Empirical models, rules, and optimization: turning positive economics on its head
This paper considers supply decisions by firms in a dynamic setting with adjustment costs and compares the behavior of an optimal control model to that of a rule-based system which relaxes the assumption that agents are explicit optimizers. In our approach, the economic agent uses believably simple...
| Autores principales: | , |
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| Formato: | Artículo preliminar |
| Lenguaje: | Inglés |
| Publicado: |
International Food Policy Research Institute
2000
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/155795 |
| _version_ | 1855528489802792960 |
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| author | Cattaneo, Andrea Robinson, Sherman |
| author_browse | Cattaneo, Andrea Robinson, Sherman |
| author_facet | Cattaneo, Andrea Robinson, Sherman |
| author_sort | Cattaneo, Andrea |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | This paper considers supply decisions by firms in a dynamic setting with adjustment costs and compares the behavior of an optimal control model to that of a rule-based system which relaxes the assumption that agents are explicit optimizers. In our approach, the economic agent uses believably simple rules in coping with complex situations. We estimate rules using an artificially generated sample obtained by running repeated simulations of a dynamic optimal control model of a firm’s hiring/firing decisions. We show that (i) agents using heuristics can behave as if they were seeking rationally to maximize their dynamic returns; (ii) the approach requires fewer behavioral assumptions relative to dynamic optimization and the assumptions made are based on economically intuitive theoretical results linking rule adoption to uncertainty; (iii) the approach delineates the domain of applicability of maximization hypotheses and describes the behavior of agents in situations of economic disequilibrium. The approach adopted uses concepts from fuzzy control theory. An agent, instead of optimizing, follows Fuzzy Associative Memory (FAM) rules which, given input and output data, can be estimated and used to approximate any non-linear dynamic process. Empirical results indicate that the fuzzy rule-based system performs extremely well in approximating optimal dynamic behavior in situations with limited noise. |
| format | Artículo preliminar |
| id | CGSpace155795 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2000 |
| publishDateRange | 2000 |
| publishDateSort | 2000 |
| publisher | International Food Policy Research Institute |
| publisherStr | International Food Policy Research Institute |
| record_format | dspace |
| spelling | CGSpace1557952025-11-06T06:33:31Z Empirical models, rules, and optimization: turning positive economics on its head Cattaneo, Andrea Robinson, Sherman decision making econometrics This paper considers supply decisions by firms in a dynamic setting with adjustment costs and compares the behavior of an optimal control model to that of a rule-based system which relaxes the assumption that agents are explicit optimizers. In our approach, the economic agent uses believably simple rules in coping with complex situations. We estimate rules using an artificially generated sample obtained by running repeated simulations of a dynamic optimal control model of a firm’s hiring/firing decisions. We show that (i) agents using heuristics can behave as if they were seeking rationally to maximize their dynamic returns; (ii) the approach requires fewer behavioral assumptions relative to dynamic optimization and the assumptions made are based on economically intuitive theoretical results linking rule adoption to uncertainty; (iii) the approach delineates the domain of applicability of maximization hypotheses and describes the behavior of agents in situations of economic disequilibrium. The approach adopted uses concepts from fuzzy control theory. An agent, instead of optimizing, follows Fuzzy Associative Memory (FAM) rules which, given input and output data, can be estimated and used to approximate any non-linear dynamic process. Empirical results indicate that the fuzzy rule-based system performs extremely well in approximating optimal dynamic behavior in situations with limited noise. 2000 2024-10-24T12:42:35Z 2024-10-24T12:42:35Z Working Paper https://hdl.handle.net/10568/155795 en Open Access application/pdf International Food Policy Research Institute Cattaneo, Andrea; Robinson, Sherman. 2000. Empirical models, rules, and optimization: turning positive economics on its head. TMD Discussion Paper 53. https://hdl.handle.net/10568/155795 |
| spellingShingle | decision making econometrics Cattaneo, Andrea Robinson, Sherman Empirical models, rules, and optimization: turning positive economics on its head |
| title | Empirical models, rules, and optimization: turning positive economics on its head |
| title_full | Empirical models, rules, and optimization: turning positive economics on its head |
| title_fullStr | Empirical models, rules, and optimization: turning positive economics on its head |
| title_full_unstemmed | Empirical models, rules, and optimization: turning positive economics on its head |
| title_short | Empirical models, rules, and optimization: turning positive economics on its head |
| title_sort | empirical models rules and optimization turning positive economics on its head |
| topic | decision making econometrics |
| url | https://hdl.handle.net/10568/155795 |
| work_keys_str_mv | AT cattaneoandrea empiricalmodelsrulesandoptimizationturningpositiveeconomicsonitshead AT robinsonsherman empiricalmodelsrulesandoptimizationturningpositiveeconomicsonitshead |