We study computational problems for two popular parliamentary voting procedures: the amendment procedure and the successive procedure. While finding successful manipulations or agenda controls is tractable for both procedures, our real-world experimental results indicate that most elections cannot be manipulated by a few voters and agenda control is typically impossible. If the voter preferences are incomplete, then finding possible winners is NP-hard for both procedures. Whereas finding necessary winners is coNP-hard for the amendment procedure, it is polynomial-time solvable for the successive one.

However, humans are proved to question: What is the connection between the arguments proposed behave differently, mixing rational and emotional by the participants of a debate and their emotional attitudes to guide their actions, and it has been status? Such question breaks down into the following subquestions: claimed that there exists a strong connection between (1) is the polarity of arguments and the relations the argumentation process and the emotions among them correlated with the polarity of the detected emotions?, felt by people involved in such process. In this paper, and (2) what is the relation between the kind and the we assess this claim by means of an experiment: amount of arguments proposed in a debate, and the mental during several debates people's argumentation engagement detected among the participants of the debate? in plain English is connected and compared to the emotions automatically detected from the participants. To answer these questions, we propose an empirical evaluation Our results show a correspondence between of the connection between argumentation and emotions.

Exploiting parallelism is a key strategy for speeding up computation. However, on hard combinatorial problems, such a strategy has been surprisingly challenging due to the intricate variable interactions.We introduce a novel way in which parallelism can be used to exploit hidden structure of hard combinatorial problems. Our approach complements divide-and-conquer and portfolio approaches. We evaluate our approach on the minimum set basis problem: a core combinatorial problem with a range of applications in optimization, machine learning, and system security. We also highlight a novel sustainability related application, concerning the discovery of new materials for renewable energy sources such as improved fuel cell catalysts. In our approach, a large number of smaller sub-problems are identified and solved concurrently. We then aggregate the information from those solutions, and use this information to initialize the search of a global, complete solver. We show that this strategy leads to a substantial speed-up over a sequential approach, since the aggregated sub-problem solution information often provides key structural insights to the complete solver. Our approach also greatly outperforms state-of-the-art incomplete solvers in terms of solution quality. Our work opens up a novel angle for using parallelism to solve hard combinatorial problems.

We study the problem of how to optimize a cloud service provider's pricing policy so as to better compete with other providers. Different from previous work, we take both the evolution of the market and the competition between multiple cloud providers into consideration while optimizing the pricing strategy for the provider. Inspired by the real situations in today's cloud market, we consider a situation in which there is only one provider who actively optimizes his/her pricing policy, while other providers adopt a follow-up policy to match his/her price cut. To compute optimal pricing policy under the above settings, we decompose the optimization problem into two steps: (1) When the market finally becomes saturated, we use Q-learning, a method of reinforcement learning, to derive an optimal pricing policy for the stationary market; (2) Based on the optimal policy for the stationary market, we use backward induction to derive an optimal pricing policy for the situation of competition in an evolutionary market. Numerical simulations demonstrate the effectiveness of our proposed approach.

Despite some success of Perfect Information Monte Carlo Sampling (PIMC) in imperfect information games in the past, it has been eclipsed by other approaches in recent years. Standard PIMC has well-known shortcomings in the accuracy of its decisions, but has the advantage of being simple, fast, robust and scalable, making it well-suited for imperfect information games with large state-spaces. We propose Presumed Value PIMC resolving the problem of overestimation of opponent's knowledge of hidden information in future game states. The resulting AI agent was tested against human experts in Schnapsen, a Central European 2-player trick-taking card game, and performs above human expert-level.

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.

Positional scoring rules are often used for rank aggregation. In this work we study how scoring rules can be formulated as the minimization of some distance measures between rankings, and we also consider a new family of aggregation methods, called biased scoring rules. This work extends a previous known observation connecting Borda count with the minimization of the sum of the Spearman distances (calculated with respect to a set of input rankings). In particular we consider generalizations of the Spearman distance that can give different weights to items and positions; we also handle the case of incomplete rank data. This has applications in the clustering of rank data, where two main steps need to be performed: aggregating rankings of the same cluster into a representative ranking (the cluster's centroid) and assigning each ranking to its closest centroid. Using the proper combination of scoring rules (for aggregation) and distances (for assignment), it is possible to perform clustering in a computationally efficient way and as well account for specific desired behaviors (give more weight to top positions, bias the centroids in favor of particular items).

In this paper we study the exchange of indivisible objects where agents’ possible preferences over the objects are strict and share a common structure among all of them, which represents a certain level of asymmetry among objects. A typical example of such an exchange model is a re-scheduling of tasks over several processors, since all task owners are naturally assumed to prefer that their tasks are assigned to fast processors rather than slow ones. We focus on designing exchange rules (a.k.a.mechanisms) that simultaneously satisfy strategyproofness, individual rationality, and Pareto efficiency. We first provide a general impossibility result for agents’ preferences that are determined in an additive manner, and then show an existence of such an exchange rule for further restricted lexicographic preferences. We finally find that for the restricted case, a previously known equivalence between the single-valuedness of the strict core and the existence of such an exchange rule does not carry over.

We consider three important, non-strategyproof assignment mechanisms: Probabilistic Serial and two variants of the Boston mechanism. Under each of these mechanisms, we study the agent’s manipulation problem of determining a best response, i.e., a report that maximizes the agent’s expected utility. In particular, we consider local manipulation strategies, which are simple heuristics based on local, greedy search. We make three main contributions. First, we present results from a behavioral experiment (conducted on Amazon Mechanical Turk) which demonstrate that human manipulation strategies can largely be explained by local manipulation strategies. Second, we prove that local manipulation strategies may fail to solve the manipulation problem optimally. Third, we show via large-scale simulations that despite this non-optimality, these strategies are very effective on average. Our results demonstrate that while the manipulation problem may be hard in general, even cognitively or computationally bounded (human) agents can find near-optimal solutions almost all the time via simple local search strategies.

Two incentive mechanisms for Boolean games were proposed recently - taxation schemes and side payments. Both mechanisms have been shown to be able to secure a pure Nash equilibrium (PNE) for Boolean games. A complete characterization of outcomes that can be transformed to PNEs is given for each of the two incentive mechanisms. Side payments are proved to be a weaker mechanism in the sense that the outcomes that they can transform to PNEs are a subset of those transformable by taxation. A family of social-network-based Boolean games, which demonstrates the differences between the two mechanisms for securing a PNE, is presented. A distributed search algorithm for finding the side payments needed for securing a PNE is proposed. An empirical evaluation demonstrates the properties of the two mechanisms on the family of social-network-based Boolean games.