New.

Geoffrey Challen
1 parent 9db30a7c
Showing 1 changed file with 14 additions and 14 deletions
certainty.tex
@@ -152,7 +152,7 @@ to drive the adaptation approaches discussed next.
 While large-scale split testing is intended to provide good coverage over all
 possible sources of uncertainty we have discussed, it still normally requires
-that only one choice be made at any given time---implying that two
+that only one decision be made at any given time---implying that two
 alternatives may never be evaluated under identical conditions. For
 \texttt{maybe} statements, however, we are exploring the idea of performing
 \textit{simultaneous} split testing. In this model the app forks at the top
@@ -168,7 +168,7 @@ interference between alternatives executing in parallel.
 \subsection{\texttt{\large maybe} Endgames}
 The entire \texttt{maybe} approach is predicated on the fact that there does
-exist, among the alternatives, a right choice, even if it depends on many
+exist, among the alternatives, a right decision, even if it depends on many
 factors and uncertainties. We continue by discussing how the dataset
 generated by post-deployment testing can be used to determine how to
 correctly choose \texttt{maybe} alternatives at runtime.
@@ -203,7 +203,7 @@ adaptation is time varying (dynamic) or not (static). We begin with the
 second, somewhat easier case.
 If the alternative is determined through static adaptation then the correct
-choice is a function of some unchanging (or very-slowly changing) aspect of
+decision is a function of some unchanging (or very-slowly changing) aspect of
 the deployed environment. Examples include the device model, average network
 conditions, the other apps installed on the device, or user characteristics
 such as gender, age, or charging habits. In this case it is possible that the
@@ -227,7 +227,7 @@ into the fragile decision-making we are trying to avoid.
 Instead, the \texttt{maybe} system allows developers to experiment with and
 evaluate a variety of different dynamic adaptation strategies deployed in a
-companion library, with the choice guided by post-deployment testing. For
+companion library, with the decision guided by post-deployment testing. For
 example, if the performance of an alternative is discovered to be correlated
 with a link providing a certain amount of bandwidth, then that adaptation
 strategy can be connected with that particular \texttt{maybe} statement.
@@ -236,18 +236,18 @@ Observe that in some cases of dynamic adaptation, what begins as a
 \texttt{maybe} statement may end as effectively \texttt{if-else} statement
 switching on a static threshold---the same approach we attacked to motivate
 our system. However, through the process of arriving at this point we have
-determined several things that were initially unknown: what the alternatives
-accomplish, that a single threshold works for all users, and what that
-threshold is. And by maintaining the choice as a \texttt{maybe} statement,
-they can continue the adaptation process as devices, users, and networks
+determined several things that were initially unknown: (1) what the
+alternatives accomplish, (2) that a single threshold works for all users, and
+(3) what that threshold is. And by maintaining the choice as a \texttt{maybe}
+statement, they can continue adaptating as devices, users, and networks
 change.
 Another benefit of this approach is that time-varying decisions can be
-outsourced to developers with expertise in the particular area driving
-adaptation policy decisions. For example, by exposing an energy-performance
-tradeoff through a \texttt{maybe} statement, a developer allows it to be
-driven by a sophisticated machine learning algorithm written by an expert in
-energy adaptation, instead of their own ad-hoc approach.
+outsourced to developers with expertise in the particular area affecting
+adaptation decisions. For example, by exposing an energy-performance tradeoff
+through a \texttt{maybe} statement, a developer allows it to be connected to
+a sophisticated machine learning algorithm written by an expert in energy
+adaptation, instead of their own ad-hoc approach.
 \subsubsection{Manual adaptation}
@@ -255,7 +255,7 @@ In some cases even our best efforts to automatically adapt may fail, and it
 may be impossible to predict which alternative is best for a particular user
 using a particular device at a particular time. If the differences between
 the alternatives are small, then it may be appropriate to simply fall back to
-a best-effort choice. However, if the differences between the alternatives
+a best-effort decision. However, if the differences between the alternatives
 are significant then the \texttt{maybe} alternatives may need to be exposed
 to the user through a settings menu. Fortunately, information obtained
 through testing can still be presented to the user to guide their decision.