Lens 3: Repetition

The final lens examines the elements that contribute to a repetition of a behavior. Not all behaviors are meant to be repeated though, so this is only when behavior designers aim at shaping a repeated behavior. Recycling is a good example of a behavior that should be repeated each time and is not meant to be a one-time behavior change.

There are two elements to examine in that lens: triggers and reinforcers.


Triggers are the elements that will make a person repeat a behavior after the first run through it. Generally, the goal would be to start with Prompts, move on to Cues and then to Habits/Routines.

Not every successful behavior change needs to progress to become a habit though. Annual health screening for example are most often prompted by a letter. If you then attend a screening, then the behavior change has been successful. You don’t need to build a habit to attend annual health screenings, all that matter is that you follow the prompts when it comes.


Prompts are triggers explicitly aiming at getting a person do an action. It can be a notification, an email, a letter, another person asking you or recommending you to do something, etc.


Cues are triggers present in the environment or as a need, that will make a person repeat a behavior. For example, if you generally use Uber for your transportation needs, if you need transport (cue) you will most likely use Uber. Or if you use Instagram and you see something you think you could post (cue), you will snap it and post it.


Habits are trigger to repeat a behavior, obviously. They are the most powerful trigger but also the most difficult to adopt and maintain over time.

Reinforcers & Deterrents

Reinforcers and Deterrents are elements that come in play after a person first complete a behavior. They affect the probability that an agent repeats this behavior by modifying his/her expected evaluation of the Incentives and Choice Architecture dimensions.

Reinforcers increase the probability that a person will repeat the same behavior by increasing the attractivity of repetitions after the first run. They provide more (perceived) value in one of two ways:

  1. increasing the net value provided for repeating a behavior (positive reinforcers)
  2. imposing costs if a behavior is not repeated, i.e. increasing relative value provided for repeating a behavior by opposition to switching to an alternative (negative reinforcers).

Deterrents decrease the probability that a person will repeat the same behavior by decreasing the attractivity of repetitions after the first run.

Positive Reinforcers

Positive reinforcers increase the probability of a repeat behavior by increasing the expected value users will get from repeating a behavior. Personalized recommendations on Netflix or other products are a typical example of getting more value out of a behavior after the first repetitions. Experiencing how easy a behavior is if you thought it was difficult is another. Partially randomized payoffs also are a positive reinforcer, though they may also be characterized as addictive reinforcers.

Negative Reinforcers

Negative reinforcers also increase the probability of a repeat behavior, but they do so by imposing a cost to not repeating a behavior or switching to an alternative. Having locked-in data or data in a proprietary format is the typical example. Network effects is another, as you will suffer should you switch to an alternative.


Deterrents decrease the probability of a repeat behavior by decreasing the expected value users will get from repeating a behavior. For example, having a terrible customer experience will decrease probability of buying again the same product/service. Going through a very lengthy bureaucratic process to complete an application for a governmental service and experiencing the full complexities of doing so will decrease probabilities of a repeat behavior. For digital applications, binge-watching cat videos on Youtube instead of working may decrease the perceived self-esteem of an agent afterwards and may decrease probabilities of doing this same behavior again (which of course will compete with the effects of powerful Reinforcers…).