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A public divisible resource is to be divided among projects. We study rules that decide on a distribution of the budget when voters have ordinal preference rankings over projects. Examples of such portioning problems are participatory budgeting, time shares, and parliament elections. We introduce a family of rules for portioning, inspired by positional scoring rules. Rules in this family are given by a scoring vector (such as plurality or Borda) associating a positive value with each rank in a vote, and an aggregation function such as leximin or the Nash product. Our family contains well-studied rules, but most are new. We discuss computational and normative properties of our rules. We focus on fairness, and introduce the SD-core, a group fairness notion. Our Nash rules are in the SD-core, and the leximin rules satisfy individual fairness properties. Both are Pareto-efficient.

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Multiagent learning literature has investigated iterated twoplayer games to develop mechanisms that allow agents to learn to converge on Nash Equilibrium strategy profiles. Such equilibrium configuration implies that there is no motivation for one player to change its strategy if the other does not. Often, in general sum games, a higher payoff can be obtained by both players if one chooses not to respond optimally to the other player. By developing mutual trust, agents can avoid iterated best responses that will lead to a lesser payoff Nash Equilibrium. In this paper we work with agents who select actions based on expected utility calculations that incorporates the observed frequencies of the actions of the opponent(s). We augment this stochasticallygreedy agents with an interesting action revelation strategy that involves strategic revealing of one's action to avoid worst-case, pessimistic moves. We argue that in certain situations, such apparently risky revealing can indeed produce better payoff than a non-revealing approach. In particular, it is possible to obtain Pareto-optimal solutions that dominate Nash Equilibrium. We present results over a large number of randomly generated payoff matrices of varying sizes and compare the payoffs of strategically revealing learners to payoffs at Nash equilibrium.

Abstract. We study a model in which a group of agents make a sequence of collective decisions on whether to remain in the current state of the system or switch to an alternative state, as proposed by one of them. Examples for instantiations of this model include the step-wise refinement of a bill of law by means of amendments to be voted on, as well as resource allocation problems, where agents successively alter the current allocation by means of a sequence of deals. We specifically focus on cases where the majority rule is used to make each of the collective decisions, as well as variations of the majority rule where different quotas need to be met to get a proposal accepted. In addition, we allow for cases in which the same proposal may be made more than once. As this can lead to infinite sequences, we investigate the effects of introducing a deadline bounding the number of proposals that can be made. We use both analytical and experimental means to characterise situations in which we can expect to see a convergence effect, in the sense that the expected payoff of each agent will become independent from the initial state of the system, as long as the deadline is chosen large enough.