Diffusion of Innovation under Limited-Trust Equilibrium

Research output: Chapter in Book/Report/Conference proceedingConference contribution


We consider the diffusion of innovation in social networks using a game-theoretic approach. In our model, each individual plays a coordination game with its neighbors and decides what alternative product to adopt to maximize its payoff. As products are used in conjunction with others and through repeated interactions, individuals are more interested in their long-term benefits and often tend to show trustworthiness to others in order to maximize their long-term payoffs. To capture such trustworthy behavior, we deviate from the expected utility theory and use a new notion of rationality based on the limited-trust equilibrium. By incorporating such rational behavior into the product diffusion model, we analyze the convergence of emerging dynamics to their equilibrium points using a mean-field approximation. In particular, we study the convergence rate of the diffusion process using the absorption probability and the expected absorption time of a reduced-size Markov chain. Simulation shows that when individuals behave trustworthily, their long-term payoffs will increase significantly compared to the case when they are solely self-interested.

Original languageEnglish (US)
Title of host publication2022 IEEE 61st Conference on Decision and Control, CDC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781665467612
StatePublished - 2022
Event61st IEEE Conference on Decision and Control, CDC 2022 - Cancun, Mexico
Duration: Dec 6 2022Dec 9 2022

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370


Conference61st IEEE Conference on Decision and Control, CDC 2022


  • Diffusion dynamics
  • Markov chains
  • Nash equilibrium
  • limited-trust equilibrium
  • social networks

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization


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