Merge branch 'master' of version.helsinki.fi:rikulain/bachelors-thesis

paper/experiments.tex

@@ -79,11 +79,9 @@ We describe both of them below.
 %
 The decisions of decision makers \humanset are based on their perception of the dangerousness of a case, to which we refer as the `{\it risk score}'.
 %
-The risk score is a function over the features \obsFeatures and \unobservable.%, denoted $f(\obsFeatures, \unobservable)$.
-%
-With synthetic data the risk score function 
+With synthetic data we compute the risk score so that
 \begin{equation} \label{eq:risk}
-f(\obsFeatures, \unobservable) = \invlogit(b_\obsFeatures \obsFeatures + b_\unobservable \unobservable).
+\text{risk score} = b_\obsFeatures \obsFeatures + b_\unobservable \unobservable.
 \end{equation}
 
 For the {\it first} type of decision makers we consider, we assume that decisions are rational and well-informed, and that a decision maker with leniency \leniencyValue makes a positive decision only for the \leniencyValue fraction of cases that are most likely to lead to a positive outcome. 
@@ -99,9 +97,6 @@ Considering a decision maker with leniency $\leniency = \leniencyValue$ who deci
 	s \leq F^{-1}(\leniencyValue).
 \end{equation} 
 %
-
-See Appendix~\ref{sec:independent} for more details. 
-%
 Since in our setting the distribution $F$ is given and fixed, such decisions for different cases happen independently based on their risk score.
 %
 Because of this, we refer to this type of decision makers as \independent.