no code implementations • 23 Apr 2018 • Daniel Y. Fu, Emily S. Wang, Peter M. Krafft, Barbara J. Grosz
In the interest of learning how to control flocking behavior, recent work in the multiagent systems literature has explored the use of influencing agents for guiding flocking agents to face a target direction.
no code implementations • 8 May 2017 • L. Elisa Celis, Peter M. Krafft, Nisheeth K. Vishnoi
Finally, we observe that our infinite population dynamics is a stochastic variant of the classic multiplicative weights update (MWU) method.
no code implementations • 2 Nov 2016 • Peter M. Krafft, Michael Macy, Alex Pentland
In this work we outline a design space for bots as virtual confederates, and we propose a set of guidelines for meeting the status quo for ethical experimentation.
no code implementations • 5 Aug 2016 • Peter M. Krafft, Julia Zheng, Wei Pan, Nicolás Della Penna, Yaniv Altshuler, Erez Shmueli, Joshua B. Tenenbaum, Alex Pentland
To address this gap, we introduce a new analytical framework: We propose that groups arrive at accurate shared beliefs via distributed Bayesian inference.
1 code implementation • 14 Mar 2016 • L. Elisa Celis, Peter M. Krafft, Nathan Kobe
Domains in which content quality can be defined exogenously and measured objectively are thus needed in order to better assess the design choices of social recommendation systems.
1 code implementation • 11 Feb 2016 • Peter M. Krafft, Chris L. Baker, Alex Pentland, Joshua B. Tenenbaum
Whether in groups of humans or groups of computer agents, collaboration is most effective between individuals who have the ability to coordinate on a joint strategy for collective action.