A comparison of adaptive management and real options approaches for environmental decisions under uncertainty

Abstract

Two approaches to sequential decisions under uncertainty in the environmental management - adaptive management and real options analysis – have evolved independently over the last decades. Adaptive management, or learning by doing, originated from adaptive control. Adaptive management is acknowledged as one of the best-practice methods to manage biological systems under structural uncertainty. Adaptive management has been used for the management of renewable natural resources (such as fisheries and waterfowl) and the conservation of species (such as assisted colonization, restoration and threatened species management). In this context, stochastic dynamic models and historical data would be valuable for describing and predicting responses of management decisions, but these are either non-existent or severely limited in their scope. Real options analysis originated from mathematical finance and is based on financial options pricing theory. The real options analysis can be viewed as both sequential decision-making and project valuation in a highly uncertain environment with non-stationary dynamics. Real options analysis has most often been used for industrial applications (such as mining, asset management, infrastructure, energy, defence, and agriculture). In this context, reasonably good stochastic dynamic models and historical data exist for describing and forecasting the behaviour of risk factors. In mathematical terms, both adaptive management and real options approaches are based on stochastic optimal control and Markov decision processes. In environmental decision-making both enable practitioners and managers to make optimal decisions under uncertainty. However, the numerical methods of solving adaptive management versus real options problems are different, as their development has been motivated by the different needs of respective application areas. An important feature of adaptive management is the presence of and need to account for a small number of hidden variables. In contrast, real options focus on the development of techniques capable of dealing with high-dimensional problems with multiple stochastic risk factors. Limited for a long time by the inefficiency of the solution methods, recent advances in both adaptive management and real options now allow us to solve more realistic environmental decision problems under uncertainty, widening the scope of their applications. Growing availability of data in the environmental management arena and an emerging need to conduct industrial operations in the proximity of conservation areas will require new decision-making approaches that can combine recent advances in adaptive management and real options. This paper reviews recent advances in both adaptive management and real options methodologies, and compares methods for solving decisions under uncertainty problems based on the type of uncertainty they are addressing, the type of decision-making approach, important assumptions, and the size of the problems they are capable of dealing with. This paper proposes new areas of development that could inspire future research and better-informed environmental decisions under uncertainty.