We argue that hedging is an activity that human and machine agents should engage in more broadly, even when the agent's value is not necessarily in monetary units. In this paper, we propose a decision-theoretic view of hedging based on augmenting a probabilistic graphical model -- specifically a Bayesian network or an influence diagram -- with a reward. Hedging is therefore posed as a particular kind of graph manipulation, and can be viewed as analogous to control/intervention and information gathering related analysis. Effective hedging occurs when a risk-averse agent finds opportunity to balance uncertain rewards in their current situation. We illustrate the concepts with examples and counter-examples, and conduct experiments to demonstrate the properties and applicability of the proposed computational tools that enable agents to proactively identify potential hedging opportunities in real-world situations.

PSDD, and then multiply them.

Undirected probabilistic graphical models are widely used to explore and represent dependencies between random variables.

In total, the paper is meticulous in suggesting the framework of chemical reaction networks and mapping belief propagation to it, but the experiments appear a little lacking in real scope. Suggestions: a)more and more convincing experiments with more complicated and/or bigger graphs b)better theoretical explanation of damped BP in relation to this work c) discuss how reaction speeds can be implemented in reality with different kappas. I expect them to be regulated through chemical compounds, which would most likely lead to discrete subsampling of the speed-space. Would this lead to local minima or other problems during inference? Are the assumptions of the'perfect chemical reaction network' based on arbitrary species realistic? Where's the catch if graphs get bigger and have largewr state spaces and hundreds/thousands of chemical species are needed to implement a problem.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The paper gives a nice introduction to sensitivity analysis in graphical models, and proposes a method to check if the MAP configuration will not change, if a simultaneous perturbation in the parameters occurs. The method works by selecting perturbed factors in such a way (using local computations) to create a worst-case scenario. The second best MAP estimation is then identified for the worst case scenario. Theoretically, the complexity is therefore equivalent to the MAP query (exponential in the tree-width), if the number of variables is a constant.

Neural Information Processing Systems http://nips.cc/

Graphs are a powerful data structure for representing relational data and are widely used to describe complex real-world systems. Probabilistic Graphical Models (PGMs) and Graph Neural Networks (GNNs) can both leverage graph-structured data, but their inherent functioning is different. The question is how do they compare in capturing the information contained in networked datasets? We address this objective by solving a link prediction task and we conduct three main experiments, on both synthetic and real networks: one focuses on how PGMs and GNNs handle input features, while the other two investigate their robustness to noisy features and increasing heterophily of the graph. PGMs do not necessarily require features on nodes, while GNNs cannot exploit the network edges alone, and the choice of input features matters. We find that GNNs are outperformed by PGMs when input features are low-dimensional or noisy, mimicking many real scenarios where node attributes might be scalar or noisy. Then, we find that PGMs are more robust than GNNs when the heterophily of the graph is increased. Finally, to assess performance beyond prediction tasks, we also compare the two frameworks in terms of their computational complexity and interpretability.