November 27
- Alex Pentland (2014). Social Physics: How Good Ideas Spread – The Lessons from A New Science. The Penguin Press.
- “If the Big Data revolution has a presiding visionary, it is MIT’s Alex “Sandy” Pentland” (book cover). From his preface: “I don’t teach traditional classes; instead, I bring in visitors with new ideas and get people to interact with others who are on the same journey. When I was academic head of the Media Lab I pushed to get rid of traditional grading; instead, we have tried to grow a community of peers where respect and collaboration on real-world projects is the currency of success and further opportunity. We live in social networks, not in the classroom or laboratory”.
- Dirk Helbing (2015). Thinking Ahead: Essays on Big Data, Digital Revolution, and Participatory Market Society. Springer.
- A book by Helbing, professor of computational social science at the ETH Zurich, and a principal investigator of FuturICT Knowledge Accelerator Crisis Relief System (see his presentation), described as an attempt to create a crystal ball, the machine that would predict the future. The project almost received €1 billion funding from the European Commission (lost in the final round).
- Erez Aiden and Jean-Baptiste Michel (2013). Uncharted: Big Data as a Lens on Human Culture. Riverhead Books.
- A good overview of what you can do with Google Books data.
- Nathan Eagle and Kate Greene (2014). Reality Mining: Using Big Data to Engineer a Better World. MIT Press.
- A book in the spirit of nudging, which provides good insights on the tools available to engineer a better world.
- Viktor Mayer-Schönberger and Kenneth Cukier (2013). Big Data: A Revolution That Will Transform How We Live, Work and Think. John Murray.
- One of the first books on the implications of Big Data. Already more than 2000 citations on Google Scholar.
- A. Hidalgo (2015). Why Information Grows. Basic Books.
- César A. Hidalgo, an economist and director of Macro Connections (MIT Media Lab) looks at an important question.
- Albert-László Barabási (2003). Linked: How Everything Is Connected to Everything Else and What It Means for Business, Science, and Everyday Life. A Plume Book.
- An easy read providing the intuition on networks. It is written by Barabási, one of the pioneers in the field of networks (his Science paper in 1999 on the emergence of scaling in random networks has been cited more than 26,000 times (Google Scholar)).
- Antony M. Townsend (2013). Smart Cities: Big Data, Civic Hackers, and the Quest for A New Utopia. W. W. Norton & Company.
- The move towards a new kind of city.
- Melanie Mitchell (2009). Complexity: A Guided Tour. Oxford University Press.
- An excellent overview in the spirit of the Santa Fe Institute.
- John H. Miller and Scott E. Page (2007). Complex Adaptive Systems: An Introduction to Computational Models of Social Science. Princeton University Press.
- Two economists also attached to the Santa Fe Institute provide a clear discussion on complex adaptive systems.
- Thomas C. Schelling (2006). Micro Motives and Macro Behavior. W. W. Norton.
- A classic by Nobelist Schelling (cited more than 6500 times (Google Scholar)). A good foundation for Agent-Based Computational Modelling.
- Joshua M. Epstein (2006). Generative Social Science: Studies in Agent-Based Computational Modeling. Princeton University Press.
- A creative overview on what can be done with ABM.
Two books on Behavioural Computational Social Science:
- Riccardo Boero (2015). Behavioral Computational Social Science. Wiley
- Scott de Marchi (2005). Computational and Mathematical Modeling in the Social Sciences. Cambridge University Press.
Three textbooks by leadings scholars on networks:
- Albert-László Barabási (2016). Network Science. Cambridge University Press.
- Matthew O. Jackson (2008). Social and Economic Networks. Princeton University Press.
- Mark E. J. Newman (2010). Networks: An Introduction. Oxford University Press.