Collecting data from hundreds of religions across the globe presents some challenges. For example, more information is known about some belief systems than others. All parts of the world are not equally represented by data. The historical record includes loads of documentation about the three major religions – Christianity, Hinduism, and Islam – but only fragments of text or material remain from other religions that played a role in world history. When it comes to research outcomes, this unevenness poses a risk of bias toward the known.
Predoctoral fellow Victor Møller Poulsen, working with Professor Simon DeDeo’s team at Carnegie Mellon University’s (CMU) Department of Social and Decision Sciences, set out to examine how human culture changes over time through the lens of cultural landscapes. This approach was rooted in a methodology begun during the 20th century when scientists studying biological evolution used mathematical tools to outline a type of landscape that explained species’ evolution.
The evolutionary landscape approach described paths of natural selection as crossing into low-lying valleys and climbing to higher peaks. For example, a particular species may not develop into a more stable form because of a valley-like barrier. The mathematics behind this way of explaining evolution proved effective in understanding species and how they change.
To test this approach against data concerning worldwide religious beliefs, Poulsen, now at the Santa Fe Institute in New Mexico, turned to the Pittsburgh Supercomputing Center’s (PSC) National Science Foundation-funded Bridges-2 supercomputer. The study required the application of mathematical tools that corrected for incomplete and biased data – presenting a real computational challenge. Being rigorous about what they did and did not know meant exploring a vast number of different possibilities in the computer, with each unknown increasing the number of calculations. The powerful nodes of Bridges-2 enabled this computational exercise.
There’s a trade-off here. By being rigorous as to how we treat missing data, by being rigorous about how we handle finite data, we get an exponentially increasing computational demand … if we simulate histories of religions evolving, we can see if our algorithm can infer the known data correctly. Bridges-2 gave us this ability.
Simon DeDeo, CMU
The results, reported in the journal Entropy, cast light on how religious beliefs emerge and develop. For example, state-endorsed religions experienced stability. Evangelical religions, non-state-sponsored religions and mystery religions each had unique stability – existing on a “floodplain” that offers stability and the ability to change. Extreme traditions, such as those involving human sacrifice, were unstable and did not persist over time.
You can read more about this story here: https://www.psc.edu/cultural-landscapes/
Project Details
Institution: PSC (Pittsburgh Supercomputing Center)
University: Carnegie Mellon University and the Santa Fe Institute
Funding Agency: This work was supported by the National Science Foundation (award no. ACI-1548562). Supercomputing time on Bridges-2 was funded by the NSF’s Extreme Science and Engineering Discovery Environment (award no. ACI-1928147).
Grant Number: See above.
The science story featured here, allocated through August 31, 2022, was enabled through Extreme Science and Engineering Discovery Environment (XSEDE) and supported by National Science Foundation grant number #1548562. Projects allocated September 1, 2022 and beyond are enabled by the ACCESS program, which is supported by National Science Foundation grants #2138259, #2138286, #2138307, #2137603, and #2138296.