Showing papers by "Duncan J. Watts published in 2017"

Redefine Statistical Significance

Abstract: We propose to change the default P-value threshold for statistical significance for claims of new discoveries from 0.05 to 0.005.

Prediction and explanation in social systems

Abstract: Historically, social scientists have sought out explanations of human and social phenomena that provide interpretable causal mechanisms, while often ignoring their predictive accuracy. We argue that the increasingly computational nature of social science is beginning to reverse this traditional bias against prediction; however, it has also highlighted three important issues that require resolution. First, current practices for evaluating predictions must be better standardized. Second, theoretical limits to predictive accuracy in complex social systems must be better characterized, thereby setting expectations for what can be predicted or explained. Third, predictive accuracy and interpretability must be recognized as complements, not substitutes, when evaluating explanations. Resolving these three issues will lead to better, more replicable, and more useful social science.

Should social science be more solution-oriented?

Abstract: Duncan Watts considers whether many branches of social science could benefit from setting research goals aimed at specific and manageable real-world problems. He gives examples and discusses how more solution-oriented social science might work.

Resilient cooperators stabilize long-run cooperation in the finitely repeated Prisoner's Dilemma

Abstract: Learning in finitely repeated games of cooperation remains poorly understood in part because their dynamics play out over a timescale exceeding that of traditional lab experiments. Here, we report results of a virtual lab experiment in which 94 subjects play up to 400 ten-round games of Prisoner's Dilemma over the course of twenty consecutive weekdays. Consistent with previous work, the typical round of first defection moves earlier for several days; however, this unravelling process stabilizes after roughly one week. Analysing individual strategies, we find that approximately 40% of players behave as resilient cooperators who avoid unravelling even at significant cost to themselves. Finally, using a standard learning model we predict that a sufficiently large minority of resilient cooperators can permanently stabilize unravelling among a majority of rational players. These results shed hopeful light on the long-term dynamics of cooperation, and demonstrate the importance of long-run experiments.

Fostering reproducibility in industry-academia research

Abstract: Many companies have proprietary resources and/or data that are indispensable for research, and academics provide the creative fuel for much early-stage research that leads to industrial innovation. It is essential to the health of the research enterprise that collaborations between industrial and university researchers flourish. This system of collaboration is under strain. Financial motivations driving product development have led to concerns that industry-sponsored research comes at the expense of transparency ( 1 ). Yet many industry researchers distrust quality control in academia ( 2 ) and question whether academics value reproducibility as much as rapid publication. Cultural differences between industry and academia can create or increase difficulties in reproducing research findings. We discuss key aspects of this problem that industry-academia collaborations must address and for which other stakeholders, from funding agencies to journals, can provide leadership and support.

Redefine Statistical Significance

Abstract: We propose to change the default P-value threshold for statistical significance for claims of new discoveries from 0.05 to 0.005.

Don’t blame the election on fake news. Blame it on the media.

Abstract: Since the 2016 presidential election, an increasingly familiar narrative has emerged concerning the unexpected victory of Donald Trump. Fake news, much of it produced by Russian sources, was amplified on social networks such as Facebook and Twitter, generating millions of views among a segment of the electorate eager to hear stories about Hillary Clinton’s untrustworthiness, unlikeability, and possibly even criminality. “Alt-right” news sites like Breitbart and The Daily Caller supplemented the outright manufactured information with highly slanted and misleading coverage of their own. The continuing fragmentation of the media and the increasing ability of Americans to self-select into like-minded “filter bubbles”…

Response to Turco and Zuckerman’s “Verstehen for Sociology”

Abstract: I am grateful to Catherine Turco and Ezra Zuckerman for writing such a thoughtful critique of my article, “Common Sense and Sociological Explanations” (AJS 120 [2014]: 313–51). There ismuch of value in their discussion— including, not the least, pointers to some interesting sociological research— and I encourage everyone to read it. As I will explain, however, I think there is less daylight between my own position and theirs than they contend. I suspect, in fact, that much (although not all) of their objection to my argument rests on a misunderstanding of my claim that the interpretability of sociological explanations sits in tension with their scientific (i.e., causal) validity. That said, it is a misunderstanding that, on reflection, is easily made, so I welcome the opportunity to clarify my initial claim. The misunderstanding appears to derive from a sentence (p. 315) in which I argue that “if sociologists want their explanations to be causal, they must place less emphasis on understandability (i.e., sense making) and more on their ability tomake predictions.”From this sentence, Turco and Zuckerman infer that “the strong implication is that the pursuit of verstehen is a diversion—a waste of sociological time and energy” (2017, p. 1273). Actually, I didn’t intend to imply any such thing. Quite to contrary, I later argue that neither social life nor sociology would be possible without the ability of humans to put themselves in the place of others via some process of mental simulation (pp. 326–27). I am certainly not advocating, as Turco and Zuckerman seem to infer, for some form of verstehen-free sociology—in fact, I can’t even imagine what that would look like. On reflection, what that sentence should have said is “if sociologists want their explanations to be causal, then when evaluating them they must place less emphasis on understandability.” This phrase is just four extra words, but with it I would have more clearly articulated my actual point, which is somewhat different than the one that Turco and Zuckerman spend much of their commentary rebutting. As far as I’mconcerned, sociological explanations can be generated in many ways: from data mining, from mathematical models, from the historical record, from ethnographic observations, from survey results, from everyday anecdotal experience, or simply from sitting and thinking about why people do what they do. All these modes of inquiry are useful in their own way and verstehen plays a role in all of them, as Turco

Rebuilding legitimacy in a post-truth age

Abstract: The current state of public and political discourse is in disarray. Politicians lie with impunity. Traditional news organizations amplify fact-free assertions, while outright fake news stories circulate on social media. Public trust in the media, science, and expert opinion has fallen, while segregation into like-minded communities has risen. Millions of citizens blame their economic circumstances on caricatures like “elites” rather than on specific economic forces and policies. Enormously complex subjects like mitigating climate change, or balancing economic growth and inequality, are reduced to slogans. Magical thinking (e.g., that millions of manufacturing jobs can be created by imposing trade restrictions; that everyone can have affordable, high quality, healthcare without individual mandates or subsidies; that vaccines cause autism) has proliferated.