Imagine that the goal of human life isn’t to win the game; the goal of human life is to win the set of all possible games. And in order to win the set of all possible games, you don’t need to win any particular game, you have to play in a manner that ensures that you will be invited to play more and more games.
So when you tell your children to “be good sports” and to “play properly,” what you mean is, play to win, but play to win in such a way that people on your team are happy to play with you, and people on the other teams are happy to play with you so that you keep getting invited to games.
Kadushin, emeritus Professor of Sociology at the CUNY Graduate Center, has been engaged in social science research on network topics since the mid 1960s and has example after example of not only his own work with networks in social science, but also citations of all of the other social scientists I’d expect to see: Ron Burt, Ed Laumann, Stanley Milgram, Stephen Borgatti, Daniel Brass, and Barry Wellman, to name only a few.
Kadushin takes a decided and purposefully social approach to social networks, noting in his introduction that although network science can be applied to power grids, for example, understanding social networks really requires examining them “as if people mattered.” Kadushin proceeds to explore both the psychological and sociological theories underpinning networks as well as the social consequences of networks and their structures.
The first few chapters provide an overview of network concepts, moving from individual network members (Chapter 2) through entire social networks and their subcomponents and network properties (Chapter 3) and finally network segmentation (Chapter 4).
Chapter 5 explores the psychological foundations of social networks and the book continues through successive levels, next examining small groups and leaders (Chapter 6), then entire organizations (Chapter 7), small-world networks and community structures (Chapter 8), followed by network processes like influence and diffusion (Chapter 9). Chapter 10 explores social capital as a function of networks and network position and Chapter 11 gives much-needed attention to ethical dilemmas in social network research. Finally, Chapter 12 reviews “ten master ideas” of social networks.
I found Kadushin’s book extremely helpful in pointing to citations of social network analysis applied to social science. For any social scientist interested in social networks, I’d strongly recommend starting with Understanding Social Networks (with Borgatti, Everett, and Johnson’s Analyzing Social Networks as a second choice). I will also note that while Kadushin focuses on social science, he does not shy away from covering the work of physicists and others on networks, though he avoids mathematics in his explanations (but references the appropriate papers).
Likewise, for the general reader, I can’t think of a better book that explains social networks and their applications to social science and social ideas than what Kadushin offers here. An additional strength of the book is Kadushin’s enjoyable writing style and clear and concise recap at the end of each chapter in which he informs the reader “where we are now.”
I’ve been fielding more questions about research ethics and protecting individuals with regard to data science and big data. The topic warrants a much more in-depth discussion than this blog post, but I’ve noticed one trend that’s worth pointing out: academics previously working at research universities either leaving academia temporarily or permanently for tech companies and industry.
Academic researchers are almost always required to submit their research proposals to their organization’s Institutional Review Board (IRB), an interdisciplinary group of researchers charged with protecting human subjects as outlined in the 1979 Belmont Report and overseeing research ethics training at most universities and research organizations. Private companies are under no such obligation, as the controversial Facebook study (PDF) of emotional contagion demonstrated. These companies rely on the permissions granted by users who consent to the Terms of Service agreements prior to signing up for the service.
For me, it remains an open question whether researchers in private industry are adhering to a “do no harm” maxim. The obvious tension is that profit-motivated entities like startups and publicly-traded tech companies are interested in maximizing investor or shareholder value and are not subject to the same research ethics requirements as publicly-funded research universities.
I’m encouraged that some academic researchers like Jessica Vitak are tackling these issues and looking for ways to increase transparency in big data use. Vitak’s Privacy + Security Internet Research Lab is tackling exactly these questions. I had the opportunity to hear Vitak speak at the recent Human-Computer Interaction Laboratoryannual symposium at the University of Maryland, College Park. One of the potential solutions that Vitak suggests is that the peer review process for academic publications and conferences needs to fill gaps left by insufficient IRB expertise in some areas of data science. This won’t necessarily change what private companies do with individual data, but it’s certainly a start. The controversial Facebook study now includes an “Editorial Expression of Concern,” which appeared after the publication of the study. Had the editor and peer reviewers at PNAS been more attuned to research ethics and human subjects protection during the peer review process, the Facebook authors might have been asked to do a much better job of addressing the ethical implications in their research.
Of course, this raises the thornier question of rejecting research that does not adhere to accepted human subjects protections: in this case, we do not reward the authors for failing to conduct research in an ethical manner, but we prevent information about the research from entering the public domain. I don’t have a good answer to this issue.
I don’t specifically intend to pick on the tech companies here. Plenty of other industries have, in the name of profit-driven research, done harm. But tech companies also represent a particularly desirable organization in which to do research. Traditionally, researchers, especially in the social sciences, had to painstakingly collect their own experimental or correlational data. This was both time consuming and expensive, and perhaps too often resulted in non-significant findings because the research sample was too small. Tech companies, on the other hand, are awash in data that represents a potential intellectual gold mine for social scientists.
My hope is that those who leave academia for the bountiful data available at tech companies remember and abide by their research ethics training, even when they aren’t required to. I also hope that tech companies are engaging with experts in research ethics and taking any objections by those experts seriously.
A recent NPRHidden Brain podcast episode “This is Your Brain on Uber” featured an interview with Keith Chen, who appears to be both Head of Economic Research at Uber and also tenured professor at Yale. If he indeed holds dual roles, it raises important ethical questions about the research he is conducting for Uber. Does Chen conform to the same human subjects protection protocols at Uber that he must when working “at” Yale? Or is there an artificial separation because Uber isn’t Yale and isn’t subject to the same requirements?
During the episode, Shankar Vendantam at one point asks Chen about the implications for individual users’ privacy in research projects based on users’ data. Chen seemed concerned about the implications Vendantam raised, but also somewhat dismissive, simply suggesting that Uber has a Privacy Officer, a hire that was made only after a user outcry when it was discovered that an Uber executive may have inappropriately used his access to track the movements of a reporter. Chen said he didn’t usually worry about his behavioral data being used by tech companies, but that Vendantam’s question is now making him think more about it.
I am encouraged that reporters are challenging researchers and industry on their data and research practices and I certainly don’t believe we should throw the proverbial baby out with the bathwater here. There is much to be gained by using these first-ever datasets of human behavior that will add to what we know and understand about humans and social behavior.
It’s also the case that with great power comes great responsibility. Greater transparency, the involvement of research ethicists, and ensuring truly informed participants should be required not just for academic researchers, but also for researchers working in industry.
Look for a future post on the role of psychologists in the ethical conduct of research, and why I believe that a professional code of ethics is a vital component of protecting individuals.
I picked up Yuval Noah Harari’s Sapiens (find in a library) because of my academic interest in early social complexity and specifically how we humans became the complex social creatures embedded in networks that we are today.
Ostensibly a “history” book (Harari’s PhD at Oxford was in history), Sapiens unexpectedly turned out to be much more than a history book full of names, dates, and places. Instead, Harari focuses on what I can best describe as large-scale shifts in the population (both form and quantity) of the Earth, and, of critical importance, the origins of these shifts.
While Harari doesn’t specifically bring a complexity science perspective to Sapiens, he is erudite and obviously exposed to a broad range of ideas and academic disciplines in addition to history. Biological anthropology, archaeology, cognitive psychology, environmental science, and economics are all very well represented.
Harari doesn’t pull any punches and relies heavily on research and science throughout the book. He discusses both sides of issues and notes if evidence is scant and debate continues, for example, in the competing hypotheses regarding what happened to Homo neanderthalensis, commonly known as Neanderthals. This question appears to have been answered in recent years by DNA evidence, though some “how” questions still remain.
The book is heavy at just over 400 pages, but Harari’s style drew me in from the start: The book opens with a two-page “Timeline of History,” in which he starts at 13.5 billion years ago goes through the Industrial Revolution and ends, interestingly, at “the Future.”
To give an example of his style:
13.5 billion years ago: Matter and energy appear. Beginning of physics. Atoms and molecules appear. Beginning of chemistry.
3.8 billion years ago: Emergence of organisms. Beginning of biology.
500 years ago: The Scientific Revolution. Humankind admits its ignorance and begins to acquire unprecedented power.
I know of no other author who’d pen “Beginning of physics” and “Beginning of biology” in this manner.
Harari is both concise and a contrarian, and I love a contrarian thinker.
Moreover, he gets complexity and the means by which large-scale cascades and changes can occur as the result of many small interactions (“tipping points,” to borrow Malcolm Gladwell’s book title).
In one of my favorite passages, Harari invokes chaos theory in explaining why history can’t be explained deterministically nor can the future be predicted. He writes on page 240:
So many forces are at work and their interactions are so complex that extremely small variations in the strength of the forces and the way they interact produce huge differences in outcomes.
He continues, explaining that exacerbating the problem of predicting the future is the fact that history is a Level Two chaotic system. A Level One chaotic system, like the weather, does not react to predictions made about it. A Level Two chaotic system, on the other hand, reacts to predictions made about it. Example: stock marketscog.
Harari also talks about the spread of ideas over networks: culture (the idea of “memetics”) and nationalism. [Robert Axelrod’s model The Dissemination of Culture uses agent-based modeling to explain the process of cultural dissemination.]
Perhaps the most helpful idea in Sapiens is Harari’s discussion of how we evolved to become Homo sapiens from our chimpanzee forbears and, importantly, what differentiated the Sapiens species from our closest relatives (i.e., the now extinct other members of genus Homo: Homo rudolfensis, Homo erectus, Homo neaderthalensis, Homo denisova, Homo floresiensis, Homo ergaster, Homo soloensis and, quite possibly, others which have simply not been discovered in the archaeological signatures to date). This section of the book, the Cognitive Revolution, tackles the implausibility of the Sapiens catapulting from “an animal of no significance” to the very top of the food chain and spreading like wildfire across an entire planet. Rich with discussions of extant research in psychology and genetics, Harari argues that the collective ability of Sapiens to create shared mental models and myths very likely explains the successes that simply could not be achieved without collective action on such a massive scale. This idea is key for both cognitive and social psychologists seeking to understand how individual cognition results in the emergence of behaviors at the aggregate level of groups, and sometimes enormous groups.
For those looking for a quick exposure to Harari and his ideas, while I heartily recommend reading Sapiens, Harari’s 2015 TED talk nicely covers his take on the role of shared mental models (or “stories”) in the Cognitive Revolution of Homo sapiens:
I’d be remiss if I stopped here, since Harari goes on to discuss the transition of Sapiens from hunter-gatherer bands to agricultural pastoralists during the Agricultural Revolution and then, provocatively, the surprisingly very few forces that have managed to unite mankind into what is increasingly one single global society on planet Earth: money (and, importantly, trust in what money represents), empires, and religions. Harari does not shy away from frank discussion of religion, including humanism.
Finally, Harari covers the Scientific Revolution and how a fundamental shift in our thinking – namely, that despite what empires or religions might profess to know, Sapiens in fact, were ignorant of many things that science could answer – that ultimately spurred so much progress in what is truly the blink of an eye in the very long history of Homo sapiens.
The combination of history, science, and critical interpretation made Sapiens (find in a library) a thoroughly enjoyable read.