arXiv subjects

Bo Tang

Publications and source records attributed to Bo Tang.

151 records · Page 9Linked to original sources

arXiv2015

On the Efficiency of the Proportional Allocation Mechanism for Divisible Resources

We study the efficiency of the proportional allocation mechanism, that is widely used to allocate divisible resources. Each agent submits a bid for each divisible resource and receives a fraction proportional to her bids. We quantify the inefficiency of Nash equilibria by studying the Price of Anarchy (PoA) of the induced game under complete and incomplete information. When agents' valuations are concave, we show that the Bayesian Nash equilibria can be arbitrarily inefficient, in contrast to the well-known 4/3 bound for pure equilibria. Next, we upper bound the PoA over Bayesian equilibria by 2 when agents' valuations are subadditive, generalizing and strengthening previous bounds on lattice submodular valuations. Furthermore, we show that this bound is tight and cannot be improved by any simple or scale-free mechanism. Then we switch to settings with budget constraints, and we show an improved upper bound on the PoA over coarse-correlated equilibria. Finally, we prove that the PoA is exactly 2 for pure equilibria in the polyhedral environment.

cs.GT↗

arXiv2015

Updated reduced CMB data and constraints on cosmological parameters

We obtain the reduced CMB data $\{l_A, R, z_*\}$ from WMAP9, WMAP9+BICEP2, Planck+WP and Planck+WP+BICEP2 for the $Λ$CDM and $w$CDM models with or without spatial curvature. We then use these reduced CMB data in combination with low-redshift observations to put constraints on cosmological parameters. We find that including BICEP2 results in a higher value of the Hubble constant especially when the equation of state of dark energy and curvature are allowed to vary. For the $Λ$CDM model with curvature, the estimate of the Hubble constant with Planck+WP+Lensing is inconsistent with the one derived from Planck+WP+BICEP at about 1.3 $σ$ confidence level.

astro-ph.CO↗

arXiv2014

Constraints on the $Λ$CDM model with redshift tomography

Recently released Planck data favor a lower value of the Hubble constant and a higher value of the fraction matter density in the standard $Λ$CDM model, which are discrepant with some of the low-redshift measurements. Within the context of this cosmology, we examine the consistency of the estimated values for the Hubble constant and fraction matter density with redshift tomography. Using the SNe Ia, Hubble parameter, BAO and CMB data, which are divided into three bins, we find no statistical evidence for any tension in the three redshift bins, although there exists a 1.4$σ$ deviation of the Hubble constant in the middle redshift from the one in the high redshift bin.

astro-ph.CO↗

arXiv2013

Pricing Ad Slots with Consecutive Multi-unit Demand

We consider the optimal pricing problem for a model of the rich media advertisement market, as well as other related applications. In this market, there are multiple buyers (advertisers), and items (slots) that are arranged in a line such as a banner on a website. Each buyer desires a particular number of {\em consecutive} slots and has a per-unit-quality value $v_i$ (dependent on the ad only) while each slot $j$ has a quality $q_j$ (dependent on the position only such as click-through rate in position auctions). Hence, the valuation of the buyer $i$ for item $j$ is $v_iq_j$. We want to decide the allocations and the prices in order to maximize the total revenue of the market maker. A key difference from the traditional position auction is the advertiser's requirement of a fixed number of consecutive slots. Consecutive slots may be needed for a large size rich media ad. We study three major pricing mechanisms, the Bayesian pricing model, the maximum revenue market equilibrium model and an envy-free solution model. Under the Bayesian model, we design a polynomial time computable truthful mechanism which is optimum in revenue. For the market equilibrium paradigm, we find a polynomial time algorithm to obtain the maximum revenue market equilibrium solution. In envy-free settings, an optimal solution is presented when the buyers have the same demand for the number of consecutive slots. We conduct a simulation that compares the revenues from the above schemes and gives convincing results.

cs.GT↗

arXiv2013

Constraining the Anisotropic Expansion of Universe

We study the possibly existing anisotropy in the accelerating expansion universe with the Union2 Type Ia supernovae data and Gamma-ray burst data. We construct a direction-dependent dark energy model and constrain the anisotropy direction and strength of modulation. We find that the maximum anisotropic deviation direction is $(l,\,b)=(126^{\circ},\,13^{\circ})$ (or equivalently $(l,\,b)=(306^{\circ},\,-13^{\circ})$), and the current anisotropy level is $g_0=0.030_{+0.010}^{-0.030}$ ($1σ$ confidence level with Union2 data). Our results do not show strong evidence for the anisotropic dark energy model. We also discuss potential methods that may distinguish the peculiar velocity field from the anisotropic dark energy model.

astro-ph.CO↗

arXiv2013

The Simulated Greedy Algorithm for Several Submodular Matroid Secretary Problems

We study the matroid secretary problems with submodular valuation functions. In these problems, the elements arrive in random order. When one element arrives, we have to make an immediate and irrevocable decision on whether to accept it or not. The set of accepted elements must form an {\em independent set} in a predefined matroid. Our objective is to maximize the value of the accepted elements. In this paper, we focus on the case that the valuation function is a non-negative and monotonically non-decreasing submodular function. We introduce a general algorithm for such {\em submodular matroid secretary problems}. In particular, we obtain constant competitive algorithms for the cases of laminar matroids and transversal matroids. Our algorithms can be further applied to any independent set system defined by the intersection of a {\em constant} number of laminar matroids, while still achieving constant competitive ratios. Notice that laminar matroids generalize uniform matroids and partition matroids. On the other hand, when the underlying valuation function is linear, our algorithm achieves a competitive ratio of 9.6 for laminar matroids, which significantly improves the previous result.

cs.DS↗

arXiv2011

Optimal Pricing in Social Networks with Incomplete Information

In revenue maximization of selling a digital product in a social network, the utility of an agent is often considered to have two parts: a private valuation, and linearly additive influences from other agents. We study the incomplete information case where agents know a common distribution about others' private valuations, and make decisions simultaneously. The "rational behavior" of agents in this case is captured by the well-known Bayesian Nash equilibrium. Two challenging questions arise: how to compute an equilibrium and how to optimize a pricing strategy accordingly to maximize the revenue assuming agents follow the equilibrium? In this paper, we mainly focus on the natural model where the private valuation of each agent is sampled from a uniform distribution, which turns out to be already challenging. Our main result is a polynomial-time algorithm that can exactly compute the equilibrium and the optimal price, when pairwise influences are non-negative. If negative influences are allowed, computing any equilibrium even approximately is PPAD-hard. Our algorithm can also be used to design an FPTAS for optimizing discriminative price profile.

cs.GT↗