Arianna Novaro

AAMAS Conference 2026 Conference Paper

Computational Social Choice: Research & Development

• Dorothea Baumeister
• Ratip Emin Berker
• Niclas Boehmer
• Sylvain Bouveret
• Andreas Darmann
• Piotr Faliszewski
• Martin Lackner
• Jérôme Lang

Computational social choice (COMSOC) studies principled ways to aggregate conflicting individual preferences into collective decisions. In this paper, we call for an increased effort towards Computational Social Choice: Research & Development (COMSOC-R&D), a problem-driven research agenda that explicitly aims to design, implement, and test collective decision-making systems in the real world. We articulate the defining features of COMSOC-R&D, argue for its value, and discuss various roadblocks and possible solutions.

AAAI Conference 2024 Conference Paper

Repeated Fair Allocation of Indivisible Items

• Ayumi Igarashi
• Martin Lackner
• Oliviero Nardi
• Arianna Novaro

The problem of fairly allocating a set of indivisible items is a well-known challenge in the field of (computational) social choice. In this scenario, there is a fundamental incompatibility between notions of fairness (such as envy-freeness and proportionality) and economic efficiency (such as Pareto-optimality). However, in the real world, items are not always allocated once and for all, but often repeatedly. For example, the items may be recurring chores to distribute in a household. Motivated by this, we initiate the study of the repeated fair division of indivisible goods and chores, and propose a formal model for this scenario. In this paper, we show that, if the number of repetitions is a multiple of the number of agents, there always exists a sequence of allocations that is proportional and Pareto-optimal. On the other hand, irrespective of the number of repetitions, an envy-free and Pareto-optimal sequence of allocations may not exist. For the case of two agents, we show that if the number of repetitions is even, it is always possible to find a sequence of allocations that is overall envy-free and Pareto-optimal. We then prove even stronger fairness guarantees, showing that every allocation in such a sequence satisfies some relaxation of envy-freeness. Finally, in case that the number of repetitions can be chosen freely, we show that envy-free and Pareto-optimal allocations are achievable for any number of agents.

EUMAS Conference 2022 Conference Paper

Iterative Goal-Based Approval Voting

• Leyla Ade
• Arianna Novaro

Abstract In iterative voting, a group of agents who has to take a collective decision has the possibility to individually and sequentially alter their vote, to improve the outcome for themselves. In this paper, we extend with an iterative component the recent framework of goal-based voting, where agents submit compactly expressed individual goals. For the aggregation, we focus on an adaptation of the classical Approval rule to this setting, and we model agents having optimistic or pessimistic satisfaction functions based on the Hamming distance. The results of our analysis are twofold: first, we provide conditions under which the application of the Approval rule is guaranteed to converge to a stable outcome; second, we study the quality of the social welfare yielded by the iteration process.

IJCAI Conference 2022 Conference Paper

Representation Matters: Characterisation and Impossibility Results for Interval Aggregation

• Ulle Endriss
• Arianna Novaro
• Zoi Terzopoulou

In the context of aggregating intervals reflecting the views of several agents into a single interval, we investigate the impact of the form of representation chosen for the intervals involved. Specifically, we ask whether there are natural rules we can define both as rules that aggregate separately the left and right endpoints of intervals and as rules that aggregate separately the left endpoints and the interval widths. We show that on discrete scales it is essentially impossible to do so, while on continuous scales we can characterise the rules meeting these requirements as those that compute a weighted average of the endpoints of the individual intervals.

JAAMAS Journal 2021 Journal Article

Unravelling multi-agent ranked delegations

• Rachael Colley
• Umberto Grandi
• Arianna Novaro

Abstract We introduce a voting model with multi-agent ranked delegations. This model generalises liquid democracy in two aspects: first, an agent’s delegation can use the votes of multiple other agents to determine their own—for instance, an agent’s vote may correspond to the majority outcome of the votes of a trusted group of agents; second, agents can submit a ranking over multiple delegations, so that a backup delegation can be used when their preferred delegations are involved in cycles. The main focus of this paper is the study of unravelling procedures that transform the delegation ballots received from the agents into a profile of direct votes, from which a winning alternative can then be determined by using a standard voting rule. We propose and study six such unravelling procedures, two based on optimisation and four using a greedy approach. We study both algorithmic and axiomatic properties, as well as related computational complexity problems of our unravelling procedures for different restrictions on the types of ballots that the agents can submit.

IJCAI Conference 2020 Conference Paper

Smart Voting

• Rachael Colley
• Umberto Grandi
• Arianna Novaro

We propose a generalisation of liquid democracy in which a voter can either vote directly on the issues at stake, delegate her vote to another voter, or express complex delegations to a set of trusted voters. By requiring a ranking of desirable delegations and a backup vote from each voter, we are able to put forward and compare four algorithms to solve delegation cycles and obtain a final collective decision.

AAMAS Conference 2019 Conference Paper

Strategic Majoritarian Voting with Propositional Goals

• Arianna Novaro
• Umberto Grandi
• Dominique Longin
• Emiliano Lorini

We study strategic behaviour in goal-based voting, where agents take a collective decision over multiple binary issues based on their individual goals (expressed as propositional formulas). We focus on three generalizations of the issue-wise majority rule, and study their resistance to manipulability in the general case, as well as for restricted languages for goals. We also study how computationally hard it is for an agent to know if they can profitably manipulate.

AAMAS Conference 2018 Conference Paper

From Individual Goals to Collective Decisions

• Arianna Novaro
• Umberto Grandi
• Dominique Longin
• Emiliano Lorini

We introduce the problem of aggregating the individual goals of a group of agents to find a collective decision. Goals are represented by propositional formulas on a finite set of binary issues. We define some rules for carrying out the aggregation of goals and we show how to adapt axiomatic properties from the literature on Social Choice Theory to this setting. The type of problems we are interested in studying for our rules are axiomatic characterizations, as well as the computational complexity of computing the outcome.

IJCAI Conference 2018 Conference Paper

Goal-Based Collective Decisions: Axiomatics and Computational Complexity

• Arianna Novaro
• Umberto Grandi
• Dominique Longin
• Emiliano Lorini

We study agents expressing propositional goals over a set of binary issues to reach a collective decision. We adapt properties and rules from the literature on Social Choice Theory to our setting, providing an axiomatic characterisation of a majority rule for goal-based voting. We study the computational complexity of finding the outcome of our rules (i. e. , winner determination), showing that it ranges from Nondeterministic Polynomial Time (NP) to Probabilistic Polynomial Time (PP).

AAMAS Conference 2018 Conference Paper

Multi-issue Voting with Propositional Goals

• Arianna Novaro

KR Conference 2018 Conference Paper

Preference Aggregation with Incomplete CP-nets

• Adrian Haret
• Arianna Novaro
• Umberto Grandi

Generalized CP-nets (gCP-nets) extend standard CP-nets by allowing conditional preference tables to be incomplete. Such generality is desirable, as in practice users may want to express preferences over the values of a variable that depend only on partial assignments for other variables. In this paper we study aggregation of gCP-nets, under the name of multiple gCP-nets (mgCP-nets). Inspired by existing research on mCP-nets, we define different semantics for mgCP-nets and study the complexity of prominent reasoning tasks such as dominance, consistency and various notions of optimality.

TARK Conference 2017 Conference Paper

Relaxing Exclusive Control in Boolean Games

• Francesco Belardinelli
• Umberto Grandi
• Andreas Herzig
• Dominique Longin
• Emiliano Lorini
• Arianna Novaro
• Laurent Perrussel

In the typical framework for boolean games (BG) each player can change the truth value of some propositional atoms, while attempting to make her goal true. In standard BG goals are propositional formulas, whereas in iterated BG goals are formulas of Linear Temporal Logic. Both notions of BG are characterised by the fact that agents have exclusive control over their set of atoms, meaning that no two agents can control the same atom. In the present contribution we drop the exclusivity assumption and explore structures where an atom can be controlled by multiple agents. We introduce Concurrent Game Structures with Shared Propositional Control (CGS-SPC) and show that they ac- count for several classes of repeated games, including iterated boolean games, influence games, and aggregation games. Our main result shows that, as far as verification is concerned, CGS-SPC can be reduced to concurrent game structures with exclusive control. This result provides a polynomial reduction for the model checking problem of specifications in Alternating-time Temporal Logic on CGS-SPC.

AAMAS Conference 2017 Conference Paper

Strategic Disclosure of Opinions on a Social Network

• Umberto Grandi
• Emiliano Lorini
• Arianna Novaro
• Laurent Perrussel

This paper starts from a simple model of strategic reasoning in situations of social influence. Agents express binary views on a set of propositions, and iteratively update their views by taking into account the expressed opinion of their influencers. We empower agents with the ability to disclose or hide their opinions, in order to attain a predetermined goal. We study classical game-theoretic solution concepts in the resulting games, observing a non-trivial interplay between the individual goals and the structure of the underlying network. By making use of different logics for strategic reasoning, we show how apparently simple problems in strategic opinion diffusion require a complex logical machinery to be properly formalized and handled.

AAMAS Conference 2016 Conference Paper

Group Manipulation in Judgment Aggregation

• Sirin Botan
• Arianna Novaro
• Ulle Endriss

We introduce the concept of group manipulation into the study of judgment aggregation and investigate the circumstances under which an aggregation rule may be subject to strategic misrepresentation of judgments by a group of agents. Our focus is on neutral aggregation rules, which treat all propositions to be judged symmetrically, and we assume that agents strategise to minimise the number of propositions on which they disagree with the outcome of a rule. We find that strategic manipulation by groups of two agents can be ruled out for the independent and monotonic aggregation rules. This family of rules, which is precisely the family of rules for which manipulation by a single agent can be ruled out, includes the widely used uniform quota rules. When three or more agents may coordinate their manipulation, on the other hand, essentially all attractive rules are susceptible to strategic manipulation. However, we are able to recover the family of independent and monotonic rules as being immune to manipulation, if we add the assumption that the members of a group of manipulating agents fear that the others might opt out of the jointly agreed plan.