Empathy as a Topological Data Experiment

Abstract

I introduce and solve a class of information asymmetries problems in which agents have uncoordinated perspectives of objective information. I first present a new primitive of literal personal perspective frames: coordinate systems through which agents view phenomena based on their subjective experiences. In the model, the transformation of coordinate systems from one perspective frame to another represents empathy. Changing perspective frames as described resembles conducting experiments, but on topological data. I extend topological data analysis to randomized controlled trials by introducing the existence and estimation of average treatment effects in topological data environments. The settings imply that being embedded in significantly varying topology is a unique reason why different individuals might update beliefs differently when faced with an identical statistical experiment. For similar topological reasons, the provided average treatment effects may not necessarily be replicable or externally valid when other metrics and topological environments are analyzed. Collectively, the findings have implications for behavioral aspects of experimentation and research transparency issues.