A new Bayesian analysis of remote work data supports one of the oldest theories of social networks, with new implications for the future of work environments.
Weak ties describe the infrequent connections we have with acquaintances, occasional colleagues, and regular friends in our social networks.1. Despite their ‘weakness’, these ties often lead to new ideas, opportunities and advice in organizational settings.1,2,3,4. Although this finding has largely stood the test of time, much is still unknown about its causal mechanisms.4. What dynamically generates these links? What keeps them going? Can they withstand “exogenous” shocks from the outside, such as location changes driven by the COVID-19 pandemic? This evolving landscape gives new urgency to calls for “knowledge in particular [social] processes at specific moments” through new data, methods and analytical rigor5. Now, an article a Nature Computational Science answers this call, demonstrating that new computational methods can investigate difficult questions about organizational networks. In their piece, Daniel Carmody and colleagues6 Use Bayesian time series analysis to provide evidence supporting an important and understudied theory in social media called proximity, which states that spatial proximity increases the likelihood of creating new connections and strengthening existing ones, in the context of COVID -19.
The formation of weak ties often begins when family, community, or organizational activities bring people together. Simply being in physical proximity to someone increases the likelihood of serendipitous interactions. In turn, these interactions give people the opportunity to explore shared qualities, interests, and behaviors with each other and thus form bonds. These steps describe the social process of proximity: the closer we are physically to another person, the more likely we are to form a new bond or reify an existing one with them.7 (Fig. 1a). Existing work has found that sharing socially meaningful qualities can amplify the effects of proximity8 and this proximity extends to virtual proximity9,10. But the concept is often taken for granted even though it has important implications for how we design organizations and social gatherings. This leaves a surprising paucity of evidence showing this process as it happens, and so we have no knowledge of how the process can improve everything from the diffusion of technology to inequalities.4.
Carmody et al. provided an important empirical demonstration that weak ties are formed and degraded by proximity. Most social network studies compare a few snapshots of social networks over a time interval because the collection of granular temporal network data is often quite difficult, both logistically and ethically. Ultimately, this prevents us from witnessing when and how most bonds are formed. The authors overcome this obstacle by estimating the number of weak ties between researchers at the Massachusetts Institute of Technology (MIT). Their email dataset spans two dramatic changes in researchers’ work locations over a year and a half during the COVID-19 pandemic. The first transition took place on March 23, 2020, when MIT halted most in-person research activities. The researchers began working from home, hypothetically preventing weak ties from forming through proximity. The second transition occurred on July 15, 2021, when researchers began returning to campus, hypothetically increasing the formation of weak ties through proximity. The authors examined the email network spanning these real-world location transitions through a counterfactual synthetic email network with these transitions absent to estimate how much proximity affected the formation of weak ties.
Methodologically, the authors constructed their synthetic counterfactual using a Bayesian Structural Time Series (BSTS) approach that separates the effect of a treatment (here, remote work) from qualities unaffected by the treatment (such as linear trends and cyclical variations). This allowed them to construct a credible range for the expected number of weak ties with and without remote work. Their analysis showed that telecommuting may have cost nearly 5,100 weak ties during the telecommuting period, about 1.8 ties per person, at the cost, of course, of important public health goals due to the pandemic Furthermore, researchers were more likely to lose weak ties with people working in nearby laboratories than with those working in the same or distant laboratories (Fig. 1b). Consequently, the researchers got “stuck” by strengthening their existing ties. As a validation of this finding, they designed a generative network simulation to replicate various bond formation mechanisms (such as sharing a lab, mutual friends, and co-location). In doing so, they show qualitatively that a proximity factor replicates the finding of their BSTS analysis.
Carmody and his colleagues demonstrated the potential of modern computational techniques to support old social science theories and identify new phenomena. Their research could provide rigorous tools for verifying elusive causal hypotheses of social networks and information4.5. Future studies should consider how other confounders might affect these results. For example, the authors note that having insufficient data from before the pandemic limited their ability to predict cyclical effects. This echoes the importance of properly constructing controls for quasi-experiments5. Size and location of the building, researcher demographics, university-required activities, and type of information exchanged (professional versus friendly).11) may confound the results, but may also reveal unexplored questions. A word of caution, however: the benefits of computing power become moot without an adequate framework of qualitative and theoretical social science.5. Naïve computational studies run the risk of drawing incorrect conclusions. Interdisciplinary collaborations between scholars with computational and theoretical perspectives could begin to answer tough and persistent questions, but with patience and curiosity on all sides, in ways that may benefit the way we design opportunities for social interaction in the years to come. .
Granovetter, MS Am. J. Sociol. 781360–1380 (1973).
Constant, D., Sproull, L. and Kiesler, S. organ science 7119–135 (1996).
Hansen, MT Admin. science Q. 4482–111 (1999).
Aral, S. Am. J. Sociol. 1211931–1939 (2016).
Davis, GF organ Nothing. Meth. 13690–709 (2010).
Carmody, D. et al. born computer science science https://doi.org/10.1038/s43588-022-00296-z (2022).
McPherson, M., Smith-Lovin, L. and Cook, JM course rev. sociol. 27415–444 (2001).
Reagans, R. organ science 22835–849 (2011).
Walther, JB and Bazarov, NN common Nothing. 35622–645 (2008).
Lewis, K. & Kaufman, J. Am. J. Sociol. 1231684–1742 (2018).
Balkundi, P. & Harrison, DA Acad. manage J. 4949–68 (2006).
The author declares that there are no competing interests.
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Meluso, J. Model supports for social network theory.
Nat Comput Sci 2, 471–472 (2022). https://doi.org/10.1038/s43588-022-00302-4