Whether it be in politics, public health, or corporate finance, why are people more likely to interpret facts or data in a way that fits their preconceived notions about the world as opposed to searching for the fundamental truth?
A new paper from the Harvard Business School called, Sharing Models to Interpret Data (by Joshua Schwartzstein and Adi Sunderam)
studies the propensity for people to adopt interpretations to data based on their community’s beliefs, and why this can lead to less accurate conclusions. Hosts and finance professors Jonathan Berk and Jules van Binsbergen are joined by the paper’s co-author Adi Sunderam, who is a professor of corporate finance at Harvard Business School, a research associate at the National Bureau of Economic Research, and a co-editor of the Journal of Finance.
The conversation covers the complexity of Bayesian updating and how the process is improperly deployed in today’s thinking, not only in corporate decision-making but also on a sociological level. They also discuss Sunderam’s model for explaining how people interpret data, why people are more likely to fall into group-belief dynamics, and if there are any interventions that would lead to better decision-making.
Read Adi Sunderam and Joshua Schwartzstein’s paper: Sharing Models to Interpret Data
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