Big data and artificial intelligence get most of the press about computational social science, but maybe the most complex aspect of it refers to using computational tools to explore and develop social science theory. This course shows how computer simulations are being used to explore the realm of what is theoretically possible. Computer simulations allow us to study why societies are the way they are, and to dream about the world we would like to live in. This can be as intuitive as playing a video game. Much like the well-known video game SimCity is used to build and manage an artificial city, we use agent-based models to grow and study artificial societies. Without hurting anyone in the real world, computer simulations allow us explore how to make the world a better place. We play hands-on with several practical computer simulation models and explore how we can combine hypothetical models with real world data. Finally, you will program a simple artificial society yourself, bottom-up. This will allow you to feel the complexity that arises when designing social systems, while at the same time experiencing the ease with which our new computational tools allow us to pursue such daunting endeavors.
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Spécialisation Computational Social ScienceUniversité de Californie à DavisÀ propos de ce cours
Offert par
Université de Californie à Davis
UC Davis, one of the nation’s top-ranked research universities, is a global leader in agriculture, veterinary medicine, sustainability, environmental and biological sciences, and technology. With four colleges and six professional schools, UC Davis and its students and alumni are known for their academic excellence, meaningful public service and profound international impact.
Programme du cours : ce que vous apprendrez dans ce cours
Getting Started and Computer Simulations
In this module, you will be able to define theoretical computer simulations, specifically agent-based models (ABM). You will be able to recall how and why agent-based models can be useful and you'll be able to examine Schelling's famous segregation model.
Artificial Societies: Sugarscape
In this module, you will be able to identify how to mix different models to create new and more complex models. You will be able to explore how to create sophisticated versions of artificial societies. You'll also be able to examine an artificial society called Sugarscape.
Computer Simulations and Characteristics of ABM
In this module, you will be able to discover how one uses computer simulations to solve practical problems. You will be able to discuss agent-based models (ABM) and identify how ABM can be used in social science.
Model Thinking and Coding Artificial Societies
In this module, you will be able to describe what agent-based models are. You will be able to identify their capabilities and limitations. You will be able to define and use vocabulary and terminology around model thinking. You'll also be able to code using NetLogo and be able to grow your own artificial society.
Avis
Meilleurs avis pour COMPUTER SIMULATIONS
Very good course with great explanations. The wordings on questions of the quizzes could have been clearer.
À propos du Spécialisation Computational Social Science
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What do students say after completion?
These are some reflections shared by students who have worked through the content of the Specialization on Computational Social Science:
- "Highly enjoyable and most importantly, giving me exceptionally important skills to fulfill my job requirements at a new position in Munich. You may be interested to know the impact of your course on salary and in my case, the knowledge and certification gained adds about another Euro 20.000 on the annual salary (taking it to about Euro 120.000 p.a.)."
- "My overall impression of this was: I can't wait to use this for other stuff!!"
- "I absolutely think that these tools could be used in my future jobs, or even as a personal reflection. If you scrape and analyze the comments/reactions that your business gets on Youtube, Twitter, Instagram, etc., what does their language use say about how they interact with your brand — or what your brand brings out in them?"
- "Wow, this is cool and fun stuff. Even though I may not pursue anything social-science related in the near future, it is still nice to learn and get to experience all of these tools that computational social science offers and benefits in all kinds of careers and fields of study."
- "I particularly enjoyed the web-scraping for some reason. It feels very advanced although its very easy. ...It seems to be a very fast and efficient way of grabbing data."
- "I enjoyed playing around with machine learning! ...It was also amazing to me how quickly it was able to grasp and learn our input in seconds. It makes me wonder how much more technology will advance in these next few years... It's scary but fascinating."
- "The fact that these tools are so easily usable and attainable is incredible in my mind. Not only do we have access to them like we have access to things like Facebook and Twitter, but they're FREE."
- "The most interesting aspect was the fact that these tools are all free and online. In the past, only researchers at well-funded universities had access to programs like the ones we used in all of our labs. But now, even someone without much technical knowledge on complex software can use these tools."
- "I am so surprised that these tools are available to anyone through a simple download, and even more so that they are very user friendly and easy to learn how to navigate. I plan on starting a clothing line company in the future and I think it will be really helpful for me to be able to analyze so much online data."
- "As an Environmental Policy Analysis and Planning major, I was fascinated to learn that there is a feasible way to simulate policy implementation and impact multiple times within a short span of time."
- "UCCSS has allowed me to feel more confident in my abilities with a computer and to better understand companies like Facebook or Twitter. ...these tools really are powerful but also dangerous. ...It allows powerful individuals to manipulate ideas."
- "Throughout the course, the content was challenging, but when it was finally applied to the labs at the end of each module, it was really rewarding to see everything play out. It was even more rewarding when it made sense too! ... I'm really glad I took this course! It was definitely a challenge, but I'm glad I got to experience and learn about so many topics I never knew even existed."
- "It was fun seeing the results of the code that I made, and I never thought that I would be doing something like this in my life. The results also showed me what the society would look like.... Social network analysis and web scraping could be the tools that I use in my future job as all the internship that I'm looking now all related to social media or digital media."
- "My career aspiration is to be a digital marketing expert. These computational tools have enormous implications for the field."
- "I really really loved that this class let me learn hands-on and gave me experience with tools that have real world application and combine STEM & social science. I think that a lot of these tools are useful far beyond homework activities."
- "Best course I have taken. I wish more online courses structured like this would be offered."
Since this Specialization is a collective effort from all UC campuses, who teaches it?
This Specialization on Computational Social Science is the result of a collective effort with contributions from Professors from all 10 campuses of the University of California. It is coordinated by Martin Hilbert, from UC Davis, and counts with lectures from:
1) UC Berkeley: Joshua Blumenstock, Prof. iSchool; Stuart Russell, Professor of Computer Science and Engineering.
2) UC Davis: Martin Hilbert, Prof., Dpt. of Communication & Seth Frey, Prof., Dpt. of Communication & Cynthia Gates, Director of the IRB.
3) UC Irvine: Lisa Pearl, Prof. Cognitive Sciences.
4) UC Los Angeles: PJ Lamberson, Assistant Prof. Communication Studies.
5) UC Merced: Paul Smaldino, Prof. Cognitive and Information Sciences.
6) UC Riverside: Christian Shelton, Prof. Computer Science.
7) UC San Diego: James Fowler, Prof. Global Public Health and Political Science.
8) UC San Francisco: Maria Glymour, Associate Prof. School of Medicine, Social Epidemiology & Biostatistics.
9) UC Santa Barbara: René Weber, Prof. Dpt. of Communication & Media Neuroscience Lab (with Frederic Hopp).
10) UC Santa Cruz: Marilyn Walker, Prof. Computer Science, Director, Computational Media.
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