Posted on 10 February 2021 by Jesica Murcia López (Centre for Environmental and Climate Science) and Juan Ocampo (Department of Business Administration).
This post was originally published in Agenda2030 Graduate School blogg

“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 – expediciontribuga.com
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.
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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 – whc.unesco.org.

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.

Conclusion

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. https://doi.org/10.2307/20048043
[4] Zellner, M. L. (2008). Embracing complexity and uncertainty: The potential of agent-based modeling for environmental planning and policy. Planning Theory and Practice.
https://doi.org/10.1080/14649350802481470 pg.443
[5] OECD (2012), “Social unrest and agent based models”, in Systemic Financial Risk, OECD Publishing, Paris, https://doi.org/10.1787/9789264167711-11-en.
[6] https://www.oecd-ilibrary.org/agriculture-and-food/economic-and-environmental-sustainability-performance-of-environmental-policies-in-agriculture_3d459f91-en
[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.
https://doi.org/10.1016/j.envsoft.2018.08.028
[8] Holling, C., S. 2001. Understanding the complexity of economic, ecological, and social systems. Ecosystems, 4 (5), pp. 390-405. https://doi.org/10.1007/s10021-001-0101-5
[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:
https://books.google.se/books?hl=en&lr=&id=NFqFbXYbjLEC&oi=fnd&pg=PR1&ots=6pIYWDX_J9&sig=xyytGaz3hBRJ_gfrMaItSdAfpow&redir_esc=y#v=onepage&q&f=false]
[10] Costanza, R., Wainger, L., Folke, C., and Mäler, K.-G. 1993. Modelling complex ecological economic systems, BioScience, 43(8), 545–555.
https://doi.org/10.2307/1311949
[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

The sustainable development agenda has a special focus on people in vulnerable conditions. Adger and Winkels[1] argue that in order for development to be sustainable it is important to address the underlying components in vulnerable societies. Vulnerability is a complex concept that embeds different dimensions, some of them including aspects such as social relations, capabilities, assets, and social exclusion[2]. In consequence, it is worth analyzing the Sustainable Development Goals (SDG), from a vulnerability perspective.
 
Using SDG wrapping as a point of departure, it is of interest in this document to reflect on some of the SDG indicators, specifically those with a focus on (digital) financial systems, which include banking, and credit availability (i.e Target 8.10). Financial systems support communities in (re)producing, distributing and accessing goods and services. Therefore, Target 8.10 could be interpreted as an attempt to stimulate the local markets through financial services. This interpretation of the underlying objective of the target, allows for exploring, or perhaps forking, alternatives that support the economic system and can be better held accountable for how they address vulnerable communities.
 
An example of a financially inclusive solution coming from the business sector is micro-credits. Through this, financial service banks offer poor people access to cash liquidity but might also lock people in debt loops. From a vulnerability and resilience perspective, it is worth questioning if giving people access to credits is really building resilience capabilities or if these businesses are perpetuating vulnerability. Are the current targets of financial inclusion just a way of wrapping (framing) unsustainable practices into more "marketable” ones?
 
An alternative coming from the grassroots is Complementary Currencies (CC), which are social technologies that create complementary monetary systems and aim to have economic and social benefits in the communities. CC can be designed for different purposes, for example to tackle social exclusion and unemployment, localize economies, and build social capital and civic commitment[3]. Today, several organizations are introducing digital CC in vulnerable communities, thus making them an interesting alternative for sustainable development. Surprisingly, they won’t necessarily have an impact on achieving the SDGs since this financial instrument won’t increase the number of ATMs in a region, or raise the amount of bank accounts in a community, which are the basic indicators for the financial inclusion component in the SDGs.
 
CC leverages the strength of the local social, economic, and political infrastructures, therefore, to implement this social technology, it is important to develop the capabilities of the target communities. This process of implementation, financial, self-organization and social capabilities being developed, allows us to envision a potential increase in the community’s adaptive capacity, and hopefully a decrease in the community’s vulnerability. It is out of the scope of this document to describe in detail how these CC are being implemented, however, in line with the objective of this document, some reflections about how actors account for their implementation process might be in place.
 
The development of CC is a complex endeavor that requires the interaction of different collective actors. Figure 1 presents four possible actors that interact in the development of the CC for development. These are: communities, donors/impact investors, facilitators, and observers (i.e Civil Society, NGO, Academia). Based on Fligstein and MacAdam[4] (2011), it is possible to argue that these collective actors, “interact with knowledge of one another under a set of common understandings about the purposes of the field, the relationships in the field (including who has power and why), and the field’s rules”.
 
Actors are embedded in complex webs of fields[5] thus, are accountable to different stakeholders[6]. To construct these accountability frames, donors, facilitators, communities and observers make use of different frame constructs in the micro, meso, and macro level[7]. In this framing construction, accounting becomes an interpretative art[8] with a major role in shaping reality in order to create and maintain stable social worlds[9].
 
Accountancy becomes a construction of narratives in which hard numbers and soft language come to interplay. Digital currencies open the opportunity to represent performance both through the use of numbers, measures and statistics of impact, and through textualizations and contextualization of these numbers[10]. For example, facilitators frame strategic actions by highlighting the number of people that are being supported through the CC and developing emotional stories about why their solution is relevant to the world. Donors can communicate the number of people that are now eating “warm meals" thanks to their funds, attracting donors or impact investors. In one of the sessions held by the facilitators to train the community in implementing a CC, a community member claimed, “we will spend, spend, spend”, as an act of commitment to the project and thus increasing the number of local transactions to later be seen in the data. Finally, we the Observers, with our inquiry lenses analyze, reflect, discuss, make sense of these numbers, pictures, and texts. Through our knowledgeable accounts, we aim to inform the world about what is happening out there in reality.
 
The question that motivated this document asked, “how does the SDG increase and/or shape accountability in the relevant field”? Well, they don’t. The accounts that the different actors develop, are constructing, redefining and contesting what the SDG are. The different CC actors transform (or wrap perhaps) the meaning of sustainability through their actions and later shape reality through their framing accounts. Realities, that hopefully, address the underlying components of vulnerability.


[1] W. Neil Adger & Alexandra Winkels, 2014. "Vulnerability, poverty and sustaining well- being," Chapters, in: Giles Atkinson & Simon Dietz & Eric Neumayer & Matthew Agarwala (ed.), Handbook of Sustainable Development, chapter 13, pages 206-216, Edward Elgar Publishing.

[2] Adger, W. N. (2006). Vulnerability. Global Environmental Change, 16(3), 268–281. doi: 10.1016/j.gloenvcha.2006.02.006; FAO, “AnalysIng Resilience for better targeting and action” (2016); Frankenberger, T., Mueller M., Spangler T., and Alexander S. October 2013. Community Resilience: Conceptual Framework and Measurement Feed the Future Learning Agenda. Rockville, MD: Westat

[3] Smith, A., Fressoli, M., & Thomas, H. (2014). Grassroots innovation movements: challenges and contributions. Journal of Cleaner Production, 114-124

[4] Fligstein, N., & MacAdam, D. (2012). A theory of fields. New York: Oxford University Press.

[5] Ibid.

[6] Lounsbury, M., M. Ventresca & Hirsch, P.M. 2003. Social movements, field frames and industry emergence: a cultural–political perspective on US recycling. Socio-economic Review, 1: 71–104

[7] Joep P. Cornelissen & Mirjam D. Werner (2014) Putting Framing in Perspective: A Review of Framing and Frame Analysis across the Management and Organizational Literature, The Academy of Management Annals, 8:1, 181-235, DOI: 10.1080/19416520.2014.875669

[8] Morgan, G. 1988. Accounting as reality construction: towards a new epistemology for accounting practice. Accounting, Organizations & Society, 13, 477-485.

[9] Fligstein, N., & MacAdam, D. (2012). A theory of fields. New York: Oxford University Press.

[10] Sandell N. & Svensson, P (2014). The Language of Failure: The Use of Accounts in Financial Reports International Journal of Business Communication

By Juan Ocampo and Santiago Botía

This post is part of the series Times of crisis and is composed by collaborative posts, informative videos and a discussion episode. Each content can be read or watched independently, which means they don´t follow a specific narrative thread or are prerequisites for understanding the whole series. However, if it catches your interest we recommend you to read and watch them all to get a more holistic perspective of a creative, but researched based, exercise. If you find it interesting you are welcome to watch the complete product and please comment or reach out.

You can find the complete episode of the series here, and Santiago’s presentation on the content here. Feel free to comment and reach out. The code for some of the graphs can be found in Santiago’s Github. The figure of the post can be found here.
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What a better start than by looking at what has become a “habit” for many of us during the COVID-19 crisis, looking at time series graphs of COVID-19 cases and the stock market behaviour. The exponential surge in detected cases and the effect the stock market has experienced is shown in Figure 1. The upper graph exposes a high and imminent threat to our health, while that in the bottom impacts our pockets. These two cases show just a few amongst many of the different consequences of the COVID-19 crisis. This collaboration started as a reaction to the global situation and the role that scholars could have in these times, regardless of their/our-own disciplines.

Figure 1 COVID-19 cases shown as a 3-day moving average (upper panel) and the normalized Dow Jones Industrial average and the Deutscher Aktienindex. Own elaboration based on data from ourworldindata.org and Yahoo Finance.

Two of the authors of this blog post come from Colombia, a country with many social and economic struggles that has been placed under more stress during this pandemic. In some way we felt challenged by being far from our country and not being able to contribute, in some way, to the discussion that was being held. We then decided to do what we are good at: research.

Reflecting on how the spread of the COVID-19 could affect our country we ended in a discussion on how, if even possible, to compare the economy and health.  Neither of us are economists or health specialists, so it was not clear where to start. As we both have engineering backgrounds, we decide to rely on what society expects us to be good at: numbers.  We started by exploring some economic indicators of countries we thought could be interesting. These were: Italy, which at that time was at its peak of COVID-19 cases; Germany as a North-European example; Canada as a least studied example in North America; Ecuador which is going through an  indescribable health management challenge, and, of course, our country Colombia.  

Figure 2 Economic baseline of selected countries. Own elaboration based on data retrieved from https://data.worldbank.org

In Figure 2, it is possible to observe some interesting points in regard to the economic context of countries.  The above figure shows the Gini index as an indicator for inequality, the GDP per capita reflects the wealth of the country, and finally the health expenditure per capita allows for a comparison in regards to health and economic context. Note that the poorest countries have a higher inequality and less health expenditure per capita, while countries with a lower Gini index showed both a greater GDP and health expenditure per capita.

With an economic baseline drawn, it was now important to look at these countries from a health perspective.  Again, drowned in data, websites, and sources we decided to observe some indicators that could give a reference of how “prepared” the selected countries were to respond to the COVID-19 health emergency. Figure 3 exposes the population of the countries and what we labelled health infrastructure, which includes the indicators found in the World Bank data bank and that measure physicians and beds per 1000 inhabitants. From these figures it is possible to observe how a country like Colombia with a population of almost 50M habitants, has less health infrastructure than a country like Canada that is around 35M habitants. Having high population a low health infrastructure  is definitely something to consider in this times of crisis where countries that fail to flatten the COVID-19 curve, will see their health infrastructure suffer.

Figure 3 Health baseline of selected countries. Own elaboration based on data retrieved from https://data.worldbank.org

Take a look at the graphs, what do these tell you? If you are thinking about inequality, we agree with you! Even though discussing inequality tempted our spirits, we thought this issue required a whole project by itself. Even though these figures shed some light on the importance of “Leaving no one behind” in times of crisis, unfortunately, these figures didn’t help us much in our economic vs health impact discussion. We needed to reframe our question.

Seeking a new approach, we decided to start again from the beginning: Colombia. First we thought about comparing the deaths of COVID-19 with the thousands of lives Colombians have lost in a conflict with no apparent expiration date, regardless of a recent peace treaty; important but perhaps out of the scope.  We then decided to make use of our deduction logic. Stopping the economy and isolating the population had a tremendous effect on everyone’s pocket but more importantly on the low-income population. Colombia had a unemployment rate of ≈9% by 2018, which is not a very good start, but another interesting/worrying fact is that 60% of the Colombian labour population depends on informal employment (≈60% for 2018). Informal employment in developing countries means that people are living on wages that depend on their daily work. Therefore, the isolation measures that the government was pressured to implement/set could lead to a further increase in the unemployment rate. But, is there a clear link between unemployment and health? No, at least it is not crystal clear for us amateurs in the field. We then tried to identify a unit for comparison, and as soulless as numbers tend to be, death became a point of reference. Here we spotted a possible missing link: high unemployment lowers income, which then reduces food access and as a consequence, increases hunger and undernourishment. Seemed reasonable, but how could we test this? Again data.

Santiago´s work is based on analysing huge chunks of atmospheric data coming from the ATTO project, and thus his skill in handling databases and developing analytical visualisations came in handy. As economists have suggested and as suggested in Figure 1, the COVID-19 pandemic is triggering an economic crisis. Thus, as a reference observing how unemployment and income have behaved during times of crisis could be useful to fathom what could come in the foreseeable future (i.e. 1990 recession and 2008 financial crisis). If you look at both graphs, during these previous crises there was a (slight) decrease in income and a clear increase in unemployment. However, keep in mind that this is an “informed” thought experiment rather than a conclusive research, but we have some confidence when we assume that unemployment affects the income of the people. Wouldn’t you think so?

Figure 4. Percent of people unemployed in the age group 25-54 (upper panel) and income per person (GDP/capita). Data download from gapminder.org.

To complete our second economic argument, and aware of the limitations of looking at correlations, we decided to look to what extent unemployment and undernourishment are related. Making any conclusion was more difficult since a relationship between these two variables, we believe, is not linear and depends on the context. In addition, as Figure 4 shows, trends have changed over time. Anyway, we got some interesting observations. First unemployment seems to be completely decoupled from undernourishment in developed countries. Even at unemployment rates, of about 10% Germany managed to keep undernourishment very low. This could be explained by their public policies or just by a lack of data reliability (even though this comes from the world bank databank). For Colombia in the early 2000s the country managed to decrease unemployment rates with little impact on undernourishment. However, from 2010 to 2014, unemployment decreased together with undernourishment and for the last years of record an increase in unemployment did not show impact on undernourishment. For Ecuador, we see some interesting patterns. The country has decreased undernourishment since the beginning of the century, maintaining, in general, a low percentage of unemployment. But there have been periods in which both variables have decreased together like from 2002 to 2006 and from 2008 to 2012. Recently, unemployment rates have increased with almost no impact on undernourishment. However, the data is rather inconclusive and we leave it to our readers to develop their own conclusions.

Figure 5. Prevalence of undernourishment in percentage of population as a function of unemployment rate in the age group 25-54. The numbers show the years for Ecuador and Colombia. Data from gapminder.org.

The definition of prevalence of undernourishment can be found here.

Bear with us in our soulless analysis, since it was a thorny analytical path. The COVID-19 is an event that has no precedence and we are all constantly trying to make sense of this. In this entry we tried to make sense of our Colombian background and make use of data to shed some light on what many countries like ours are experiencing. Thinking about health and economy in terms of death, is just a reflection of how soulless data can be.
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