259 research outputs found

    Effects of Time Horizons on Influence Maximization in the Voter Dynamics

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    In this paper we analyze influence maximization in the voter model with an active strategic and a passive influencing party in non-stationary settings. We thus explore the dependence of optimal influence allocation on the time horizons of the strategic influencer. We find that on undirected heterogeneous networks, for short time horizons, influence is maximized when targeting low-degree nodes, while for long time horizons influence maximization is achieved when controlling hub nodes. Furthermore, we show that for short and intermediate time scales influence maximization can exploit knowledge of (transient) opinion configurations. More in detail, we find two rules. First, nodes with states differing from the strategic influencer's goal should be targeted. Second, if only few nodes are initially aligned with the strategic influencer, nodes subject to opposing influence should be avoided, but when many nodes are aligned, an optimal influencer should shadow opposing influence.Comment: 22 page

    TOPOLOGY-AWARE APPROACH FOR THE EMERGENCE OF SOCIAL NORMS IN MULTIAGENT SYSTEMS

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    Social norms facilitate agent coordination and conflict resolution without explicit communication. Norms generally involve restrictions on a set of actions or behaviors of agents to a particular strategy and can significantly reduce the cost of coordination. There has been recent progress in multiagent systems (MAS) research to develop a deep understanding of the social norm formation process. This includes developing mechanisms to create social norms in an effective and efficient manner. The hypoth- esis of this dissertation is that equipping agents in networked MAS with “network thinking” capabilities and using this contextual knowledge to form social norms in an effective and efficient manner improves the performance of the MAS. This disser- tation investigates the social norm emergence problem in conventional norms (where there is no conflict between individual and collective interests) and essential norms (where agents need to explicitly cooperate to achieve socially-efficient behavior) from a game-theoretic perspective. First, a comprehensive investigation of the social norm formation problem is performed in various types of networked MAS with an emphasis on the effect of the topological structures on the process. Based on the insights gained from these network-theoretic investigations, novel topology-aware decentralized mech- anisms are developed that facilitate the emergence of social norms suitable for various environments. It addresses the convention emergence problem in both small and large conventional norm spaces and equip agents to predict the topological structure to use the suitable convention mechanisms. It addresses the cooperation emergence prob- lem in the essential norm space by harnessing agent commitments and altruism where appropriate. Extensive simulation based experimentation has been conducted on dif- ferent network topologies by varying the topological features and agent interaction models. Comparisons with state-of-the-art norm formation techniques show that pro- posed mechanisms facilitate significant improvement in performance in a variety of networks

    State-of-the-art in aerodynamic shape optimisation methods

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    Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners

    INFLUENCE MAXIMIZATION IN SOCIAL NETWORKS

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    In the social network era, every decision an individual makes, whether it is watching a movie or purchasing a book, is influenced by his or her personal network to a certain degree. This thesis investigates the power of the “word-of-mouth” effect within social networks. Given a network G = (V, E, t) where t(v) denotes the threshold of node v, we model the spread of influence as follows. Influence propagates deterministically in discrete steps. An influenced node u exerts a fixed amount of influence bu,w on any neighbor w. For any uninfluenced node v, if the total amount of influence it receives from all its already influenced neighbors at time step t− 1 is at least t(v), node v will be influenced in step t. Given a social network G, we study the problem of introducing an already activated external influencer v into the network, and choosing links from v to nodes in G that can maximize the spread of influence in G. We study two problems: the Minimum Links problem, which is to choose the minimum number of links that can activate the entire network, and the Maximum Influence problem, which is to choose k neighbors that will maximize the size of the influenced set. We prove that the Maximum Influence problem is NP-hard, even for bipartite graphs in which thresholds of all nodes are either one or two. We also study both problems in paths, rings, trees and cliques, and we give polynomial time algorithms that find optimal solutions to both problems for all these topologies

    KEY DETERMINANTS OF ACHIEVING TRUST IN APPLYING BLOCKCHAIN TO EMISSION TRADING IN FINLAND

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    A thesis presented on the critical moment of sustainability transition especially regarding reducing emission of carbon dioxide. The disruption of blockchain application has become one of the innovations that can facilitate this transition. This thesis research focuses on the investigating the most influential trust determinants of applying blockchain to emission trading system (ETS) in the context of Finland. This is exploratory qualitative research which combines grounded theory applied for eight empirical papers and three semi-structured expert interviews. The interviewees include two Finnish ETS experts and two blockchain application experts. Through the qualitative analysis, the author suggested structural assurance trust, knowledge-based familiarity trust and control-based trust to be the most deterministic trust factors in applying blockchain to emission trading. Meanwhile, situational normality trust, peer-based trust and ethnical trust can also be influential. A sequential model and a pyramid model of different trust factors mentioned have been proposed based on the interpretative findings of interviews. Therewith, a consolidated trust model was suggested involving all the relevant trust factors. Finally, the research results can be highly generalisable towards member countries of EU ETS other than Finland

    What is the optimal level of tariffs for African countries?

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    This paper traces the economic underpinnings of tariff formation and implications of different tariff rates. It posits that there is no magic formula in determining the appropriate level of tariff pertinent to the implementation of a medium- term growth strategy in sub-Saharan African countries. Ultimately the particular circumstances of each country will determine the structure of tariff rates. A rate that maximizes economic welfare for any developing country has to take into account the particular economic circumstances, the institutional structures available for trade liberalization, and complementary instruments for trade and growth facilitation, as well as the process, speed, and sequencing of liberalization

    Modelling financial lead-lag interactions with Kinetic Ising Models

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    Applied (Meta)-Heuristic in Intelligent Systems

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    Engineering and business problems are becoming increasingly difficult to solve due to the new economics triggered by big data, artificial intelligence, and the internet of things. Exact algorithms and heuristics are insufficient for solving such large and unstructured problems; instead, metaheuristic algorithms have emerged as the prevailing methods. A generic metaheuristic framework guides the course of search trajectories beyond local optimality, thus overcoming the limitations of traditional computation methods. The application of modern metaheuristics ranges from unmanned aerial and ground surface vehicles, unmanned factories, resource-constrained production, and humanoids to green logistics, renewable energy, circular economy, agricultural technology, environmental protection, finance technology, and the entertainment industry. This Special Issue presents high-quality papers proposing modern metaheuristics in intelligent systems

    Fairness in Social Networks

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    In professional and other social settings, networks play an important role in people\u27s lives. The communication between individuals and their positions in the network, may have a large impact on many aspects of their lives.In this work, I evaluate fairness from different perspectives.First,tomeasurefairnessfromgroupperspective,Iproposethenovelinformation unfairness criterion, which measures whether information spreads fairly to different groups in a network. Using this criterion, I perform a case study and measure fairness in information flow in different computer science co-authorship networks with respect to gender. Then, I consider two applications and show how to increase fairness with respect to a fairness metric. The first application is increasing fairness in information flow by adding a set of edges. I propose two algorithms- MaxFair and MinIUF- which are based on detecting those pairs of nodes whose connection would increase flow to disadvantaged groups. The second application is increasing fairness in organizational networks through employee hiring and assignment. I propose FairEA, a novel algorithm that allows organizations to gauge their success in achieving a diverse network. Next,Iexaminefairnessfromanindividualperspective.Iproposestratification assortativity, a novel metric that evaluates the tendency of the network to be divided into ordered classes. Then, I perform a case study on several co-authorship networks and examine the evolution of these networks over time and show that networks evolve into a highly stratified state. Finally, I introduce an agent-based model for network evolution to explain why social stratification emerges in a network
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