Influence Diagrams

By R. Howard, J. Matheson. Decision Analytics, 2005

1,064 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/cbefa1a15f53a0ee9196eeff113feeea8bfbcee6

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In Artificial Intelligence

In Management Science and Operations Research

In Statistics and Probability

Citations in the corpus

A decision network account of reasoning about other people's choices. (2015, methodology): “Decision nets were originally developed as tools for computing optimal decisions under uncertain conditions (Howard & Matheson, 2005).”
Bayesian reasoning and machine learning (2012, neutral): “3 Extending Bayesian Networks for Decisions An influence diagram is a Bayesian Network with additional Decision nodes and Utility nodes [137, 148, 162].”
Statistical Methods for Eliciting Probability Distributions (2005, background): “Efforts to influence how people consider probabilities have also been explored, such as asking them to suggest scenarios that would lead to an unlikely event (Slovic and Fischhoff 1977), influence diagrams (Howard and Matheson 1984), getting subjects to think carefully about the substantive details ”
Settable Systems: An Extension of Pearl's Causal Model with Optimization, Equilibrium, and Learning (2009, background): “, Heckerman and Shachter, 1995) in the spirit of influence diagrams (Howard and Matheson, 1984).”
Modeling AGI Safety Frameworks with Causal Influence Diagrams (2019, background): “In this paper, we show that causal inﬂuence diagrams (CIDs) [Koller and Milch, 2003; Howard and Matheson, 1984] offer a uniﬁed way of clearly representing the most prominent frameworks discussed in the AGI safety literature.”
Modeling Interaction via the Principle of Maximum Causal Entropy (2010, methodology): “We now apply MaxCausalEnt to the Inﬂuence Dia-gram (Howard & Matheson, 1984), a graphical framework that subsumes Bayesian Networks, augmenting their capabilities to reason about latent variables with decisions and utilities so that inference includes optimal decision making.”
Predictive Statistical Models for User Modeling (2001, positive): “In addition, BNs can be extended to include temporal information (dynamic Bayesian networks, Dean and Wellman, 1991) and utilities (influence diagrams, Howard and Matheson, 1984).”
Causal identifiability via Chain Event Graphs (2013, background): “Clearly the value of W must be known before X is manipulated, so in this ID representation there must be an edge from W to X (see for example [23]) and so W is a parent of X .”
Bayesian Artificial Intelligence (2004, background): “Adding an explicit representation of both the actions under consideration and the value or utility of the resultant outcomes gives us decision networks (also called influence diagrams [115]).”
Enhancing the Ostrom social-ecological system framework through formalization (2014, background): “Biomass” refers to the biomass of an entire forest.”
Probabilistic models with unknown objects (2006, background): “Thus, in addition to specifying CPDs, a BN asserts the directed local Markov property [Howard and Matheson, 1984; Kiiveri et al., 1984]: each variable is independent of its nondescendants given its parents.”