Which of the following models are susceptible to
a feedback loop?
A traffic-forecasting model that predicts congestion at highway exits
near the beach, using beach crowd size as one of its features.
Some beachgoers are likely to base their plans on the traffic
forecast. If there is a large beach crowd and traffic is forecast to be
heavy, many people may make alternative plans. This may depress beach
turnout, resulting in a lighter traffic forecast, which then may
increase attendance, and the cycle repeats.
A book-recommendation model that suggests novels its users may like
based on their popularity (i.e., the number of times the books have been
Book recommendations are likely to drive purchases, and these
additional sales will be fed back into the model as input,
making it more likely to recommend these same books in the future.
A university-ranking model that rates schools in part by their
selectivity—the percentage of students who applied that were admitted.
The model's rankings may drive additional interest to top-rated
schools, increasing the number of applications they receive. If these
schools continue to admit the same number of students, selectivity will
increase (the percentage of students admitted will go down). This
will boost these schools' rankings, which will further increase
prospective student interest, and so on…
An election-results model that forecasts the winner of a
mayoral race by surveying 2% of voters after the polls have closed.
If the model does not publish its forecast until after the polls have
closed, it is not possible for its predictions to affect voter behavior.
A housing-value model that predicts house prices, using
size (area in square meters), number of bedrooms, and geographic location
It is not possible to quickly change a house's location,
size, or number of bedrooms in response to price forecasts,
making a feedback loop unlikely. However, there is potentially
a correlation between size and number of bedrooms (larger homes
are likely to have more rooms) that may need to be teased apart.
A face-attributes model that detects whether a person is smiling
in a photo, which is regularly trained on a database of stock photography
that is automatically updated monthly.
There is no feedback loop here, as model predictions don't have
any impact on our photo database. However, versioning of our input
data is a concern here, as these monthly updates could potentially
have unforeseen effects on the model.