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
New Working Paper: A Simple Explanation Reconciles “Algorithm Aversion” and “Algorithm Appreciation”: Hypotheticals vs. Real Judgments
The Psychology of Big Data
I created the Psychology of Big Data class and teach the topic in three different formats:
- NEW: First Year Seminar
- NEW: Georgetown Undergrad Elective (MGMT 295 People Analytics)
- Georgetown's Master of Science in Business Analytics
Examine data analytics from a psychological perspective.
“Effectively communicating and sharing analytic insights
is as important as finding them.” -Schrage
“Effectively communicating and sharing analytic insights
is as important as finding them.” -Schrage
Course Description
With the rise of big data, more and more organizations are trying to leverage the accuracy of algorithmic advice to inform managerial decisions. The issue is that while many organizations are swimming in data and investing in algorithms, many are trying to understand how they can maximize the benefits of algorithmic advice. How can they do this? By understanding when decision makers are willing to listen to algorithmic advice. Focusing on the case of Moneyball, you will consider strategies for communicating insights from big data to different audiences. How will you leverage big data to solve consequential organizational and societal issues?
What is the Problem?
The Last Mile Problem is the communication gap between producing and utilizing insights.
Why is this problem important?
Algorithms can help us improve the accuracy of our predictions but only if people are willing to listen.
How do we solve the Last Mile Problem?
Data analytics needs psychology to understand how people respond to algorithmic advice and broadly re-examine what we know about decision making in the age of big data and automation. This class was created specifically to bridge the gap between analysts and decision makers, who are often siloed in their organizations.
Your Competitive Advantage
At its heart, this class sharpens your critical thinking to improve empirical literacy, including how to identify and critically assess different types of evidence: anecdotal, correlational, and experimental. You can gain a competitive advantage in your career by combining empirical literacy and insights from psychology and behavioral economics to better understand and predict how people think and behave.
With the rise of big data, more and more organizations are trying to leverage the accuracy of algorithmic advice to inform managerial decisions. The issue is that while many organizations are swimming in data and investing in algorithms, many are trying to understand how they can maximize the benefits of algorithmic advice. How can they do this? By understanding when decision makers are willing to listen to algorithmic advice. Focusing on the case of Moneyball, you will consider strategies for communicating insights from big data to different audiences. How will you leverage big data to solve consequential organizational and societal issues?
What is the Problem?
The Last Mile Problem is the communication gap between producing and utilizing insights.
Why is this problem important?
Algorithms can help us improve the accuracy of our predictions but only if people are willing to listen.
How do we solve the Last Mile Problem?
Data analytics needs psychology to understand how people respond to algorithmic advice and broadly re-examine what we know about decision making in the age of big data and automation. This class was created specifically to bridge the gap between analysts and decision makers, who are often siloed in their organizations.
Your Competitive Advantage
At its heart, this class sharpens your critical thinking to improve empirical literacy, including how to identify and critically assess different types of evidence: anecdotal, correlational, and experimental. You can gain a competitive advantage in your career by combining empirical literacy and insights from psychology and behavioral economics to better understand and predict how people think and behave.
How can decision-makers critically assess results and, in turn, clearly communicate insights from big data to different audiences?
Course Objectives: Empirical Literacy
Course Objectives: Empirical Literacy
- Identify and scope consequential problems
- Consider appropriate metrics in order to leverage data effectively
- Critically evaluate evidence from analytics
- Persuasively communicate insights to a wide audience including technical and non-technical roles
Students from my Spring 2019 class in the Harvard Extension Program
proposed solutions to organizational problems using big data.
Listed below are select proposals with
independent variables (IVs) used to predict
key outcomes, dependent variables (DVs).
proposed solutions to organizational problems using big data.
Listed below are select proposals with
independent variables (IVs) used to predict
key outcomes, dependent variables (DVs).
