The Choice Architecture Lab

Goal: This course will empower you to apply behavioral science concepts and methods outside the classroom for real-world impact in organizations. 

Overview: It’s one thing to learn about decision heuristics and biases, choice architecture tools, and nudging in the classroom. It’s an entirely other thing to successfully apply these insights and methods to real-world problems. This semester-long lab is designed to give you that hands-on experience, pairing small teams of students with external organizations (a mix of for-profit and nonprofit, large-scale and startup companies) in mini-consulting engagements and providing coaching along the way. Whatever your post-Wharton career – brand management, product design, human capital management, policymaking, finance, consulting, etc. – if you’re interested in applying behavioral science to your professional work, this course may be a good fit for you.

Throughout the semester, you will strengthen your muscles across: 

• diagnosing a decision or choice process using psychological theories,

• designing ‘nudges’ to improve that decision or choice process,

• evaluating your diagnosis and design with a randomized controlled experiment, and

• communicating your findings to external stakeholders.

You will be researching, observing, writing, listening, debating, creating, testing, analyzing, and presenting—all to implement a real and valuable change in your organization’s world. 

Timing: Tuesdays 3:30-6:30pm

Teams: Students work in groups of 3-4, based on schedule and topic interests. Each team has a designated partner organization, as well as a coach whose "day job" is in behavioral science consulting.

Teaching style: Lectures and working sessions will focus on reviewing core decision and behavioral science concepts, sharing more about their nuances and complexities, tackling common challenges in applying them in the “real world”, and strengthening skills in running experiments. Class activities will be highly hands-on, including games, debates, and mini-hackathons - with a particular focus on helping you progress on your client projects. Where applicable, practitioners and experts will visit class to share their stories and give advice on your work. 

Individual Components (55%)

Take-home assignments (40%): To give you objective feedback on your progress through the course material, we will have 4 open-book, open-notes take-home individual assignments. (10% each)

Reading quizzes (5%): As a commitment device to do the reading, we will have random in-class, very short quizzes on the readings for that day. We will use a randomizer to determine whether it happens on any given day. Each quiz will take ~5 minutes.

Participation (5%): Participation includes constructive questions, comments, and debates during class. Late arrivals or missed classes that have not been pre-approved count negatively towards participation. 

Feedback from your team & organization (5%)

Group Components (45%)

Your capstone assignment as a group comprises six elements: (i) Diagnosis, (ii) Solution Design, (iii) Experimental Design, (iv) Data Analysis, (v) Final Presentation, and (vi) Final Report. 

I will share templates at the start of the semester, and you will select one “owner” for each of the intermediate elements (i)-(iv). While each of those elements counts towards 5% of your grade, it counts 15% (5+10%) towards the grade of the “owner”.

(i)-(iv): The intermediate elements are chunked to help move you through a typical project, and are due at designated deadlines (below). To accommodate the diversity of client situations, your team may request an extension as needed; be sure to explain why and think through the implications for later deadlines. Each team must also meet with me around 2/20-3/1, to make sure you are on track before Spring break.

(v) During core exams week (Tuesday April 23), you will deliver a final presentation in-class, as a dry-run for you to later present this work to stakeholders at your client organization. At least one person from your team must be present in person; all team members should block the class time to zoom in.

(vi) Your final report will be due for the class and the client during finals period (Friday May 3).

Readings

There is no required textbook for this class. All required readings will be posted to Canvas, including links to a handful of multimedia “readings” (e.g., podcasts, videos). Where I assign book chapters, I will work with the Penn library to secure online access to these chapters, subject to copyright restrictions.

Application: 

Given the hands-on nature of this course and involvement of outside organizations, the class is capped at 30 students. Interested students will be asked to complete a short application by late November.

Course credit: 

This class counts as 1 CU towards the OIDD major at Wharton.

Prerequisites: 

None, but it helps to have taken OID690 or a similar behavioral science class (or at least read Thinking, Fast & Slow and Nudge). This class is effectively an advanced decision processes and behavior change course. Students will get the most out of the experience if they already have a basic understanding of judgment and decision making biases and interventions to mitigate them. 

There is no requirement for quantitative background, but it helps to have taken a statistics class like STAT 6130 or STAT 6210, or have waived out of the statistics requirement due to past training. If you are not generally familiar with the following statistical concepts, I recommend reading about them before class: mean, standard deviation, proportion, standard error, the normal distribution, a t-test.  During the class, you will be engaging in analysis of your experiment's data, but you will have your whole team working on it together.

Students: 

Open to Wharton MBA students and, with the permission of the instructor, 3rd or 4th year Wharton undergraduates and Penn Masters of Behavioral and Decision Sciences students. Priority will be given to Wharton MBAs in their 2nd year.

Caveat emptor: 

In many ways this class is an advanced Managerial Decision Making course; if you did not like OID690, you may not like this course. Further, the bulk of the assignments center on running a research project with an external organization, much like a behavioral science consulting team might do; if you do not like consulting or group work, you may not like this course. Finally, we will cover the statistics and operations of experimentation; if you are experienced running experiments from your time in industry, you may find that those lectures and activities are redundant with your past training.

Class policies:

Given the small size of our class and interactive content, late arrivals can be quite disruptive. If you anticipate having to arrive late to any class, email me at least 2 hours beforehand to explain your tardiness. Failure to do so will count against your participation grade.

Over the years, I have experimented with allowing and prohibiting the use of laptops in class. Based on this experience and on popular opinion by your peers who took this class last year, my policy this spring is as follows: laptops and phones are NOT allowed on your desk during class, unless we are expressly using software (e.g., R, GPower) on the former. Tablets for note-taking are allowed, as long as you only use them for note-taking – i.e., no email, no social media, no internet surfing, etc.