We study a setting in which a community wishes to identify a strongly supported proposal from a space of alternatives, in order to change the status quo. We describe a deliberation process in which agents dynamically form coalitions around proposals that they prefer over the status quo. We formulate conditions on the space of proposals and on the ways in which coalitions are formed that guarantee deliberation to succeed, that is, to terminate by identifying a proposal with the largest possible support. Our results provide theoretical foundations for the analysis of deliberative processes such as the ones that take place in online systems for democratic deliberation support. Earlier versions of this article have been accepted for presentation at the 35th AAAI Conference on Artificial Intelligence, AAAI-21 [Elkind et al., 2021] and at the 8th International Workshop on Computational Social Choice, COMSOC-21.

Attention-Aware Social Choice tackles the fundamental conflict faced by some agent communities between their desire to include all members in the decision making processes and the limited time and attention that are at the disposal of the community members. Here, we investigate a combination of two techniques for attention-aware social choice, namely Natural Language Processing (NLP) and Sampling. Essentially, we propose a system in which each governance proposal to change the status quo is first sent to a trained NLP model that estimates the probability that the proposal would pass if all community members directly vote on it; then, based on such an estimation, a population sample of a certain size is being selected and the proposal is decided upon by taking the sample majority. We develop several concrete algorithms following the scheme described above and evaluate them using various data, including such from several Decentralized Autonomous Organizations (DAOs).

Participatory Budgeting (PB) is a process in which voters decide how to allocate a common budget; most commonly it is done by ordinary people -- in particular, residents of some municipality -- to decide on a fraction of the municipal budget. From a social choice perspective, existing research on PB focuses almost exclusively on designing computationally-efficient aggregation methods that satisfy certain axiomatic properties deemed "desirable" by the research community. Our work complements this line of research through a user study (N = 215) involving several experiments aimed at identifying what potential voters (i.e., non-experts) deem fair or desirable in simple PB settings. Our results show that some modern PB aggregation techniques greatly differ from users' expectations, while other, more standard approaches, provide more aligned results. We also identify a few possible discrepancies between what non-experts consider \say{desirable} and how they perceive the notion of "fairness" in the PB context. Taken jointly, our results can be used to help the research community identify appropriate PB aggregation methods to use in practice.

Any community in which membership is optional may eventually break apart, or fork. For example, forks may occur in political parties, business partnerships, social groups, cryptocurrencies, and federated governing bodies. Forking is typically the product of informal social processes or the organized action of an aggrieved minority, and it is not always amicable. Forks usually come at a cost, and can be seen as consequences of collective decisions that destabilize the community. Here, we provide a social choice setting in which agents can report preferences not only over a set of alternatives, but also over the possible forks that may occur in the face of disagreement. We study this social choice setting, concentrating on stability issues and concerns of strategic agent behavior.

Consider n agents forming an egalitarian, self-governed community. Their first task is to decide on a decision rule to make further decisions. We start from a rather general initial agreement on the decision-making process based upon a set of intuitive and self-evident axioms, as well as simplifying assumptions about the preferences of the agents. From these humble beginnings we derive a decision rule. Crucially, the decision rule also specifies how it can be changed, or amended, and thus acts as a de facto constitution. Our main contribution is in providing an example of an initial agreement that is simple and intuitive, and a constitution that logically follows from it. The naive agreement is on the basic process of decision making - that agents approve or disapprove proposals; that their vote determines either the acceptance or rejection of each proposal; and on the axioms, which are requirements regarding a constitution that engenders a self-updating decision making process.

The executive branch, or government, is typically not elected directly by the people, but rather formed by another elected body or person such as the parliament or the president. As a result, its members are not directly accountable to the people, individually or as a group. We consider a scenario in which the members of the government are elected directly by the people, and wish to achieve proportionality while doing so. We propose a formal model consisting of $k$ offices, each with its own disjoint set of candidates, and a set of voters who provide approval ballots for all offices. We wish to identify good aggregation rules that assign one candidate to each office. As using a simple majority vote for each office independently might result in disregarding minority preferences altogether, here we consider an adaptation of the greedy variant of Proportional Approval Voting (GreedyPAV) to our setting, and demonstrate -- through computer-based simulations -- how voting for all offices together using this rule overcomes this weakness. We note that the approach is applicable also to a party that employs direct democracy, where party members elect the party's representatives in a coalition government.

We study the problem of bribery in multiwinner elections, for the case where the voters cast approval ballots (i.e., sets of candidates they approve) and the bribery actions are limited to: adding an approval to a vote, deleting an approval from a vote, or moving an approval within a vote from one candidate to the other. We consider a number of approval-based multiwinner rules (AV, SAV, GAV, RAV, approval-based Chamberlin--Courant, and PAV). We find the landscape of complexity results quite rich, going from polynomial-time algorithms through NP-hardness with constant-factor approximations, to outright inapproximability. Moreover, in general, our problems tend to be easier when we limit out bribery actions on increasing the number of approvals of the candidate that we want to be in a winning committee (i.e., adding approvals only for this preferred candidate, or moving approvals only to him or her). We also study parameterized complexity of our problems, with a focus on parameterizations by the numbers of voters or candidates.

We study a generalization of the standard approval-based model of participatory budgeting (PB), in which voters are providing approval ballots over a set of predefined projects and -- in addition to a global budget limit, there are several groupings of the projects, each group with its own budget limit. We study the computational complexity of identifying project bundles that maximize voter satisfaction while respecting all budget limits. We show that the problem is generally intractable and describe efficient exact algorithms for several special cases, including instances with only few groups and instances where the group structure is close to be hierarchical, as well as efficient approximation algorithms. Our results could allow, e.g., municipalities to hold richer PB processes that are thematically and geographically inclusive.

Voting rules may fail to implement the will of the society when only some voters actively participate, and/or in the presence of sybil (fake or duplicate) voters. Here we aim to address social choice in the presence of sybils and voter abstention. To do so we assume the status-quo (Reality) as an ever-present distinguished alternative, and study Reality Enforcing voting rules, which add virtual votes in support of the status-quo. We measure the tradeoff between safety and liveness (the ability of active honest voters to maintain/change the status-quo, respectively) in a variety of domains, and show that the Reality Enforcing voting rule is optimal in this respect.

We present a unifying framework encompassing many social choice settings. Viewing each social choice setting as voting in a suitable metric space, we consider a general model of social choice over metric spaces, in which---similarly to the spatial model of elections---each voter specifies an ideal element of the metric space. The ideal element functions as a vote, where each voter prefers elements that are closer to her ideal element. But it also functions as a proposal, thus making all participants equal not only as voters but also as proposers. We consider Condorcet aggregation and a continuum of solution concepts, ranging from minimizing the sum of distances to minimizing the maximum distance. We study applications of the abstract model to various social choice settings, including single-winner elections, committee elections, participatory budgeting, and participatory legislation. For each setting, we compare each solution concept to known voting rules and study various properties of the resulting voting rules. Our framework provides expressive aggregation for a broad range of social choice settings while remaining simple for voters, and may enable a unified and integrated implementation for all these settings, as well as unified extensions such as sybil-resiliency, proxy voting, and deliberative decision making.