Abstraction and approximate decision-theoretic planning
Artificial Intelligence
The measurement of user information satisfaction
Communications of the ACM
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
A Case for Using Real Options Pricing Analysis to Evaluate Information Technology Project Investment
Information Systems Research
Real Options Analysis and Strategic Decision Making
Organization Science
Executives' perceptions of the business value of information technology: a process-oriented approach
Journal of Management Information Systems - Special issue: Impacts of information technology investment on organizational performance
Data Mining and Knowledge Discovery
| Hi-index | 0.00 |
The purpose of this research is to examine whether decision-theoretic planning techniques can be used to help managers evaluate strategic options in complex and uncertain environments. Firms faced with choices such as whether to acquire a start-up, develop a new product, or invest in updated production technology continue to make decisions based on unreliable heuristics, "gut feel" or misleading financial measures such as net present value (NPV). In this paper we show that decision-theoretic planning techniques originally developed for robot planning permit us to gain the insights provided by real options analysis without working within the restrictions of models designed to price financial options or incurring the overhead of constructing huge decision trees. A biotechnology licensing problem similar to those addressed elsewhere in the real options literature is used to illustrate the methodology and demonstrate its feasibility.