillwell's research uses big data to understand psychology.Music credit: David Cutter Music / @dcuttermusic Hosted on Acast. See acast.com/privacy for more information.

We need a radical overhaul of our understanding of human and machine interactions to combat new and urgent social challenges, according to a new international study, just published in the journal 'Nature Human Behaviour' calling for a new 'sociology of humans and machines'. From fake social media accounts to self-driving vehicles and stock trading algorithms to AI chatbots, the need for a deeper understanding of human-machine interactions cannot be overstated, say the authors. The research, undertaken by a team of social scientists from Ireland, UK, USA and Germany, reviewed situations where algorithms, bots, and robots compete, cooperate, coordinate, communicate, and make decisions with humans. "For the last two centuries, social science research has focused on humans as the only entities with agency, social cognition, and creative communication, relegating animals, the environment, and technology to the background," explains Taha Yasseri, Professor of Technology and Society, Trinity College Dublin and one of the study's co-authors. "We are entering a new era in which agency, social cognition, and creative communication are no longer exclusive to humans only - algorithms, bots, and robots are now participating in society alongside humans, driving on roads, leading online conversations, and trading stocks." The paper calls on social scientists to start paying more attention to machines, AI engineers to explicitly design for human-machine and machine-machine interactions, and policymakers to strive for ecological diversity in human-machine social systems. "To ensure more robust and resilient human-machine communities, we need a much deeper understanding of human-machine social systems. In short, we need a new sociology of humans and machines," commented lead co-author Milena Tsvetkova, Associate Professor of Computational Social Science at The London School of Economics and Political Science. "This new sociology of humans and machines will help us address many new and urgent social challenges: online misinformation, market flash crashes, cybersecurity, labour market resilience, and road safety, to name a few." The paper, 'A new sociology of humans and machines', Nature Human Behaviour, by Milena Tsvetkova, Taha Yasseri, Niccolo Pescetelli, and Tobias Werner, can be viewed on the journal website here: https://www.nature.com/articles/s41562-024-02001-8.

Prof. Sebastian Stier, Scientific Director of Computational Social Science at GESIS and Professor of CSS at the University of Mannheim, discusses how web tracking data can inform social science questions.  We discuss the data structure of web browsing data, how it is collected, and the types of incentives used to recruit participants. Prof. Stier also shares his insights and research integrating web browsing data with survey data, as well as how LLMs are opening up new methodological avenues in simulated data.Here are the resources mentioned in the episode: Analysis of Web Browsing Data: A Guide (2023)Integrating Survey Data and Digital Trace Data: Key Issues in Developing an Emerging Field (2020)Post Post-Broadcast Democracy? News Exposure in the Age of Online Intermediaries (2022)The two R packages: webtrackR and adaR

In unserer 61. Episode führen Felix und Rudi ein Gespräch mit Daniel Mayerhoffer, Assistant Professor in Computational Social Science an der Universität Amsterdam, und Jan Schulz-Gebhard, wissenschaftlicher Mitarbeiter am Lehrstuhl für Volkswirtschaftslehre an der Otto-Friedrich-Universität in Bamberg. Das Thema: Die Wahrnehmung ökonomischer Ungleichheit. Im ersten Teil sprechen wir darüber, wie das persönliche Umfeld zur Unterschätzung der ökonomischen Ungleichheit beiträgt. Dabei werfen wir auch einen Blick auf das Agent Based Modelling als angewandte Methode. Im zweiten Teil vertiefen wir uns in konkrete Auswirkungen der Homophilie, darunter Wahlentscheidungen, Nachahmungskonsum sowie die Effekte auf die Gender Wage Gap und die Racial Wage Gap. Schaltet ein und erfahrt zudem, wie ihr LEGO-Bausteine in eure Vorträge und Forschung integrieren könnt. Viel Spaß beim Hören!

Join us on Monday, November 20 @ 6pm EST for an informative conversation with Josh D. Grossman as he shares his research on discrimination in college student admissions. Josh is a Ph.D. candidate in Computational Social Science at Stanford University's Department of Management Science & Engineering. Broadly, his research applies tools from data science to issues in public policy and he is particularly interested in understanding how racial disparate impact manifests itself in judicial decisions and college admissions. He recently interned as a data scientist at Recidiviz, a non-profit that builds technology to reduce incarceration. Previously, he worked as a data scientist at the Stanford Computational Policy Lab and IXL Learning, a K–12 education technology company. He completed his bachelor's degree at Harvard University, majoring in Neurobiology and minoring in Statistics. His work is supported by a National Science Foundation Graduate Research Fellowship (NSF GRFP).

Charlie Beckett, Professor and Founding Director of Polis, the international journalism think tank at the London School of Economics and Munmun De Choudhury, Associate Professor of Interactive Computing at Georgia Institute of Technology, join host Nikita Roy in this episode. Together, they explore the intricacies of AI-generated misinformation, its potential implications on election and the role of the news industry in keeping the public informed and safeguarding democracy. This episode is a recording of the virtual panel at the Online News Association's Onward 2023 event on September 29, 2023.Charlie Beckett is a professor in the Department of Media and Communications at the London School of Economics.He currently leads the Polis Journalism and AI project. Charlie was director of the LSE's Truth, Trust and Technology Commission that reported on the misinformation crisis in 2018. He was an award-winning journalist at LWT, BBC and ITN. He began his news career on the South London Press and ended it as a programme editor at Channel 4 News.Munmun De Choudhury is an Associate Professor of Interactive Computing at Georgia Tech. Associate Professor of Interactive Computing at Georgia Tech. She is best known for laying the foundation of a new line of research that develops computational techniques for understanding and improving mental health outcomes through the ethical analysis of social media data. Munmun's work has been recognized by multiple awards and recognitions, and her research has contributed to the Office of the U.S. Surgeon General's Advisory on The Healing Effects of Social Connection. Munmun serves on the Board of the International Society for Computational Social Science. She is on the National Academies of Sciences, Engineering, and Medicine committee examining research on social media's impact on the well-being of young people. Tune in for a comprehensive discussion on the evolving role of the news industry in the AI era and its pivotal role in safeguarding democracy.

Parlamente sind besondere Orte in der repräsentativen Demokratie. Versteht man Politik als argumentatives Handeln, so lassen sich Plenardebatten als Quelle nutzen, die diese sprachlich vermittelte Kommunikation abbildet. Sie sind ein aufschlussreiches Datenmaterial für die Untersuchung von Argumenten, Interpretationen und Konflikten, die die politische Entscheidungsfindung prägen. In dieser Folge geht es also um nicht weniger als die Sprache der Demokratie. Mit unserem Gast, dem Politikwissenschaftler Christoph Leonhardt, stellen wir in dieser Folge u.a. das GermaParl-Korpus vor, das sämtliche Parlamentsdebatten des Deutschen Bundestages von der ersten bis einschließlich der 19. Legislaturperiode umfasst. Es ist Teil des PolMine-Projects, das große Textbestände als Sprachressourcen für die Forschung aufbereitet. Mit Christoph tauchen wir ein in die Welt der Textdaten und sprechen darüber, was diese Daten so besonders macht, welche weiteren Textkorpora das PolMine-Project zu bieten hat und wie man mit ihnen arbeiten kann.

Today, We are joined by Petter Törnberg, an Assistant Professor in Computational Social Science at the University of Amsterdam and a Senior Researcher at the University of Neuchatel. His research is centered on the intersection of computational methods and their applications in social sciences. He joins us to discuss findings from his research papers, ChatGPT-4 Outperforms Experts and Crowd Workers in Annotating Political Twitter Messages with Zero-Shot Learning, and How to use LLMs for Text Analysis.

On today's episode, Kunle is joined by Tobi Konitzer, Founder & CEO of Ocurate, a predictive analytics platform that provides real-time LTV to D2C brands. Having a PhD in Computational Social Science at Stanford, Tobias or Tobi, worked on studying mass behavior and attitudes. He also worked as a research consultant at Facebook. He got tired of politics in the US and its long-term effects on one's health. He later founded a company called PredictWise after following his curiosities in the B2C space. With a vision to make RLTV become the central KPI for internet-to-consumer brands, Ocurate has built a playbook that can help brands identify their high-quality consumers even before the first purchase. Ocurate also helps the brands in product recommendation and running experiments. It's an interesting episode as you'd hear Kunle and Tobi talk more about LTV and RLTV, Ocurate's sophisticated data collection technology, Split-testing, and more benefits of Ocurate to D2C brands. --- SPONSORS: This episode is brought to you by: Tidio This episode is sponsored by Tidio, the highest-rated live chat app on Shopify. Tidio is a versatile platform that offers personalized shopping experiences, boosts customer satisfaction, and manages all communication channels on one dashboard. It can automate up to 47% of recurring questions with its deep Shopify integration. With the budget-friendly Tidio Plus Plan, you get a dedicated customer success manager, personalized training sessions, and more. Tidio powers over 300,000 brand websites, including Shell, Wellbots, and Valyou Furniture. Head to Tidio.com/2X to take control of your customer relationships and supercharge your growth. TreydThe 2X eCommerce Podcast is sponsored by Treyd, a revolutionary financing service transforming product launches for eCommerce brands. As the ultimate inventory purchasing solution, Treyd lets you sell first, pay suppliers later. Treyd's unsecured funding and credit model improves cash flow, supports larger orders, and even helps negotiate supplier discounts.  With a transparent, pay-as-you-go model, Treyd offers unmatched flexibility and minimal onboarding, independent of eCommerce platforms. Experience the power of "Sell first, pay suppliers later" and snooze invoices for up to 120 days. Transform your business with Treyd today on Treyd.io.

The round table offers a platform for discussion on the opportunities but also the challenges of big data in political science. Technological changes allow us to collect huge amounts of data from many different sources. Never before have we seen such a detailed and extensive collection of data about people's behavior as now, in the age of the Internet. Big data advocates see the potential to raise new questions that we were unable to answer before large-scale datasets became available. However, big data is "no free lunch" and researchers face ethical questions around data protection and intellectual property rights. Chairs: Dominik Duell and Lisa Lechner, University of Innsbruck Speakers Kevin Munger, Pennsylvania State University Julia Schulte-Cloos, European University Institute Alexandra Segerberg, Uppsala Universitet ---- Music by Lost Harmonies. This podcast gives the views of the author(s), and not the position of the Department of Political Science. This work by the Department of Political Science at the University of Innsbruck is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0).

On the show today, Dino Carpentras, a post-doctoral researcher at the Computational Social Science group at ETH Zürich joins us to discuss how opinion dynamics models are built and validated. He explained how quantifying opinions is complex, and strategies to develop robust models for measuring and predicting public opinions.

Princeton professor Matthew Salganik's book, Bit by Bit, explores the merging worlds of computer science and social science for timely, policy relevant research in the 21st century. In the book, he shows how traditional research techniques in the social sciences can sometimes be combined with digital tools and big data to generate high-quality evidence on a larger scale, in less time, and at a much lower cost. On the five-year anniversary of his book's release, Salganik, who is also a member of Mathematica's Board of Directors, spoke with On the Evidence about the book's legacy and the evolution of the field of computational social science since he first taught a course on the subject in 2007. A full transcript of the episode is available here: https://www.mathematica.org/blogs/princetons-matthew-salganik-discusses-the-evolving-intersection-of-data-and-social-science-ethics Read a free online version of Salganik's book, Bit by Bit: https://www.bitbybitbook.com/en/1st-ed/preface/ Attend a virtual book talk on February 7, 2023 about Bit by Bit, including a Q&A with the author, hosted by the Washington, D.C. chapter of the American Association for Public Opinion Research: https://www.eventbrite.com/e/dc-aapor-book-club-20-bit-by-bit-social-research-in-the-digital-age-tickets-487575530537 Listen to a previous On the Evidence episode about the Howard-Mathematica SICSS, which features Salganik: https://mathematica.org/blogs/inside-an-initiative-to-diversify-the-field-of-computational-social-science Learn more about the Howard-Mathematica SICSS: https://mathematica.org/news/howard-university-and-mathematica-to-host-computational-social-science-summer-institute Watch Salganik give a Tedx talk at Princeton University about the tension between ready-made data (big data) and custom-made data (with which social scientists usually work): https://www.youtube.com/watch?v=2pj95xXHN2g Learn more about Salganik and his appointment in 2018 as a member of Mathematica's Board of Directors: https://mathematica.org/news/matthew-salganik-appointed-to-mathematicas-board-of-directors

I am super excited to have David Lazer (1,2) on the pod today.  David Lazer needs no introduction. But here at lazypod we're polite, so he get's one anyway. David Lazer is a University Distinguished Professor of Political Science and Computer Sciences, Northeastern University, and Co-Director, NULab for Texts, Maps, and Networks. Prior to coming to Northeastern University, he was on the faculty at the Harvard Kennedy School (1998-2009). In 2019, he was elected a fellow to the National Academy of Public Administration. His research has been published in such journals as Science, Nature, Proceedings of the National Academy of Science, the American Political Science Review, Organization Science, and the Administrative Science Quarterly, and has received extensive coverage in the media, including the New York Times, NPR, the Washington Post, the Wall Street Journal, and CBS Evening News.He is among the leading scholars in the world on misinformation and computational social science and has served in multiple leadership and editorial positions, including as a board member for the International Network of Social Network Analysts (INSNA), reviewing editor for Science, associate editor of Social Networks and Network Science, numerous other editorial boards and program committees.As always we talk about David path through science, with a particular emphasis on Computational Social Science (3) - a field that he has been absolutely instrumental in establishing. But we also cover many other topics in this wide-ranging converstation which ends up covering his paper “Product diffusion through on-demand information-seeking behaviour” (4) which is one of his favorite papers and least cited, and which has a super-interesting backstory.References(1) https://lazerlab.net(2) https://cssh.northeastern.edu/faculty/david-lazer/(3) https://www.science.org/doi/10.1126/science.1167742(4) https://royalsocietypublishing.org/doi/full/10.1098/rsif.2017.0751

Komplexe Systeme – viele kleine Teile, große globale Wirkung. Jana Lasser ist Postdoktarandin mit dem Schwerpunkt auf Computational Social Science an der TU Graz und gibt einen Einblick in die Komplexitätsforschung. Der Beitrag Was ist Komplexitätsforschung? erschien zuerst auf AirCampus.

Creative flow is a desirable but often elusive state. Many of us crave those moments where it feels like everything just comes together and we're in the zone. But how exactly do we get there? And what can we do to facilitate more flow in our everyday lives? In this episode we explore the flow state with the help of researcher Dr. Richard Huskey and writer Bonnie Tsui. We take a look at the science behind flow, what it can do for us, and how we can facilitate more of it in our everyday lives. Head over to CreativeFuelCollective.com for more creative inspiration, prompts, online workshops and a robust creative community.Hosted by Anna BronesCo-Produced by Anna Brones & Gale StraubTheme Music is by cleod9 musicSeason 1 is Made with Support by Big CartelFeaturing: Dr. Richard Huskey: Richard Huskey (PhD, University of California Santa Barbara) is an assistant professor in the Department of Communication and the Cognitive Science Program at the University of California Davis. Dr. Huskey is the principal investigator in the Cognitive Communication Science Lab, a researcher in the Computational Communication Research Lab, an affiliated faculty member at the Center for Mind and Brain, an affiliated faculty member in the Designated Emphasis in Computational Social Science, and Chair of the International Communication Association Communication Science and Biology interest group. He studies how motivation influences the attitudes people hold and the behaviors they adopt. He researches these questions using a variety of methodological techniques including: functional magnetic resonance imaging (fMRI), computational methods, and lab-based experimentation. In his free time, he enjoys hiking, snowboarding, and walking his dog Turner.Links: Cognitive Communication Science LabBonnie Tsui: Bonnie Tsui is a longtime contributor to The New York Times and the author of American Chinatown, winner of the 2010 Asian/Pacific American Award for Literature. Her latest book, Why We Swim, was a New York Times Book Review Editors' Choice and a Time magazine and NPR Best Book of 2020; it is currently being translated into nine languages. Her first children's book, Sarah and the Big Wave, about the first woman to surf Northern California's Mavericks, was published last year. Her work has been recognized and supported by Harvard University, the National Press Foundation, and the Mesa Refuge.Links: Bonnie TsuiResources Mentioned & Places to Learn MoreWhy We Swim by Bonnie TsuiMihaly CsikszentmihalyiFlow: The Psychology of Optimal Experience by Mihaly Csikszentmihalyi“A computational theory of the subjective experience of flow” Melnikoff et al., 2022“Why does experiencing ‘flow' feel so good?” By Dr. Richard HuskeyTo learn more about somatic movement, Tori recommends checking out Bartenieff Institute of Movement Studies and the book The Body Keeps ScoreFeatured listenersAndrea SlusarskiSheryl WiserKerri Anne StebbinsMike Sowden and his Everything is Amazing newsletterTori DuhaimeSponsor LinksBig Cartel believes you don't have to sell out to sell online. With their simple stores for artists, makers, and creators, you won't be surprised by hidden fees and they don't take a cut of your sales like some other platforms. The sky's the limit on your sales and your success. Open your own shop at bigcartel.com.

Gabriel Bayomi is the Co-Founder at OpenLayer, a tool that tests & debugs machine learning models. OpenLayer was in the YCombinator's batch in 2021, building tools for machine learning model testing. Previously he was a machine learning engineer at Apple working on Siri. He has a master degree in computer science from Carnegie Mellon. He is passionate about Natural Language Processing, Machine Learning, and Computational Social Science. We talked about how to test and debug machine learning models, his experience at Apple, and career lessons. If you like the show subscribe to the channel and give us a 5-star review. Subscribe to Daliana's newsletter on www.dalianaliu.com/ for more on data science and career. Gabriel's LinkedIn: https://www.linkedin.com/in/gbayomi Daliana's LinkedIn: https://www.linkedin.com/in/dalianaliu/ Daliana's Twitter: https://twitter.com/DalianaLiu (0:00) Intro (01:01:39) How he got into machine learning (01:06:43) His experience at Apple, Siri (01:15:55) How to validate the solution (01:19:39) Benefits of using external error analysis framework (01:21:30) How to build a model evaluation pipeline (01:28:26) Don't overfit the subset of data (01:33:19) Your validation set shouldn't be fixed (01:41:03) Become one with data (01:44:05) Three model interpretability library you should use (01:50:47) Common mistakes people made in model validation (01:53:33) How to create an adversarial test (01:55:43) How to check data quality (01:06:46) Transition from engineer to executive (01:10:04) Things he learnt from his favorite coworker (01:17:57) how job roles would evolve

Today on the Pod we are lucky to have Laura Alessandretti (1) visiting us.Laura is an Assistant Professor in Modelling of Human Dynamics at the Technical University of Denmark. She is interested in Computational Social Science, Data Science and Complex Networks. She studies aspects of human behavior combining analysis of large-scale datasets, analytical models and numerical simulations. Previously, she was a PostDoctoral researcher at the Copenhagen Centre for Social Data Science and at DTU Compute. Before that, Laura got her PhD in Mathematics at City, University of London, and her Master's in Physics of Complex Systems at École normale supérieure de Lyon. She's also doing many things to serve the scientific community, for example Laura will be the general chair (with Luca Aiello) of the IC2S2 conference in Copenhagen.Laura is a close collaborator and a good friend of mine, so today's podcast is a little bit different than many others. We discuss the long and winding road leading to our joint paper “The Scales of Human Mobility” (2).The sound is a little less perfect than sometimes, but the content is top-notch, so I hope you'll stick with it in spite of that.References(1) https://laura.alessandretti.com(2) https://www.nature.com/articles/s41586-020-2909-1

This week on Culture by Design, Timothy R. Clark is joined by James Evans, Director of Knowledge Lab at the University of Chicago. They talk about how to activate diversity, how to harness collective intelligence, and the paradoxical interplay between innovation and execution. You won't want to miss the gems from this episode:What is computational social science? (2:30) Turns out, it's hard to explain. It's how we engage in collective action, thinking, and memory. It's reducing the data to make sense of it. It's using computation to create better social policy. In short, it's unprecedented.Insights and findings in collective intelligence (8:00). We all need help with maximizing collective intelligence. Dr. Evans shares his insights on the subjects. He highlights the power of diversity of experiences, perspectives, and identities across contexts. How to activate the diversity of their team members (12:20). Teams that are “flat” are more likely to combine elements in novel ways and produce disruptive outputs. Success is its own worst future failure (13:00). If something succeeds too well., it crystallizes itself and destroys any of the diversity that could make it better. We need to find creative destruction, not destructive creation. Many organizations are compositionally diverse but don't harness that power. Ways to activate diversity in a team (15:45). Big teams are less likely to activate their underlying diversity (more efficient, but less innovative). Cultural flatness dramatically improves innovation. Interdisciplinary teams maximize the possibilities of creation.Cultural flatness neutralizes hierarchies (23:00). We experience cultural flatness when we're agnostic to title, position, authority, and power distance. We can debate issues on their merits and remove the exaggerated difference in the chain of command.What really facilitates productive difference? (36:00) Dr. Evans discusses where innovation actually lies in teams and organizations. Older participants crystallize the system. Diverse expeditions are the real game-changers.Protecting boundaries and silos create a reservoir of ideas (40:30). This is why innovation is so difficult! It's a complete paradox. Teams cannot execute and innovate at the same time. We must surf the boundaries between execution and innovation in order to be successful.About Our Guest:James Evans is the Director of Knowledge Lab at the University of Chicago, which has collaborative, granting and employment opportunities, as well as ongoing seminars. He also founded and now directs the Computational Social Science program at Chicago, and sponsors the associated Computational Social Science workshop. He teaches courses in augmented intelligence, the history of modern science, science studies, computational content analysis, and Internet and Society. Before Chicago, he received his doctorate in sociology from Stanford University, served as a research associate in the Negotiation, Organizations, and Markets group at Harvard Business School, started a private high school focused on project-based arts education, and completed a B. A. in Anthropology at Brigham Young University. He is especially interested in innovation—how new ideas and practices emerge—and the role that social and technical institutions (e.g., the Internet, markets, collaborations) play in collective cognition and discovery.

Paul is CEO of data science and research startup League of Scholars, which works with a wide range of organizations including Nature and News Corporation, and the cofounder of a number of other ventures, He is an Adjunct Professor at U of NSW and Honorary Research Fellow at Western Sydney University, and the author of Online Gravity, a successful book on how technology is rebooting economics.

In this and the following episodes, we will be talking about data and algorithms with a special focus on their uses in and effects on politics. In this episode, we start with data.

In computational social science there are great hopes and enthusiasms connected with the availability of new data sources. In this episode, we will be talking about working with one of these new data sources: digital trace data.

Text analysis is a prominently used approach from computational social science. In this episode, we examine three recent studies closely, that are using text analysis in interesting and constructive ways.

Our discussion of computational social science and its promises and challenges has remained rather abstract. It is time to turn to CSS as a practice. For this, let's have a look at the typical CSS project pipeline.

Computational social science (CSS) is an interdisciplinary scientific field that studies human behavior and social systems using computational methods and research practices. In this episode, we will discuss CSS, what makes it different from other approaches in the social sciences, and attempt a definition.

The future. It's all there, in front of us. It could go wonderfully. Or it could go badly wrong. It will inevitably require our passions and our ingenuity. So how do we see the challenges early on, find solutions and help make the world a better place? For ourselves, for our families, for everyone. Welcome to The Futureverse, a new series brought to you by Intelligence Squared and in partnership with Y TREE. In the first episode of The Futureverse podcast, From the Ancients to AI, host Kamal Ahmed and a panel of experts explore the history of the future as an idea. Dr Aleks Krotoski, social psychologist, researcher and science communicator, explains why planning for the future is at the heart of being human. Dr Amanda Rees, a historian of science based at the University of York, and Alexander Boxer, author of A Scheme of Heaven, look back at the history of the future as a concept; how have we juggled planning and prediction from ancient times through to modernity? And Dirk Helbing, Professor of Computational Social Science at the Swiss Federal Institute of Technology in Zurich, reveals how we might be able to stream data into a giant simulator that could help us predict – and prepare for – events in the future. Come with us into The Futureverse: http://intelligencesquared.com/futureverse For more information and to register to watch an event featuring Sir Antony Gormley, Mo Gawdat and Clover Hogan, please visit: y-tree.com/futureverse Find out more about Dirk's latest project - how to build a “digital twin” of the Earth, here: https://www.researchgate.net/publication/358571489 Learn more about your ad choices. Visit megaphone.fm/adchoices

How does big data help make large public health decisions? How do we set up the collection, the analysis, and interoperate the data to create infrastructure? Leoson Hoay serves as a Research Analyst and Data Steward at the University of Chicago Urban Labs, working with the Health Lab team to support the creation of data-driven solutions to public health problems. Prior to joining the Urban Labs, he had worked in various fields spanning data engineering, mental health counseling, and environmental remediation with organizations in the US, Singapore, and Australia. Leoson received his MA in Computational Social Science from the University of Chicago, and his undergraduate degree in Psychology from the National University of Singapore. He is currently pursuing an MS in Computer Science at the Georgia Institute of Technology.In this episode we discuss many big questions invovling the use of big data to solve large problems. We dive into two projects Leoson has contributed to and generally discuss what big data means in the context of health decisions. Check it out! Leoson's Social Media: Linkedin Show Notes Tell us a little bit about what lead you to this path and what you do on a day to day basis?What does preventive medicine mean to you?How are large amounts of data able to be used to solve problems? What does the process look like? What are the challenges with this process?You have done research into the homeless population cycling between the streets, hospitals, and jails. Can you tell us about this project?Can you tell us a little bit more about your research with insurance backed psychiatric placement and providers?How do you measure the impact of these projects?What impact does the collection of data make on these projects and what changes were or could be made? How does data lead to infrastructure?Do you think that community social services can help prevent problems from occurring or are they most active at solving acute problems?Healthcare adds on a level of complexity for data collection with HIPPA and insurance companies. Are hospitals and insurance companies willing partners?What are some of the protective measures to safeguard privacy when working with healthcare data?If someone asks you how data contributes to health, what do you tell them? Join our Mailing List HERE: Mailchimp

The future. It's all there, in front of us. It could go wonderfully. Or it could go badly wrong. It will inevitably require our passions and our ingenuity. So how do we see the challenges early on, find solutions and help make the world a better place? For ourselves, for our families, for everyone. Welcome to The Futureverse, a new series brought to you by Intelligence Squared and in partnership with Y TREE. In the first episode of The Futureverse podcast, From the Ancients to AI, host Kamal Ahmed and a panel of experts explore the history of the future as an idea. Dr Aleks Krotoski, social psychologist, researcher and science communicator, explains why planning for the future is at the heart of being human. Dr Amanda Rees, a historian of science based at the University of York, and Alexander Boxer, author of A Scheme of Heaven, look back at the history of the future as a concept; how have we juggled planning and prediction from ancient times through to modernity? And Dirk Helbing, Professor of Computational Social Science at the Swiss Federal Institute of Technology in Zurich, reveals how we might be able to stream data into a giant simulator that could help us predict – and prepare for – events in the future. Come with us into The Futureverse: http://intelligencesquared.com/futureverse For more information and to register to watch an event featuring Sir Antony Gormley, Mo Gawdat and Clover Hogan, please visit: y-tree.com/futureverse Find out more about Dirk's latest project - how to build a “digital twin” of the Earth, here: https://www.researchgate.net/publication/358571489 Learn more about your ad choices. Visit megaphone.fm/adchoices

The future. It's all there, in front of us. It could go wonderfully. Or it could go badly wrong. It will inevitably require our passions and our ingenuity. So how do we see the challenges early on, find solutions and help make the world a better place? For ourselves, for our families, for everyone. Welcome to The Futureverse, a new series brought to you by Intelligence Squared and in partnership with Y TREE. In the first episode of The Futureverse podcast, From the Ancients to AI, host Kamal Ahmed and a panel of experts explore the history of the future as an idea. Dr Aleks Krotoski, social psychologist, researcher and science communicator, explains why planning for the future is at the heart of being human. Dr Amanda Rees, a historian of science based at the University of York, and Alexander Boxer, author of A Scheme of Heaven, look back at the history of the future as a concept; how have we juggled planning and prediction from ancient times through to modernity? And Dirk Helbing, Professor of Computational Social Science at the Swiss Federal Institute of Technology in Zurich, reveals how we might be able to stream data into a giant simulator that could help us predict – and prepare for – events in the future. Come with us into The Futureverse: http://intelligencesquared.com/futureverse For more information and to register to watch an event featuring Sir Antony Gormley, Mo Gawdat and Clover Hogan, please visit: y-tree.com/futureverse Find out more about Dirk's latest project - how to build a “digital twin” of the Earth, here: https://www.researchgate.net/publication/358571489 Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode, we talk with David Lazer, the University Distinguished Professor of Political Science and Computer Sciences at Northeastern University and the Co-Director of the NULab for Texts, Maps, and Networks. We discuss two seminal papers in computational social science he co-authored a decade apart: "Life in the network: the coming age of computational social science" (Science 2009) and "Computational social science: Obstacles and opportunities" (Science 2020). David shares with us events in his long and distinguished CSS research career. In the early 2000s, he helped gather a small group of people working on new "data streams" and how they intentionally created the term computational social science. He also talks about his own struggles on the academic job market, advice for aspiring CSS researchers, and a wish for better data availability structures.

Apa sih computational social science itu? Orang dengan latar belakang seperti apa yang bisa bekerja atau riset di bidang tersebut? Di episode spesial ini, saya kedatangan dua narasumber dengan latar belakang berbeda yang dipertemukan oleh bidang yang sedang naik daun akhir-akhir ini. Dr. Ahmad Raf'ie Pratama (@ahmadrafie) dan Dr. Firman M. Firmansyah (@firmansm) bercerita tentang latar belakang pendidikan mereka dan beberapa publikasi mereka di bidang computational social science. Alumni Stony Brook University ini juga bercerita tentang bagaimana akhirnya mereka bisa memboyong SICSS ke Jogja, Indonesia - jadi bagian pertama dari summer school bergengsi ini di Asia Tenggara dan Pasifik. Tautan: https://sicss.io/2022/jogja/ https://www.bitbybitbook.com/ https://osf.io/preprints/socarxiv/dacu7/ (1:43) Perkenalan Ahmad R. Pratama (4:55) Perkenalan Firman M. Firmansyah (7:43) Awal kerja sama penelitian dan peran masing-masing (17:26) Paper Naming Practice and Gender Equality (32:45) Penelitian dengan agent-based simulation (43:49) Proses belajar teknologi untuk computational social science (48:08) Dalam penelitian multidisiplin, mana yang lebih baik: menjadi generalis atau spesialis? (1:00:08) Summer Institute in Computational Social Science (1:12:21) Penutup

Europa wird immer digitaler. Handel, Wirtschaft, Verwaltung und der politische Diskurs sind zu großen Teilen ins Internet gewandert. Das stellt die Gesellschaft und die Gesetzgebenden vor neue Herausforderungen. Wer hegt die (Informations-) Macht der Gatekeeper wie Facebook, Google und Amazon ein? Wer bestimmt die politischen Diskussionen im Netz? Diese Fragen werden aktuell auf europäischer Ebene diskutiert. Mit dem Digital Services Act gibt es neben der Datenschutzgrundverordnung einen weiteren politischen und gesetzgeberischen Hebel. Was sonst noch geschehen muss, um die Digitalisierung sozial gerecht zu gestalten, das diskutieren wir in diesem Podcast. Ein Podcast mit:• Carla Hustedt, Leiterin der Abteilung „Digitalisierte Gesellschaft“ der Stiftung Mercator • Ulf Buermeyer, Jurist und Vorsitzende der Gesellschaft für Freiheitsrechte• Dirk Helbing, Professor für Computational Social Science an der ETH Zürich• Professorin Jeanette Hofmann, Politikwissenschaftlerin • Wolfgang Hoffmann-Riem, ehemaliger Richter des Bundesverfassungsgerichts Shownotes:Gesetz über digitale Dienste/ Digital Services Act:https://ec.europa.eu/info/strategy/priorities-2019-2024/europe-fit-digi… Mitschnitte der Konferenz „Visionen für ein digitales Europa 2025“:https://eu.boell.org/en/DigitalEU2025

Europa wird immer digitaler. Handel, Wirtschaft, Verwaltung und der politische Diskurs sind zu großen Teilen ins Internet gewandert. Das stellt die Gesellschaft und die Gesetzgebenden vor neue Herausforderungen. Wer hegt die (Informations-) Macht der Gatekeeper wie Facebook, Google und Amazon ein? Wer bestimmt die politischen Diskussionen im Netz? Diese Fragen werden aktuell auf europäischer Ebene diskutiert. Mit dem Digital Services Act gibt es neben der Datenschutzgrundverordnung einen weiteren politischen und gesetzgeberischen Hebel. Was sonst noch geschehen muss, um die Digitalisierung sozial gerecht zu gestalten, das diskutieren wir in diesem Podcast. Ein Podcast mit:• Carla Hustedt, Leiterin der Abteilung „Digitalisierte Gesellschaft“ der Stiftung Mercator • Ulf Buermeyer, Jurist und Vorsitzende der Gesellschaft für Freiheitsrechte• Dirk Helbing, Professor für Computational Social Science an der ETH Zürich• Professorin Jeanette Hofmann, Politikwissenschaftlerin • Wolfgang Hoffmann-Riem, ehemaliger Richter des Bundesverfassungsgerichts Shownotes:Gesetz über digitale Dienste/ Digital Services Act:https://ec.europa.eu/info/strategy/priorities-2019-2024/europe-fit-digi… Mitschnitte der Konferenz „Visionen für ein digitales Europa 2025“:https://eu.boell.org/en/DigitalEU2025

Europa wird immer digitaler. Handel, Wirtschaft, Verwaltung und der politische Diskurs sind zu großen Teilen ins Internet gewandert. Das stellt die Gesellschaft und die Gesetzgebenden vor neue Herausforderungen. Wer hegt die (Informations-) Macht der Gatekeeper wie Facebook, Google und Amazon ein? Wer bestimmt die politischen Diskussionen im Netz? Diese Fragen werden aktuell auf europäischer Ebene diskutiert. Mit dem Digital Services Act gibt es neben der Datenschutzgrundverordnung einen weiteren politischen und gesetzgeberischen Hebel. Was sonst noch geschehen muss, um die Digitalisierung sozial gerecht zu gestalten, das diskutieren wir in diesem Podcast. Ein Podcast mit: • Carla Hustedt, Leiterin der Abteilung „Digitalisierte Gesellschaft“ der Stiftung Mercator • Ulf Buermeyer, Jurist und Vorsitzende der Gesellschaft für Freiheitsrechte • Dirk Helbing, Professor für Computational Social Science an der ETH Zürich • Professorin Jeanette Hofmann, Politikwissenschaftlerin • Wolfgang Hoffmann-Riem, ehemaliger Richter des Bundesverfassungsgerichts Shownotes: Gesetz über digitale Dienste/ Digital Services Act:https://ec.europa.eu/info/strategy/priorities-2019-2024/europe-fit-digi… Mitschnitte der Konferenz „Visionen für ein digitales Europa 2025“:https://eu.boell.org/en/DigitalEU2025

Bear F. Braumoeller is Professor in the Department of Political Science at The Ohio State University and the holder of the Baronov and Timashev Chair in Data Analytics. He conducts research in the areas of international relations, political methodology, and complexity and human behavior. He received his B.A. from the University of Chicago and his Ph.D. from the University of Michigan, and he held faculty positions at the University of Illinois at Urbana-Champaign and Harvard University before moving to Ohio State. His present work focuses on the decline-of-war thesis, the relationship between international order and international conflict, and causal inference based on observational data. He currently co-directs the Computational Social Science community of practice under the Translational Data Analytics Institute at Ohio State. For more information, visit the Computational Social Science CoP page at TDAI.

On this episode of On the Evidence, we focus on a creative initiative designed to build a more diverse pipeline of researchers who use methods and tools from data science and social science. Earlier this year, Howard University and Mathematica sponsored a free, two-week training for graduate students, postdoctoral researchers, and beginning faculty in the fields of data science and social science. The training was part of a broader instructional program held at 20 sites across the globe called the Summer Institutes in Computational Social Science (SICSS). The Howard-Mathematica SICSS was unique in that it was the first site to be hosted by a historically Black college or university (Howard) and the first to focus on anti-Black racism and inequity. This episode will include the following guests: • Nicole Jenkins, an assistant professor at Howard University in the Department of Sociology and Criminology whose ethnographic research focuses on studying the experiences of Black women in institutions • Jeremy Prim, a Ph.D. candidate in the Department of Sociology at the University of California, Davis whose research focuses on race, policing, exclusionary discipline, and educational outcomes • Felix Owusu, a Ph.D. candidate in public policy at Harvard whose dissertation research centers on racial disparities in the criminal legal system • Naniette Coleman, founder and lead organizer of the SICSS-Howard/Mathematica and a Ph.D. candidate in the Sociology Department at the University of California, Berkeley • Matt Salganik, a professor of sociology at Princeton University who is a member of Mathematica's Board of Directors and the author of Bit by Bit: Social Research in the Digital Age • Akira Bell, a senior vice president and the chief information officer at Mathematica • Wayne A.I. Frederick, president of Howard University and a surgical oncologist whose medical research focuses on narrowing racial, ethnic, and gender disparities in cancer-care outcomes • Paul Decker, president and chief executive officer of Mathematica Read an op-ed in The Hechinger Report by Wayne A.I. Frederick and Paul Decker, the presidents of Howard University and Mathematica, respectively, about the need to increase diversity in research and analytics: https://hechingerreport.org/opinion-a-lack-of-diversity-in-research-and-analytics-is-not-just-unethical-it-is-dangerous/ Read more about the launch of the SICSS-Howard/Mathematica: https://mathematica.org/events/howard-university-mathematica-computational-social-science-institute-on-countering-anti-black-racism A full transcript of the episode is available here: mathematica.org/blogs/inside-an-initiative-to-diversify-the-field-of-computational-social-science

This unique episode centers on a "meta" discussion on interdisciplinary work involving large-scale social data. We interview Chris Bail, a Professor of Sociology and Public Policy at Duke University. Last year, Chris and co-authors Achim Edelman, Tom Wolff, and Danielle Montagne published an overview paper titled "Computational Social Science and Sociology" in the Annual Review of Sociology. We discuss the challenges of defining this large research area, the benefits of making "lateral connections" with potential colleagues as a graduate student, and taking risks in pursuing new research directions. We also highlight the process behind the creation and growth of the Summer Institute in Computational Social Science, which Chris co-founded with Matt Salganik.

In this episode of Pekingology, Freeman Chair in China Studies Jude Blanchette is joined by Mary Gallagher, Professor of Political Science and Director of the Center for Chinese Studies at the University of Michigan, and Blake Miller, Assistant Professor of Computational Social Science at the London School of Economics, to discuss their recent paper, Who Not What: The Logic of China's Information Control Strategy, which examines how the Chinese Party-state controls social media. References: https://www.cambridge.org/core/journals/china-quarterly/article/who-not-what-the-logic-of-chinas-information-control-strategy/4DC69883679770CBCDB1F1B87A34F09E

In this episode of "Keen On", Andrew is joined by Chris Bail, the author of "Breaking the Social Media Prism", to discuss the ironic dynamics of social media platforms, which on the surface flatter to provide information and facilitate human connection but rather serve to distort individuals' perspectives on reality. Chris Bail is Professor of Sociology and Public Policy at Duke University, where he directs the Polarization Lab. He studies political tribalism, extremism, and social psychology using data from social media and tools from the emerging field of computational social science. A Guggenheim Fellow and Carnegie Fellow, Chris's research appears in leading journals such as Science, The Proceedings of the National Academy of Sciences, and the American Journal of Public Health. His 2015 book, Terrified: How Anti-Muslim Fringe Organizations Became Mainstream, received three awards and resulted in an invitation to address the 2016 Democratic National Convention. Bail has also written for the Sunday Op-Ed page of the New York Times, CNN, and The Washington Post Blog. His research has been covered by more than fifty media outlets, including Wired, The Atlantic, Scientific American, Foreign Policy, The Washington Post, The Los Angeles Times, The Boston Globe, The Guardian, Vox, Daily Kos, National Public Radio, NBC News, C-Span, and the BBC. Bail regularly lectures to audiences in government, business, and the non-profit sector. He also consults with social media platforms struggling to combat polarization. Chris is passionate about building the field of computational social science. He is the Editor of the Oxford University Press Series in Computational Social Science and the Co-Founder of the Summer Institutes in Computational Social Science, which are free training events designed to introduce junior scholars to the field that are held concurrently in a range of universities around the world each year. He also serves on the Advisory Committee to the National Science Foundation's Social Behavioral and Economic Sciences Directorate, and helped create Duke's Interdisciplinary Data Science Program. Most of the funding for Bail's research has been provided by the National Science Foundation, the Carnegie Foundation, the Guggenheim Foundation, the Robert Wood Johnson Foundation, and the Russell Sage Foundation, among others described on the C.V. below. Chris received his PhD from Harvard University in 2011. Learn more about your ad choices. Visit megaphone.fm/adchoices

Twitter’s ban on Donald Trump has been welcomed by a lot of people who were shocked by last week’s riot on Capitol Hill. But long time Trump critic and German Chancellor Angela Merkel is not one of those people. She has criticised the move calling it “problematic”. This has raised many questions about social media as a platform for public discourse. Marie Boran is a Freelance Tech Journalist & Researcher in Computational Social Science at DCU.

La tercera temporada continúa su curso. En esta ocasión acompaña a Bart y Yis por la intrincada política estadounidense comentando el Gerrymandering y cómo las Matemáticas y la Computación pueden ayudar a detectar, probar y corregir los mapas electorales y así colaborar en el proceso democrático. ¿Por qué no estás fluxeando? ¡Dale al play!Bibliografía:Greer, C. (2020, 9 septiembre). Gerrymandering: How drawing jagged lines can impact an election. TED Talk. https://www.ted.com/talks/christina_greer_gerrymandering_how_drawing_jagged_lines_can_impact_an_electionVox. (2019, 17 octubre). The man who rigged America's election maps. YouTube. https://www.youtube.com/watch?v=KpamjJtXqFIVox. (2017, 10 abril). The algorithm that could help end partisan gerrymandering. YouTube. https://www.youtube.com/watch?v=gRCZR_BbjToGuest, O., Kanayet, F. J., & Love, B. C. (2019). Gerrymandering and computational redistricting. Journal of Computational Social Science, 2(2), 119-131. https://doi.org/10.1007/s42001-019-00053-9Herschlaga, G., Ravier, R., & Mattingly, J. (2017, septiembre). Evaluating Partisan Gerrymandering in Wisconsin. Duke University. https://services.math.duke.edu/~jonm/Redistricting/wisconsinRedistricting-InitialVersion.pdfEllenberg, J. (2017, 6 octubre). How Computers Turned Gerrymandering Into a Science. The New York Times. https://www.nytimes.com/2017/10/06/opinion/sunday/computers-gerrymandering-wisconsin.htmlCarnegie Mellon University. (2019, 6 noviembre). Mathematicians' Work Helps Change How People Vote - News - Carnegie Mellon University. https://www.cmu.edu/news/stories/archives/2019/november/gerrymandering-pegden.htmlCMU Algorithm Sniffs Out Gerrymandering. (2018, octubre). Carnegie Mellon University's Heinz College. https://www.heinz.cmu.edu/media/2018/October/wes-pegden-gerrymanderingPedgen, W., Procaccia, A. D., & Yu, D. (2017, octubre). A partisan districting protocol with provably nonpartisan outcomes. Carnegie Mellon University. https://arxiv.org/abs/1710.08781Música: • > Red, White, Black & Blue by PEG & The Rejected is licensed under a Attribution-NonCommercial-ShareAlike License. • > Shipping Lanes by Chad Crouch is licensed under a Attribution-NonCommercial 3.0 International License. • > Organisms by Chad Crouch is licensed under a Attribution-NonCommercial 3.0 International License. • > Moonrise by Chad Crouch is licensed under a Attribution-NonCommercial 3.0 International License. • > Algorithms by Chad Crouch is licensed under a Attribution-NonCommercial 3.0 International License.

Dr. Solomon Messing, Chief Scientist at ACRONYM and Affiliated Researcher at Georgetown University, shares his insights on data science across academia, the tech industry, and political campaigning space. We discuss how computational social science methods have changed over time, and how system architectures can be built to protect social media users' privacy. We also chat about current trends that Dr. Messing is observing at ACRONYM relating to the persuasiveness and cost of political ads on social media. Here's the paper we discuss on differential privacy, and the Facebook URLs Dataset Codebook. 

In this interview De Kai talks about Artificial Intelligence from his cross-disciplinary perspective. We talk about the meaning of creativity, consciousness and mindfullness for modern AI, various misconceptions about modern AI and whether it is still programmable in the classical sense at all, about AI ethics and what AI means for us as a society, humanity and our future.About De KaiDe Kai is Professor of Computer Science and Engineering at HKUST and Distinguished Research Scholar at Berkeley's International Computer Science Institute. He is among only 17 scientists named Founding Fellow by the Association for Computational Linguistics, for his pioneering contributions to machine learning foundations of machine translation that led to the Google/Microsoft/Yahoo/etc translators. De Kai was recruited as founding faculty of the University of Science and Technology in Hong Kong directly from UC Berkeley, where his PhD thesis was one of the first to argue for the paradigm shift toward machine learning based natural language processing. He holds a Kellogg-HKUST Executive MBA and a BS in Computer Engineering (Phi Beta Kappa, cum laude, Revelle College honors) from UCSD.For his work on AI, machine learning, machine translation, natural language processing, music technology, computational creativity, Debrett's HK 100 recognized De Kai as one of the 100 most influential figures of Hong Kong.For his work on AI ethics and society, De Kai was one of eight inaugural members selected by Google in 2019 for its AI ethics council.See http://dek.ai for details.De Kai’s LinksWebsite: http://dek.aiUniversal Masking Project: http://dek.ai/masks4allTwitter: @dekai123Facebook: https://www.facebook.com/dekai1YouTube, 26 Apr 22, Visual simulations show why we all need to wear masks now https://youtu.be/yfeW2l8G_W4South China Morning Post, 25 May 2020, Coronavirus spread would dramatically drop if 80% of a population wore masks, AI researcher says https://www.scmp.com/video/coronavirus/3085971/coronavirus-spread-would-dramatically-drop-if-80-population-wore-masks-aiBoma COVID-19 Summit, 23 Mar 2020, The disastrous consequences of information disorder erupting around COVID-19: AI is preying upon our unconscious cognitive biases https://youtu.be/ZidC7oRd7PcTEDxChiangMai, Thailand, 7 Sep 2019, The Paradox of AI Ethics: Why Rule-based AI Ethics Will Fail https://www.youtube.com/watch?v=rKPhvb_9tawTEDxOakland, California, 18 Nov 2018, Why AI is impossible without mindfulness https://www.youtube.com/watch?v=w_Sd2ZPPhv8TEDxKlagenfurt, Austria, 16 Jun 2018, Artificial Gossips https://www.youtube.com/watch?v=hHyjSgCoNlwTEDxBlackRockCity, Nevada, 29 Aug 2017, Artificial Children (no video due to technical difficulties)TEDxZhujiangNewTown, Guangzhou, 14 Jan 2017 Why Meaningful AI is Musical https://v.youku.com/v_show/id_XMzA4MDQwODE4MA==.html or https://v.qq.com/x/page/i05088lu78h.htmlTEDxBlackRockCity, Nevada, 31 Aug 2016 (no video due to technical difficulties)TEDxXi'Support the show (https://www.patreon.com/xerxesre)

How the $2 Trillion in Pandemic Relief Is Supposed to Help the Economy (0:31)Guest: Richard Evans, PhD, Associate Director and Senior Lecturer, Master's Program in Computational Social Science, University of ChicagoCongress has so far approved a little more than $2-trillion in stimulus and relief to deal with the economic impact of the coronavirus pandemic, but will it be enough? How Much Do We Actually Know About Medical Marijuana? (18:02)Guest: Staci Gruber, PhD, Associate Professor of Psychiatry at Harvard Medical School, Director of the Marijuana Investigations for Neuroscientific Discovery Program at McLean Hospital.Marijuana use for medicinal purposes is now the norm in America – all but a few states allow it. And in 11 states, it's legal for adults to use marijuana recreationally – no doctor's note required. But we still know very little about how cannabis affects the body and brain. This is a case of policy outpacing science. Growing Lettuce (And Other Food) in Space (34:51)Guest: Gioia Massa, PhD, Plant Scientist, NASA Kennedy Space Center, Veggie Project Lead NASA is working on sending humans back to the moon and then onto Mars in the next decade.  Gioia Massa is working on making sure the astronauts who go on those long space missions have fresh produce in their diet. How to Protect Your Children From Accidental Poisoning (50:39)Guest: Sadiqa Kendi MD, Medical Director, Safe Kids DC and Children's National Safety Center at Children's National HospitalParents working at home all day with little kids underfoot is a tricky situation. It's impossible to keep a close eye on them at every moment. So these pandemic quarantines may boost the risk of kids getting into medicines. During normal times, a kid with accidental medicine poisoning shows up in an emergency room somewhere in America every ten minutes. Immune Booster Myths (1:06:54)Guest: Alice Knoedler, M.D., Fellow in the Division of Asthma, Allergy and Immunology, University of Virginia School of MedicineWe're all trying to stay healthy right now. Is there anything that can boost the immune system's ability to fight off infection? Quick fixes abound on the internet.  Movies to Help Kids Talk About Pandemic Fears (1:19:26)Guest: Kirsten Hawkes, ParentPreviews.comHow's the stress level in your home right now? Movies can be a great way for families to spark a conversation about feelings and coping with scary stuff, so Kirsten Hawkes of Parent Previews.com has a few recommendations for you today.

In an era of fake news, our society is in a bit of a moral panic. But Dr. Jeff Hancock, an expert in social media behavior, has found that it's human psychology, not technology, which drives deception and distrust. In this episode, he and Bill Coppel — Managing Director and Chief Client Growth Officer at First Clearing — discuss how to navigate the digital landscape to maintain trust and how we can use technology to help build it. In this episode, you'll hear: ·         How trust has evolved over time ·         The many definitions and kinds of trust ·         The new forms of trust we're seeing today ·         How we evaluate someone's trustworthiness ·         What role social media plays in the trust formula ·         How reputation has changed with the rise of social media ·         How differences in communication lead to distrust between generations ·         What role technology will play in maintaining trust between advisor and client   Dr. Jeff Hancock is a psychologist and professor of communications at Stanford University. He is the founder of the Social Media Lab at Stanford and also serves as director of Stanford's Center for Computational Social Science. A leading expert in social media behavior and the psychology of online interaction, Jeff studies how social technologies and artificial intelligence affect us, from how we trust, to how we develop and maintain relationships, to understanding how technology is affecting our well-being. His view isn't that social media is good or bad, but that it amplifies everything and we must learn to navigate the effects.   Before earning his PhD in psychology, Jeff served as a customs officer in Canada. Prior to joining the Stanford faculty, he was an associate professor of cognitive science and communication at Cornell University. He lives in Silicon Valley with his wife and daughter, and still enjoys playing hockey regularly and does his best to keep pucks out of the net.   Ways to contact Jeff: Email: hancockj@stanford.edu Website: Stern Speakers: Jeff Hancock

In this episode we interview Dr. Seth Frey about his work on understanding online social communities. We even get a sneak peak at his recent study on the behaviors in online poker that lead to winning or losing! 

Dr. Deen Freelon, Associate Professor in the School of Media and Journalism at the University of North Carolina at Chapel Hill, discusses how researchers collect and analyze social media data to study politics. We talk about Facebook's recent API shut-down, the new Social Science One initiative, differences between Python and R programming languages, and one of his recent reports analyzing how minority communities engage with news on Twitter.

Z tego odcinka dowiesz się:- czym zajmuje się Konrad Błaszkiewicz,- czym są obliczeniowe nauki społeczne Computational Social Science,- jakie są większe problemy do rozwiązania w obszarach psychologii, socjologii,- jak smartfony zmieniają badania psychologiczne,- jaki ma cel i jaki problem ma rozwiązać program Menthal,- czy ludzie piszą prawdę w kwestionariuszach,- czy smartfony mogą pomóc w badaniach nad depresją i wczesnym jej wykrywaniu,- co robią najbardziej inspirujące osoby z dziedziny Computational Social Science,- co może pomóc poznać, co myślą i czują dzieci z autyzmem,- jakie informacje o człowieku można poznać analizując jego aktywność na Facebooku,- jakie zastosowanie mogą mieć badania na sieciach społecznościowych w biznesie,- czy w sieciach społecznościowych można się schować,- jak naukowcy podchodzą do tematu etyki,- do jakich celów można wykorzystać badania prowadzone w sieciach społecznościowych.http://biznesmysli.pl/31

Z tego odcinka dowiesz się:- czym zajmuje się Konrad Błaszkiewicz,- czym są obliczeniowe nauki społeczne Computational Social Science,- jakie są większe problemy do rozwiązania w obszarach psychologii, socjologii,- jak smartfony zmieniają badania psychologiczne,- jaki ma cel i jaki problem ma rozwiązać program Menthal,- czy ludzie piszą prawdę w kwestionariuszach,- czy smartfony mogą pomóc w badaniach nad depresją i wczesnym jej wykrywaniu,- co robią najbardziej inspirujące osoby z dziedziny Computational Social Science,- co może pomóc poznać, co myślą i czują dzieci z autyzmem,- jakie informacje o człowieku można poznać analizując jego aktywność na Facebooku,- jakie zastosowanie mogą mieć badania na sieciach społecznościowych w biznesie,- czy w sieciach społecznościowych można się schować,- jak naukowcy podchodzą do tematu etyki,- do jakich celów można wykorzystać badania prowadzone w sieciach społecznościowych.http://biznesmysli.pl/31

What can traffic lights tell us about how our economy could work? It may seem a strange question to ask, but Dirk Helbing, is the person who makes the connection. In this episode, he explains what we can learn from complex dynamic systems and how that knowledge can help us understand and implement a feedback-rich circular economy. Dirk Helbing is Professor of Computational Social Science at the Department of Humanities, Social and Political Sciences at ETH Zurich.

Richard Evans is a Senior Fellow in Computational Social Science at the University of Chicago, and Fellow here at the institute. Evans sees immense potential in the methods, practices, and even workflows that computer engineers have implemented in their own discipline, and is working to bring those skills into Chicago economics through his role both here at the institute and via the Masters in Computational Social Science, a graduate program he's a part of. Evans spoke to us about how we can expect to see computation shape different aspects of economic study, as well as the ways that computer scientists and software engineers can teach economists how to work smarter. Music in this episode:— Auscultation: https://auscultation.bandcamp.com/album/spiritland Boris Mann 2: https://soundcloud.com/boriscloud/acid-jazz-sunday

What can traffic lights tell us about how our economy could work? It may seem a strange question to ask, but Dirk Helbing, is the person who makes the connection. In this episode, he explains what we can learn from complex dynamic systems and how that knowledge can help us understand and implement a feedback-rich circular economy. Dirk Helbing is Professor of Computational Social Science at the Department of Humanities, Social and Political Sciences at ETH Zurich. Listen to the previous episodes of _A New Dynamic podcast series _ _This podcast series presented by Colin Webster explores the recently published book A New Dynamic 2: Effective systems in a circular economy. Each programme features an interview with authors of the book’s chapters. These leading experts on architecture, agriculture, design, business or engineering, provide unique insights that reflect on the necessity to develop a whole-system approach to re-think our economy. _ Order your copy of A New Dynamic 2: Effective systems in a circular economy

An increasing fraction of human interactions are digitally captured. These digital breadcrumbs create enormous opportunities for ground breaking social science, to look at societal phenomena at a scale and granularity heretofore impossible. This talk will discuss what some of the potential scientific opportunities are, as well the potential barriers to the emergence of a “computational social science".