COVID-19 and Networked Sensemaking
MARCH 24, 2020
In a past newsletter, we discussed the promise of collective intelligence in our newly networked age:
There can be no return to a world of information scarcity, nor to the stable authority of a centralized broadcast media...What's more, a decentralized collective intelligence with abundant information is, at least in principle, far more powerful and agile than any centralized model could hope to be. The question, therefore, is not how to reassert centralized control over our sensemaking. Rather, it is how to steward the transition to a decentralized collective intelligence that actually orients toward truth.
The escalating coronavirus pandemic has been a proving ground for this decentralized collective intelligence. In the early days of the crisis, online social networks proved indispensable in sourcing and disseminating relevant information. Technologists and others issued dire warnings while many traditional media sources downplayed the threat. These outlets aren't malicious or stupid. Rather, they are adapted a world of linear change. Traditional media excels at making sense of new information within the scope of existing narratives. But where change is exponential and precedent is scarce, the agility of the network prevails.
The networked sensemaking we see on a site like Twitter can be usefully contrasted with the predominately hierarchical models we see in media and government. These hierarchies have much to recommend them: they militate against misinformation and ill-informed speculation by relying on authority and expertise. As the historian Niall Ferguson points out, "a hierarchy is just a special kind of network," one "designed to maximize [the top node's] ability both to access and to control information." When these top nodes are knowledgable, the structure works. In his book The Square and the Tower, Ferguson argues that history has seen numerous alternations between the order and stability of hierarchy and the dynamism of networks. Communication technology often plays a role: the advent of the printing press allowed Martin Luther to build a network and disrupt the hierarchy of the church. (Interestingly, the coronavirus has led the Catholic Church disrupt its own hierarchy by encouraging confessors to "take [their] sorrow directly to God.")
The coronavirus has shifted the balance away from stable hierarchy and toward network dynamism. One of the most widely shared and influential pieces on the virus came from a non-expert publishing on Medium. The lauded Imperial College study, which warned of healthcare system overwhelm, was anticipated by another Medium article days earlier. These networks are not flat or random: they cluster around experts or epistemically savvy amateurs. They also depend crucially on reliable knowledge generated by hierarchical institutions, like academic papers and studies. But they are able to aggregate information more quickly and spread it far more widely than institutions. Whereas institutional authority comes from expertise and reliability, the network's authority stems from its responsiveness. In a rapidly changing crisis, this is extremely compelling.
Of course, the network also generates and spreads misinformation at an alarming rate. This has led various platforms to scale up their policing efforts. Medium even took down an article cautioning against COVID "hysteria." But as the tech writer Ben Thompson notes, the ability to find misinformation when looking for it is an inevitable consequence of information abundance. "...[T]he implication of the Internet making everyone a publisher is that there is far more misinformation on an absolute basis," Thompson writes, "but that also suggests there is far more valuable information that was not previously available." Moreover, the World Health Organization has itself come under fire for producing misleading data, to say nothing of the mixed messages coming from government. It's hard not to be grateful for the network in this context.
We can draw some lessons about when and why to trust networked sensemaking by comparing two of the most viral Medium articles: Tomas Pueyo's "Coronavirus: Why You Must Act Now" and Aaron Ginn's "COVID-19: Evidence over hysteria." The former has garnered hundreds of endorsements from experts; the latter, as previously mentioned, was taken down by Medium. Both articles were written by non-experts working in technology; both claimed some measure of insight into exponential growth from working with viral applications. Both authors aggregated a variety of studies and data from respected sources. But the authors' approaches sharply diverged. Ginn emphasized the ability of the data to speak for itself: "You don't need a special degree to understand what the data says and doesn't say. Numbers are universal." Pueyo, by contrast, encouraged his readers not to trust him. He highlighted his role as a synthesizer and spreader of expert opinion, not an independent interpreter of the data.
This is key: stable institutions generate lasting theoretical frameworks that are vital for accurately interpreting bottom-up data. But dynamic networks can aggregate, synthesize, and spread information at a rate that matches the pace of exponential change. The two will surely have to work together to meet the challenge of this crisis.