Teaching the Modeling of Human-Environment Systems: Acknowledging Complexity with an Agent-Based Model

Abstract

Agent-based modeling is a promising tool for familiarizing students with complex systems as well as programming skills. Human-environment systems, for instance, entail complex interdependencies that need to be considered when modeling these systems. This complexity is often neglected in teaching modeling approaches. For a heterogeneous group of master's students at a German university, we pre-built an agent-based model. In class, this was used to teach modeling impacts of land use policies and markets on ecosystem services. As part of the course, the students had to perform small research projects with the model in groups of two. This study aims to evaluate how well students could deal with the complexity involved in the model based on their group work outcomes. Chosen indicators were, e.g., the appropriateness of their research goals, the suitability of the methods applied, and how well they acknowledged the limitations. Our study results revealed that teaching complex systems does not need to be done with too simplistic models. Most students, even with little background in modeling and programming, were able to deal with the complex model setup, conduct small research projects, and have a thoughtful discussion on the limitations involved. With adequate theoretical input during lectures, we recommend using models that do not hide the complexity of the systems but foster a realistic simplification of the interactions.

Supplementary information: The online version contains supplementary material available at 10.1007/s10956-022-10022-z.

Keywords: Blended learning; Higher education; Individual-based models; Socio-ecological systems; University teaching; Wicked problems.

Fig. 1

Results of a query shown to all students (n = 28) attending the lecture part of the module “Land use policies, markets, and ecosystems” (1 = strongly disagree to 5 = strongly agree), conducted with the interactive presentation tool Mentimeter (Mentimeter, 2021)

Fig. 2

Different actions by the dairy farms in the model World of Cows within one year (equivalent to one time step in the model). Each year, the farms start by deciding if they want to stop farming. Each action is represented by one submodel. The background shows part of the “world” of the graphical user interface of the model in NetLogo (in the setup, based on real-world data for the agricultural fields)

Fig. 3

Sections of the model interface World of Cows. The users can adjust the policy options and economic settings through sliders and choosers. In the chosen example policy option, users can set a limit of organic nitrogen (Norg) per hectare, how much subsidies will be deducted if farms exceed it, and what percentage of farms are checked. Ecosystem services and farm indicators are presented in output plots and monitors. In the presented example output monitor, the profitability (in terms of Euro per hectare) for the different management intensity classes of farms is displayed. Here, the presented model “world” setup is based on the second alternative of a “random” configuration of agricultural fields (in contrast to Fig. 2)

Fig. 4

Effect of one of the five policy options (nitrate tax) in the model World of Cows on produced milk (originating from farms operating in different intensity levels)

Fig. 5

Counts of student groups showing the proportions of (i) approach and (ii) complexity of research goals, (iii) tools used for the method of analysis, and (iv) their suitability to reach the defined research goal, as well as (v) discussed types of limitations

Fig. 6

Two examples of group work results showing a an identification of the impact of cow numbers through heatwaves and b the height of a greenhouse gas tax (Euro per ton CO₂ equivalents) needed to significantly impact ecosystem services. In this case, climate regulation (120 €/t)