Business Opportunity Assessment With Costly, Imperfect Information

An increasing number of decision makers are required to assess business opportunities, as new technologies have been developing at an astonishing rate and technology transfer has been facilitated in numerous institutions. We present a partially observed Markov decision process model of the assessment. At each point in time, a reward-maximizing decision maker chooses to either accept the opportunity, reject it, or gather costly, imperfect information and update her belief on the status of the opportunity. The resulting optimal policy balances the costs and benefits of decision accuracy, information gathering, and the window of opportunity closing. We show that the optimal policy is characterized by a pair of probability thresholds, derive bounds for these thresholds, and investigate their sensitivity to changes in information cost, the payoff structure, and information quality. Our approach unifies and contributes to three distinct streams of research: technology adoption, partially observed optimal stopping, and comparison of experiments. We also articulate how real decision makers can implement this opportunity assessment framework and offer implications for those who fund these opportunities.