1,387 research outputs found

    The influence of topology and information diffusion on networked game dynamics

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    This thesis studies the influence of topology and information diffusion on the strategic interactions of agents in a population. It shows that there exists a reciprocal relationship between the topology, information diffusion and the strategic interactions of a population of players. In order to evaluate the influence of topology and information flow on networked game dynamics, strategic games are simulated on populations of players where the players are distributed in a non-homogeneous spatial arrangement. The initial component of this research consists of a study of evolution of the coordination of strategic players, where the topology or the structure of the population is shown to be critical in defining the coordination among the players. Next, the effect of network topology on the evolutionary stability of strategies is studied in detail. Based on the results obtained, it is shown that network topology plays a key role in determining the evolutionary stability of a particular strategy in a population of players. Then, the effect of network topology on the optimum placement of strategies is studied. Using genetic optimisation, it is shown that the placement of strategies in a spatially distributed population of players is crucial in maximising the collective payoff of the population. Exploring further the effect of network topology and information diffusion on networked games, the non-optimal or bounded rationality of players is modelled using topological and directed information flow of the network. Based on the topologically distributed bounded rationality model, it is shown that the scale-free and small-world networks emerge in randomly connected populations of sub-optimal players. Thus, the topological and information theoretic interpretations of bounded rationality suggest the topology, information diffusion and the strategic interactions of socio-economical structures are cyclically interdependent

    The influence of topology and information diffusion on networked game dynamics

    Get PDF
    This thesis studies the influence of topology and information diffusion on the strategic interactions of agents in a population. It shows that there exists a reciprocal relationship between the topology, information diffusion and the strategic interactions of a population of players. In order to evaluate the influence of topology and information flow on networked game dynamics, strategic games are simulated on populations of players where the players are distributed in a non-homogeneous spatial arrangement. The initial component of this research consists of a study of evolution of the coordination of strategic players, where the topology or the structure of the population is shown to be critical in defining the coordination among the players. Next, the effect of network topology on the evolutionary stability of strategies is studied in detail. Based on the results obtained, it is shown that network topology plays a key role in determining the evolutionary stability of a particular strategy in a population of players. Then, the effect of network topology on the optimum placement of strategies is studied. Using genetic optimisation, it is shown that the placement of strategies in a spatially distributed population of players is crucial in maximising the collective payoff of the population. Exploring further the effect of network topology and information diffusion on networked games, the non-optimal or bounded rationality of players is modelled using topological and directed information flow of the network. Based on the topologically distributed bounded rationality model, it is shown that the scale-free and small-world networks emerge in randomly connected populations of sub-optimal players. Thus, the topological and information theoretic interpretations of bounded rationality suggest the topology, information diffusion and the strategic interactions of socio-economical structures are cyclically interdependent

    Incentive Mechanisms for Participatory Sensing: Survey and Research Challenges

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    Participatory sensing is a powerful paradigm which takes advantage of smartphones to collect and analyze data beyond the scale of what was previously possible. Given that participatory sensing systems rely completely on the users' willingness to submit up-to-date and accurate information, it is paramount to effectively incentivize users' active and reliable participation. In this paper, we survey existing literature on incentive mechanisms for participatory sensing systems. In particular, we present a taxonomy of existing incentive mechanisms for participatory sensing systems, which are subsequently discussed in depth by comparing and contrasting different approaches. Finally, we discuss an agenda of open research challenges in incentivizing users in participatory sensing.Comment: Updated version, 4/25/201

    The Design of Free-Market Economies in a Post-Neoclassical World

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    The ‘Washington Consensus’ supporting competitive frames and market solutions in economics and law was shown inadequate to address social problems in non-U.S. settings. So would diversity and dynamics suggest theories in need of adjustment to other realities such as culture, increasing returns and market power. Reform must account for an economics of falling cost, ecological limits and complementarity in our relations. Such shall open new applications for economics and law. In this paper a theory of planning horizons is introduced and then employed to raise some meaningful questions about the neoclassical view with respect to its substitution, decreasing returns and independence assumptions. Suppositions of complementarity, increasing returns and interdependence suggest that competition is inefficient by upholding a myopic culture resistant to change. Growth – though long believed to rise from markets and competitive values – may not derive from these sources. Instead, as civilizations advance, shifting from material wants to higher-order intangible output, they evolve from market tradeoffs (substitution and scarcity) into realms of common need (complementarity and abundance). If so, then neoclassical arguments shall no longer apply to any advanced information economy also restrained by its ecology. Indeed, this paper opens standard theory into a more general framework constructing ‘horizon effects’ into a case for cooperation – as more efficient than competition for all long-term problems of growth. The case is made that competition is keeping us stupid and immature, rewarding a myopic culture at the expense of learning and trust, therefore retarding economic growth instead of encouraging it as believed. The policy implications of horizonal theory are explored, with respect to regulatory aims and economic concerns. Such an approach emphasizes strict constraints against entry barriers, ecological harm, market power abuse and ethical lapses. Social cohesion – not competition – is sought as a means to extend horizons and thereby increase efficiency, equity and ecological health. The overriding importance of horizon effects for regulatory assessment dominates other orthodox standards in economics and law. In sum, much of the reason for the failure of the Washington Consensus stems from myopic concerns central to any horizonal view. Reframing economics along horizonal lines suggests some meaningful insight to how regulations should be designed to keep pace with this approach in economics and law

    Cycles in adversarial regularized learning

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    Regularized learning is a fundamental technique in online optimization, machine learning and many other fields of computer science. A natural question that arises in these settings is how regularized learning algorithms behave when faced against each other. We study a natural formulation of this problem by coupling regularized learning dynamics in zero-sum games. We show that the system's behavior is Poincar\'e recurrent, implying that almost every trajectory revisits any (arbitrarily small) neighborhood of its starting point infinitely often. This cycling behavior is robust to the agents' choice of regularization mechanism (each agent could be using a different regularizer), to positive-affine transformations of the agents' utilities, and it also persists in the case of networked competition, i.e., for zero-sum polymatrix games.Comment: 22 pages, 4 figure

    Agent-Based Models and Human Subject Experiments

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    This paper considers the relationship between agent-based modeling and economic decision-making experiments with human subjects. Both approaches exploit controlled ``laboratory'' conditions as a means of isolating the sources of aggregate phenomena. Research findings from laboratory studies of human subject behavior have inspired studies using artificial agents in ``computational laboratories'' and vice versa. In certain cases, both methods have been used to examine the same phenomenon. The focus of this paper is on the empirical validity of agent-based modeling approaches in terms of explaining data from human subject experiments. We also point out synergies between the two methodologies that have been exploited as well as promising new possibilities.agent-based models, human subject experiments, zero- intelligence agents, learning, evolutionary algorithms
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