Value of Information

Value of Information calculates how much you should pay to reduce uncertainty before making a decision. It compares what you expect to gain with perfect knowledge versus your current best choice. This framework prevents both overspending on useless data and costly decisions made in ignorance. Notice this: always compare the cost of information against its expected value, i.e. the expected reduction in decision loss.

How Value of Information Works

First, establish your baseline by calculating the expected value of each available action using current knowledge. Choose the best option—this becomes your starting point. Then imagine what you would do if you had perfect information about the uncertain factors, considering each possible state of the world. The difference between these two scenarios represents the maximum amount you should pay to reduce uncertainty.

Examples in Action

Healthcare Policy: Governments use VoI to prioritize which clinical trials to fund, estimating that a study on cancer treatment A could save $1 billion in unnecessary spending, while research on treatment B yields only $10 million in value. Personal Life: You might pay $50 for a mechanic's inspection before buying a used car worth $10,000, since avoiding a single major repair (costing $2,000) makes the inspection worthwhile.

Practical Application

To apply VoI, identify what you need to know, estimate how much each possible answer would change your decision, and multiply by the probability of each outcome. This reveals whether research costs justify the expected improvement. The approach is most powerful when decisions are irreversible, stakes are high, and uncertainty is substantial—making it essential for strategic planning, medical research prioritization, and major investments.

Value of Information (VoI) analysis provides a rigorous framework for quantifying the worth of additional information before making decisions under uncertainty. The EVPI represents the maximum amount a rational decision-maker should pay to eliminate all uncertainty, calculated as the difference between the expected value of the optimal decision with perfect information and the expected value of the optimal decision with current knowledge. The EVSI extends this concept to evaluate specific, imperfect sources of information.

• Raiffa and Schlaifer (1961): Established the foundational theory of Value of Information in their seminal work on applied statistical decision theory, defining EVPI as the difference between the expected value with perfect information and the expected value under current uncertainty and formalizing the relationship between information value and decision quality. [1]
• Jackson et al. (2019): Demonstrated that VoI methods generalize beyond simple decision problems to complex Bayesian evidence synthesis models, showing how regression-based computation enables practical application in healthcare policy, epidemiology, and public health modeling with multiple uncertain parameters. [2]
• Claxton and Sculpher (2006): Pioneered the application of VoI analysis in health economics for research prioritization, establishing that EVPI and EVPPI calculations can identify which parameters contribute most to decision uncertainty and where further research investment yields the highest return in population health benefits. [3]

VoI analysis requires precise quantification of probabilities, outcomes, and preferences—information that is often unavailable or difficult to elicit accurately in practice. The framework assumes that the structure of the decision problem remains stable after new information arrives, which may not hold in dynamic environments where new options emerge or preferences evolve. Computational complexity grows rapidly with the number of uncertain parameters, making VoI challenging for large-scale models. The method also struggles with deep uncertainty where probabilities cannot be reliably assigned, and with structural uncertainties that are difficult to parameterize within a single model. Additionally, VoI calculations are sensitive to the choice of prior distributions, requiring sensitivity analysis or robust methods to ensure conclusions are not driven by arbitrary assumptions.