Simulations: How Ride-Share Companies Use Behavioral Science To "Nudge" Drivers
Lengthy article in NY Times (5,000 words, about 30 minutes) recently about the behavioral tactics utilized by Uber and Lyft to nudge its drivers to take certain actions. Not necessary for students to read the article as I focus this post on 3 of the 4 simulations embedded within it and suggest mini-activities for students to experience the tradeoffs inherent in being a ride-share driver. The simulations also demonstrate how the best interests of the company often diverge from the interest of their drivers. I see these simulations as a fun way to bring behavioral science into the math, psychology, economics or personal finance class.
- How changing number of drivers impacts idleness of drivers.
Using the simulation:
- Reload the article so counters get reset.
- Have students select each of the driver counts and wait 30 seconds to see how it impacts passenger wait times and drivers idling:
- 50 drivers: _______ minutes a passenger is waiting/_________%age of drivers waiting
- 75 drivers: _______ minutes a passenger is waiting/_________%age of drivers waiting
- 125 drivers: _______ minutes a passenger is waiting/_________%age of drivers waiting
- 250 drivers: _______ minutes a passenger is waiting/_________%age of drivers waiting
- Based on the data above, how does the number of drivers impact passenger wait times and idleness of drivers.
- What situation is best for the drivers? Best for the ride-share company?
2. What motivates you more: seeing gains or fearing losses? From the article:
At the time, Lyft drivers could voluntarily sign up in advance for shifts. The consultants devised an experiment in which the company showed one group of inexperienced drivers how much more they would make by moving from a slow period like Tuesday morning to a busy time like Friday night — about $15 more per hour. For another group, Lyft reversed the calculation, displaying how much drivers were losing by sticking with Tuesdays. The latter had a more significant effect on increasing the hours drivers scheduled during busy periods.
Using the simulation:
- Have students reload the page so the simulation resets to 0,0. Press the WORKING Button and describe their feelings after one minute of completing rides. How many rides did they complete in one minute?
- Now reload the page again and click on NOT WORKING button and wait one minute. How do they feel now? How many dropped rides did they experience?
- Compare their feelings from WORKING vs. NOT WORKING. Which was a stronger emotion?
- This concept of loss aversion has relevance to investing also. Here’s a few posts focused on this topic; here and here.
3. Would you focus on dollar targets (e.g., drive until you earn $100) or only drive during busy times?
Over the past 20 years, behavioral economists have found evidence for a phenomenon known as income targeting, in which workers who can decide how long to work each day, like cabdrivers, do so with a goal in mind — say, $100 — much the way marathon runners try to get their time below four hours or three hours.
Why is income targeting bad for drivers?
Strict income targeting is highly inefficient because it leads drivers to work long hours on days when business is slow and their hourly take is low, and to knock off early on days when business is brisk.
Using the simulation:
- Which do you think is better for driver: to work until they earn $100 or to only work during busy times? Why do you think drivers might choose one strategy over another?
- Reload the page
- Jot down the numbers for each strategy after one minute
- Works till $100: Earned: $___________, Hours: ____________, Wage: $_____________
- Works busiest times: Earned: $___________, Hours: ____________, Wage: $_____________
- Which strategy is best for driver to choose? How did you determine that?
- What strategy is best for ride-share companies?
About the Author
Tim's saving habits started at seven when a neighbor with a broken hip gave him a dog walking job. Her recovery, which took almost a year, resulted in Tim getting to know the bank tellers quite well (and accumulating a savings account balance of over $300!). His recent entrepreneurial adventures have included driving a shredding truck, analyzing executive compensation packages for Fortune 500 companies and helping families make better college financing decisions. After volunteering in 2010 to create and teach a personal finance program at Eastside College Prep in East Palo Alto, Tim saw firsthand the impact of an engaging and activity-based curriculum, which inspired him to start a new non-profit, Next Gen Personal Finance.