Samuel D. Johnson
Samuel D. Johnson has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Dana Warmsley
Intelligent Systems Center, United States
1 shared publication Jemin George
United States Army Research Laboratory
1 shared publication- Jemin George
U.S. Army Research Laboratory, Adelphi, MD, USA
1 shared publication - Jiejun Xu
HRL Laboratories California USA
1 shared publication - Raissa M. D'Souza
Department of Computer Science and Department of Mechanical and Aerospace Engineering, University of California, Davis 2 , Davis, California 95616
1 shared publication Raissa M. D’souza
University of California
1 shared publication
Latest Publications
- 14 September 2024
- Published by Springer Nature
- p. 246-256
Assessing the Sociocultural Alignment of Large Language Models: An Empirical Study of Chinese-Speaking Populations
- 14 September 2024
- Published by Springer Nature
- p. 246-256
Assessing the Sociocultural Alignment of Large Language Models: An Empirical Study of Chinese-Speaking Populations
- 26 November 2016
- Published by Springer Nature
- p. 3-12
Strategic Seeding of Rival Opinions
- Open Access
Strategic Seeding of Rival Opinions
We present a network influence game that models players strategically seeding the opinions of nodes embedded in a social network. A social learning dynamic, whereby nodes repeatedly update their opinions to resemble those of their neighbors, spreads the seeded opinions through the network. After a fixed period of time, the dynamic halts and each player's utility is determined by the relative strength of the opinions held by each node in the network vis-a-vis the other players. We show that the existence of a pure Nash equilibrium cannot be guaranteed in general. However, if the dynamics are allowed to progress for a sufficient amount of time so that a consensus among all of the nodes is obtained, then the existence of a pure Nash equilibrium can be guaranteed. The computational complexity of finding a pure strategy best response is shown to be NP-complete, but can be efficiently approximated to within a (1 - 1/e) factor of optimal by a simple greedy algorithm.
Open AccessStrategic Seeding of Rival Opinions
We present a network influence game that models players strategically seeding the opinions of nodes embedded in a social network. A social learning dynamic, whereby nodes repeatedly update their opinions to resemble those of their neighbors, spreads the seeded opinions through the network. After a fixed period of time, the dynamic halts and each player's utility is determined by the relative strength of the opinions held by each node in the network vis-a-vis the other players. We show that the existence of a pure Nash equilibrium cannot be guaranteed in general. However, if the dynamics are allowed to progress for a sufficient amount of time so that a consensus among all of the nodes is obtained, then the existence of a pure Nash equilibrium can be guaranteed. The computational complexity of finding a pure strategy best response is shown to be NP-complete, but can be efficiently approximated to within a (1 - 1/e) factor of optimal by a simple greedy algorithm.