Linear Aggregation Of Information In Hierarchical Games

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Aspects of Networking in Multiplayer Computer Games

supportable by one server demonstrates that sublinear capacity requirement is needed. The hierarchical system best supports this, although a little space could have been devoted to discussing why this is true. The authors suggest using interest management, as well as compression and aggregation techniques to achieve this sub-linear requirement.

Iterative bidding in electricity markets: rationality and

generators to share information with the intent of tricking the system to obtain a higher payoff. 1 INTRODUCTION As part of the plan to integrate distributed energy resources (DERs) into the electricity grid, regulating authorities envision a hierarchical architecture where, at the lower layer, different sets of DERs coordinate

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select televised sportscasters for Olympic Games. Dağdeviren (2010) employed ANP and modified TOPSIS to select personnel. Dursun and Karsak (2010) used the principles of fuzzy information fusion, 2-tuple linguistic representation model, and

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(linear time-invariant) system approximation of the aggregate nonlinear model, which is possible through application of results from [12]. The scalar input in this linear model is a parameter that appears in the MDP cost function. The LTI approximation is convenient for control design at the grid level: the input becomes the control signal that

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2. Hierarchical Aggregation of Linear Systems This section is a brief overview of the results in [1]. For a complete proof of the main theorem, as well as precise mathematical statements, the reader is referred to 1l]. We begin by postulating a dynamic model which describes the entire evolution of the system when no decisions are applied. We

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supply chain data science. First, a hierarchical linear model is used to empirically investigate the conflicting observation of the magnitude and prevalence of demand distortion in supply chains. Results corroborate with the theoretical literature and find that data aggregation obscure the true

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Chapter 1

to a form of hierarchical optimization (Sethi and Zhang (1994), Phillips and Kokotovic (1981)). The paper is organized as follows. In Section 1.2 we introduce the dynamic model. Section 1.3 gives preliminary results on linear track-ing. Section 1.4 contains the individual and mass behaviour analysis via a state aggregation procedure.

Semantic Scholar

includingthedominatingplayer,hisdelayplaysasahiddenrandomvariablecomingfromacommon an optimal solution that can serve as an lution for markets with leader and

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systems are almost all based on very limited abilities to represent information. Planning Planning starts with general facts about the world (especially facts about the effects of actions), facts about the particular situation and a statement of a goal. From these, planning programs generate a strategy for achieving the goal.

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2. Hierarchical Aggregation of Linear Systems This section is a brief overview of the results in 11]. For a complete proof of the main theorem, as well as precise mathematical statements, the reader is referred to 1l]. We begin by postulating a dynamic model which describes the

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Then, Section 5 provides simulation results and the

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The switching lemma can be based on any k-linear hardness assumptions on one of the groups. In particular, this enables convenient information theoretic arguments in the construction of sequence of games proving security of cryptographic schemes, mimicking proofs and constructions in the random oracle model.

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in [7], the computational complexity of the kernel is linear in the number of features). It must also be noted that we did not observe any significant increase in performance beyond M = 200 and L =2, where the concatenated histograms are only 4200-dimensional. 1In principle, it is possible to integrate geometric information directly