论文标题
具有认知行为不对称的记忆社交网络中的成本功能学习
Cost Function Learning in Memorized Social Networks with Cognitive Behavioral Asymmetry
论文作者
论文摘要
本文研究了社交信息网络中的成本函数学习，其中明确考虑了人类记忆对信息消费的影响。我们首先提出了一个社会信息扩散动力学的模型，重点是对不对称认知偏见的系统建模，以确认偏见和新颖性偏见为代表。然后，在提出的社会模型的基础上，我们提出了M $^{3} $ irl：一个模型和最大值的基于逆向的逆增强学习框架，用于学习记忆的社交网络中目标个人的成本功能。与现有的贝叶斯IRL，最大熵IRL，相对熵IRL和最大因果熵IRL相比，M $^{3} $ IRL的特征在这里有很大不同：不依赖Markov决策过程原理，仅需要单个有限的轨迹轨迹样本，并且需要单个有限的决策变量。最后，在线社交媒体数据验证了拟议的社会信息扩散模型和M $^{3} $ IRL算法的有效性。
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.
