Optimizing high-dimensional stochastic forestry via reinforcement learning

Olli Tahvonen, Antti Suominen, Pekka Malo, Lauri Viitasaari and Vesa-Pekka Parkatti

Journal of Economic Dynamics and Control, 2022, vol. 145, issue C

Abstract: In proceeding beyond the generic optimal rotation model, forest economic research has applied various specifications that aim to circumvent the problems of high dimensionality. We specify an age- and size-structured mixed-species optimal harvesting model with binary variables for harvest timing, stochastic stand growth, and stochastic prices. Reinforcement learning allows solving this high-dimensional model without simplifications. In addition to presenting new features in reservation price schedules and effects of stochasticity, our setup allows evaluating the simplifications in the existing research. We find that one- or two-dimensional models lose a high fraction of attainable economic output while the commonly applied size-structured matrix model overestimates economic profitability, yields deviations in harvest timing, including optimal rotation, and dilutes the effects of stochasticity. Reinforcement learning is found to be an efficient and promising method for detailed age- and size-structured optimization models in resource economics.

Keywords: Artificial intelligence; Reinforcement learning; Forestry; Stochasticity; Curse of dimensionality; Optimal rotation; Natural resources (search for similar items in EconPapers)
JEL-codes: C61 Q23 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:dyncon:v:145:y:2022:i:c:s0165188922002561

DOI: 10.1016/j.jedc.2022.104553

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