Communication Network Design: Balancing Modularity and Mixing via Optimal Graph Spectra

By leveraging information technologies, organizations now have the ability to design their communication networks and crowdsourcing platforms to pursue various performance goals, but existing research on network design does not account for the specific features of social networks, such as the notion of teams. We fill this gap by demonstrating how desirable aspects of organizational structure can be mapped parsimoniously onto the spectrum of the graph Laplacian allowing the specification of structural objectives and build on recent advances in non-convex programming to optimize them. This design framework is general, but we focus here on the problem of creating graphs that balance high modularity and low mixing time, and show how "liaisons" rather than brokers maximize this objective

Approximate Strategyproofness

The standard approach of mechanism design theory insists on equilibrium behavior by participants. This assumption is captured by imposing incentive constraints on the design space. But in bridging from theory to practice, it often becomes necessary to relax incentive constraints in order to allow tradeoffs with other desirable properties. This article surveys a number of different options that can be adopted in relaxing incentive constraints, providing a current view of the state-of-the-art.Engineering and Applied Science

Quantifying the Strategyproofness of Mechanisms via Metrics on Payoff Distributions

Strategyproof mechanisms provide robust equilibrium with minimal assumptions about knowledge and rationality but can be unachievable in combination with other desirable properties such as budget-balance, stability against deviations by coalitions, and computational tractability. In the search for maximally-strategyproof mechanisms that simultaneously satisfy other desirable properties, we introduce a new metric to quantify the strategyproofness of a mechanism, based on comparing the payoff distribution, given truthful reports, against that of a strategyproof “reference” mechanism that solves a problem relaxation. Focusing on combinatorial exchanges, we demonstrate that the metric is informative about the eventual equilibrium, where simple regret-based metrics are not, and can be used for online selection of an effective mechanism.Engineering and Applied Science

Expressive Power-Based Resource Allocation for Data Centers

As data-center energy consumption continues to rise, efficient power management is becoming increasingly important. In this work, we examine the use of a novel market mechanism for finding the right balance between power and performance. The market enables a separation between a 'buyer side' that strives to maximize performance and a 'seller side' that strives to minimize power and other costs. A concise and scalable description language is defined for agent preferences that admits a mixedinteger program for computing optimal allocations. Experimental results demonstrate the robustness, flexibility, practicality and scalability of the architecture.Engineering and Applied Science

TBBL: A Tree-Based Bidding Language for Iterative Combinatorial Exchanges

We present a novel tree-based logical bidding language, TBBL, for preference elicitation in combinatorial exchanges (CEs). TBBL provides new expressiveness for two-sided markets with agents that are both buying and selling goods. Moreover, the rich semantics of TBBL allow the language to capture new structure, making it exponentially more concise than OR* and LGB for preferences that are realistic in important domains for CEs. With simple extensions TBBL can subsume these earlier languages. TBBL can also explicitly represent partial information about valuations. The language is designed such that the structure in TBBL bids can be concisely captured directly in mixed-integer programs for the allocation problem. We illustrate TBBL through examples drawn from domains to which it can be (and is being) applied, and motivate further extensions we are currently pursuing.Engineering and Applied Science

RCS2 J232727.6-020437: An Efficient Cosmic Telescope at z=0.6986z=0.6986

We present a detailed gravitational lens model of the galaxy cluster RCS2 J232727.6-020437. Due to cosmological dimming of cluster members and ICL, its high redshift ($z=0.6986$) makes it ideal for studying background galaxies. Using new ACS and WFC3/IR HST data, we identify 16 multiple images. From MOSFIRE follow up, we identify a strong emission line in the spectrum of one multiple image, likely confirming the redshift of that system to $z=2.083$. With a highly magnified ($\mu\gtrsim2$) source plane area of $\sim0.7$ arcmin$^2$ at $z=7$, RCS2 J232727.6-020437 has a lensing efficiency comparable to the Hubble Frontier Fields clusters. We discover four highly magnified $z\sim7$ candidate Lyman-break galaxies behind the cluster, one of which may be multiply-imaged. Correcting for magnification, we find that all four galaxies are fainter than $0.5 L_{\star}$. One candidate is detected at ${>10\sigma}$ in both Spitzer/IRAC [3.6] and [4.5] channels. A spectroscopic follow-up with MOSFIRE does not result in the detection of the Lyman-alpha emission line from any of the four candidates. From the MOSFIRE spectra we place median upper limits on the Lyman-alpha flux of $5-14 \times 10^{-19}\, \mathrm{erg \,\, s^{-1} cm^{-2}}$ ($5\sigma$).Comment: 14 pages, 9 figures, submitted to ApJ on 3/06/201