Periodic Reporting for period 3 - PRAGMA (Pragmatics of Multiwinner Voting: Algorithms and Preference Data Analysis)
Berichtszeitraum: 2024-06-01 bis 2025-11-30
PRAGMA is in the area of computational social choice, an
interdisciplinary field of study focused on computational analysis of
group-decision making, including voting and elections. The project has
two main goals: The first one is to adapt, develop, and extend the theory
of multiwinner voting---i.e. the theory of choosing committees---so that
it would become more practical and applicable in real-life settings.
The second one is to build better understanding of preference data, with
a focus on the type of data that appears in multiwinner voting. This way
the second goal is facilitating the progress on the first one, by allowing
the discovered results to be tested on realistic data. The main vision is to
enable the use of fair, efficient, explainable multiwinner voting
mechanisms by societies, institutions, and algorithm designers alike.
Examples of multiwinner elections that PRAGMA focuses on include,
e.g. choosing committees to perform particular tasks, choosing
members of universities' senates, choosing projects to fund within
participatory budgeting exercises in cities, as well as virtual
elections that could help in designing better search
algorithms. Indeed, many real-life elections are carried out using
suboptimal voting rules. For example, many cities use a basic greedy
voting rule for participatory budgeting, which does not ensure proportional
representation of the voters. The goal of the PRAGMA project is to offer
voting rules and means of using them that would avoid these problems and
that would both easy to use and to understand.
To achieve these goals, PRAGMA is strongly focused on understanding
the voting data. In particular, we aim to develop a technique for
analyzing and such data. The idea is that this will enable us to evaluate
how similar is the data generated using synthetic models to real-life one,
and to optimize these models and make them more practical.
Issues that PRAGMA is dedicated to solving are important to the
society because they are at the foundation of democratic processes.
With better voting tools, institutions will be able to make better
decisions. Improved participatory budgeting mechanisms will help
cities develop faster and people feel more involved. Better
algorithmic tools will lead to more fair, more useful search and
recommendation engines. Finally, better understanding of voting data
will be useful both internally within the project, but also beyond,
for follow-up research on the nature of democracy.
interdisciplinary field of study focused on computational analysis of
group-decision making, including voting and elections. The project has
two main goals: The first one is to adapt, develop, and extend the theory
of multiwinner voting---i.e. the theory of choosing committees---so that
it would become more practical and applicable in real-life settings.
The second one is to build better understanding of preference data, with
a focus on the type of data that appears in multiwinner voting. This way
the second goal is facilitating the progress on the first one, by allowing
the discovered results to be tested on realistic data. The main vision is to
enable the use of fair, efficient, explainable multiwinner voting
mechanisms by societies, institutions, and algorithm designers alike.
Examples of multiwinner elections that PRAGMA focuses on include,
e.g. choosing committees to perform particular tasks, choosing
members of universities' senates, choosing projects to fund within
participatory budgeting exercises in cities, as well as virtual
elections that could help in designing better search
algorithms. Indeed, many real-life elections are carried out using
suboptimal voting rules. For example, many cities use a basic greedy
voting rule for participatory budgeting, which does not ensure proportional
representation of the voters. The goal of the PRAGMA project is to offer
voting rules and means of using them that would avoid these problems and
that would both easy to use and to understand.
To achieve these goals, PRAGMA is strongly focused on understanding
the voting data. In particular, we aim to develop a technique for
analyzing and such data. The idea is that this will enable us to evaluate
how similar is the data generated using synthetic models to real-life one,
and to optimize these models and make them more practical.
Issues that PRAGMA is dedicated to solving are important to the
society because they are at the foundation of democratic processes.
With better voting tools, institutions will be able to make better
decisions. Improved participatory budgeting mechanisms will help
cities develop faster and people feel more involved. Better
algorithmic tools will lead to more fair, more useful search and
recommendation engines. Finally, better understanding of voting data
will be useful both internally within the project, but also beyond,
for follow-up research on the nature of democracy.
The work within the PRAGMA project regards (a) various ways of facilitating
the use of multiwinner elections, and (b) building the ``map of elections''
for preference data analysis. Below we provide the project's main achievements.
One important issue regarding multiwinner elections is explaining the
results to both voters and the candidates. Indeed, many multiwinner
rules, such as the single transferable vote rule (STV), do not even
have a naturally defined score, and those rules that do, such as
Proportional Approval Voting (PAV) only provide scores for the whole
committees and not for individual candidates. Hence, within PRAGMA we
have developed a number of techniques for explaining why particular
candidates were or were not selected. These techniques are
particularly useful in participatory budgeting scenarios: We evaluate,
e.g. how many additional votes a project needed to be funded, how
much it cost should have been decreased, which other projects stood in
its way, how it would perform if we added some random noise on top of
the cast votes, etc. Altogether, our methodology provides a
multidimensional evaluation of project performance. While our
techniques often involve solving computationally challenging problems,
we have provided a number of techniques to deal with this
issues. Following this line of research, we have also studied several
game-theoretic models, which guide which candidates and projects
should be nominated in an election, to be successful (in case of
participatory budgeting, we also analyzed how to choose project
costs).
In terms of data analysis, the most important achievement of the
PRAGMA project is developing the ``map of elections'' framework. The
idea is to collect election instances (either those from repositories
of real-life ones or generated synthetically), compute distances
between them, and, based on that, analyze similarities between
them. This, however, requires so-called isomorphic distances, which
are invariant to candidate and voter identities. Within PRAGMA, we
have developed and analyzed such distances, and used them to analyze
elections. One of the imporant recent achievements in this respect is
providing distances that work seamlessly over elections of different
sizes. This way we were able to provide maps of large fractions of
elections from PrefLib and PabuLib databases, which are the most
important collections of real-life elections in computational social
choice.
the use of multiwinner elections, and (b) building the ``map of elections''
for preference data analysis. Below we provide the project's main achievements.
One important issue regarding multiwinner elections is explaining the
results to both voters and the candidates. Indeed, many multiwinner
rules, such as the single transferable vote rule (STV), do not even
have a naturally defined score, and those rules that do, such as
Proportional Approval Voting (PAV) only provide scores for the whole
committees and not for individual candidates. Hence, within PRAGMA we
have developed a number of techniques for explaining why particular
candidates were or were not selected. These techniques are
particularly useful in participatory budgeting scenarios: We evaluate,
e.g. how many additional votes a project needed to be funded, how
much it cost should have been decreased, which other projects stood in
its way, how it would perform if we added some random noise on top of
the cast votes, etc. Altogether, our methodology provides a
multidimensional evaluation of project performance. While our
techniques often involve solving computationally challenging problems,
we have provided a number of techniques to deal with this
issues. Following this line of research, we have also studied several
game-theoretic models, which guide which candidates and projects
should be nominated in an election, to be successful (in case of
participatory budgeting, we also analyzed how to choose project
costs).
In terms of data analysis, the most important achievement of the
PRAGMA project is developing the ``map of elections'' framework. The
idea is to collect election instances (either those from repositories
of real-life ones or generated synthetically), compute distances
between them, and, based on that, analyze similarities between
them. This, however, requires so-called isomorphic distances, which
are invariant to candidate and voter identities. Within PRAGMA, we
have developed and analyzed such distances, and used them to analyze
elections. One of the imporant recent achievements in this respect is
providing distances that work seamlessly over elections of different
sizes. This way we were able to provide maps of large fractions of
elections from PrefLib and PabuLib databases, which are the most
important collections of real-life elections in computational social
choice.
Some of the most exciting progress that we expect within PRAGMA
regards data analysis and the ``map of elections'' framework. Through
the course of the project, we moved from being able to make maps of
synthetic elections of fixed sizes to being able to visualize large
repositories of real-life elections of different sizes and nature.
Some of our most recent progress regards the ability to evaluate features
of elections, such as voter agreement, diversity, and polarization. We
We have also transferred the technique to a number of other settings,
building maps of stable roommates problems, maps of fair allocation
problems, maps of voting rules, or maps of tournament graphs. We
expect that it will have strong effect on experimental studies
regarding these problems, and we already observe this influence.
Further, the introduction of isomorphic distances allowed us to
develop a new technique for learning parameters of statistical
distributions and, more generally, analyzing these distibutions. This
has lead to a significant progress, e.g. in understanding the Mallows
model.
We also invest significant effort into developing algorithmic
infrastructure that would facilitate running participatory budgeting
elections. In particular, we have built a whole spectrum of tools for
analyzing election results and explaining their results. While fair
budgeting rules, such as Method of Equal Shares (MES), may generate
result that at first sight appear surprising (such as funding a more
expensive project that is supported by fewer voters instead of a
cheaper one, with greater support), our techniques provide evidence
why such results are justified.
Altogether, we expect that by the end of the project, we will have a
set of algorithms that will make it easy to run fair, convincing
participatory budgeting elections (as well as other types of
elections, ranging from small scale ones in institutions to virtual
ones, held within computer systems). The algorithms will allow both to
generate realistic data (e.g. for evaluation and testing), compute
election results, and explain these results, if needed.
regards data analysis and the ``map of elections'' framework. Through
the course of the project, we moved from being able to make maps of
synthetic elections of fixed sizes to being able to visualize large
repositories of real-life elections of different sizes and nature.
Some of our most recent progress regards the ability to evaluate features
of elections, such as voter agreement, diversity, and polarization. We
We have also transferred the technique to a number of other settings,
building maps of stable roommates problems, maps of fair allocation
problems, maps of voting rules, or maps of tournament graphs. We
expect that it will have strong effect on experimental studies
regarding these problems, and we already observe this influence.
Further, the introduction of isomorphic distances allowed us to
develop a new technique for learning parameters of statistical
distributions and, more generally, analyzing these distibutions. This
has lead to a significant progress, e.g. in understanding the Mallows
model.
We also invest significant effort into developing algorithmic
infrastructure that would facilitate running participatory budgeting
elections. In particular, we have built a whole spectrum of tools for
analyzing election results and explaining their results. While fair
budgeting rules, such as Method of Equal Shares (MES), may generate
result that at first sight appear surprising (such as funding a more
expensive project that is supported by fewer voters instead of a
cheaper one, with greater support), our techniques provide evidence
why such results are justified.
Altogether, we expect that by the end of the project, we will have a
set of algorithms that will make it easy to run fair, convincing
participatory budgeting elections (as well as other types of
elections, ranging from small scale ones in institutions to virtual
ones, held within computer systems). The algorithms will allow both to
generate realistic data (e.g. for evaluation and testing), compute
election results, and explain these results, if needed.