Agenda 2030 Graduate School blog

Lund University Agenda 2030 Graduate School is a global, cutting-edge research school and collaboration platform for issues related to societal challenges, sustainability and the 2030 Agenda. The 17 PhD students from all faculties at Lund University enrolled with the Agenda 2030 Graduate School relate their specific research topics to the Sustainable Development Goals. In this blog the PhD students of the Graduate School discuss topical research and societal issues related to the 2030 Agenda.

Modeling complexity at the Agenda 2030 Graduate School

Child looking at high building with binoculars. Photo.
Photo by Maarten van den Heuvel on Unsplash.

Posted on 10 February 2021 by Jesica Murcia López (Centre for Environmental and Climate Science) and Juan Ocampo (Department of Business Administration).

The views expressed in this publication are those of the authors and do not necessarily represent those of the Agenda 2030 Graduate School or Lund University. The present document is being issued without formal editing.

In this post Jesica and Juan provide us with some ideas of how modeling techniques are being used in sustainable development, specifically forest cover dynamics and money engineering. But be aware as they write: “models are not just one-to-one reflections of reality (…) so we have to be aware of this when we use them or read about their results”

“The world in which you were born is just one model of reality. Other cultures are not failed attempts at being you; they are unique manifestations of the human spirit.”

– Wade Davis

Sharing a common background, from Colombia, where social, environmental and economical challenges contrast with the beauty of its mountains, rivers, biodiversity, and culture. Complexity is embedded in the way we, Jesica and Juan, approach our research. Therefore, it is not surprising for us to recognise the differences and nuances that emerge from the systems we study. To navigate and resolve the interplay of sustainable-related problems in real cases, being political displays of power, economic transfers of ownership, or environmental transformations, complex thinking becomes an appropriate state of mind. Our collaboration was triggered by a
popular-science documentary called Tribuga –
inspired by books of Wade Davies (see Magdalena or The River) and resulted in conversations and discussions about ways to study complex problems.

There are different ways of studying complex systems. An example is sustainometrics. This technique aims to describe  and represent the interconnectedness of five domains of human activity, namely environmental, socio-cultural, technological, economics, and public policy, and their interaction with regard to achieving the goals of sustainability[1]. As sustianometrics, there are plenty of different methods that help us to engage with problems as the ones presented in sustainable development, many of these involved modelling. Modelling – the production and revision of models – has been seen as the essence of the dynamic and non-linear processes involved in the development of scientific knowledge[2].  And this post is about models!

Models are not one-to-one reflections of reality, however, if constructed with rigour they have the potential to be ‘illuminating’ abstractions[3], and can be used to explain social, ecological and economic systems. Today we require methods that offer us new perspectives and understandings to tackle the complex problems that hinders sustainable development. Our common interest was to start a first glimpse of the technicalities behind the use of such models, and how other researchers and ourselves are attempting to highlight the benefits and challenges of these approaches in the contribution to scientific debates.  In this line of thought, this post introduces Agent-Based modeling (ABM) and System Dynamics (SD), both of them increasingly being used in environmental, social and economic sciences.

Using Agent-based Models for money engineering

In his research Juan studies the engineering of Money. Specifically, he studies Complementary Currencies (CC) which are the voluntary agreement within a community to: (i) use a standardised unit of account to value their contributions, these being cleaning the streets, taking care of the elderly, or their produced good and services; (ii) accept these units as a mode of payment form other members; and (iii) use this units as a medium of exchange inside the community. In his research he has found it is not unusual to find situations in which stakeholders are not aware of basic socio-technical components of money until problems emerge. Money is a complex topic, it embedded social, ideological and technical components that if they are not openly discussed they might jeopardize the relations and hinder the success of the CC. Therefore, it is important for interested stakeholders to assure there is a proper description on the socio-cultural and spatial context of target communities and a thoughtful analysis on the economic theories, their mathematical formulations, and their influence in the communities’ socio-economic relations.

Agent-Based Models (ABM) are a type of computer-based technique that can represent the behaviours and interactions of human and non-human actors (e.g. animals, institutions) and allow rich and dynamic representations of individuals in a particular ecosystem, and during a specific range of time. In other words, ABM “support a metaphorical representation of complexity by programming actions, decisions and mechanisms in explicit form”[4]. Increasingly, ABM have been developed to assist the management, governance, and research of different complex scenarios.  For example, the OECD has used this computational tool to analyse systemic financial risks[5], or economic and environmental systems[6].

Juan’s project aims to use ABM through a practice called participatory modeling. This way of modeling is a learning process for action that invite the stakeholders to share their implicit and explicit knowledge and create shared models that represent the complex problems[7] they deal with. In other words, ABM serves as a way for designers of Complementary Currencies to be open about their interests, be aware of each other’sassumptions, and integrate their needs. It would be impossible to model it all, however through a compromise it is possible to program key variables and decision-making behaviours into explicit computational models. By programming different monetary configurations and modelling different human-behaviours it will be possible to simulate different scenarios and reflect on possible challenges and opportunities that Complementary Currencies might offer in search for an equal and inclusive monetary system.

Systems thinking and system dynamic modelling for sustainometrics

Systems thinking is a way to understand the complexity of core dimensions of sustainability, economic, social and ecological systems[8]. In our current times, sustainable development problems can be evaluated with network analysis. Any complex system is a set of interacting variables that behave according to governing mechanisms[9] and as the complexity of sustainability-related problems increases, it is more and more difficult to understand the related models.  However, according to Costanza et al[10]  “(m)odels are analogous to maps… they have many possible purposes and uses, and no one map, or model is right for the entire range of uses”. Additionally, these models are only possible with the current advances in information technology and information theory.

In her research Jesica uses such models of land use change as primary “diagrams” for analysing the causes and consequences of land use changes. The use of system dynamics modeling methodology will serve to compare causal loop diagrams of forest cover dynamics in the Northwest Amazon region of Colombia generated by key actors working to tackle deforestation as a result of extensive cattle ranching activities in the area. Complementary, using supportive geographical information system (GIS) methods and software to assess the impacts of land use change on ecosystems to support land use planning and policy makers, represent a way to translate the complexity involved in these kinds of accurate system dynamic (SD) models. System Dynamics covers a set of qualitative tools for the analysis of dynamic processes, e.g. Causal Loop Diagrams (CLD) and Stock and Flow Diagrams (SFD) simulation and optimisation software. Since the introduction of systems thinking in terms of sustainability attained by Forrester in 1971 on his book World Dynamics[11], and then Meadows and collaborators (1972) work[12], a well-known effort concerning the topic, “The Limits to Growth”, focused on the simulation of the issues of sustainability worldwide with a more deep conceptual understanding of the modelled socio-economic and environmental systems as well.

Her study looks at the interactions between pristine forests ecosystems, livestock, and the deforestation rates in protected areas, located in
the northern part of the Chiribiquete National Park –
The study focuses on modelling forest land degradation in the Savannas of Yarí from 2016-2020 and simulates future scenarios up to 2030 using system dynamics modelling. In similar studies, in the Brazilian Amazon[13] the use of these models provided a first approximation of the loss of ecosystem services that is attributable to deforestation, considering that the patterns and processes of land use—and the economic incentives that drive them—continue unabated. In the Philippines[14] researchers used group model-building exercises, involving both researchers and community members, and they found that systemic understanding of deforestation can generate more robust reforestation initiatives.


The urgent need to understand human activity, namely environmental, socio-cultural, technological, economics, and public policy and their related sustainability-related problems as mentioned before, requires simultaneous integration of economic, social and ecological knowledge. In this way we can manage to understand sustainable development not in an incompatible way but as human evolution within a constantly changing natural world. Hence, the modelling of sustainability-related complex systems can support us to interpret holistic approaches without leaving key variables or agents of change outside the box. Consequently, aiming to better decisions making towards concrete actions on planetary goals for 2030, there is an urgent need to comprehend the interplay of the pillars for sustainability, through modelling of real-world problems.

However, models are tools constructed by people and we have to be aware of this when we use them or read about their results. In both cases using ABM or SD is not only about the quantitative results and analysis that can be simulated in regards to inequality or sustainable development. If developed in a participatory way ABM and SD become an object to trigger dialogue amongst stakeholders and serve as a tool for learning more about each other’s way of thinking. This way the importance of insights into points of leverage for any system can help us get solutions into action.

[1] Steward, W. C., & Kuska, S. 2011. Sustainometrics: Measuring sustainability—design, planning, and public administration for sustainable living (p. 144). Norcross, GA: Greenway Communications.

[2] Justi, R. S. & John K. Gilbert, J. K. 2002. Modelling, teachers’ views on the nature of modelling, and implications for the education of modellers, International. Journal of Science Education, 24:4, 369-387. DOI: 10.1080/09500690110110142

[3] Fukuyama, F., Epstein, J. M., & Axtell, R. (1997). Growing Artificial Societies: Social Science from the Bottom Up. Foreign Affairs.

[4] Zellner, M. L. (2008). Embracing complexity and uncertainty: The potential of agent-based modeling for environmental planning and policy. Planning Theory and Practice. pg.443

[5] OECD (2012), “Social unrest and agent based models”, in Systemic Financial Risk, OECD Publishing, Paris,


[7] Voinov, A., Jenni, K., Gray, S., Kolagani, N., Glynn, P. D., Bommel, P., Prell, C., Zellner, M., Paolisso, M., Jordan, R., Sterling, E., Schmitt Olabisi, L., Giabbanelli, P. J., Sun, Z., Le Page, C., Elsawah, S., BenDor, T. K., Hubacek, K., Laursen, B. K., … Smajgl, A. (2018). Tools and methods in participatory modeling: Selecting the right tool for the job. Environmental Modelling and Software.

[8] Holling, C., S. 2001. Understanding the complexity of economic, ecological, and social systems. Ecosystems, 4 (5), pp. 390-405.

[9] Walker, B & Salt, D. 2006. Resilience Thinking: Sustaining Ecosystems and People in a Changing World. (first ed.), Island Press, Washington. Consulted online [Accessed on 29 January 2021:]

[10] Costanza, R., Wainger, L., Folke, C., and Mäler, K.-G. 1993. Modelling complex ecological economic systems, BioScience, 43(8), 545–555.

[11] Forrester, J.W. 1971. World Dynamics, vol. 59, Wright-Allen Press, Cambridge.

[12] Meadows, D.H., Meadows, D.L. and J. Randers, W.W. 1972. The Limits to Growth: A Report of the Club of Rome’s Project on the Predicament of Mankind. Universe Books.

[13] Portela, R and Rademacher, I. 2001. A dynamic model of patterns of deforestation and their effect on the ability of the Brazilian Amazonia to provide ecosystem services. Ecological Modelling (143): 115–146. DOI: 10.1016/S0304-3800(01)00359-3

[14] Olabisi, L. S. 2010. System Dynamics of Forest Cover in the Developing World: Researcher Versus Community Perspectives. Sustainability (2): 1523-1535. doi:10.3390/su2061523

February 10, 2021

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