How Three Social Scientists Predicted the New Pope
- Miriam Grobman
- Jun 25
- 3 min read
A few days ago, I was procrastinating on LinkedIn (more here about why I do this) when I stumbled upon a story headline: "Scientists literally used math to predict the next Pope."
The post went on to explain how pure math beats AI.
I was triple-triggered: first by the word "literally" used inappropriately, then by the story itself, and finally by the misrepresentation/oversimplification of what these folks did or did not do.
Yes, I know. Nerd^3 here.
The true story was the following: three social scientists from the University of Bocconi used a very well-known theory and method, called social network analysis, to map out the relationships between the members of the college of cardinals, identify the most powerful/influential figures, and based on several metrics, individual characteristics (ex: age) and basic statistical analysis (regression) predict who will be the next pope.
The clever part of this analysis is NOT the use of math (network analysis software and EVEN ChatGPT do the math automatically for you behind the scenes). It is identifying the types of relationships that lend someone status (see the note below) in the Vatican and finding data that shows who is connected to whom.
In this case, researchers described the following sources of data to construct the Vatican network:
Official co-memberships (Roman curia dicasteries, commissions, councils, academies): these data reveal who works with whom and in what institutional contexts.
Lines of episcopal consecration: each cardinal was ordained by others, and these “spiritual genealogies” build strong bonds of loyalty and recognition.
Informal relationships: mapped through authoritative journalistic sources, these include ideological affinities, mentoring relationships, and membership in patronage networks.
Here’s a visual take on one way to measure a cardinal’s status—or, in social network lingo, their centrality—shown by the size of each bubble in the graph. It’s based on overlapping connections through the Vatican organizations they participate in and through spiritual bonds (consecration).
And look who’s right smack in the middle: Robert Francis Prevost.
As we now know, he went on to become Pope Leo XIV.
If you want to know more about the analysis, you can see all the details and some more interesting graphs in the authors' original paper.
Why am I telling you this?
I am not a Catholic.
I just love studying human behavior, and social network analysis (SNA) is like a secret detective tool that can help us figure out things such as:
Who are the informal leaders in your company?
How influential is the formal leadership?
Does being the corporate social butterfly make one more or less productive?
How does work arrangement (fully-remote, hybrid, or fully in-office) affect work interactions between teams and within teams?
Who are the people that everyone avoids?
Who are the bridges or bottlenecks in cross-functional projects?
SNA is not only applicable to people but also to entities in all kinds of environments. It has been applied in many domains. Here are just a few examples:
How informal networks help during disaster response, or hinder it through misinformation
How community stakeholder engagement can help strengthen water resources public policy
How diseases spread in a community (we heard all about those R factors during the COVID-19 pandemic),
How to prevent money laundering through identifying illicit flows through shell companies
How drug dealers transport money and products across geographies
At this point, you are probably either very curious or very bored. In future posts, I will share a few practical ideas on how to get started with SNA to understand the structure of relationships in your organization and how they affect the culture and performance.
This knowledge can be quite handy when the next AI tool or fancy consultant comes knocking on your door and asking you to pay them $$$ for such insights.
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