Modular ABM development in NRM for improved dissemination and training
Agent-based models (ABM) have become an important tool for natural resource management in recent decades, and for the study of agricultural change in particular (e.g., Becu, Perez, Walker, Barreteau, & Page, 2003; Bell, 2011; Bert et al., 2010). Where household-level decisions, made in interaction w...
| Main Authors: | , , , |
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| Format: | Artículo preliminar |
| Language: | Inglés |
| Published: |
International Food Policy Research Institute
2014
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/149888 |
| _version_ | 1855528648999698432 |
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| author | Bell, Andrew R. Robinson, Derek Malik, Ammar Dewal, Snigdha |
| author_browse | Bell, Andrew R. Dewal, Snigdha Malik, Ammar Robinson, Derek |
| author_facet | Bell, Andrew R. Robinson, Derek Malik, Ammar Dewal, Snigdha |
| author_sort | Bell, Andrew R. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Agent-based models (ABM) have become an important tool for natural resource management in recent decades, and for the study of agricultural change in particular (e.g., Becu, Perez, Walker, Barreteau, & Page, 2003; Bell, 2011; Bert et al., 2010). Where household-level decisions, made in interaction with other households and the natural environment, shape outcomes at the landscape scale, ABM can provide insights that coarser equation-based models (EBM) or statistical models may not (Bankes, 2002; Paranuk et al. 1998). ABM provide as well a unique point of entry for non-expert stakeholders into the analytic process because it offers a 1:1 mapping of real-world actors to computational agents, which provides a level of conceptual understanding and familiarity that is not available when EBM or statistical models are used. Furthermore, the actor behaviours that are formalized within the computational code that defines an agent’s behavior can be represented in a variety of ways (e.g., heuristic decision trees, utility functions) that are again easier for non-expert stakeholders to than the equations and constraints of other modeling approaches. |
| format | Artículo preliminar |
| id | CGSpace149888 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2014 |
| publishDateRange | 2014 |
| publishDateSort | 2014 |
| publisher | International Food Policy Research Institute |
| publisherStr | International Food Policy Research Institute |
| record_format | dspace |
| spelling | CGSpace1498882025-11-06T06:34:27Z Modular ABM development in NRM for improved dissemination and training Bell, Andrew R. Robinson, Derek Malik, Ammar Dewal, Snigdha bioeconomics mathematical models macroeconomics Agent-based models (ABM) have become an important tool for natural resource management in recent decades, and for the study of agricultural change in particular (e.g., Becu, Perez, Walker, Barreteau, & Page, 2003; Bell, 2011; Bert et al., 2010). Where household-level decisions, made in interaction with other households and the natural environment, shape outcomes at the landscape scale, ABM can provide insights that coarser equation-based models (EBM) or statistical models may not (Bankes, 2002; Paranuk et al. 1998). ABM provide as well a unique point of entry for non-expert stakeholders into the analytic process because it offers a 1:1 mapping of real-world actors to computational agents, which provides a level of conceptual understanding and familiarity that is not available when EBM or statistical models are used. Furthermore, the actor behaviours that are formalized within the computational code that defines an agent’s behavior can be represented in a variety of ways (e.g., heuristic decision trees, utility functions) that are again easier for non-expert stakeholders to than the equations and constraints of other modeling approaches. 2014 2024-08-01T02:50:09Z 2024-08-01T02:50:09Z Working Paper https://hdl.handle.net/10568/149888 en Open Access application/pdf International Food Policy Research Institute Bell, Andrew R.; Robinson, Derek;l Malik, Ammar and Dewal, Snigdha. 2014. Modular ABM development in NRM for improved dissemination and training. Washington, DC: International Food Policy Research Institute (IFPRI). https://hdl.handle.net/10568/149888 |
| spellingShingle | bioeconomics mathematical models macroeconomics Bell, Andrew R. Robinson, Derek Malik, Ammar Dewal, Snigdha Modular ABM development in NRM for improved dissemination and training |
| title | Modular ABM development in NRM for improved dissemination and training |
| title_full | Modular ABM development in NRM for improved dissemination and training |
| title_fullStr | Modular ABM development in NRM for improved dissemination and training |
| title_full_unstemmed | Modular ABM development in NRM for improved dissemination and training |
| title_short | Modular ABM development in NRM for improved dissemination and training |
| title_sort | modular abm development in nrm for improved dissemination and training |
| topic | bioeconomics mathematical models macroeconomics |
| url | https://hdl.handle.net/10568/149888 |
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