Committee scoring rules form an important class of multiwinner voting rules. As computing winning committees under such rules is generally intractable, in this paper we investigate efficient heuristics for this task. We design two novel heuristics for computing approximate results of multiwinner elections under arbitrary committee scoring rules; notably, one of these heuristics uses concepts from cooperative game theory. We then provide an experimental evaluation of our heuristics (and two others, known from the literature): we compare the scores of the committees output by our algorithms to the scores of the optimal committees, and also use the two-dimensional Euclidean domain to compare the visual representations of the outputs of our algorithms.

We introduce tool auctions, a novel market mechanism for constructing a cost-efficient assembly line for producing a desired set of products from a given set of goods and tools. Such tools can be used to transform one type of good into a different one. We then study the computational complexity of tool auctions in detail, using methods from both classical and parameterized complexity theory. While solving such auctions is intractable in general, just as for the related frameworks of combinatorial and mixed auctions, we are able to identify several special cases of practical interest where designing efficient algorithms is possible.

We study the problem of allocating indivisible goods among n agents in a fair manner. For this problem, maximin share (MMS) is a well-studied solution concept which provides a fairness threshold. Specifically, maximin share is defined as the minimum utility that an agent can guarantee for herself when asked to partition the set of goods into n bundles such that the remaining (n-1) agents pick their bundles adversarially. An allocation is deemed to be fair if every agent gets a bundle whose valuation is at least her maximin share. Even though maximin shares provide a natural benchmark for fairness, it has its own drawbacks and, in particular, it is not sufficient to rule out unsatisfactory allocations. Motivated by these considerations, in this work we define a stronger notion of fairness, called groupwise maximin share guarantee (GMMS). In GMMS, we require that the maximin share guarantee is achieved not just with respect to the grand bundle, but also among all the subgroups of agents. Hence, this solution concept strengthens MMS and provides an ex-post fairness guarantee. We show that in specific settings, GMMS allocations always exist. We also establish the existence of approximate GMMS allocations under additive valuations, and develop a polynomial-time algorithm to find such allocations. Moreover, we establish a scale of fairness wherein we show that GMMS implies approximate envy freeness. Finally, we empirically demonstrate the existence of GMMS allocations in a large set of randomly generated instances. For the same set of instances, we additionally show that our algorithm achieves an approximation factor better than the established, worst-case bound.

We consider ordinal approximation algorithms for a broad class of utility maximization problems for multi-agent systems. In these problems, agents have utilities for connecting to each other, and the goal is to compute a maximum-utility solution subject to a set of constraints. We represent these as a class of graph optimization problems, including matching, spanning tree problems, TSP, maximum weight planar subgraph, and many others. We study these problems in the ordinal setting: latent numerical utilities exist, but we only have access to ordinal preference information, i.e., every agent specifies an ordering over the other agents by preference. We prove that for the large class of graph problems we identify, ordinal information is enough to compute solutions which are close to optimal, thus demonstrating there is no need to know the underlying numerical utilities. For example, for problems in this class with bounded degree b a simple ordinal greedy algorithm always produces a (b + 1)-approximation; we also quantify how the quality of ordinal approximation depends on the sparsity of the resulting graphs. In particular, our results imply that ordinal information is enough to obtain a 2-approximation for Maximum Spanning Tree; a 4-approximation for Max Weight Planar Subgraph; a 2-approximation for Max-TSP; and a 2- approximation for various Matching problems.

Treatable infectious diseases are a critical challenge for public health. Outreach campaigns can encourage undiagnosed patients to seek treatment but must be carefully targeted to make the most efficient use of limited resources. We present an algorithm to optimally allocate limited outreach resources among demographic groups in the population. The algorithm uses a novel multiagent model of disease spread which both captures the underlying population dynamics and is amenable to optimization. Our algorithm extends, with provable guarantees, to a stochastic setting where we have only a distribution over parameters such as the contact pattern between agents. We evaluate our algorithm on two instances where this distribution is inferred from real world data: tuberculosis in India and gonorrhea in the United States. Our algorithm produces a policy which is predicted to avert an average of least 8,000 person-years of tuberculosis and 20,000 person-years of gonorrhea annually compared to current policy.

In traditional models for word-of-mouth recommendations and viral marketing, the objective function has generally been based on reaching as many people as possible. However, a number of studies have shown that the indiscriminate spread of a product by word-of-mouth can result in overexposure, reaching people who evaluate it negatively. This can lead to an effect in which the over-promotion of a product can produce negative reputational effects, by reaching a part of the audience that is not receptive to it. How should one make use of social influence when there is a risk of overexposure? In this paper, we develop and analyze a theoretical model for this process; we show how it captures a number of the qualitative phenomena associated with overexposure, and for the main formulation of our model, we provide a polynomial-time algorithm to find the optimal marketing strategy. We also present simulations of the model on real network topologies, quantifying the extent to which our optimal strategies outperform natural baselines.

Pirate syndicates capturing tankers to siphon oil, causing an estimated cost of $5 billion a year, has become a serious security issue for maritime traffic. In response to the threat, coast guards and navies deploy patrol boats to protect international oil trade. However, given the vast area of the sea and the highly time and space dependent behaviors of both players, it remains a significant challenge to find efficient ways to deploy patrol resources. In this paper, we address the research challenges and provide four key contributions. First, we construct a Stackelberg model of the oil-siphoning problem based on incident reports of actual attacks; Second, we propose a compact formulation and a constraint generation algorithm, which tackle the exponentially growth of the defender’s and attacker’s strategy spaces, respectively, to compute efficient strategies of security agencies; Third, to further improve the scalability, we propose an abstraction method, which exploits the intrinsic similarity of defender’s strategy space, to solve extremely large-scale games; Finally, we evaluate our approaches through extensive simulations and a detailed case study with real ship traffic data. The results demonstrate that our approach achieves a dramatic improvement of scalability with modest influence on the solution quality and can scale up to realistic-sized problems.

Scientific disciplines, such as Behavioural Psychology, Anthropology and recently Social Signal Processing are concerned with the systematic exploration of human behaviour. A typical work-flow includes the manual annotation (also called coding) of social signals in multi-modal corpora of considerable size. For the involved annotators this defines an exhausting and time-consuming task. In the article at hand we present a novel method and also provide the tools to speed up the coding procedure. To this end, we suggest and evaluate the use of Cooperative Machine Learning (CML) techniques to reduce manual labelling efforts by combining the power of computational capabilities and human intelligence. The proposed CML strategy starts with a small number of labelled instances and concentrates on predicting local parts first. Afterwards, a session-independent classification model is created to finish the remaining parts of the database. Confidence values are computed to guide the manual inspection and correction of the predictions. To bring the proposed approach into application we introduce NOVA - an open-source tool for collaborative and machine-aided annotations. In particular, it gives labellers immediate access to CML strategies and directly provides visual feedback on the results. Our experiments show that the proposed method has the potential to significantly reduce human labelling efforts.

Blockchain systems and ubiquitous computing are changing the way we do business and lead our lives. Its easy to compare blockchain with multi-agents systems communication a technology, which provides a way for multiple interacting intelligent agents to communicate with each other and with environment. Blockchain is a mystery story or provides the foundation for cryptocurrencies like Bitcoin. Some time I get confused with my school time memories data structure; ehe way blockchain is represented as a singly linked list. Each block has a hash of the previous block which can be thought of as a pointer to previous block.

Because the groups are SMARTER when thinking TOGETHER than the individuals would be on their own.