J. Stephen Judd

dblp:87/4980 · DBLP profile ↗
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8ranked-venue papers
5as first author
0since 2021 · last 2012
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 7 · 4 first-authorTheory of computation · 4 · 2 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
4 papers
Algorithmic game theory and mechanism design · 99% Coding theory · 1%
Interdisciplinary, comprehensive, and emerging computing
2 papers
Computational social science and digital humanities · 78% Computational finance and economics · 22%
Artificial intelligence
3 papers
Learning theory · 72% Deep learning architectures and training · 28%

Topics — the 8 heaviest of 10, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computational social science and digital humanities
human subject experiments
0.222012
A behavioral study of bargaining in social networks · EC 2010
Behavioral experiments on a network formation game · EC 2012
Algorithmic game theory and mechanism design › network games
network formation game
0.112012
Behavioral experiments on a network formation game · EC 2012
Algorithmic game theory and mechanism design › cooperative game theory
bargaining
0.112010
A behavioral study of bargaining in social networks · EC 2010
Algorithmic game theory and mechanism design › cooperative game theory
bargaining networks
0.112010
A behavioral study of bargaining in social networks · EC 2010
Computational finance and economics
behavioral economics
0.012012
Behavioral experiments on a network formation game · EC 2012
Machine learning › Learning theory
generalization
0.011993
Optimal Stopping and Effective Machine Complexity in Learning · NIPS 1993
Machine learning › Deep learning architectures and training › feedforward neural network
shallow neural networks
0.011991
Constant-Time Loading of Shallow 1-Dimensional Networks · NIPS 1991
Coding theory
error-correcting codes
0.011992
Nets with Unreliable Hidden Nodes Learn Error-Correcting Codes · NIPS 1992

Methods — techniques the papers use, named apart from their topics

network formation game · 0.3generative models of social networks · 0.3human-subject experiment · 0.1optimal stopping · 0.0
YearPublicationVenuePosition
2012 Behavioral experiments on a network formation game
abstract
We report on an extensive series of behavioral experiments in which 36 human subjects collectively build a communication network over which they must solve a competitive coordination task for monetary compensation. There is a cost for creating network links, thus creating a tension between link expenditures and collective and individual incentives. Our most striking finding is the poor performance of the subjects, especially compared to our long series of prior experiments. We demonstrate that the subjects built difficult networks for the coordination task, and compare the structural properties of the built networks to standard generative models of social networks. We also provide extensive analysis of the individual and collective behavior of the subjects, including free riding and factors influencing edge purchasing decisions.
Michael Kearns, J. Stephen Judd, Yevgeniy Vorobeychik
EC2
2010 A behavioral study of bargaining in social networks
abstract
We report on a series of highly controlled human subject experiments in networked bargaining. The basic interaction between two players is the decision of how to share a mutual payment; we extend this to situate the players in a network. Various theories predict, to different levels of uniqueness, what the shares will be. We analyze our experimental results from three points of view: social efficiency, nodal differences, and human differences; and contrast our behavioral results with the theories.
Tanmoy Chakraborty 0001, J. Stephen Judd, Michael Kearns, Jinsong Tan
EC2
2008 Behavioral experiments in networked trade
abstract
We report on an extensive series of highly controlled human subject experiments in networked trade. Our point of departure is a simple and well-studied bipartite network exchange model, for which previous work has established a detailed equilibrium theory relating wealth to network topology. A notable feature of this theory is its prediction that there may be significant local variation in equilibrium wealths and prices purely as a result of structural asymmetries in the network.
J. Stephen Judd, Michael Kearns
EC1
1993 Optimal Stopping and Effective Machine Complexity in Learning
Changfeng Wang, Santosh S. Venkatesh, J. Stephen Judd
NIPS3
1992 Nets with Unreliable Hidden Nodes Learn Error-Correcting Codes
J. Stephen Judd, Paul W. Munro
NIPS1
1992 A Reply to Honavar's Book Review of Neural Network Design and the Complexity of Learning
J. Stephen Judd
Mach. Learn.1
1991 Constant-Time Loading of Shallow 1-Dimensional Networks
J. Stephen Judd
NIPS1
1988 On the complexity of loading shallow neural networks
J. Stephen Judd
J. Complex.1