Coherent Frameworks for Statistical Inference serving Integrating Decision Support Systems
arXiv:1507.07394
Abstract
A subjective expected utility policy making centre, managing complex, dynamic systems, needs to draw on the expertise of a variety of disparate panels of experts and integrate this information coherently. To achieve this, diverse supporting probabilistic models need to be networked together, the output of one model providing the input to the next. In this paper we provide a technology for designing an integrating decision support system and to enable the centre to explore and compare the efficiency of different candidate policies. We develop a formal statistical methodology to underpin this tool. In particular, we derive sufficient conditions that ensure inference remains coherent before and after relevant evidence is accommodated into the system. The methodology is illustrated throughout using examples drawn from two decision support systems: one designed for nuclear emergency crisis management and the other to support policy makers in addressing the complex challenges of food poverty in the UK.
References in corpus (10)
- Object-Oriented Bayesian Networks
- Causal Discovery from a Mixture of Experimental and Observational Data
- Identifying the consequences of dynamic treatment strategies: A decision-theoretic overview
- Network Engineering for Complex Belief Networks
- Searching Multiregression Dynamic Models of Resting-State fMRI Networks Using Integer Programming
- Estimating Food Consumption and Poverty Indices with Mobile Phone Data
- Causal Reasoning in Graphical Time Series Models
- From Science to Management: Using Bayesian Networks to Learn about Lyngbya
- UPDATE February 2012 - The Food Crises: Predictive validation of a quantitative model of food prices including speculators and ethanol conversion
- UPDATE July 2012 | The Food Crises: The US Drought