259 lines
9.0 KiB
TeX

\documentclass{beamer}
\beamertemplatenavigationsymbolsempty
\usetheme{Boadilla}
\usecolortheme{dolphin}
\usepackage{graphicx}
\usepackage{tikz}
\usepackage{dsfont}
\usepackage{comment}
\usetikzlibrary{arrows}
\begin{document}
\title[Participatory Budgeting]{Participatory Budgeting}
\subtitle{Algorithms and Complexity}
\author{Tobias Eidelpes}
\begin{frame}
\maketitle
\end{frame}
\begin{frame}
\frametitle{Table of Contents}
\tableofcontents
\end{frame}
\section{Introduction}
\begin{frame}
\frametitle{What is Participatory Budgeting?}
\begin{quote}
Participatory Budgeting (PB) is a democratic process in which
community members decide how to spend part of a public budget.
\end{quote}
\vspace{1cm}
\begin{itemize}
\setlength{\itemsep}{1.1\baselineskip}
\item Participatory part: community members propose projects
\item Budgeting part: each project requires a fixed amount of money
\item Goal: Fund the \emph{best} projects without exceeding the budget
\end{itemize}
\end{frame}
\begin{frame}
\frametitle{How does it work?}
\tikzstyle{blue} = [rectangle,rounded
corners=3pt,draw=blue!50,fill=blue!20,node distance=1cm]
\tikzstyle{red} = [rectangle,rounded corners=3pt,draw=red!50,fill=red!20]
\tikzstyle{arrow} = [line width=1pt,->,>=triangle 60,draw=black!70]
\begin{center}
\begin{tikzpicture}[shorten >= 2pt,level distance=1.3cm]
\node[blue](design){Design the process}
child { node [blue](collect){Collect ideas}
child { node [blue](develop){Develop feasible projects}
child { node [red](vote){Vote on projects}
child { node [red](aggregate){Aggregate votes \& fund winners}
}
}
}
}
;
\draw [arrow] (design) to (collect);
\draw [arrow] (collect) to (develop);
\draw [arrow] (develop) to (vote);
\draw [arrow] (vote) to (aggregate);
\end{tikzpicture}
\end{center}
\end{frame}
\begin{frame}
\frametitle{A general framework for PB}
\begin{itemize}
\setlength{\itemsep}{1\baselineskip}
\item Projects $P=\{p_1,\dots,p_m\}$
\begin{itemize}
\setlength{\itemsep}{.7\baselineskip}
\item Each project $p\in P$ has associated cost
$c(p):P\rightarrow\mathbb{R}$
\item Projects are either divisible or indivisible (discrete)
\end{itemize}
\item Select a set $P'\subseteq P$ as \emph{winning projects} not
exceeding total budget $B$
\begin{itemize}
\setlength{\itemsep}{.7\baselineskip}
\item Discrete case: $\sum_{p\in P'}c(p)\leq B$
\item Divisible case: $\mu(p): P\rightarrow [0,1]$ with
$\sum_{p\in P'}c(\mu(p))\leq B$
\end{itemize}
\end{itemize}
\end{frame}
\begin{frame}
\frametitle{A general framework for PB ctd.}
\begin{itemize}
\setlength{\itemsep}{1\baselineskip}
\item Voters $V=\{v_1,\dots,v_n\}$
\begin{itemize}
\setlength{\itemsep}{.5\baselineskip}
\item Express preferences over individual projects in $P$ or
over subsets in $\mathcal{P}(P) := \{P'\,|\,P'\subseteq P\}$
\item Preference elicitation is dependent on the input method
(approval-based, ranked orders)
\end{itemize}
\item Aggregation methods
\begin{itemize}
\item Aggregation methods combine votes to determine a set
of winning projects
\item Are usually tied to the input method
\item Rules are used to select projects w.r.t. desired
properties of the outcome (fairness, welfare)
\end{itemize}
\end{itemize}
\end{frame}
\begin{frame}
\frametitle{Input and aggregation methods}
Example input methods:
\begin{itemize}
\item Approval preferences
\item Ranked orders
\item Utility-based preferences
\end{itemize}
\vspace{0.2cm}
Example aggregation methods:
\begin{itemize}
\item Maximizing social welfare
\item Greedy selection
\item Fairness-based selection
\end{itemize}
\vspace{0.2cm}
Aggregation methods depend on how voters elicit their preferences.
\end{frame}
\begin{frame}
\frametitle{Approval-based budgeting methods}
\begin{itemize}
\item Voters approve a subset of projects
\item Voter preferences are assumed to be \emph{dichotomous}
\item A \emph{satisfaction function} provides a metric for voter
satisfaction
\end{itemize}
\begin{block}{An approval-based budgeting scenario}
A budgeting scenario is a tuple $E = (P,V,c,B)$ where $P =
\{p_1,\dots,p_m\}$ is a set of projects, $V$ is a set of voters, $c :
P\rightarrow\mathbb{N}$ is a cost function associating each project
$p\in P$ with its cost $c(p)$ and $B\in\mathbb{N}$ is a budget limit. A
voter $v\in V$ specifies $P_v\subseteq P$, containing all approved
items.
\end{block}
\begin{block}{Budgeting method $\mathcal{R}$}
A budgeting method $\mathcal{R}$ takes a budgeting scenario $E$ and
returns a bundle $A\subseteq P$ where the total cost of the items in
$A$ does not exceed the budget limit $B$.
\end{block}
\end{frame}
\begin{frame}
\frametitle{Satisfaction functions}
\begin{block}{Satisfaction function}
A satisfaction function $sat : 2^P\times 2^P\rightarrow\mathbb{R}$ with
a set $P$ of items, a voter $v$ and her approval set $P_v$ and a bundle
$A\subseteq P$ provides the satisfaction $sat(P_v,A)$ of $v$ from the
bundle $A$. The set of approved items by $v$ that end up in the winning
bundle is denoted by $A_v = P_v\cap A$.
\end{block}
\begin{exampleblock}{$sat_\#(P_v,A)$}
$sat_\#(P_v,A) = |A_v|$: The satisfaction of voter $v$ is the number of
funded items that are approved.
\end{exampleblock}
\end{frame}
\begin{frame}
\frametitle{Satisfaction functions ctd.}
\begin{exampleblock}{$sat_\$(P_v,A)$}
$sat_{\$}(P_v,A) = \sum_{p\in A_v}{c(p) = c(A_v)}$: The satisfaction of voter
$v$ is the total cost of her approved and funded items.
\end{exampleblock}
\begin{exampleblock}{$sat_{0/1}(P_v,A)$}
\[ sat_{0/1}(P_v,A) =
\begin{cases}
1 & \text{if } |A_v|>0 \\
0 & \text{otherwise}
\end{cases}
\]
A voter $v$ has satisfaction 1 if at least one of her approved items is
funded and 0 otherwise.
\end{exampleblock}
\end{frame}
\begin{frame}
\frametitle{Rules for selecting a winning bundle}
{\Large Let $sat$ be a satisfaction function:}
\begin{block}{Max rules}
The rule $\mathcal{R}_{sat}^m$ selects a bundle which maximizes the sum
of voters' satisfaction: $\mathsf{max}_{A\subseteq P}\sum_{v\in
V}{sat(P_v,A)}$
\end{block}
\begin{block}{Greedy rules}
The rule $\mathcal{R}_{sat}^g$ iteratively adds an item $p\in P$ to $A$,
seeking to maximize $\sum_{v\in V}{sat(P_v,A\cup\{p\})}$.
\end{block}
\begin{block}{Proportional greedy rules}
The rule $\mathcal{R}_{sat}^p$ iteratively adds an item $p\in P$ to $A$
seeking to maximize the sum of satisfaction per unit of cost.
\end{block}
\end{frame}
\begin{frame}
\frametitle{Example budgeting scenarios}
\begin{block}{A budgeting scenario}
Items $P = \{p_2,p_3,p_4,p_5,p_6\}$ and their associated cost $p_i$
where $p_i$ costs $i$. Budget limit $B=10$ and 5 voters vote
$v_1=\{p_2,p_5,p_6\}$, $v_2=\{p_2,p_3,p_4,p_5\}$, $v_3=\{p_3,p_4,p_5\}$,
$v_4=\{p_4,p_5\}$ and $v_5=\{p_6\}$.
\end{block}
\begin{exampleblock}{Combining max rule with $sat_\#$}
Under $\mathcal{R}_{|A_v|}^m$ the winning bundle is $\{p_2,p_3,p_5\}$.
The total satisfaction is 8.
\end{exampleblock}
\begin{exampleblock}{Combining greedy rule with $sat_\#$}
Under $\mathcal{R}_{|A_v|}^g$ the winning bundle is $\{p_4,p_5\}$ (first
selecting $p_5$). The total satisfaction is 7.
\end{exampleblock}
\begin{exampleblock}{Combining max rule with $sat_{0/1}$}
Under $\mathcal{R}^m_{sat_{0/1}}$ the winning bundle is $\{p_4,p_6\}$,
achieving max satisfaction.
\end{exampleblock}
\end{frame}
\begin{frame}
\frametitle{}
\end{frame}
\section{Future Directions}
\begin{frame}
\frametitle{Future Areas of Interest}
\begin{itemize}
\item Multi-dimensional constraints
\item Hybrid models
\item Complex resident preferences
\item Market-based approaches
\item The role of information
\item Research spanning the entire PB process
\end{itemize}
\end{frame}
\begin{frame}
\centering
\Large
Thank you for your attention! \\
Questions \& Answers
\end{frame}
\end{document}