822,221 research outputs found
Allocation in Practice
How do we allocate scarcere sources? How do we fairly allocate costs? These
are two pressing challenges facing society today. I discuss two recent projects
at NICTA concerning resource and cost allocation. In the first, we have been
working with FoodBank Local, a social startup working in collaboration with
food bank charities around the world to optimise the logistics of collecting
and distributing donated food. Before we can distribute this food, we must
decide how to allocate it to different charities and food kitchens. This gives
rise to a fair division problem with several new dimensions, rarely considered
in the literature. In the second, we have been looking at cost allocation
within the distribution network of a large multinational company. This also has
several new dimensions rarely considered in the literature.Comment: To appear in Proc. of 37th edition of the German Conference on
Artificial Intelligence (KI 2014), Springer LNC
The Complexity of Fairness through Equilibrium
Competitive equilibrium with equal incomes (CEEI) is a well known fair
allocation mechanism; however, for indivisible resources a CEEI may not exist.
It was shown in [Budish '11] that in the case of indivisible resources there is
always an allocation, called A-CEEI, that is approximately fair, approximately
truthful, and approximately efficient, for some favorable approximation
parameters. This approximation is used in practice to assign students to
classes. In this paper we show that finding the A-CEEI allocation guaranteed to
exist by Budish's theorem is PPAD-complete. We further show that finding an
approximate equilibrium with better approximation guarantees is even harder:
NP-complete.Comment: Appeared in EC 201
Power Allocation for Adaptive OFDM Index Modulation in Cooperative Networks
In this paper, we propose a power allocation strategy for the adaptive
orthogonal frequency-division multiplexing (OFDM) index modulation (IM) in
cooperative networks. The allocation strategy is based on the
Karush-Kuhn-Tucker (KKT) conditions, and aims at maximizing the average network
capacity according to the instantaneous channel state information (CSI). As the
transmit power at source and relay is constrained separately, we can thus
formulate an optimization problem by allocating power to active subcarriers.
Compared to the conventional uniform power allocation strategy, the proposed
dynamic strategy can lead to a higher average network capacity, especially in
the low signal-to-noise ratio (SNR) region. The analysis is also verified by
numerical results produced by Monte Carlo simulations. By applying the proposed
power allocation strategy, the efficiency of adaptive OFDM IM can be enhanced
in practice, which paves the way for its implementation in the future,
especially for cell-edge communications
Institutional Allocation In Initial Public Offerings: Empirical Evidence
We analyze institutional allocation in initial public offerings (IPOs) using a new dataset of US offerings between 1997 and 1998. We document a positive relationship between institutional allocation and day one IPO returns. This is partly explained by the practice of giving institutions more shares in IPOs with strong pre-market demand, consistent with book-building theories. However, institutional allocation also contains private information about first-day IPO returns not reflected in pre-market demand and other public information. Our evidence supports book-building theories of IPO underpricing, but suggests that institutional allocation in underpriced issues is in excess of that explained by book-building alone.
Cost Allocation and Convex Data Envelopment
This paper considers allocation rules. First, we demonstrate that costs allocated by the Aumann-Shapley and the Friedman-Moulin cost allocation rules are easy to determine in practice using convex envelopment of registered cost data and parametric programming. Second, from the linear programming problems involved it becomes clear that the allocation rules, technically speaking, allocate the non-zero value of the dual variable for a convexity constraint on to the output vector. Hence, the allocation rules can also be used to allocate inefficiencies in non-parametric efficiency measurement models such as Data Envelopment Analysis (DEA). The convexity constraint of the BCC model introduces a non-zero slack in the objective function of the multiplier problem and we show that the cost allocation rules discussed in this paper can be used as candidates to allocate this slack value on to the input (or output) variables and hence enable a full allocation of the inefficiency on to the input (or output) variables as in the CCR model.cost allocation; convex envelopment; data envelopment analysis; slack allocation
Automating allocation of development assurance levels: An extension to HiP-HOPS
Controlling the allocation of safety requirements across a system's architecture from the early stages of development is an aspiration embodied in numerous major safety standards. Manual approaches of applying this process in practice are ineffective due to the scale and complexity of modern electronic systems. In the work presented here, we aim to address this issue by presenting an extension to the dependability analysis and optimisation tool, HiP-HOPS, which allows automatic allocation of such requirements. We focus on aerospace requirements expressed as Development Assurance Levels (DALs); however, the proposed process and algorithms can be applied to other common forms of expression of safety requirements such as Safety Integrity Levels. We illustrate application to a model of an aircraft wheel braking system
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