All Else Equal: Making Better Decisions

Ep73 “The Dangers of Group Think on Decision Making” with Adi Sunderam

12 snips
Feb 19, 2026
Adi Sunderam, Harvard Business School professor and NBER research associate, explains why people adopt community-driven interpretations of data. He discusses closed model sets, social pressures that exclude alternatives, how post-hoc story selection can make unlikely explanations stick, and organizational remedies like model-driven teams to counteract echo chambers.
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ANECDOTE

COVID Lab-Origin Example

  • Jonathan used the early COVID lab-origin debate as an example of excluded models forcing convoluted natural-origin stories.
  • As discrepant evidence mounted, proponents invented increasingly unlikely natural explanations.
INSIGHT

Post-Hoc Model Choice Can Outcompete Truth

  • People interpret the same data through different focal models and prefer explanations that fit well.
  • Under this behavior, truth need not prevail because post-hoc stories can always be crafted to fit.
INSIGHT

Deep Data Dives Can Still Mislead Groups

  • Highly data-focused groups can still go astray by picking and overfitting favored interpretations.
  • Online communities (e.g., inflation hawks vs doves) repeatedly reframe evidence to preserve prior views.
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