Lior Seeman

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—The algorithmic challenge of maximizing information diffusion through word-of-mouth processes in social networks has been heavily studied in the past decade. While there has been immense progress and an impressive arsenal of techniques has been developed, the algorithmic frameworks make idealized assumptions regarding access to the network that can often(More)
Nakamoto's famous blockchain protocol enables achieving consensus in a so-called permis-sionless setting—anyone can join (or leave) the protocol execution, and the protocol instructions do not depend on the identities of the players. His ingenious protocol prevents " sybil attacks " (where an adversary spawns any number of new players) by relying on(More)
We show that by modeling people as bounded finite automata, we can capture at a qualitative level the behavior observed in experiments. We consider a decision problem with incomplete information and a dynamically changing world, which can be viewed as an abstraction of many real-world settings. We provide a simple strategy for a finite automaton in this(More)
Coordination is a challenging everyday task; just think of the last time you organized a party or a meeting involving several people. As a growing part of our social and professional life goes online, an opportunity for an improved coordination process arises. Recently, Gupta et al. proposed entangled queries as a declarative abstraction for data-driven(More)
The Adaptive Seeding problem is an algorithmic challenge motivated by influence maximiza-tion in social networks: One seeks to select among certain accessible nodes in a network, and then select, adaptively, among neighbors of those nodes as they become accessible in order to maximize a global objective function. More generally, adaptive seeding is a(More)
Adapting Seeding is a key algorithmic challenge of influence maximization in social networks. One seeks to select among certain available nodes in a network, and then, adaptively, among neighbors of those nodes as they become available, in order to maximize influence in the overall network. Despite recent strong approximation results [Seeman and Singer(More)
There have been two major lines of research aimed at capturing resource-bounded players in game theory. The first, initiated by Rubinstein (), charges an agent for doing costly computation; the second, initiated by Neyman (), does not charge for computation, but limits the computation that agents can do, typically by modeling agents as finite automata. We(More)
We study the problem of finding a subgame-perfect equilibrium in repeated games. In earlier work [Halpern, Pass and Seeman 2014], we showed how to efficiently find an (approximate) Nash equilibrium if assuming that players are computationally bounded (and making standard cryptographic hardness assumptions); in contrast, as demonstrated in the work of Borgs(More)