{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/optimal-management-of-a-stochastically","title":"Optimal management of a stochastically varying population when policy adjustment is costly","arxiv_id":"1507.07037","date":"2015-07-24","proceeding":null,"authors":[],"abstract":"Ecological systems are dynamic and policies to manage them need to respond to\nthat variation. However, policy adjustments will sometimes be costly, which\nmeans that fine-tuning a policy to track variability in the environment very\ntightly will only sometimes be worthwhile. We use a classic fisheries\nmanagement question -- how to manage a stochastically varying population using\nannually varying quotas in order to maximize profit -- to examine how costs of\npolicy adjustment change optimal management recommendations. Costs of policy\nadjustment (here changes in fishing quotas through time) could take different\nforms. For example, these costs may respond to the size of the change being\nimplemented, or there could be a fixed cost any time a quota change is made. We\nshow how different forms of policy costs have contrasting implications for\noptimal policies. Though it is frequently assumed that costs to adjusting\npolicies will dampen variation in the policy, we show that certain cost\nstructures can actually increase variation through time. We further show that\nfailing to account for adjustment costs has a consistently worse economic\nimpact than would assuming these costs are present when they are not.","url_abs":"http://arxiv.org/abs/1507.07037v1","url_pdf":"http://arxiv.org/pdf/1507.07037v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"optimal-management-of-a-stochastically","repo_url":"https://github.com/cboettig/pdg_control","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"management","task_name":"Management"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}