Zobrazeno 1 - 10
of 116
pro vyhledávání: '"KRAFFT, PETER"'
Whether we recognize it or not, the Internet is rife with exciting and original institutional forms that are transforming social organization on and offline. Issues of governance in these Internet platforms and other digital institutions have posed a
Externí odkaz:
http://arxiv.org/abs/1902.08728
Autor:
Adjodah, Dhaval, Calacci, Dan, Dubey, Abhimanyu, Goyal, Anirudh, Krafft, Peter, Moro, Esteban, Pentland, Alex
Publikováno v:
AAMAS 2020
A common technique to improve learning performance in deep reinforcement learning (DRL) and many other machine learning algorithms is to run multiple learning agents in parallel. A neglected component in the development of these algorithms has been h
Externí odkaz:
http://arxiv.org/abs/1902.06740
People naturally bring their prior beliefs to bear on how they interpret the new information, yet few formal models exist for accounting for the influence of users' prior beliefs in interactions with data presentations like visualizations. We demonst
Externí odkaz:
http://arxiv.org/abs/1901.02949
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and
Externí odkaz:
http://hdl.handle.net/1721.1/113931
Autor:
Adjodah, Dhaval, Calacci, Dan, Dubey, Abhimanyu, Krafft, Peter, Moro, Esteban, Pentland, Alex `Sandy'
In this empirical paper, we investigate how learning agents can be arranged in more efficient communication topologies for improved learning. This is an important problem because a common technique to improve speed and robustness of learning in deep
Externí odkaz:
http://arxiv.org/abs/1811.12556
Flocking is a coordinated collective behavior that results from local sensing between individual agents that have a tendency to orient towards each other. Flocking is common among animal groups and might also be useful in robotic swarms. In the inter
Externí odkaz:
http://arxiv.org/abs/1804.08667
Publikováno v:
Peter Krafft, Nicol\'as Della Penna, Alex Pentland. (2018). An Experimental Study of Cryptocurrency Market Dynamics. ACM CHI Conference on Human Factors in Computing Systems (CHI)
As cryptocurrencies gain popularity and credibility, marketplaces for cryptocurrencies are growing in importance. Understanding the dynamics of these markets can help to assess how viable the cryptocurrnency ecosystem is and how design choices affect
Externí odkaz:
http://arxiv.org/abs/1801.05831
In this study, we build on previous research to understand the conditions within which the Wisdom of the Crowd (WoC) improves or worsens as a result of showing individuals the predictions of their peers. Our main novel contributions are: 1) a dataset
Externí odkaz:
http://arxiv.org/abs/1712.10284
Being able to correctly aggregate the beliefs of many people into a single belief is a problem fundamental to many important social, economic and political processes such as policy making, market pricing and voting. Although there exist many models a
Externí odkaz:
http://arxiv.org/abs/1712.09960
We draw upon a previously largely untapped literature on human collective intelligence as a source of inspiration for improving deep learning. Implicit in many algorithms that attempt to solve Deep Reinforcement Learning (DRL) tasks is the network of
Externí odkaz:
http://arxiv.org/abs/1711.11180