Innovator’s Dilemmas: real options? (1)
“Real options are created whenever you face a decision that is costly to reverse (…) The solving of real options has two parts. The first part is ’spotting options’ and the second part is ‘valuing options’. To spot options, we need to have a deep understanding of the whole decision landscape.”
Read Andrew Metrick’s book, “Venture Capital and the Finance of Innovation“, published by Wiley & Sons.
“Opportunity cost is the cost (sacrifice) incurred by choosing one option over an alternative one that may be equally desired. Thus, opportunity cost is the cost of pursuing one choice instead of another. Every action has an opportunity cost.”
Wikipedia’s entry for “opportunity cost“
“The primary value of investments in emerging technologies is in the options created through opportunities for future development and profitable commercialization (…) Just how and when (and if) this might happen would depend significantly on the outcome of R&D efforts over the next two or three years, and no one could hope to make credible estimates of of possible payoffs until then.”
Read William F. Hamilton’s paper, “Managing Real Options”, published on “Wharton on Emerging Technologies” by Wiley and Sons. This quote refers to a specific example provided by the author for illustration purposes.
Investing in emerging technologies is a tricky business. In many cases, decisions are made based on ‘high return/high risk’ assumptions or on the theoretical ’strategic value’ of the product, which can dramatically change over the planning horizon.
A decision tree is often depicted as a graph that helps visualize the decision making process by charting decision points, possible paths, outcomes and consequences, resource costs, etc.
When assessing a given R&D investment, it makes sense to develop a decision tree analysis as, often times, we are facing alternative architectures, features, implementations, timelines and market targets, which can yield different results.
This kind of exercise should enable discussions and analysis related to how success is being defined (e.g. qualitative and quantitative targets), impact of ‘what if’ scenarios, and what specific things we could get done in exchange for giving up something else (e.g. trade-offs).
More over, the decision tree analysis can portray contingencies helping the team spot next best alternatives. One of the branches should be about the implications of ‘doing nothing’, clearly showing whether no taking action involves greater risks or it just happens to save us from some kind of unnecessary trouble.
This is my first post on this subject. I will be sharing more insights on the following days. In the meantime, please feel free to leave comments (see link below) or send me an email.
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| J. de Francisco |  |
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| Chicago, 29 April 08 |  |
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