Is Uncertainty Always Bad?: Effect of Topic Competence on Uncertain Opinions

The proliferation of information disseminated by public/social media has made decision-making highly challenging due to the wide availability of noisy, uncertain, or unverified information. Although the issue of uncertainty in information has been studied for several decades, little work has investigated how noisy (or uncertain) or valuable (or credible) information can be formulated into people's opinions, modeling uncertainty both in the quantity and quality of evidence leading to a specific opinion. In this work, we model and analyze an opinion and information model by using Subjective Logic where the initial set of evidence is mixed with different types of evidence (i.e., pro vs. con or noisy vs. valuable) which is incorporated into the opinions of original propagators, who propagate information over a network. With the help of an extensive simulation study, we examine how the different ratios of information types or agents' prior belief or topic competence affect the overall information diffusion. Based on our findings, agents' high uncertainty is not necessarily always bad in making a right decision as long as they are competent enough not to be at least biased towards false information (e.g., neutral between two extremes)

A Novel Approach in Strategic Planning of Power Networks Against Physical Attacks

The reported work points at developing a practical approach for power transmission planners to secure power networks from potential deliberate attacks. We study the interaction between a system planner (defender) and a rational attacker who threatens the operation of the power grid. In addition to the commonly used hardening strategy for protecting the network, a new sort of resource is introduced under the deception concept. Feint and deception are acknowledged as effective tools for misleading the attacker in strategic planning. To this end, the defender deception is mathematically formulated by releasing misinformation about his plan in the shared cognition-based model. To reduce the risk of damage in case of deception failure, preemptive-goal programming is utilized to prioritize the hardening strategy for the vital components. Furthermore, the value of posturing is introduced which is the benefits that the deception brings to the system. The problems are formulated as tri-level mixed-integer linear programming and solved by the constraint-and-column generation method. Comprehensive simulation studies performed on WSCC 9-bus and IEEE 118-bus systems indicate how the defender will save significant cost from protecting his network with posturing rather than hardening and the proposed approach is a promising development to ensure the secure operation of power networks.Comment: Accepted to be published in Journal of Electric Power Systems Research, 201

Deep Learning for Predicting Dynamic Uncertain Opinions in Network Data

Subjective Logic (SL) is one of well-known belief models that can explicitly deal with uncertain opinions and infer unknown opinions based on a rich set of operators of fusing multiple opinions. Due to high simplicity and applicability, SL has been substantially applied in a variety of decision making in the area of cybersecurity, opinion models, trust models, and/or social network analysis. However, SL and its variants have exposed limitations in predicting uncertain opinions in real-world dynamic network data mainly in three-fold: (1) a lack of scalability to deal with a large-scale network; (2) limited capability to handle heterogeneous topological and temporal dependencies among node-level opinions; and (3) a high sensitivity with conflicting evidence that may generate counterintuitive opinions derived from the evidence. In this work, we proposed a novel deep learning (DL)-based dynamic opinion inference model while node-level opinions are still formalized based on SL meaning that an opinion has a dimension of uncertainty in addition to belief and disbelief in a binomial opinion (i.e., agree or disagree). The proposed DL-based dynamic opinion inference model overcomes the above three limitations by integrating the following techniques: (1) state-of-the-art DL techniques, such as the Graph Convolutional Network (GCN) and the Gated Recurrent Units (GRU) for modeling the topological and temporal heterogeneous dependency information of a given dynamic network; (2) modeling conflicting opinions based on robust statistics; and (3) a highly scalable inference algorithm to predict dynamic, uncertain opinions in a linear computation time. We validated the outperformance of our proposed DL-based algorithm (i.e., GCN-GRU-opinion model) via extensive comparative performance analysis based on four real-world datasets.Comment: IEEE Bigdata 201