Social Physics: How Good Ideas Spread-The Lessons from a New Science, by Alex Pentland

In Social Physics: How Good Ideas Spread-The Lessons from a New Science, Alex Pentland introduces the readers to a new field of science: social physics. This new field is defined by the author as “a quantitative social science that describes reliable, mathematical connections between information and idea flow on the one hand and people’s behavior on the other” (Pentland, 2014). This science is geared towards developing insights about the “flow of ideas between people” with the objective of creating better social structures such as companies and cities, for example. Pentland notes how the current studies in the social sciences suffer from using static and limited datasets, through which scientists can discover statistical regularities of social phenomena, but can’t exactly build a “causal theory of social structure.” The growing capability of big data technologies, which facilitate the retrieval, storage and analysis of massive amount of individual and peer to peer interaction data, act as the primary enabler of social physics. Through these technologies, the researcher can build a “living laboratory” and mine the “realities” occurring within the social structure using a diverse set of data-rich platforms such as digital sensing platforms and actual individual behavior. It is important to note that social physics rests upon a key premise—a lot of human behavior is habitual and primarily influenced by social learning, which occurs via peer to peer interaction within a social network. Pentland provides sufficient amount of empirical evidence throughout the book in support of this. The author organizes the book into four main parts following an introductory chapter (Chapter 1: From Ideas to Action). The Part I focuses on the field of social physics, whereas the Part II is dedicated to “idea machines”, which discusses the generation of collective intelligence and how social network incentives can be used as a tool to induce organizational change. Parts III and IV are dedicated to data-driven cities and societies respectively. In addition, the author provides four appendices to elaborate on key topics: Reality Mining (Appendix 1), OpenPDS (Appendix 2), Fast, Slow and Free Will (Appendix 3) and Math (Appendix 4). The author concludes this book by providing detailed notes for each chapter, references and index of key terms used in the book. In the upcoming paragraphs, I will provide a review of the key ideas discussed in each part of the book and conclude my review.