MBA201a: The Value of Information

Understanding probabilities A probability reflects a set of information. Collecting information can change the best estimate of a probability. A probability can be conditional on the outcomes of previous events or it can be independent of previous events. After the fact (ex post), the probability of an event is either 0 or 1. Professor Wolfram MBA201a - Fall 2009

Another decision tree… Developing a new product to cobrand with a soon-to-be released movie. Develop new product Drop the new product Movie is a hit [p=0.8] -$1m $.1m $2.5m Movie flops [p= ] Professor Wolfram MBA201a - Fall 2009

Would it be valuable to get information about the success of the movie before developing the product? Movie will be a hit [p=0.8] Movie will be a flop [p= ] -$1m + $2.5m Don’t develop product Develop product -$1m + $.1m Professor Wolfram MBA201a - Fall 2009

Obtaining information first changes your decision The information helps you avoid developing the product if the movie flops. This happens with 0.2 probability. In the event the movie flops, you save $.9. The expected value of this is $.18, which is the difference in the value of the two trees: $1.2-$1.02. Professor Wolfram MBA201a - Fall 2009

The value of information Information is valuable only if it may affect your choices. Valuation of information is from an ex ante (before the fact) position. In other words, don’t confuse a good outcome with a good decision. Professor Wolfram MBA201a - Fall 2009

Takeaways Remember: decision trees are a tool to formalize the decision process. Decision-makers do not always maximize expected value. Probabilities reflect a set of information. Decision trees can help you think about the value of obtaining new information. Ask yourself: What, if anything, would I do differently with new information? Would new information be worth more than the cost, if any, of acquiring it? Professor Wolfram MBA201a - Fall 2009