Value of information

Value of information (VOI or VoI) is the amount a decision maker would be willing to pay for information prior to making a decision.

Similar terms

VoI is sometimes distinguished into value of perfect information, also called value of clairvoyance (VoC), and value of imperfect information. They are closely related to the widely known expected value of perfect information and expected value of sample information. Note that VoI is not necessarily equal to "value of decision situation with perfect information" - "value of current decision situation" as commonly understood.

Definitions

Simple

A simple example best illustrates the concept. Consider the decision situation with one decision, for example deciding on a 'Vacation Activity'; and one uncertainty, for example what will the 'Weather Condition' be? But we will only know the 'Weather Condition' after we have decided and begun the 'Vacation Activity'.

• The Value of perfect information on Weather Condition captures the value of being able to know Weather Condition even before making the Vacation Activity decision. It is quantified as the highest price the decision-maker is willing to pay for being able to know Weather Condition before making the Vacation Activity decision.
• The Value of imperfect information on Weather Condition, however, captures the value of being able to know the outcome of another related uncertainty, e.g., Weather Forecast, instead of Weather Condition itself before making Vacation Activity decision. It is quantified as the highest price the decision-maker is willing to pay for being able to know Weather Forecast before making Vacation Activity decision. Note that it is essentially the value of perfect information on Weather Forecast.

Formal

The above definition illustrates that the value of imperfect information of any uncertainty can always be framed as the value of perfect information, i.e., VoC, of another uncertainty, hence only the term VoC will be used onwards.

Standard

Consider a general decision situation having n decisions (d₁, d₂, d₃, ..., d_n) and m uncertainties (u₁, u₂, u₃, ..., u_m). Rationality assumption in standard individual decision-making philosophy states that what is made or known are not forgotten, i.e., decision-has perfect recall. This assumption translates into the existence of a linear ordering of these decisions and uncertainties such that;

• d_i is made prior to making d_j if and only if d_i comes before d_j in the ordering
• d_i is made prior to knowing u_j if and only if d_i comes before u_j in the ordering
• d_i is made after knowing u_j if and only if d_i comes after u_j in the ordering

Consider cases where the decision-maker is enabled to know the outcome of some additional uncertainties earlier in his/her decision situation, i.e., some u_i are moved to appear earlier in the ordering. In such case, VoC is quantified as the highest price which the decision-maker is willing to pay for all those moves.

Generalized

The standard definition is further generalized in team decision analysis framework where there is typically incomplete sharing of information among team members under the same decision situation. In such case, what is made or known might not be known in later decisions belonging to different team members, i.e., there might not exist linear ordering of decisions and uncertainties satisfying perfect recall assumption. VoC thus captures the value of being able to know "not only additional uncertainties but also additional decisions already made by other team members" before making some other decisions in the team decision situation.

Characteristics

There are two extremely important characteristics of VoI that always hold for any decision situation;

• Value of information can never be less than zero since the decision-maker can always ignore the additional information and makes decision as if such information is not available.
• No other information gathering/sharing activities can be more valuable than that quantified by value of clairvoyance.

Computation

VoC is derived strictly following its definition as the monetary amount that is big enough to just offset additional benefit of getting more information. In other words; VoC is calculated iteratively until;

"value of decision situation with perfect information while paying VoC" = "value of current decision situation".

A special case is when the decision-maker is risk neutral where VoC can be simply computed as;

VoC = "value of decision situation with perfect information" - "value of current decision situation"

This special case is how expected value of perfect information and expected value of sample information are calculated where risk neutrality is implicitly assumed. For cases where decision-maker is risk averse or risk seeking, this simple calculation does not necessary yield correct result, and iterative calculation is the only way to ensure correctness.

Decision tree and influence diagram are most commonly used in representing and solving decision situation as well as associated VoC calculation. Influence diagram, in particular, is structured to accommodate team decision situation where incomplete sharing of information among team members can be represented and solved very efficiently. While decision tree is not designed to accommodate team decision situation, it can do so by augmenting it with information set widely used in game tree.

See also

• Decision analysis
• Decision tree
• Expected value of perfect information (EVPI)
• Expected value of including uncertainty (EVIU)
• Expected value of sample information
• Influence diagram
• Value of control
• Information theory

Bibliography

• Cardenas, I. et al. (April 2015). "Modeling the Influence of Unknown Factors in Risk Analysis using Bayesian Networks" (PDF). Under review by a refereed journal.  CS1 maint: Explicit use of et al. (link)
• Detwarasiti, A. (2005). Team decision analysis and influence diagrams. Ph.D. Dissertation, Department of Management Science and Engineering, Stanford University.
• Howard, R.A. (1966). Information value theory. IEEE Transactions on Systems Science and Cybernetics (SSC-2), 22-26.
• Howard, R.A. and J.E. Matheson, "Influence diagram" (1981), in Readings on the Principles and Applications of Decision Analysis, eds. R.A. Howard and J.E. Matheson, Vol. II (1984), Menlo Park CA: Strategic Decisions Group.
• Kuhn, H.W. (1953). Extensive games and the problem of information. Contributions to the Theory of Games II, eds. H.W. Kuhn and A.W. Tucker, 193-216.
• Stratonovich, R. L. (1965). On value of information. Izvestiya of USSR Academy of Sciences, Technical Cybernetics 5, 3–12. In Russian.