1,721,060 research outputs found
Persuading Voters in District-based Elections
No full textWe focus on the scenario in which an agent can exploit his information advantage to manipulate the outcome of an election. In particular, we study district-based elections with two candidates, in which the winner of the election is the candidate that wins in the majority of the districts. District-based elections are adopted worldwide (e.g., UK and USA) and are a natural extension of widely studied voting mechanisms (e.g., k-voting and plurality voting). We resort to the Bayesian persuasion framework, where the manipulator (sender) strategically discloses information to the voters (receivers) that update their beliefs rationally. We study both private signaling, in which the sender can use a private communication channel per receiver, and public signaling, in which the sender can use a single communication channel for all the receivers. Furthermore, for the first time, we introduce semi-public signaling in which the sender can use a single communication channel per district. We show that there is a sharp distinction between private and (semi-)public signaling. In particular, optimal private signaling schemes can provide an arbitrarily better probability of victory than (semi-)public ones and can be computed efficiently, while optimal (semi-)public signaling schemes cannot be approximated to within any factor in polynomial time unless P = NP. However, we show that reasonable relaxations allow the design of multi-criteria PTASs for optimal (semi-)public signaling schemes. In doing so, we introduce a novel property, namely comparative stability, and we design a bi-criteria PTAS for public signaling in general Bayesian persuasion problems beyond elections when the sender’s utility function is state-dependent
Online Mechanism Design for Information Acquisition
No full textWe study the problem of designing mechanisms for information acquisition scenarios. This setting models strategic interactions between an uninformed receiver and a set of informed senders. In our model the senders receive information about the underlying state of nature and communicate their observation (either truthfully or not) to the receiver, which, based on this information, selects an action. Our goal is to design mechanisms maximizing the receiver's utility while incentivizing the senders to report truthfully their information. First, we provide an algorithm that efficiently computes an optimal incentive compatible (IC) mechanism. Then, we focus on the online problem in which the receiver sequentially interacts in an unknown game, with the objective of minimizing the cumulative regret w.r.t. the optimal IC mechanism, and the cumulative violation of the incentive compatibility constraints. We investigate two different online scenarios, i.e., the full and bandit feedback settings. For the full feedback problem, we propose an algorithm that guarantees Õ(√T) regret and violation, while for the bandit feedback setting we present an algorithm that attains Õ(Tα) regret and Õ(T1-α/2) violation for any α ∈ [1/2,1]. Finally, we complement our results providing a tight lower bound
Election Control in Social Networks via Edge Addition or Removal
No full textWe focus on the scenario in which messages pro and/or against one or multiple candidates are spread through a social network in order to affect the votes of the receivers. Several results are known in the literature when the manipulator can make seeding by buying influencers. In this paper, instead, we assume the set of influencers and their messages to be given, and we ask whether a manipulator (e.g., the platform) can alter the outcome of the election by adding or removing edges in the social network. We study a wide range of cases distinguishing for the number of candidates or for the kind of messages spread over the network. We provide a positive result, showing that, except for trivial cases, manipulation is not affordable, the optimization problem being hard even if the manipulator has an unlimited budget (i.e., he can add or remove as many edges as desired). Furthermore, we prove that our hardness results still hold in a reoptimization variant, where the manipulator already knows an optimal solution to the problem and needs to compute a new solution once a local modification occurs (e.g., in bandit scenarios where estimations related to random variables change over time)
Committing to correlated strategies with multiple leaders
No full textWe address multi-agent Stackelberg settings involving many leaders and followers. In order to effectively model this kind of interactions, we extend the idea of commitment to correlated strategies to Stackelberg games with multiple leaders and followers. Correlation can be easily implemented by resorting to a device that sends signals to the players, and it also enables the leaders to reach better solutions than those achieved by committing independently. In this setting, a crucial question is how the leaders agree on a correlated-strategy commitment. To this end, we introduce a preliminary agreement stage that implements a natural non-cooperative negotiation protocol. The protocol proposes a correlated strategy to the leaders, who can then decide, in turn, whether to participate in the commitment or defect from it by losing the possibility of being part of the agreement. The goal is to design stable agreements in which no leader defects. We distinguish three solution concepts on the basis of the constraints that they enforce on the agreement reached by the leaders. We provide a comprehensive study of the properties of our solution concepts, in terms of existence, relation with other solution concepts, and computational complexity. As for the computational analysis, we prove that our solutions can be computed in polynomial time for certain classes of succinctly represented games. Interestingly, our results show that, in these games, introducing the agreement stage does not make computing equilibria a more challenging task, as finding a solution in our setting is as hard as computing an optimal correlated equilibrium
A framework for safe decision making: A convex duality approach
No full textWe study the problem of online interaction in general decision making problems, where the objective is not only to find optimal strategies, but also to satisfy certain safety guarantees, expressed in terms of costs accrued. In particular, we focus on the online learning problem in which an agent has to find the optimal solution of a linear objective. Moreover, the agent has to satisfy a linear safety constraint at each round. We propose a theoretical framework to address such problems and present BAN-SOLO, a UCB-like algorithm that, in an online interaction with an unknown environment, attains sublinear regret of order O(√T) and satisfies a safety constraint with high probability at each iteration. BAN-SOLO provides a general framework that can be applied to any setting in which estimators of the objective and the cost function are available. At its core, it relies on tools from convex duality to manage environment exploration while satisfying the safety constraint imposed by the problem. To show the applicability of our framework, we provide two game theoretical applications: normal-form games and sequential decision-making problems
Persuading Voters: It's Easy to Whisper, It's Hard to Speak Loud
Full text linkWe focus on the following natural question: is it possible to influence the outcome of a voting process through the strategic provision of information to voters who update their beliefs rationally? We investigate whether it is computationally tractable to design a signaling scheme maximizing the probability with which the sender's preferred candidate is elected. We resort to the model recently introduced by Arieli and Babichenko (2019) (i.e., without inter-agent externalities), and focus on, as illustrative examples, k-voting rules and plurality voting. There is a sharp contrast between the case in which private signals are allowed and the more restrictive setting in which only public signals are allowed. In the former, we show that an optimal signaling scheme can be computed efficiently both under a k-voting rule and plurality voting. In establishing these results, we provide two contributions applicable to general settings beyond voting. Specifically, we extend a well-known result by Dughmi and Xu (2017) to more general settings and prove that, when the sender's utility function is anonymous, computing an optimal signaling scheme is fixed-parameter tractable in the number of receivers' actions. In the public signaling case, we show that the sender's optimal expected return cannot be approximated to within any factor under a k-voting rule. This negative result easily extends to plurality voting and problems where utility functions are anonymous
Leadership in singleton congestion games: What is hard and what is easy
No full textWe study the problem of computing Stackelberg equilibria in Stackelberg games whose underlying structure is a congestion game, focusing on singleton congestion games, i.e., on congestion games where each player can choose a single resource, and assuming that one of them acts as leader while the other ones act as followers. We provide a comprehensive picture of the computational complexity of finding equilibria in these games, investigating different forms of commitment (pure-strategy and mixed-strategy) and followers' tie-breaking rules (optimistic and pessimistic). We identify two features of such games, namely, identical/different action spaces and monotonic/generic cost functions, by which we provide a complete characterization of the cases in which the equilibrium-finding problem is either easy or hard. In particular, we show that, in the case where the action spaces are different, the cost the leader incurs in an optimistic or pessimistic Stackelberg equilibrium cannot be approximated in polynomial time up to any polynomial factor in the size of the game unless , independently of the cost functions being monotonic or generic. This result holds even when the commitment is restricted to pure strategies. For general mixed-strategy commitments, we show that a similar result also holds when the players have generic cost functions, even if their action spaces are identical. Furthermore, we prove that the case with identical action spaces and monotonic cost functions is easy. We also improve the efficiency of a polynomial-time algorithm available in the literature for the computation of a socially optimal Nash equilibrium in non-Stackelberg singleton congestion games with identical action spaces and generic cost functions, and we extend it to the computation of a Stackelberg equilibrium for the case where the leader is restricted to playing pure strategies. For the cases in which the problem of finding an equilibrium is hard, we show how, in the optimistic setting where the followers break ties in favor of the leader, the problem can be formulated via mixed-integer linear programming techniques. We also provide an experimental evaluation of our algorithms both on random instances and on instances generated from our inapproximability reductions
Historic urban landscape e turistificazione. il centro storico Unesco di Napoli
No full textIl lavoro affronta il tema del difficile rapporto tra conservazione del patrimonio Unesco e sviluppo turistico partendo da uno specifico caso di studio riferito a Napoli e al suo centro storico, patrimonio dell’umanità dal 1995. Negli ultimi anni l’intensa crescita dei flussi turistici sperimentata dalla città partenopea si è accompagnata ad una altrettanto formidabile crescita degli affitti brevi e ad una trasformazione del retailscape delle zone a maggiore attrattività turistica, tutte incluse nel perimetro Unesco. Utilizzando dati provenienti da sorgenti informative diverse, il paper indaga patterns spaziali ed effetti di tali processi con l’intento di fornire una prima preliminare valutazione delle ricadute territoriali derivanti dal recente sviluppo turistico della città
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