Book Chapter: The Psychology of Big Data: Developing a “Theory of Machine” 
​New Working Paper: A Simple Explanation Reconciles “Algorithm Aversion” and “Algorithm Appreciation”: Hypotheticals vs. Real Judgments 
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Research Overview

My research examines why people fail to view themselves and their work realistically.  My primary line of research examines how individuals can assess themselves and the world more accurately by using advice and feedback produced by algorithms (scripts for mathematical calculations).  ​

Theory of Machine
My research examines "big data" from a psychological perspective.  While many organizations focus on producing analytical insights, there is a gap between producing and utilizing those insights (the "last mile" problem). I call my primary line of research Theory of Machine; a twist on the classic "theory of mind" from philosophy.  It examines how people respond to the increasing prevalence of information produced by algorithms. Broadly, this work examines how people expect algorithmic and human judgment to differ from each other in their: input, process, and output. Understanding how people respond to algorithms will help solve the last mile problem and realize the full potential of algorithms.  Read more about Theory of Machine in my book chapter. 

This research asks:
  • Algorithm Appreciation: When are people most likely to leverage the power of algorithmic advice to improve the accuracy of their judgments about the world?
  • Algorithmic Hiring: ​When do applicants prefer to have their application packet assessed by an algorithm or person?
  • Robo-Coaching: ​When do people prefer performance feedback from an algorithm or a person?  

Overconfidence
My second line of research investigates overconfidence, the most pervasive bias.  My work offers new insights into both the antecedents and consequences of overly positive self-views. 

This research asks: