Value Of Information Article Index for
Value Of 		Website Links For
Value 		 

Information About

Value Of Information
  CATEGORIES ABOUT VALUE OF INFORMATION
   
information, economics and uncertainty
   
APPAREL
BABY
BEAUTY
BOOKS
CAR TOYS
CELL PHONES
DVD'S
ELECTRONICS
GOURMET FOOD
GROCERIES
HEALTH & PERSONAL
HOME & GARDEN
JEWELRY
MUSIC
MUSIC INSTRUMENTS
OFFICE PRODUCTS
SOFTWARE
SPORTING GOODS
TOOLS & HARDWARE
TOYS
VIDEO GAMES
SHOPPING HOME
MORE SHOPPING...




SIMPLE DEFINITION


A simple example best illustrates the concept. Consider a decision situation with one decision ''Vacation Activity'' and one uncertainty ''Weather Condition'' which will be resolved only after the ''Vacation Activity'' decision has been made.

  • 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 in which the decision-maker is willing to pay for being able to know ''Weather Condition'' before making ''Vacation Activity'' decision.

  • 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 in which 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 DEFINITION


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 Definition


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

  • ''di'' is made prior to making ''dj'' if and only if ''di'' comes before ''dj'' in the ordering

  • ''di'' is made prior to knowing ''uj'' if and only if ''di'' comes before ''uj'' in the ordering

  • ''di'' is made after knowing ''uj'' if and only if ''di'' comes after ''uj'' in the ordering


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


Generalized Definition


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 OF VALUE OF INFORMATION


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

  • 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 OF VALUE OF INFORMATION


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 .


NOTES


Special care is needed when the choice being made for a decision can influence how an uncertainty resolves in the future. Having a perfect or imperfect information on such uncertainty implies that the choice to be made can be inferred prior to making such choice. This circular logic is against Free Will principle and thus extra works are needed to represent and solve for VoI properly.


BIBLIOGRAPHY

  • 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 diagrams" (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.



SEE ALSO