Cost Function Learning in Memorized Social Networks With Cognitive Behavioral Asymmetry

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.