Game-Theoretic Protection Against Networked SIS Epidemics by Human Decision-Makers

  title={Game-Theoretic Protection Against Networked SIS Epidemics by Human Decision-Makers},
  author={Ashish Ranjan Hota and Shreyas Sundaram},

Figures from this paper

Game-Theoretic Vaccination Against Networked SIS Epidemics and Impacts of Human Decision-Making

This paper establishes the existence and uniqueness of a threshold equilibrium where nodes with degrees larger than a certain threshold vaccinate, and proves tight bounds on the ratio of equilibrium thresholds under behavioral and true perceptions of probabilities.

Impacts of Game-Theoretic Activation on Epidemic Spread over Dynamical Networks

This work analyzes the susceptible-asymptomatic-infected-recovered (SAIR) epidemic in the framework of activity-driven networks with heterogeneous node degrees and time-varying activation rates, and derives both individual and degree-based mean-field approximations of the exact state evolution.

Game-Theoretic Frameworks for Epidemic Spreading and Human Decision-Making: A Review

This review presents and reviews various solved and open problems in developing, analyzing, and mitigating epidemic spreading processes under human decision-making, and develops a multi-dimensional taxonomy, which categorizes existing works based on multiple dimensions.

Infection-Curing Games over Polya Contagion Networks

This work investigates infection-curing games on a network epidemics model based on the classical Polya urn scheme that accounts for spatial contagion among neighbouring nodes and proves the existence of a Nash equilibrium that can be determined numerically using gradient descent algorithms.

Networked SIS Epidemics With Awareness

It is shown that adding awareness reduces the expectation of any epidemic metric on the space of sample paths, e.g., eradication time or total infections, in terms of the coupling distribution.

Initialization and Curing Policies for Polya Contagion Networks

This paper investigates optimization policies for resource distribution in network epidemics using a model that derives from the classical Polya process, and introduces heuristic policies that primarily function on the basis of limiting the number of targeted nodes within a particular network setup.

Optimization Policies for Polya Contagion Networks

This thesis investigates optimization policies for resource distribution in network epidemics, using a model that derives from the classical Polya process, and introduces heuristic policies that primarily function on the basis of limiting the number of targeted nodes within a particular network setup.

Security Against Impersonation Attacks in Distributed Systems

The potential for adversarial manipulation in a class of graphical coordination games where the adversary can pose as a friendly agent in the game, thereby influencing the decision-making rules of a subset of agents is studied.

Risk-perception-aware control design under dynamic spatial risks

This work uses Cumulative Prospect Theory (CPT) to model the risk perception of a decision maker (DM) and uses it to construct perceived risk functions that transform the uncertain dynamic spatial cost to deterministic perceived risks of a DM.

Planning under risk and uncertainty based on Prospect-theoretic models

This work develops a novel sampling-based motion planing approach to generate plans in a risky and uncertain environment and proposes an adaption of Cumulative Prospect Theory to the setting of path planning, leading to the definition of a non-rational continuous cost envelope associated with an obstacle environment.



Interdependent Security Games on Networks Under Behavioral Probability Weighting

This paper characterize graph topologies that achieve the largest and smallest worst case average attack probabilities at Nash equilibria in Total Effort games, and equilibrium investments in Weakest Link and Best Shot games.

Interdependent Security With Strategic Agents and Cascades of Infection

  • R. La
  • Economics
    IEEE/ACM Transactions on Networking
  • 2016
It is demonstrated that, at least for some parameter regimes, the cascade probability increases with the average degree of nodes, in networks consisting of strategic agents with interdependent security.

The Impact of Imitation on Vaccination Behavior in Social Contact Networks

This work uses network-based mathematical models to study the effects of both imitation behavior and contact heterogeneity on vaccination coverage and disease dynamics and integrates contact network epidemiological models with a framework for decision-making.

Selfish Response to Epidemic Propagation

This work studies the best-response dynamic in a network whose users repeatedly activate or de-activate security, depending on what they learn about the infection level, and finds that the equilibrium level of infection increases as the users' learning rate increases.

Imitation dynamics of vaccination behaviour on social networks

This work sheds light on how imitation of peers shapes individual vaccination choices in social networks, and integrates an epidemiological process into a simple agent-based model of adaptive learning, which suggests parallels to historical scenarios in which vaccination coverage provided herd immunity for some time, but then rapidly dropped.

Hub nodes inhibit the outbreak of epidemic under voluntary vaccination

It is found that disease outbreak can be more effectively inhibited on scale-free networks than on random networks, indicating that real-world networks, which are often claimed to be scale free, can be favorably and easily controlled under voluntary vaccination.

Protecting Against Network Infections: A Game Theoretic Perspective

It is shown that its quality, in terms of overall network security, largely depends on the underlying topology, and is observed that the price of anarchy may be prohibitively high, hence a scheme for steering users towards socially efficient behavior is proposed.

Disease dynamics in a stochastic network game: a little empathy goes a long way in averting outbreaks

A stochastic network disease game model is proposed that captures the self-interests of individuals during the spread of a susceptible-infected-susceptible disease and shows that empathy is more effective than risk-aversion in the role played by the response of the infected versus the susceptible individuals on disease eradication.

Analysis of Exact and Approximated Epidemic Models over Complex Networks

This work provides a complete global analysis of the epidemic dynamics of the nonlinear mean-field approximation of the Markov chain model and shows that depending on the largest eigenvalue of the underlying graph adjacency matrix and the rates of infection, recovery, and vaccination, the global dynamics takes on one of two forms.