Cost Function Learning in Memorized Social Networks With Cognitive Behavioral Asymmetry

Yanbing Mao, Jinning Li, Naira Hovakimyan, Tarek Abdelzaher, Christian Lebiere

Research output: Contribution to journalArticlepeer-review


This article investigates the cost function learning in social information networks, wherein human memory and cognitive bias are explicitly taken into account. We first propose a model for social information-diffusion dynamics, with a focus on the systematic modeling of asymmetric cognitive bias represented by confirmation bias and novelty bias. Building on the dynamics model, we then propose the M3IRL - a memorized model and maximum-entropy-based inverse reinforcement learning - for learning cost functions. Compared with the existing model-free IRLs, the characteristics of M3IRL are significantly different here: no dependence on the Markov decision process principle, the need for only a single finite-time trajectory sample, and bounded decision variables. Finally, the effectiveness of the proposed social information-diffusion model and the M3IRL algorithm is validated by the online social media data.

Original languageEnglish (US)
Pages (from-to)418-430
Number of pages13
JournalIEEE Transactions on Computational Social Systems
Issue number1
StatePublished - Feb 1 2024


  • Asymmetric confirmation bias
  • asymmetric novelty bias
  • cost function learning
  • human memory
  • inverse reinforcement learning (IRL)
  • social information-diffusion dynamics

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Social Sciences (miscellaneous)
  • Modeling and Simulation


Dive into the research topics of 'Cost Function Learning in Memorized Social Networks With Cognitive Behavioral Asymmetry'. Together they form a unique fingerprint.

Cite this