6 research outputs found
Distributed Computing with Adaptive Heuristics
We use ideas from distributed computing to study dynamic environments in
which computational nodes, or decision makers, follow adaptive heuristics (Hart
2005), i.e., simple and unsophisticated rules of behavior, e.g., repeatedly
"best replying" to others' actions, and minimizing "regret", that have been
extensively studied in game theory and economics. We explore when convergence
of such simple dynamics to an equilibrium is guaranteed in asynchronous
computational environments, where nodes can act at any time. Our research
agenda, distributed computing with adaptive heuristics, lies on the borderline
of computer science (including distributed computing and learning) and game
theory (including game dynamics and adaptive heuristics). We exhibit a general
non-termination result for a broad class of heuristics with bounded
recall---that is, simple rules of behavior that depend only on recent history
of interaction between nodes. We consider implications of our result across a
wide variety of interesting and timely applications: game theory, circuit
design, social networks, routing and congestion control. We also study the
computational and communication complexity of asynchronous dynamics and present
some basic observations regarding the effects of asynchrony on no-regret
dynamics. We believe that our work opens a new avenue for research in both
distributed computing and game theory.Comment: 36 pages, four figures. Expands both technical results and discussion
of v1. Revised version will appear in the proceedings of Innovations in
Computer Science 201
Self-stabilizing uncoupled dynamics
Dynamics in a distributed system are self-stabilizing if they are guaranteed
to reach a stable state regardless of how the system is initialized. Game
dynamics are uncoupled if each player's behavior is independent of the other
players' preferences. Recognizing an equilibrium in this setting is a
distributed computational task. Self-stabilizing uncoupled dynamics, then, have
both resilience to arbitrary initial states and distribution of knowledge. We
study these dynamics by analyzing their behavior in a bounded-recall
synchronous environment. We determine, for every "size" of game, the minimum
number of periods of play that stochastic (randomized) players must recall in
order for uncoupled dynamics to be self-stabilizing. We also do this for the
special case when the game is guaranteed to have unique best replies. For
deterministic players, we demonstrate two self-stabilizing uncoupled protocols.
One applies to all games and uses three steps of recall. The other uses two
steps of recall and applies to games where each player has at least four
available actions. For uncoupled deterministic players, we prove that a single
step of recall is insufficient to achieve self-stabilization, regardless of the
number of available actions
Plurality Voting under Uncertainty
Understanding the nature of strategic voting is the holy grail of social
choice theory, where game-theory, social science and recently computational
approaches are all applied in order to model the incentives and behavior of
voters.
In a recent paper, Meir et al.[EC'14] made another step in this direction, by
suggesting a behavioral game-theoretic model for voters under uncertainty. For
a specific variation of best-response heuristics, they proved initial existence
and convergence results in the Plurality voting system.
In this paper, we extend the model in multiple directions, considering voters
with different uncertainty levels, simultaneous strategic decisions, and a more
permissive notion of best-response. We prove that a voting equilibrium exists
even in the most general case. Further, any society voting in an iterative
setting is guaranteed to converge.
We also analyze an alternative behavior where voters try to minimize their
worst-case regret. We show that the two behaviors coincide in the simple
setting of Meir et al., but not in the general case.Comment: The full version of a paper from AAAI'15 (to appear