80 citations · 236 across the 21 of their papers we have counts for
22 papers
Flexible social inference facilitates targeted social learning when rewards are not observable
Robert D. Hawkins, Andrew M. Berdahl, Alex "Sandy" Pentland +3
Groups coordinate more effectively when individuals are able to learn from others' successes. But acquiring such knowledge is not always easy, especially in real-world environments…
Bias amplification in experimental social networks is reduced by resampling
Mathew D. Hardy, Bill D. Thompson, P. M. Krafft +1
Large-scale social networks are thought to contribute to polarization by amplifying people's biases. However, the complexity of these technologies makes it difficult to identify th…
Social Learning and the Accuracy-Risk Trade-off in the Wisdom of the Crowd
Dhaval Adjodah, Yan Leng, Shi Kai Chong +3
How do we design and deploy crowdsourced prediction platforms for real-world applications where risk is an important dimension of prediction performance? To answer this question, w…
Defining AI in Policy versus Practice
P. M. Krafft, Meg Young, Michael Katell +2
Recent concern about harms of information technologies motivate consideration of regulatory action to forestall or constrain certain developments in the field of artificial intelli…
An Algorithmic Equity Toolkit for Technology Audits by Community Advocates and Activists
Michael Katell, Meg Young, Bernease Herman +6
A wave of recent scholarship documenting the discriminatory harms of algorithmic systems has spurred widespread interest in algorithmic accountability and regulation. Yet effective…
Designing for Participation and Change in Digital Institutions
Peter Krafft, Brian Keegan, Seth Frey
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 i…
Leveraging Communication Topologies Between Learning Agents in Deep Reinforcement Learning
Dhaval Adjodah, Dan Calacci, Abhimanyu Dubey +4
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…
A Bayesian Cognition Approach to Improve Data Visualization
Yea-Seul Kim, Logan A Walls, Peter Krafft +1
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 i…
How to Organize your Deep Reinforcement Learning Agents: The Importance of Communication Topology
Dhaval Adjodah, Dan Calacci, Abhimanyu Dubey +3
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…
The Wisdom of the Network: How Adaptive Networks Promote Collective Intelligence
Abdullah Almaatouq, Alejandro Noriega-Campero, Abdulrahman Alotaibi +3
Social networks continuously change as new ties are created and existing ones fade. It is widely noted that our social embedding exerts a strong influence on what information we re…
Influencing Flock Formation in Low-Density Settings
Daniel Y. Fu, Emily S. Wang, Peter M. Krafft +1
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…
An Experimental Study of Cryptocurrency Market Dynamics
Peter M Krafft, Nicolás Della Penna, Alex Pentland
As cryptocurrencies gain popularity and credibility, marketplaces for cryptocurrencies are growing in importance. Understanding the dynamics of these markets can help to assess how…
Large-Scale Experiment on the Importance of Social Learning and Unimodality in the Wisdom of the Crowd
Dhaval Adjodah, Shi Kai Chong, Yan Leng +2
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 pred…
Social Bayesian Learning in the Wisdom of the Crowd
Dhaval Adjodah, Yan Leng, Shi Kai Chong +2
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…
Improved Learning in Evolution Strategies via Sparser Inter-Agent Network Topologies
Dhaval Adjodah, Dan Calacci, Yan Leng +3
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 atte…
A Distributed Learning Dynamics in Social Groups
L. Elisa Celis, Peter M. Krafft, Nisheeth K. Vishnoi
We study a distributed learning process observed in human groups and other social animals. This learning process appears in settings in which each individual in a group is trying t…
Bots as Virtual Confederates: Design and Ethics
Peter M Krafft, Michael Macy, Alex Pentland
The use of bots as virtual confederates in online field experiments holds extreme promise as a new methodological tool in computational social science. However, this potential tool…
Inferring Population Preferences via Mixtures of Spatial Voting Models
Alison Nahm, Alex Pentland, Peter Krafft
Understanding political phenomena requires measuring the political preferences of society. We introduce a model based on mixtures of spatial voting models that infers the underlyin…
Human collective intelligence as distributed Bayesian inference
Peter M. Krafft, Julia Zheng, Wei Pan +5
Collective intelligence is believed to underly the remarkable success of human society. The formation of accurate shared beliefs is one of the key components of human collective in…
Sequential Voting Promotes Collective Discovery in Social Recommendation Systems
L. Elisa Celis, Peter M. Krafft, Nathan Kobe
One goal of online social recommendation systems is to harness the wisdom of crowds in order to identify high quality content. Yet the sequential voting mechanisms that are commonl…
Modeling Human Ad Hoc Coordination
Peter M. Krafft, Chris L. Baker, Alex Pentland +1
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…
Popularity and Performance: A Large-Scale Study
Peter Krafft, Julia Zheng, Erez Shmueli +3
Social scientists have long sought to understand why certain people, items, or options become more popular than others. One seemingly intuitive theory is that inherent value drives…