22,557 research outputs found
Evolutionary Poisson Games for Controlling Large Population Behaviors
Emerging applications in engineering such as crowd-sourcing and
(mis)information propagation involve a large population of heterogeneous users
or agents in a complex network who strategically make dynamic decisions. In
this work, we establish an evolutionary Poisson game framework to capture the
random, dynamic and heterogeneous interactions of agents in a holistic fashion,
and design mechanisms to control their behaviors to achieve a system-wide
objective. We use the antivirus protection challenge in cyber security to
motivate the framework, where each user in the network can choose whether or
not to adopt the software. We introduce the notion of evolutionary Poisson
stable equilibrium for the game, and show its existence and uniqueness. Online
algorithms are developed using the techniques of stochastic approximation
coupled with the population dynamics, and they are shown to converge to the
optimal solution of the controller problem. Numerical examples are used to
illustrate and corroborate our results
Evolutionary games on graphs
Game theory is one of the key paradigms behind many scientific disciplines
from biology to behavioral sciences to economics. In its evolutionary form and
especially when the interacting agents are linked in a specific social network
the underlying solution concepts and methods are very similar to those applied
in non-equilibrium statistical physics. This review gives a tutorial-type
overview of the field for physicists. The first three sections introduce the
necessary background in classical and evolutionary game theory from the basic
definitions to the most important results. The fourth section surveys the
topological complications implied by non-mean-field-type social network
structures in general. The last three sections discuss in detail the dynamic
behavior of three prominent classes of models: the Prisoner's Dilemma, the
Rock-Scissors-Paper game, and Competing Associations. The major theme of the
review is in what sense and how the graph structure of interactions can modify
and enrich the picture of long term behavioral patterns emerging in
evolutionary games.Comment: Review, final version, 133 pages, 65 figure
The Evolutionary Stability of Optimism, Pessimism and Complete Ignorance
We provide an evolutionary foundation to evidence that in some situations humans maintain optimistic or pessimistic attitudes towards uncertainty and are ignorant to relevant aspects of the environment. Players in strategic games face Knightian uncertainty about opponents’ actions and maximize individually their Choquet expected utility. Our Choquet expected utility model allows for both an optimistic or pessimistic attitude towards uncertainty as well as ignorance to strategic dependencies. An optimist (resp. pessimist) overweights good (resp. bad) outcomes. A complete ignorant never reacts to opponents’ change of actions. With qualifications we show that optimistic (resp. pessimistic) complete ignorance is evolutionary stable / yields a strategic advantage in submodular (resp. supermodular) games with aggregate externalities. Moreover, this evolutionary stable preference leads to Walrasian behavior in those classes of games
Are moral objections to free riding evolutionarily stable?
Game Theory;Public Goods
Two-Speed Evolution of Strategies and Preferences in Symmetric Games
Agenst in a large population are randomly matched to play a material payoff game. They may have preferences that are different from the material payoffs. Agents learn equilibrium strategies according to their preferences before evolution changes the preference distribution in the population according to fitness. When agents know the preferences of the opponent in a match, only efficient symmetric strategy profiles of the material payoff game can be stable. When agents do not know the preferences of the opponent, only Nash equilibria of the material payoff game can be stable. For 2x2 symmetric games I characterize preferences that are stable.
Allocating Limited Resources to Protect a Massive Number of Targets using a Game Theoretic Model
Resource allocation is the process of optimizing the rare resources. In the
area of security, how to allocate limited resources to protect a massive number
of targets is especially challenging. This paper addresses this resource
allocation issue by constructing a game theoretic model. A defender and an
attacker are players and the interaction is formulated as a trade-off between
protecting targets and consuming resources. The action cost which is a
necessary role of consuming resource, is considered in the proposed model.
Additionally, a bounded rational behavior model (Quantal Response, QR), which
simulates a human attacker of the adversarial nature, is introduced to improve
the proposed model. To validate the proposed model, we compare the different
utility functions and resource allocation strategies. The comparison results
suggest that the proposed resource allocation strategy performs better than
others in the perspective of utility and resource effectiveness.Comment: 14 pages, 12 figures, 41 reference
A Hierarchical Game with Strategy Evolution for Mobile Sponsored Content and Service Markets
In sponsored content and service markets, the content and service providers
are able to subsidize their target mobile users through directly paying the
mobile network operator, to lower the price of the data/service access charged
by the network operator to the mobile users. The sponsoring mechanism leads to
a surge in mobile data and service demand, which in return compensates for the
sponsoring cost and benefits the content/service providers. In this paper, we
study the interactions among the three parties in the market, namely, the
mobile users, the content/service providers and the network operator, as a
two-level game with multiple Stackelberg (i.e., leader) players. Our study is
featured by the consideration of global network effects owning to consumers'
grouping. Since the mobile users may have bounded rationality, we model the
service-selection process among them as an evolutionary-population follower
sub-game. Meanwhile, we model the pricing-then-sponsoring process between the
content/service providers and the network operator as a non-cooperative
equilibrium searching problem. By investigating the structure of the proposed
game, we reveal a few important properties regarding the equilibrium existence,
and propose a distributed, projection-based algorithm for iterative equilibrium
searching. Simulation results validate the convergence of the proposed
algorithm, and demonstrate how sponsoring helps improve both the providers'
profits and the users' experience
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