McCardle Abstract Valuing Learning and Flexibility Firms routinely make risky investment decisions: they must decide whether to invest some amount today in exchange for an uncertain stream of future payoffs. For example, oil companies invest in exploration hoping to find valuable oil reserves; pharmaceutical firms invest in R&D hoping to develop valuable new drugs; manufacturing firms build plants in foreign countries to tap new markets or achieve significant cost reductions. In these kinds of investments, the firm learns more and more about the value of the project as time passes - as exploration wells are drilled, clinical trials completed, supplier contracts negotiated - and uncertainties resolve. A smart firm will adapt their investment and management strategies s these uncertainties resolve. There are two major competing procedures for evaluating risky projects where managerial flexibility plays an important role: one is option pricing theory, based on the no-arbitrage theory of financial markets, and the other is decision analysis, based on stochastic dynamic programming. In previous work, the principal investigators developed a new procedure for evaluating risky projects that integrates these two approaches and applied this procedure on several real and gas investments. One of the key lessons learned from these applications was the critical impact that the models of learning have on the values and optimal policies. The goal of the current research project is to develop better models of learning about uncertainties over time and study their impact on the firm's optimal investment and management strategies. Rather than address these questions in an abstract way, we plan to address them in the context of specific application related to valuing oil and gas investments, working with industry experts to ensure the appropriateness of the modeling assumptions.
