Victor Naroditskiy

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In this paper, we combine two approaches to handling uncertainty: we use techniques for finding optimal solutions in the expected sense to solve combinatorial optimization problems in an online setting. The problem we address is the scheduling component of the Trading Agent Competition in Supply Chain Management (TAC SCM) problem, a combinatorial(More)
The paper describes the architecture of Brown Universityýs agent, Botticelli, a finalist in the 2003 Trading Agent Competition in Supply Chain Management (TAC SCM). In TAC SCM, a simulated computer manufacturing scenario, Botticelli competes with other agents to win customer orders and negotiates with suppliers to procure the components necessary to(More)
The paper describes the design of the agent BOTTICELLI, a finalist in the 2003 Trading Agent Competition in Supply Chain Management (TAC SCM). In TAC SCM, a simulated computer manufacturing scenario, BOTTICELLI competes with other agents to win customer orders and negotiates with suppliers to procure the components necessary to complete its orders. We(More)
We present autonomous bidding strategies for ad auctions, first for a stylized problem, and then for the more realistic Trading Agent Competition for Ad Auctions (TAC AA)—a simulated market environment that tries to capture some of the complex dynamics of bidding in ad auctions. We decompose the agent’s problem into a modeling subproblem, where we estimate(More)
We model budget-constrained keyword bidding in sponsored search auctions as a stochastic multiple-choice knapsack problem (S-MCKP) and design an algorithm to solve S-MCKP and the corresponding bidding optimization problem. Our algorithm selects items online based on a threshold function which can be built/updated using historical data. Our algorithm(More)
We study the problem of how to allocate <i>m</i> identical items among <i>n</i> &#62; <i>m</i> agents, assuming each agent desires exactly one item and has a private value for consuming it. We assume the items are jointly owned by the agents, not by one uninformed center, so an auction cannot be used to solve our problem. Instead, the agents who receive(More)
We study the problem of an advertising agent who needs to intelligently distribute her budget across a sequence of online keyword bidding auctions. We assume the closing price of each auction is governed by the same unknown distribution, and study the problem of making provably optimal bidding decisions. Learning the distribution is done under censored(More)
Many scenarios where participants hold private information require payments to encourage truthful revelation. Some of these scenarios have no natural residual claimant who would absorb the budget surplus or cover the deficit. Faltings [7] proposed the idea of excluding one agent uniformly at random and making him the residual claimant. Based on this idea,(More)
Word-of-mouth, referral, or viral marketing is a highly sought-after way of advertising. In this paper, we investigate whether such marketing can be encouraged through incentive mechanisms, thus allowing an organisation to effectively crowdsource their marketing. Specifically, we undertake a field experiment that compares several mechanisms for(More)