We study strategic candidate nomination by parties in elections decided by Plurality voting. Each party selects a nominee before the election, and the winner is chosen from the nominated candidates based on the voters' preferences. We introduce a new restriction on these preferences, which we call party-aligned single-peakedness: all voters agree on a common ordering of the parties along an ideological axis, but may differ in their perceptions of the positions of individual candidates within each party. The preferences of each voter are single-peaked with respect to their own axis over the candidates, which is consistent with the global ordering of the parties. We present a polynomial-time algorithm for recognizing whether a preference profile satisfies party-aligned single-peakedness. In this domain, we give polynomial-time algorithms for deciding whether a given party can become the winner under some (or all) nominations, and whether this can occur in some pure Nash equilibrium. We also prove a tight result about the guaranteed existence of pure strategy Nash equilibria for elections with up to three parties for single-peaked and party-aligned single-peaked preference profiles.

Cooperation among independent learning agents is desirable as it enables reaching collectively rewarding states. Recent work has shown that artificial agents can learn to act pro-socially without the need for predefined cooperative preferences or behavioural heuristics, provided that they can observe others' actions or policies and select them as partners accordingly. This paper relaxes this constraint, studying reinforcement learning (RL) agents operating with only minimal information about others' behaviour. We propose a novel `Observer Model', where agents gain insights from direct experience and limited, indirect observations. We show that direct experience alone cannot sustain cooperation, particularly in large societies. However, even minimal observations of third-party interactions, allowing as few as one observer per gameplay, lead to significant improvements, enabling the population to achieve and sustain robust cooperation across varying population sizes. Through numerical analysis, we show the co-evolution of strategy and interaction structure and disentangle how learning happens under various settings. Analysing the partner selection graph, we identify the reasons for cooperation to emerge, and we explore how different learning and exploration rates affect the outcome of social dilemmas played among RL agents.

Social dilemmas are an important benchmark to study the emergence of cooperation among autonomous learning agents and impressive results were recently achieved in two-player games by reinforcement learning agents equipped with a partner selection module. However, the same cannot be said for games on networks. When surrounded by many other defectors, cooperators suffer harsher punishments and find it hard to replicate, making mass defection quickly take over. The frameworks studied so far for the emergence of cooperation in social dilemmas on networks have shown the key role of dynamical linking, the capacity of agents to select their own neighbours, but they have also relied on hard-wired heuristics, such as imitation dynamics, designed to favour cooperation. In this paper, we remove this constraint and study a population of agents that can autonomously learn whether to cooperate or defect with any of their neighbours in a social dilemma, as well as whether to form or sever social ties with others. Building on a seminal framework for the emergence of cooperation in complex social networks with dynamical linking, we implement our agents as Sarsa learners with Boltzmann exploration and equipped with partner selection actions. We show, for the first time, that these agents can reach a fully cooperative society without requiring ad-hoc heuristics. In doing so, we confirm the fundamental role of timescales, the relative speed at which strategy and structure updates occur, for the emergence of cooperation, highlighting the intricate interplay between network dynamics and decision-making in agent societies.

Majority illusion occurs in a social network when the majority of the network vertices belong to a certain type but the majority of each vertex's neighbours belong to a different type, therefore creating the wrong perception, i.e., the illusion, that the majority type is different from the actual one. From a system engineering point of view, this motivates the search for algorithms to detect and, where possible, correct this often undesirable phenomenon. In this we provide a computational study of majority illusion in social networks, paying particular attention to the problem of its verification, i.e., whether majority illusion can occur on social networks, and elimination, i.e., how can we eliminate majority illusion by social network rewiring. While we show that the problems we consider are generally NP-complete, we also provide a parameterised complexity analysis, showing FPT-algorithms for the detection problem and W[1]-hardness for the elimination problem, using natural graph-theoretic parameters.

Majority illusion occurs in a social network when the majority of the network vertices belong to a certain type but the majority of each vertex's neighbours belong to a different type, therefore creating the wrong perception, i.e., the illusion, that the majority type is different from the actual one. From a system engineering point of view, this motivates the search for algorithms to detect and, where possible, correct this undesirable phenomenon. In this paper we initiate the computational study of majority illusion in social networks, providing NP-hardness and parametrised complexity results for its occurrence and elimination.

The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of six AI assignments from the 2023 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http://modelai.gettysburg.edu .

Structural causal models provide a formalism to express causal relations between variables of interest. Models and variables can represent a system at different levels of abstraction, whereby relations may be coarsened and refined according to the need of a modeller. However, switching between different levels of abstraction requires evaluating a trade-off between the consistency and the information loss among different models. In this paper we introduce a family of interventional measures that an agent may use to evaluate such a trade-off. We consider four measures suited for different tasks, analyze their properties, and propose algorithms to evaluate and learn causal abstractions. Finally, we illustrate the flexibility of our setup by empirically showing how different measures and algorithmic choices may lead to different abstractions.

In peer selection a group of agents must choose a subset of themselves, as winners for, e.g., peer-reviewed grants or prizes. We take a Condorcet view of this aggregation problem, assuming that there is an objective ground-truth ordering over the agents. We study agents that have a noisy perception of this ground truth and give assessments that, even when truthful, can be inaccurate. Our goal is to select the best set of agents according to the underlying ground truth by looking at the potentially unreliable assessments of the peers. Besides being potentially unreliable, we also allow agents to be self-interested, attempting to influence the outcome of the decision in their favour. Hence, we are focused on tackling the problem of impartial (or strategyproof) peer selection – how do we prevent agents from manipulating their reviews while still selecting the most deserving individuals, all in the presence of noisy evaluations? We propose a novel impartial peer selection algorithm, PeerNomination, that aims to fulfil the above desiderata. We provide a comprehensive theoretical analysis of the recall of PeerNomination and prove various properties, including impartiality and monotonicity. We also provide empirical results based on computer simulations to show its effectiveness compared to the state-of-the-art impartial peer selection algorithms. We then investigate the robustness of PeerNomination to various levels of noise in the reviews. In order to maintain good performance under such conditions, we extend PeerNomination by using weights for reviewers which, informally, capture some notion of reliability of the reviewer. We show, theoretically, that the new algorithm preserves strategyproofness and, empirically, that the weights help identify the noisy reviewers and hence to increase selection performance.1

Abstract The emergence of cooperation among self-interested agents has been a key concern of the multi-agent systems community for decades. With the increased importance of network-mediated interaction, researchers have shifted the attention to the impact of social networks and their dynamics in promoting or hindering cooperation, drawing various context-dependent conclusions. For example, some lines of research, theoretical and experimental, suggest the existence of a threshold effect in the ratio of timescales of network evolution, after which cooperation will emerge, whereas other lines dispute this, suggesting instead a Goldilocks zone. In this paper we provide an evolutionary game theory framework to understand coevolutionary processes from a bottom up perspective - in particular the emergence of a cooperator-core and defector-periphery - clarifying the impact of partner selection and imitation strategies in promoting cooperative behaviour, without assuming underlying communication or reputation mechanisms. In doing so we provide a unifying framework to study imitation-based cooperation in dynamic social networks and show that disputes in the literature can in fact coexist in so far as the results stem from different equally valid assumptions.

When individuals interact in a social network their opinions can change, at times quite significantly, as a result of social influence. In elections, for example, while they might initially support one candidate, what their friends say may lead them to support another. But how do opinions settle in a social network, as a result of social influence? A recently proposed graph-theoretic metric, the influence gap, has shown to be a reliable predictor of the effect of social influence in two-party elections, albeit only tested on regular and scale-free graphs. Here, we investigate whether the influence gap is able to predict the outcome of multi-party elections on networks exhibiting community structure, i.e., made of highly interconnected components, and therefore more resembling of real-world interaction. To encode communities we build on the classical model of caveman graphs, which we extend to a richer graph family that displays different levels of homophily, i.e., how many connections and opinions are intertwined. Our contribution is three-fold. First, we study the predictive power of the influence gap in the presence of communities. We show that when there is no clear initial majority the influence gap is not a good predictor of the election outcome. When we instead allow for varying majorities, although the influence gap improves as a predictor, counting the initial partisan majority does consistently better, across all levels of homophily. Second, we study the combined effect of the more predictive metrics, as function of the homophily levels. Using regression models, we demonstrate that the influence gap combined with the initial votes count does increase the overall predictive power for some levels of homophily. Third, we study elections with more than two parties. Specifically, we extend the definition of the influence gap to any number of parties, considering various generalisations, and show that the initial votes count has an even higher predictive power when compared to influence gap than it did in the two-party case.

We analyse opinion diffusion in social networks, where a finite set of individuals is connected in a directed graph and each simultaneously changes their opinion to that of the majority of their influencers. We study the algorithmic properties of the fixed-point behaviour of such networks, showing that the problem of establishing whether individuals converge to stable opinions is PSPACE-complete.

In peer selection agents must choose a subset of themselves for an award or a prize. As agents are self-interested, we want to design algorithms that are impartial, so that an individual agent cannot affect their own chance of being selected. This problem has broad application in resource allocation and mechanism design and has received substantial attention in the artificial intelligence literature. Here, we present a novel algorithm for impartial peer selection, PeerNomination, and provide a theoretical analysis of its accuracy. Our algorithm possesses various desirable features. In particular, it does not require an explicit partitioning of the agents, as previous algorithms in the literature. We show empirically that it achieves higher accuracy than the exiting algorithms over several metrics.

Congestion games have many important applications to systems where only limited knowledge may be available to players. Here we study traffic networks with multiple origin-destination pairs, relaxing the simplifying assumption of agents having complete knowledge of the network structure. We identify a ubiquitous class of networks, i.e., rings, for which we can safely increase the agents’ knowledge without affecting their own overall performance - known as immunity to Informational Braess’ Paradox - closing a gap in the literature. By extension of this performance measure to include the welfare of all agents, i.e., minimisation of social cost, we show that IBP is a widespread phenomenon and no network is immune to it.

We study voting games on binary issues, where voters hold an objective over the outcome of the collective decision and are allowed, before the vote takes place, to negotiate their ballots with the other participants. We analyse the voters' rational behaviour in the resulting two-phase game when ballots are aggregated via non-manipulable rules and, more specifically, quota rules. We show under what conditions undesirable equilibria can be removed and desirable ones sustained as a consequence of the pre-vote phase.

We study the complexity of bribery in a network-based rating system, where individuals are connected in a social network and an attacker, typically a service provider, can influence their rating and increase the overall profit. We derive a number of algorithmic properties of this framework, in particular we show that establishing the existence of an optimal manipulation strategy for the attacker is NP-complete, even with full knowledge of the underlying network structure.

The existence of (Nash) equilibria with undesirable properties is a well-known problem in game theory, which has motivated much research directed at the possibility of mechanisms for modifying games in order to eliminate undesirable equilibria, or induce desirable ones. Taxation schemes are a well-known mechanism for modifying games in this way. In the multi-agent systems community, taxation mechanisms for incentive engineering have been studied in the context of Boolean games with costs. These are games in which each player assigns truth-values to a set of propositional variables she uniquely controls in pursuit of satisfying an individual propositional goal formula; different choices for the player are also associated with different costs. In such a game, each player prefers primarily to see the satisfaction of their goal, and secondarily, to minimise the cost of their choice, thereby giving rise to lexicographic preferences over goal-satisfaction and costs. Within this setting, where taxes operate on costs only, however, it may well happen that the elimination or introduction of equilibria can only be achieved at the cost of simultaneously introducing less desirable equilibria or eliminating more attractive ones. Although this framework has been studied extensively, the problem of precisely characterising the equilibria that may be induced or eliminated has remained open. In this paper we close this problem, giving a complete characterisation of those mechanisms that can induce a set of outcomes of the game to be exactly the set of Nash Equilibrium outcomes.

We introduce a class of extensive form games whereplayers might not be able to foresee the possible consequences of their decisions and form a model of theiropponents which they exploit to achieve a more profitable outcome. We improve upon existing models ofgames with limited foresight, endowing players with theability of higher order reasoning and proposing a novelsolution concept to address intuitions coming from realgame play. We analyse the resulting equilibria, devisingan effective procedure to compute them.

The purpose of this contribution is to set up a language to evaluate the results of concerted action among interdependent agents against predetermined properties that we can recognize as desirable from a deontic point of view. Unlike the standard view of logics to reason about coalitionally rational action, the capacity of a set of agents to take a rational decision will be restricted to what we will call agreements, which can be seen as solution concepts to a dependence structure present in a certain game. The language will identify those agreements that act accordingly or disaccordingly with the desirable properties arbitrarily set up in the beginning, and will reveal, by logical reasoning, a variety of structural properties of this type of collective action.

In the multi-agent systems community, dependence theory and game theory are often presented as two alternative perspectives on the analysis of agent interaction. The paper presents a formal analysis of a notion of dependence between players, given in terms of standard game-theoretic notions of rationality such as dominant strategy and best response. This brings the notion of dependence within the realm of game theory providing it with the sort of mathematical foundations which still lacks. Concretely, the paper presents two results: first, it shows how the proposed notion of dependence allows for an elegant characterization of a property of reciprocity for outcomes in strategic games; and second, it shows how the notion can be used to define new classes of coalitional games, where coalitions can force outcomes only in the presence of reciprocal dependencies.

Aim of this work is to provide a formal characterization of those emotions that deal with normative reasoning, such as shame and sense of guilt, to understand their relation with rational action and to ground their formalization on a cognitive science perspective. In order to do this we need to identify the factors that constitute the preconditions and trigger the reactions of shame and sense of guilt in cognitive agents, that is when agents feel ashamed or guilty and what agents do when they feel so. We will also investigate how agents can induce and silence these feelings in themselves, i.e. the analysis of defensive strategies they can employ. We will argue that agents do have control over their emotions and we will analyze some operations they can carry out on them.

In this paper we provide and discuss a language to talk about coherence, a property of interaction that ensures players' abilities non to contradict one other and the empty coalition not to make active choices. With this property we can model a closed-world interaction, such as those of a Coordination Game or of a Prisoner Dilemma, where all the outcomes are determined only by the choices of the agents that are present.