Charles Wan

Charles is broadly interested in how humans make decisions against a certain causal structure or representation of the world.  Recently, the adoption of statistical learning algorithms has introduced a novel causal context for studying the human learning-cum-decision-making process.  Charles' research explores how statistical learning algorithms interact with human agency, human causal reasoning, organizational information-processing, and organizational decision-making. 

Previously Charles worked as a commodities trader in Europe, the US, and Asia. 

For more information please visit https://wan-charles.github.io/.