203 research outputs found
Mechanism Design for Team Formation
Team formation is a core problem in AI. Remarkably, little prior work has
addressed the problem of mechanism design for team formation, accounting for
the need to elicit agents' preferences over potential teammates. Coalition
formation in the related hedonic games has received much attention, but only
from the perspective of coalition stability, with little emphasis on the
mechanism design objectives of true preference elicitation, social welfare, and
equity. We present the first formal mechanism design framework for team
formation, building on recent combinatorial matching market design literature.
We exhibit four mechanisms for this problem, two novel, two simple extensions
of known mechanisms from other domains. Two of these (one new, one known) have
desirable theoretical properties. However, we use extensive experiments to show
our second novel mechanism, despite having no theoretical guarantees,
empirically achieves good incentive compatibility, welfare, and fairness.Comment: 12 page
Defending Elections Against Malicious Spread of Misinformation
The integrity of democratic elections depends on voters' access to accurate
information. However, modern media environments, which are dominated by social
media, provide malicious actors with unprecedented ability to manipulate
elections via misinformation, such as fake news. We study a zero-sum game
between an attacker, who attempts to subvert an election by propagating a fake
new story or other misinformation over a set of advertising channels, and a
defender who attempts to limit the attacker's impact. Computing an equilibrium
in this game is challenging as even the pure strategy sets of players are
exponential. Nevertheless, we give provable polynomial-time approximation
algorithms for computing the defender's minimax optimal strategy across a range
of settings, encompassing different population structures as well as models of
the information available to each player. Experimental results confirm that our
algorithms provide near-optimal defender strategies and showcase variations in
the difficulty of defending elections depending on the resources and knowledge
available to the defender.Comment: Full version of paper accepted to AAAI 201
Predicting Human Cooperation
The Prisoner's Dilemma has been a subject of extensive research due to its
importance in understanding the ever-present tension between individual
self-interest and social benefit. A strictly dominant strategy in a Prisoner's
Dilemma (defection), when played by both players, is mutually harmful.
Repetition of the Prisoner's Dilemma can give rise to cooperation as an
equilibrium, but defection is as well, and this ambiguity is difficult to
resolve. The numerous behavioral experiments investigating the Prisoner's
Dilemma highlight that players often cooperate, but the level of cooperation
varies significantly with the specifics of the experimental predicament. We
present the first computational model of human behavior in repeated Prisoner's
Dilemma games that unifies the diversity of experimental observations in a
systematic and quantitatively reliable manner. Our model relies on data we
integrated from many experiments, comprising 168,386 individual decisions. The
computational model is composed of two pieces: the first predicts the
first-period action using solely the structural game parameters, while the
second predicts dynamic actions using both game parameters and history of play.
Our model is extremely successful not merely at fitting the data, but in
predicting behavior at multiple scales in experimental designs not used for
calibration, using only information about the game structure. We demonstrate
the power of our approach through a simulation analysis revealing how to best
promote human cooperation.Comment: Added references. New inline citation style. Added small portions of
text. Re-compiled Rmarkdown file with updated ggplot2 so small aesthetic
changes to plot
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