Real-World Applications of Influence Diagrams

Gómez, Manuel

doi:10.1007/978-3-540-39879-0_9

Cited by 6 publications

(7 citation statements)

References 63 publications

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“…So the large number of computations necessary for coherently evaluating different policies are actually trivial ones and operationally computable in real time. We note that the algorithms we define in this multi-expert system are closely related to the ones already cited for the propagation of probabilities and expected utilities in graphical structures, which have now been successfully implemented in many large applications (Gómez, 2004;Oliver and Smith, 1990). So we can be confident that our methods remain feasible for current and much larger applications.…”

Section: Discussionmentioning

confidence: 99%

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Bayesian decision support for complex systems with many distributed experts

Leonelli

Smith

2015

Ann Oper Res

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Complex decision support systems often consist of component modules which, encoding the judgements of panels of domain experts, describe a particular sub-domain of the overall system. Ideally these modules need to be pasted together to provide a comprehensive picture of the whole process. The challenge of building such an integrated system is that, whilst the overall qualitative features are common knowledge to all, the explicit forecasts and their associated uncertainties are only expressed individually by each panel, resulting from its own analysis. The structure of the integrated system therefore needs to facilitate the coherent piecing together of these separate evaluations. If such a system is not available there is a serious danger that this might drive decision makers to incoherent and so indefensible policy choices. In this paper we develop a graphically based framework which embeds a set of conditions, consisting of the agreement usually made in practice of certain probability and utility models, that, if satisfied in a given context, are sufficient to ensure the composite system is truly coherent. Furthermore, we develop new message passing algorithms entailing the transmission of expected utility scores between the panels, that enable the uncertainties within each module to be fully accounted for in the evaluation of the available alternatives in these composite systems.

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Section: Discussionmentioning

confidence: 99%

“…and substituting these expressions into equations (17)- (18). All the terms in Table 1, as well as those in equations (14) and (15), are a function of Y l 1 (2), parameters and variables in the first time slice only and can therefore be sent to panel G 1 , which sums these two incoming messages.…”

Section: A Multiregression Dynamic Model For a Nuclear Emergencymentioning

confidence: 99%

Bayesian decision support for complex systems with many distributed experts

Leonelli

Smith

2015

Ann Oper Res

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“…A compilation can be found in [26]. But this examination does not provide information about which systems are real applications and which are just prototypes.…”

Section: Modeling With Ids: Strengths and Limitationsmentioning

confidence: 99%

A review of representation issues and modeling challenges with influence diagrams

Bielza

Gómez

Shenoy

2011

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Since their introduction in the mid 1970s, influence diagrams have become a de facto standard for representing Bayesian decision problems. The need to represent complex problems has led to extensions of the influence diagram methodology designed to increase the ability to represent complex problems.In this paper, we review the representation issues and modeling challenges associated with influence diagrams. In particular, we look at the representation of asymmetric decision problems including conditional distribution trees, sequential decision diagrams, and sequential valuation networks. We also examine the issue of representing the sequence of decision and chance variables, and how it is done in unconstrained influence diagrams, sequential valuation networks, and sequential influence diagrams.We also discuss the use of continuous chance and decision variables, including continuous conditionally deterministic variables. Finally, we discuss some of the modeling challenges faced in representing decision problems in practice and some software that is currently available.

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“…Statistical and causal IDs have shown promise for a wide variety of applications. In business and medical decision making, statistical IDs provide a simple yet powerful model for optimising decisions by making assumptions explicit and revealing what information is relevant [Góm04], [KM08]. Moreover, for the design of safe and fair AI systems, causal IDs have been used to help predict the behaviour of agents arising due to their incentives in an environment [ECL + 21], [CLEL20], [EHKK21], [Hol20], [EKKL19], [LE21], and [CVH20].…”

Section: Introductionmentioning

confidence: 99%

PyCID: A Python Library for Causal Influence Diagrams

Fox¹,

Everitt²,

Carey³

et al. 2021

Proceedings of the Python in Science Conference

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Why did a decision maker select a certain decision? What behaviour does a certain objective incentivise? How can we improve this behaviour and ensure that a decision-maker chooses decisions with safer or fairer consequences? This paper introduces the Python package PyCID, built upon pgmpy, that implements (causal) influence diagrams, a widely used graphical modelling framework for decision-making problems. By providing a range of methods to solve and analyse (causal) influence diagrams, PyCID helps answer questions about behaviour and incentives in both single-agent and multi-agent settings.

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Real-World Applications of Influence Diagrams

Cited by 6 publications

References 63 publications

Bayesian decision support for complex systems with many distributed experts

Bayesian decision support for complex systems with many distributed experts

A review of representation issues and modeling challenges with influence diagrams

PyCID: A Python Library for Causal Influence Diagrams

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