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Maximal lottery (ML) schemes constitute an interesting class of randomized voting rules that were proposed by Peter Fishburn in 1984 and have been repeatedly recommended for practical use. However, the subtle differences between different ML schemes are often overlooked. Two canonical subsets of ML schemes are C1-ML schemes (which only depend on unweighted majority comparisons) and C2-ML schemes (which only depend on weighted majority comparisons). We prove that C2-ML schemes are the only homogeneous ML schemes that satisfy SD-efficiency and SD-participation, but are also among the most manipulable ML schemes. While all ML schemes are manipulable and even violate monotonicity, they are never manipulable when a Condorcet winner exists and satisfy a relative notion of monotonicity. We also evaluate the frequency of manipulable preference profiles and the degree of randomization of ML schemes via extensive computer simulations. In summary, ML schemes are rarely manipulable and often do not randomize at all, especially for few alternatives. The average degree of randomization of C2-ML schemes is consistently lower than that of C1-ML schemes.

Equality of opportunity is an important normative ideal of distributive justice. In spite of its wide acceptance and economic relevance, standard estimation approaches suffer from data limitations that can lead to both downward and upward biased estimates of inequality of opportunity. These shortcomings may be particularly pronounced for emerging economies in which comprehensive household survey data of sufficient sample size is often unavailable. In this paper, we assess the extent of upward and downward bias in inequality of opportunity estimates for a set of twelve emerging economies. Our findings suggest strongly downward biased estimates of inequality of opportunity in these countries. To the contrary, there is little scope for upward bias. By bounding inequality of opportunity from above, we address recent critiques that worry about the prevalence of downward biased estimates and the ensuing possibility to downplay the normative significance of inequality.

The experimental literature suggests that contributions to a public good made dynamically, over multiple stages are higher than contributions made in a static setting, even when players do not receive feedback about co-players’ previous contributions between stages. Because the dynamic setting without feedback is strategically equivalent to the static one, this finding is puzzling. One important difference between the two settings, however, is that the dynamic setting gives the opportunity to sink contributions while in the static one this opportunity does not exist. I test whether the sunk character of the dynamic contributions explains the higher contributions in the dynamic setting. Symmetric players contribute in two stages to a threshold public good and receive feedback after each stage. The experimental treatment differ in whether the first-stage contributions are sunk or not when deciding on the second-stage contributions. The results show that making the first-stage contributions sunk increases the second-stage individual contributions, and this is more so the case at higher levels of the first-stage contributions. This suggests that the sunk contributions do, at least partially, explain the better performance of the dynamic setting.

We consider plurality voting games being simple games in partition function form such that in every partition there is at least one winning coalition. Such a game is said to be weighted if it is possible to assign weights to the players in such a way that a winning coalition in a partition is always one for which the sum of the weights of its members is maximal over all coalitions in the partition. A plurality game is called decisive if in every partition there is exactly one winning coalition. We show that in general, plurality games need not be weighted, even not when they are decisive. After that, we prove that (i) decisive plurality games with at most four players, (ii) majority games with an arbitrary number of players, and (iii) decisive plurality games that exhibit some kind of symmetry, are weighted. Complete characterizations of the winning coalitions in the corresponding partitions are provided as well.

The current literature on mechanism design in models with social preferences discusses social-preference-robust mechanisms, i.e., mechanisms that are implementable in any environment with social preferences. The literature also discusses payoff-information-robust mechanisms, i.e., mechanisms that are implementable for any belief and higher-order beliefs of the agents about the payoff types of the other agents. In the present paper, I address the question of whether deterministic mechanisms that are robust in both of these dimensions exist. I consider environments where each agent holds private information about his personal payoff and about the existence and extent of his social preferences. In such environments, a mechanism is robust in both dimensions only if it is ex-post implementable, i.e., only if incentive compatibility holds for every realization of payoff signals and for every realization of social preferences. I show that ex-post implementation of deterministic mechanisms is impossible in such environments; i.e., deterministic mechanisms that are both social-preference-robust and payoff-information-robust do not exist.

Online social networks (OSNs) are rapidly growing and have become a huge source of all kinds of global and local news for millions of users. However, OSNs are a double-edged sword. Although the great advantages they offer such as unlimited easy communication and instant news and information, they can also have many disadvantages and issues. One of their major challenging issues is the spread of fake news. Fake news identification is still a complex unresolved issue. Furthermore, fake news detection on OSNs presents unique characteristics and challenges that make finding a solution anything but trivial. On the other hand, artificial intelligence (AI) approaches are still incapable of overcoming this challenging problem. To make matters worse, AI techniques such as machine learning and deep learning are leveraged to deceive people by creating and disseminating fake content. Consequently, automatic fake news detection remains a huge challenge, primarily because the content is designed in a way to closely resemble the truth, and it is often hard to determine its veracity by AI alone without additional information from third parties. This work aims to provide a comprehensive and systematic review of fake news research as well as a fundamental review of existing approaches used to detect and prevent fake news from spreading via OSNs. We present the research problem and the existing challenges, discuss the state of the art in existing approaches for fake news detection, and point out the future research directions in tackling the challenges.

When including outside pressure on voters as individual costs, sequential voting (as in roll call votes) is theoretically preferable to simultaneous voting (as in recorded ballots). Under complete information, sequential voting has a unique subgame perfect equilibrium with a simple equilibrium strategy guaranteeing true majority results. Simultaneous voting suffers from a plethora of equilibria, often contradicting true majorities. Experimental results, however, show severe deviations from the equilibrium strategy in sequential voting with not significantly more true majority results than in simultaneous voting. Social considerations under sequential voting—based on emotional reactions toward the behaviors of the previous players—seem to distort subgame perfect equilibria.

The tyranny of the majority is one of the most frequently discussed problems of democracy in political theory. It arises when winning majorities are fixed and permanent, and there are no checks on the majority’s ability to dominate the minority. In this paper, I investigate the effects of communication on the occurrence of majority domination. Theoretically, communication cuts both ways. On the one hand, forming and maintaining a coalition requires coordination between individuals, which is barely accomplishable without opportunities to communicate. On the other hand, communication strengthens prosocial orientations in groups and should thus prevent the permanent exclusion of minorities. I argue that publicity of communication is crucial. The prosocial effects of communication dominate when communication is public whereas exclusive majorities form under private communication. I test my claim in a series of laboratory experiments where five-member committees make distributional decisions using the voting mechanism ‘voting by veto’. Compared to a baseline treatment without communication, groups distribute benefits more equally when they have the opportunity to communicate in a public chat. When communication is private, however, majoritarian coalitions form that exclude a minority of group members from the distribution of benefits.

Establishing a standard formula (SF) for the regulation of European insurance companies is a Herculean task. It has to acknowledge very different business models and national peculiarities. In addition, regulatory authorities—as a stakeholder on their own—have a number of supervisory objectives the SF should incentivize. With the intervention of the SF in economic activities, the principle of equal treatment must be maintained. The large circle of users makes its procedural simplicity indispensable to ensure that it is applied and implemented in a proportionate manner. Above all, the SF should be risk-sensitive. Compared to Solvency I, the SF of Solvency II is considered a significant improvement, as many of the aforementioned desiderata have been much better realized. The following analysis and survey of model-theoretical aspects of the SF shows that these improvements could be achieved above all with regard to epistemic uncertainties. The stochastic model underneath the SF is still subject to considerable uncertainties; so that the probability functional of the SF is exposed to significant model risk. As part of the Own Risk and Solvency Assessment (ORSA), insurance companies must prove the adequacy of the SF for their company. The vague prior knowledge represented by the stochastic component of the SF is not sufficient for an SF intrinsic validation of the aleatoric component.

We show that prominent centrality measures in network analysis are all based on additively separable and linear treatments of statistics that capture a node’s position in the network. This enables us to provide a taxonomy of centrality measures that distills them to varying on two dimensions: (i) which information they make use of about nodes’ positions, and (ii) how that information is weighted as a function of distance from the node in question. The three sorts of information about nodes’ positions that are usually used—which we refer to as “nodal statistics”—are the paths from a given node to other nodes, the walks from a given node to other nodes, and the geodesics between other nodes that include a given node. Using such statistics on nodes’ positions, we also characterize the types of trees such that centrality measures all agree, and we also discuss the properties that identify some path-based centrality measures.