arXiv subjects

Yiannis Giannakopoulos

Publications and source records attributed to Yiannis Giannakopoulos.

23 records · Page 2Linked to original sources

arXiv2017

The VCG Mechanism for Bayesian Scheduling

We study the problem of scheduling $m$ tasks to $n$ selfish, unrelated machines in order to minimize the makespan, where the execution times are independent random variables, identical across machines. We show that the VCG mechanism, which myopically allocates each task to its best machine, achieves an approximation ratio of $O\left(\frac{\ln n}{\ln \ln n}\right)$. This improves significantly on the previously best known bound of $O\left(\frac{m}{n}\right)$ for prior-independent mechanisms, given by Chawla et al. [STOC'13] under the additional assumption of Monotone Hazard Rate (MHR) distributions. Although we demonstrate that this is in general tight, if we do maintain the MHR assumption, then we get improved, (small) constant bounds for $m\geq n\ln n$ i.i.d. tasks, while we also identify a sufficient condition on the distribution that yields a constant approximation ratio regardless of the number of tasks.

cs.GT↗

arXiv2017

Online Market Intermediation

We study a dynamic market setting where an intermediary interacts with an unknown large sequence of agents that can be either sellers or buyers: their identities, as well as the sequence length $n$, are decided in an adversarial, online way. Each agent is interested in trading a single item, and all items in the market are identical. The intermediary has some prior, incomplete knowledge of the agents' values for the items: all seller values are independently drawn from the same distribution $F_S$, and all buyer values from $F_B$. The two distributions may differ, and we make standard regularity assumptions, namely that $F_B$ is MHR and $F_S$ is log-concave. We study online, posted-price mechanisms, and analyse two objectives: that of maximizing the intermediary's profit and that of maximizing the social welfare, under a competitive analysis benchmark. First, on the negative side, for general agent sequences we prove tight competitive ratios of $\varTheta(\sqrt{n})$ and $\varTheta(\ln n)$, respectively for the two objectives. On the other hand, under the extra assumption that the intermediary knows some bound $α$ on the ratio between the number of sellers and buyers, we design asymptotically optimal online mechanisms with competitive ratios of $1+o(1)$ and $4$, respectively. Additionally, we study the model were the number of items that can be stored in stock throughout the execution is bounded, in which case the competitive ratio for the profit is improved to $O(\ln n)$.

cs.GT↗

arXiv2015

Selling Two Goods Optimally

We provide sufficient conditions for revenue maximization in a two-good monopoly where the buyer's values for the items come from independent (but not necessarily identical) distributions over bounded intervals. Under certain distributional assumptions, we give exact, closed-form formulas for the prices and allocation rule of the optimal selling mechanism. As a side result we give the first example of an optimal mechanism in an i.i.d. setting over a support of the form $[0,b]$ which is not deterministic. Since our framework is based on duality techniques, we were also able to demonstrate how slightly relaxed versions of it can still be used to design mechanisms that have very good approximation ratios with respect to the optimal revenue, through a "convexification" process.

cs.GT↗

arXiv2015

A Note on Selling Optimally Two Uniformly Distributed Goods

We provide a new, much simplified and straightforward proof to a result of Pavlov [2011] regarding the revenue maximizing mechanism for selling two goods with uniformly i.i.d. valuations over intervals $[c,c+1]$, to an additive buyer. This is done by explicitly defining optimal dual solutions to a relaxed version of the problem, where the convexity requirement for the bidder's utility has been dropped. Their optimality comes directly from their structure, through the use of exact complementarity. For $c=0$ and $c\geq 0.092$ it turns out that the corresponding optimal primal solution is a feasible selling mechanism, thus the initial relaxation comes without a loss, and revenue maximality follows. However, for $0<c<0.092$ that's not the case, providing the first clear example where relaxing convexity provably does not come for free, even in a two-item regularly i.i.d. setting.

cs.GT↗

arXiv2015

Bounding the Optimal Revenue of Selling Multiple Goods

Using duality theory techniques we derive simple, closed-form formulas for bounding the optimal revenue of a monopolist selling many heterogeneous goods, in the case where the buyer's valuations for the items come i.i.d. from a uniform distribution and in the case where they follow independent (but not necessarily identical) exponential distributions. We apply this in order to get in both these settings specific performance guarantees, as functions of the number of items $m$, for the simple deterministic selling mechanisms studied by Hart and Nisan [EC 2012], namely the one that sells the items separately and the one that offers them all in a single bundle. We also propose and study the performance of a natural randomized mechanism for exponential valuations, called Proportional. As an interesting corollary, for the special case where the exponential distributions are also identical, we can derive that offering the goods in a single full bundle is the optimal selling mechanism for any number of items. To our knowledge, this is the first result of its kind: finding a revenue-maximizing auction in an additive setting with arbitrarily many goods.

cs.GT↗