An agent-based simulation model of human-environment interactions in agricultural systems

This paper describes an agent-based software package, called Mathematical Programming-based Multi-Agent Systems (MP-MAS), which builds on a tradition of using constrained optimization to simulate farm decision-making in agricultural systems. The purpose of MP-MAS is to understand how agricultural technology, market dynamics, environmental change, and policy intervention affect a heterogeneous population of farm households and the agro-ecological resources these households command. The software is presented using the Overview, Design concepts, and Details (ODD) protocol. Modeling features are demonstrated with empirical applications to study sites in Chile, Germany, Ghana, Thailand, Uganda, and Vietnam. We compare MP-MAS with eight other simulators of human-environment interactions comparison shows that the uniqueness of MP-MAS lies in its combination of a microeconomic modeling approach and a choice of alternative biophysical modules that are either coded as part of the software or coupled with it using the Typed Data Transfer (TDT) library. 1-10 GB of free disk space (depending on application) We recommend using a high-speed processor and at least 4 GB of RAM. Software required: MS Office Excel for constructing input files. IBM OSL 1 as optimization software. STATA SE is recommended for analyzing the simulation output. Program language: C++ Availability and cost: Freeware downloadable from: https://mp-mas.uni-hohenheim.de/ including a user guide. A default data set is available online for testing the software. Most of the model features presented here, except the coupling with external software, can be tested by switching specific features on or off in this default version. 1 IBM has stopped its development of OSL and transferred the software code to an open source community, called COIN. Work is ongoing to implement COIN in MP-MAS so that it can run be run without OSL. 4 1 Introduction Simulation models have become important tools for assessing, ex ante, the impact of new technologies, policy interventions and climate change on agricultural systems (e.g. Doglioni et al. 2009, Gilfedder et al. 2009, Wang et al. 2010). Among model developers, a consensus has grown that such simulation models should ideally give a balanced representation of the economic, environmental, as well as social dimensions of a given system (Parker et al. 2002, Schaldach and Alcamo 2006). While many simulation models combine economic, environmental and social components at fine resolution, only a few incorporate the dynamic interactions between them (Argent 2004). Incorporating these interactions is required to better capture management options for agricultural systems in which the economic decisions and social …