Representation of decision-making in European agricultural agent-based models

Robert Huber*, Martha Bakker, Alfons Balmann, Thomas Berger, Mike Bithell, Calum Brown, Adrienne Grêt-Regamey, Hang Xiong, Quang Bao Le, Gabriele Mack, Patrick Meyfroidt, James Millington, Birgit Müller, J.G. Polhill, Zhanli Sun, Roman Seidl, Christian Troost, Robert Finger

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

14 Citations (Scopus)

Abstract

The use of agent-based modelling approaches in ex-post and ex-ante evaluations of agricultural policies has been progressively increasing over the last few years. There are now a sufficient number of models that it is worth taking stock of the way these models have been developed. Here, we review 20 agricultural agent-based models (ABM) addressing heterogeneous decision-making processes in the context of European agriculture. The goals of this review were to i) develop a framework describing aspects of farmers’ decision-making that are relevant from a farm-systems perspective, ii) reveal the current state-of-the-art in representing farmers’ decision-making in the European agricultural sector, and iii) provide a critical reflection of underdeveloped research areas and on future opportunities in modelling decision-making. To compare different approaches in modelling farmers’ behaviour, we focused on the European agricultural sector, which presents a specific character with its family farms, its single market and the common agricultural policy (CAP). We identified several key properties of farmers’ decision-making: the multi-output nature of production; the importance of non-agricultural activities; heterogeneous household and family characteristics; and the need for concurrent short- and long-term decision-making. These properties were then used to define levels and types of decision-making mechanisms to structure a literature review. We find most models are sophisticated in the representation of farm exit and entry decisions, as well as the representation of long-term decisions and the consideration of farming styles or types using farm typologies. Considerably fewer attempts to model farmers’ emotions, values, learning, risk and uncertainty or social interactions occur in the different case studies. We conclude that there is considerable scope to improve diversity in representation of decision-making and the integration of social interactions in agricultural agent-based modelling approaches by combining existing modelling approaches and promoting model inter-comparisons. Thus, this review provides a valuable entry point for agent-based modellers, agricultural systems modellers and data driven social scientists for the re-use and sharing of model components, code and data. An intensified dialogue could fertilize more coordinated and purposeful combinations and comparisons of ABM and other modelling approaches as well as better reconciliation of empirical data and theoretical foundations, which ultimately are key to developing improved models of agricultural systems.

Original languageEnglish
Pages (from-to)143-160
JournalAgricultural Systems
Volume167
DOIs
Publication statusPublished - Nov 2018

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decision making
farmers
agricultural industry
farm typology
ex ante analysis
family farms
Common Agricultural Policy
farms
agricultural policy
emotions
households
uncertainty
learning
farming systems
agriculture
case studies
markets

Cite this

Huber, R., Bakker, M., Balmann, A., Berger, T., Bithell, M., Brown, C., ... Finger, R. (2018). Representation of decision-making in European agricultural agent-based models. Agricultural Systems, 167, 143-160. https://doi.org/10.1016/j.agsy.2018.09.007
Huber, Robert ; Bakker, Martha ; Balmann, Alfons ; Berger, Thomas ; Bithell, Mike ; Brown, Calum ; Grêt-Regamey, Adrienne ; Xiong, Hang ; Le, Quang Bao ; Mack, Gabriele ; Meyfroidt, Patrick ; Millington, James ; Müller, Birgit ; Polhill, J.G. ; Sun, Zhanli ; Seidl, Roman ; Troost, Christian ; Finger, Robert. / Representation of decision-making in European agricultural agent-based models. In: Agricultural Systems. 2018 ; Vol. 167. pp. 143-160.
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abstract = "The use of agent-based modelling approaches in ex-post and ex-ante evaluations of agricultural policies has been progressively increasing over the last few years. There are now a sufficient number of models that it is worth taking stock of the way these models have been developed. Here, we review 20 agricultural agent-based models (ABM) addressing heterogeneous decision-making processes in the context of European agriculture. The goals of this review were to i) develop a framework describing aspects of farmers’ decision-making that are relevant from a farm-systems perspective, ii) reveal the current state-of-the-art in representing farmers’ decision-making in the European agricultural sector, and iii) provide a critical reflection of underdeveloped research areas and on future opportunities in modelling decision-making. To compare different approaches in modelling farmers’ behaviour, we focused on the European agricultural sector, which presents a specific character with its family farms, its single market and the common agricultural policy (CAP). We identified several key properties of farmers’ decision-making: the multi-output nature of production; the importance of non-agricultural activities; heterogeneous household and family characteristics; and the need for concurrent short- and long-term decision-making. These properties were then used to define levels and types of decision-making mechanisms to structure a literature review. We find most models are sophisticated in the representation of farm exit and entry decisions, as well as the representation of long-term decisions and the consideration of farming styles or types using farm typologies. Considerably fewer attempts to model farmers’ emotions, values, learning, risk and uncertainty or social interactions occur in the different case studies. We conclude that there is considerable scope to improve diversity in representation of decision-making and the integration of social interactions in agricultural agent-based modelling approaches by combining existing modelling approaches and promoting model inter-comparisons. Thus, this review provides a valuable entry point for agent-based modellers, agricultural systems modellers and data driven social scientists for the re-use and sharing of model components, code and data. An intensified dialogue could fertilize more coordinated and purposeful combinations and comparisons of ABM and other modelling approaches as well as better reconciliation of empirical data and theoretical foundations, which ultimately are key to developing improved models of agricultural systems.",
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Huber, R, Bakker, M, Balmann, A, Berger, T, Bithell, M, Brown, C, Grêt-Regamey, A, Xiong, H, Le, QB, Mack, G, Meyfroidt, P, Millington, J, Müller, B, Polhill, JG, Sun, Z, Seidl, R, Troost, C & Finger, R 2018, 'Representation of decision-making in European agricultural agent-based models', Agricultural Systems, vol. 167, pp. 143-160. https://doi.org/10.1016/j.agsy.2018.09.007

Representation of decision-making in European agricultural agent-based models. / Huber, Robert; Bakker, Martha; Balmann, Alfons; Berger, Thomas; Bithell, Mike; Brown, Calum; Grêt-Regamey, Adrienne; Xiong, Hang; Le, Quang Bao; Mack, Gabriele; Meyfroidt, Patrick; Millington, James; Müller, Birgit; Polhill, J.G.; Sun, Zhanli; Seidl, Roman; Troost, Christian; Finger, Robert.

In: Agricultural Systems, Vol. 167, 11.2018, p. 143-160.

Research output: Contribution to journalArticleAcademicpeer-review

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AU - Huber, Robert

AU - Bakker, Martha

AU - Balmann, Alfons

AU - Berger, Thomas

AU - Bithell, Mike

AU - Brown, Calum

AU - Grêt-Regamey, Adrienne

AU - Xiong, Hang

AU - Le, Quang Bao

AU - Mack, Gabriele

AU - Meyfroidt, Patrick

AU - Millington, James

AU - Müller, Birgit

AU - Polhill, J.G.

AU - Sun, Zhanli

AU - Seidl, Roman

AU - Troost, Christian

AU - Finger, Robert

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AB - The use of agent-based modelling approaches in ex-post and ex-ante evaluations of agricultural policies has been progressively increasing over the last few years. There are now a sufficient number of models that it is worth taking stock of the way these models have been developed. Here, we review 20 agricultural agent-based models (ABM) addressing heterogeneous decision-making processes in the context of European agriculture. The goals of this review were to i) develop a framework describing aspects of farmers’ decision-making that are relevant from a farm-systems perspective, ii) reveal the current state-of-the-art in representing farmers’ decision-making in the European agricultural sector, and iii) provide a critical reflection of underdeveloped research areas and on future opportunities in modelling decision-making. To compare different approaches in modelling farmers’ behaviour, we focused on the European agricultural sector, which presents a specific character with its family farms, its single market and the common agricultural policy (CAP). We identified several key properties of farmers’ decision-making: the multi-output nature of production; the importance of non-agricultural activities; heterogeneous household and family characteristics; and the need for concurrent short- and long-term decision-making. These properties were then used to define levels and types of decision-making mechanisms to structure a literature review. We find most models are sophisticated in the representation of farm exit and entry decisions, as well as the representation of long-term decisions and the consideration of farming styles or types using farm typologies. Considerably fewer attempts to model farmers’ emotions, values, learning, risk and uncertainty or social interactions occur in the different case studies. We conclude that there is considerable scope to improve diversity in representation of decision-making and the integration of social interactions in agricultural agent-based modelling approaches by combining existing modelling approaches and promoting model inter-comparisons. Thus, this review provides a valuable entry point for agent-based modellers, agricultural systems modellers and data driven social scientists for the re-use and sharing of model components, code and data. An intensified dialogue could fertilize more coordinated and purposeful combinations and comparisons of ABM and other modelling approaches as well as better reconciliation of empirical data and theoretical foundations, which ultimately are key to developing improved models of agricultural systems.

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