Stackelberg game models of security have received much attention, with a number of approaches for computing Stackelberg equilibria in games with a single defender protecting a collection of targets. In contrast, multi-defender security games have received significantly less attention, particularly when each defender protects more than a single target. We fill this gap by considering a multidefender security game, with a focus on theoretical characterizations of equilibria and the price of anarchy. We present the analysis of three models of increasing generality, two in which each defender protects multiple targets. In all models, we find that the defenders often have the incentive to overprotect the targets, at times significantly. Additionally, in the simpler models, we find that the price of anarchy is unbounded, linearly increasing both in the number of defenders and the number of targets per defender. Surprisingly, when we consider a more general model, this results obtains only in a “corner” case in the space of parameters; in most cases, however, the price of anarchy converges to a constant when the number of defenders increases.

Unfair rating attacks happen in existing trust and reputation systems, lowering the quality of the systems. There exists a formal model that measures the maximum impact of independent attackers [Wang et al., 2015] — based on information theory. We improve on these results in multiple ways: (1) we alter the methodology to be able to reason about colluding attackers as well, and (2) we extend the method to be able to measure the strength of any attacks (rather than just the strongest attack). Using (1), we identify the strongest collusion attacks, helping construct robust trust system. Using (2), we identify the strength of (classes of) attacks that we found in the literature. Based on this, we help to overcome a shortcoming of current research into collusion-resistance — specific (types of) attacks are used in simulations, disallowing direct comparisons between analyses of systems.

We study voting games on binary issues, where voters might hold an objective over some issues at stake, while willing to strike deals on the remaining ones, and can influence one another’s voting decision before the vote takes place. We analyse voters’ rational behaviour in the resulting two-phase game, showing under what conditions undesirable equilibria can be removed as an effect of the pre-vote phase.

Motivated by a radically new peer review system that the National Science Foundation recently experimented with, we study peer review systems in which proposals are reviewed by PIs who have submitted proposals themselves. An (m,k)-selection mechanism asks each PI to review m proposals, and uses these reviews to select (at most) k proposals. We are interested in impartial mechanisms, which guarantee that the ratings given by a PI to others' proposals do not affect the likelihood of the PI's own proposal being selected. We design an impartial mechanism that selects a k-subset of proposals that is nearly as highly rated as the one selected by the non-impartial (abstract version of) the NSF pilot mechanism, even when the latter mechanism has the "unfair" advantage of eliciting honest reviews.

Recent work has shown that the quality of work produced in a crowdsourcing working session can be influenced by the presence of performance-contingent financial incentives, such as bonuses for exceptional performance, in the session. We take an algorithmic approach to decide when to offer bonuses in a working session to improve the overall utility that a requester derives from the session. Specifically, we propose and train an input-output hidden Markov model to learn the impact of bonuses on work quality and then use this model to dynamically decide whether to offer a bonus on each task in a working session to maximize a requester’s utility. Experiments on Amazon Mechanical Turk show that our approach leads to higher utility for the requester than fixed and random bonus schemes do. Simulations on synthesized data sets further demonstrate the robustness of our approach against different worker population and worker behavior in improving requester utility.

Manipulation can be performed when intermediate voting results are known; voters might attempt to vote strategically and try and manipulate the results during an iterative voting process. When only partial voting preferences are available, preference elicitation is necessary. In this paper, we combine two approaches of iterative processes: iterative preference elicitation and iterative voting and study the outcome and performance of a setting where manipulative voters submit partial preferences. We provide practical algorithms for manipulation under the Borda voting rule and evaluate those using different voting centers: the Careful voting center that tries to avoid manipulation and the Naive voting center. We show that in practice, manipulation happens in a low percentage of the settings and has a low impact on the final outcome. The Careful voting center reduces manipulation even further.

In this paper we perform a large-scale homophily analysis on Twitter using a hierarchical representation of users' interests which we call a Twixonomy. In order to build a population, community, or single-user Twixonomy we first associate "topical" friends in users' friendship lists (i.e. friends representing an interest rather than a social relation between peers) with Wikipedia categories. A word-sense disambiguation algorithm is used to select the appropriate wikipage for each topical friend. Starting from the set of wikipages representing "primitive" interests, we extract all paths connecting these pages with topmost Wikipedia category nodes, and we then prune the resulting graph G efficiently so as to induce a direct acyclic graph. This graph is the Twixonomy. Then, to analyze homophily, we compare different methods to detect communities in a peer friends Twitter network, and then for each community we compute the degree of homophily on the basis of a measure of pairwise semantic similarity.We show that the Twixonomy provides a means for describing users' interests in a compact and readable way and allows for a fine-grained homophily analysis. Furthermore, we show that mid-low level categories in the Twixonomy represent the best balance between informativeness and compactness of the representation.

Voting rules are powerful tools that allow multiple agents to aggregate their preferences in order to reach joint decisions. A common flaw of some voting rules, known as the no-show paradox, is that agents may obtain a more preferred outcome by abstaining from an election. We study strategic abstention for set-valued voting rules based on Kelly's and Fishburn's preference extensions. Our contribution is twofold. First, we show that, whenever there are at least five alternatives, every Pareto-optimal majoritarian voting rule suffers from the no-show paradox with respect to Fishburn's extension. This is achieved by reducing the statement to a finite---yet very large---problem, which is encoded as a formula in propositional logic and then shown to be unsatisfiable by a SAT solver. We also provide a human-readable proof which we extracted from a minimal unsatisfiable core of the formula. Secondly, we prove that every voting rule that satisfies two natural conditions cannot be manipulated by strategic abstention with respect to Kelly's extension. We conclude by giving examples of well-known Pareto-optimal majoritarian voting rules that meet these requirements.

Artistic creation is often based on the concept of blending. Linguistic creativity is no exception, as demonstrated for instance by the importance of metaphors in poetry. Blending can also be used to evoke a secondary concept while playing with an already given piece of language, either with the intention of making the secondary concept well perceivable to the reader, or instead, to subtly evoke something additional. Current language technology can do a lot in this connection, and automated language creativity can be useful in cases where input or target are to change continuously, making human production not feasible. In this work we present a system that takes existing well-known expressions and innovates them by bringing in a novel concept coming from evolving news. The technology is composed of several steps concerned with the selection of the sortable concepts and the production of novel expressions, largely relying on state of the art corpus-based methods. Proposed applications include: i) producing catchy news headlines by "parasitically" exploiting well known successful expressions and adapting them to the news at hand; ii) generating adaptive slogans that allude to news of the day and give life to the concept evoked by the slogan; iii) providing artists with an application for boosting their creativity.

This paper studies a new renewable energy investment model through crowdfunding, which is motivated by emerging community solar farms. In this paper we develop a sequential game theory model to capture the interactions among crowdfunders, the solar farm owner, and an electricity company who purchases renewable energy generated by the solar farm in a multi-period framework. By characterizing a unique subgame-perfect equilibrium, andcomparing it with a benchmark model without crowdfunding, we find that under crowdfunding although the farm owner reduces its investment level, the overall green energy investment level is increased due to the contribution of crowdfunders. We also find that crowdfunding can increase the penetration of green energy in consumption and thus reduce the energy procurement cost of the electricity company. Finally, the numerical results based on real data indicates crowdfunding is a simple but effective way to boost green generation.