Models Based on Positive Mathematical Programming: State of the Art and Further Extensions

Abstract

Mathematical programming models have received renewed interest in the area of agricultural and agri-environmental policy analysis. Their ability to explicitly represent physical constraints makes them specifically suited for connecting economic and bio-physical aspects of agricultural systems. Furthermore, they allow for a direct representation of many current agricultural policy measures related to production activity levels. The introduction of Positive Mathematical Programming (PMP) addressed problems with plausibility of simulated behaviour and lack of empirical validation of these models. This paper reviews the development of PMP in its various forms and takes a look at approaches beyond PMP contributing to the quest for empirically specified programming models.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 91 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.