  Algorithmic predictions are increasingly used to inform the allocations of
goods and interventions in the public sphere. In these domains, predictions
serve as a means to an end. They provide stakeholders with insights into
likelihood of future events as a means to improve decision making quality, and
enhance social welfare. However, if maximizing welfare is the ultimate goal,
prediction is only a small piece of the puzzle. There are various other policy
levers a social planner might pursue in order to improve bottom-line outcomes,
such as expanding access to available goods, or increasing the effect sizes of
interventions.
  Given this broad range of design decisions, a basic question to ask is: What
is the relative value of prediction in algorithmic decision making? How do the
improvements in welfare arising from better predictions compare to those of
other policy levers? The goal of our work is to initiate the formal study of
these questions. Our main results are theoretical in nature. We identify
simple, sharp conditions determining the relative value of prediction
vis-\`a-vis expanding access, within several statistical models that are
popular amongst quantitative social scientists. Furthermore, we illustrate how
these theoretical insights may be used to guide the design of algorithmic
decision making systems in practice.

Computers and Society;Machine Learning;Theoretical Economics;Machine Learning