  This paper investigates the cost function learning in social information
networks, wherein the influence of humans' memory on information consumption is
explicitly taken into account. We first propose a model for social
information-diffusion dynamics with a focus on systematic modeling of
asymmetric cognitive bias, represented by confirmation bias and novelty bias.
Building on the proposed social model, we then propose the M$^{3}$IRL: a model
and maximum-entropy based inverse reinforcement learning framework for learning
the cost functions of target individuals in the memorized social networks.
Compared with the existing Bayesian IRL, maximum entropy IRL, relative entropy
IRL and maximum causal entropy IRL, the characteristics of M$^{3}$IRL are
significantly different here: no dependency on the Markov Decision Process
principle, the need of only a single finite-time trajectory sample, and bounded
decision variables. Finally, the effectiveness of the proposed social
information-diffusion model and the M$^{3}$IRL algorithm are validated by the
online social media data.
