4 Primary Concepts of Complexity Economics: Complexity Economics Series Part 3

Published on INFORMS Analytics Magazine (Joseph Byrum)

Author’s note: This series Seeing Economic Collapse and Recovery Through the Lens of Complexity Economics will look at the pandemic-related economic problems we currently face and how we might apply important concepts of complexity economics to better understand how to move forward. Part Two described the impact of equilibrium and nonequilibrium on economic recovery.

W. Brian Arthur summarized the main concepts of complexity economics in a 2013 white paper: “Complexity economics holds that the economy is not necessarily in equilibrium, that computation as well as mathematics is useful in economics, that increasing as well as diminishing returns may be present in an economic situation, and that the economy is not something given and existing but forms from a constantly developing set of institutions, arrangements and technological innovations” [1].

In other words, complexity economics turns on equilibrium vs. nonequilibrium systems, agent-based modeling, increasing returns and emergence – topics that will be covered in this installment, along with related research and recommendations. 

1. Agent-based modeling

A type of computer modeling based on the behavior of individual agents as they interact interdependently to create a pattern that transcends the agents. “Agent-based modeling works forward from an individual agent’s rules to observe the pattern that is created,” says Bill Rand on a Santa Fe Institute course on the topic [2]. This is different from an ordinary differential equation (ODE) because the ODE approach does not capture fluctuations [3]. That is, it does not allow for the factors in the equation to change their natures. Agent-based modeling is important for complexity economics, which uses computer programs to understand consequences being explored in a model.

Research: Agent-based modeling can predict the extent of economic recovery. In 2014, following economic damage caused by earthquakes (exogenous event), Boston et al. ran an agent-based model on community recovery after a natural event and concluded that community resilience is a “function of the performance of individual buildings, organizations and people within the community” and further that a community’s recovery is defined by how well and how quickly the individual components of a community and the networked community are able to return to normalcy after a disaster [4].

Recommendation: Policymakers and companies should identify the key agents for recovery and support them all to the highest extent possible. For example, a policy that focuses on tax relief but ignores infrastructure needs may slow recovery, and the converse is true as well.

2. Increasing returns 

An economic law that says network effects will lead to lock-in and dominance of one or a few players irrespective of their merits. Mainstream economics recognizes a law of diminishing returns for example when a market becomes saturated. But complexity economics notes that “if one company or product or technology gets advantage it gains further advantage – there are increasing returns or positive feedbacks” [5]. This helps to explain the “rich get richer,” aka the 1% rule, and the “poor get poorer,” aka, the poverty traps rule, as well as the “first mover advantage.” The winner wins based more on timing and chance than merit.

Research: Early in the last economic crisis, a researcher made some interesting discoveries about increasing returns in relation to poverty traps, which is the converse of increasing returns (in this case, poverty, not wealth, increases) [6]. Francisco Rodriguez found evidence in some counties of a “self-reinforcing cycle whereby initial declines of income translate into further declines in productivity.” He disputes the notion that poverty traps are caused by increasing returns, at least not in manufacturing.

Recommendation: When evaluating policies and programs, recognize the power of positive returns and beware of vicious cycles.

3. Emergence 

The state of becoming (as opposed to existing). This is a key concept in complexity theory. According to W. Brian Arthur, there are two great problems in economics: allocation – who gets what – and formation, which is how things come to be. The former is well studied, but the latter is only now being explored. “Complexity economics looks at structures forming in the economy, so it’s just as much concerned with formation as with allocation” [7].

Research: The concept of emergence has influenced a great deal of research. There is even a scholarly journal called Emergence [8]. One of the papers recently featured in this journal looked at the issue of environmental sustainability as part of a larger framework. “We can no longer speak meaningfully about social, environmental and economic sustainability issues as isolated, independent incidents. With growing acceptance that ‘everything is connected to everything else’ (citing B. Commoner), we recognize that we must progress beyond sole use of conventional reductionist epistemologies” [9].

Recommendation: Businesses should consider the big picture of the world as holistically as possible. The notion of a “planetary emergency,” noted by the World Economic Forum, is apt [10]. As noted by Rockstrom and Dixson-Decleve, “like COVID-19, climate change, biodiversity loss, and financial collapse do not observe national or even physical borders. These problems … must be acted upon not as singular threats but as a potential series of shocks and long-term risks to human health and livelihoods, economic prosperity and planetary stability” [11].

4. Nonequilibrium state

The condition of a system that is not in equilibrium. As stated by economist W. Brian Arthur of the Santa Fe Institute in New Mexico, “Complexity economics is an extension of equilibrium economics to the nonequilibrium case. And since nonequilibrium contains equilibrium, it’s a widening of economics – a generalization …” [12]. Other concepts include “far-from-equilibrium” systems and “diffusive” systems [13]. Far-from-equilibrium systems are very common in nature. A problem with studying them is that while it is possible to see the system as a whole, it is more difficult to detect the behavior of its component parts.

Research: A number of studies have drawn upon the concept of a nonequilibrium economy since it arose in the late 20th century. Barbawi and Cozzi used a historical data series to compare three scenarios for Europe from 2015 to 2025: continued austerity, a gender-neutral expansionary scenario and a gendered expansionary scenario. They found the last scenario to be the most favorable [14]. The important thing about this research is not so much the feminist angle, but rather that it shows the power of modeling using a variety of assumptions. On this point, Pranti et al. (citing Barbawi and Cozzi, among others) have proposed “Interactive Macroeconomics – A Pluralist Simulator” in 2019 [15]. Also, Bill Miller, an investor associated with the Santa Fe Institute, has noted that markets do not behave the way that classical economics would suggest. “Being complex adaptive systems, they are nonlinear and constantly changing as circumstances and conditions and information warrant, and those changes can be abrupt, violent and frightening” [16]. For this reason, he concludes that it is a good time to buy [17].

Recommendation: When planning for economic recovery and testing models for outcomes, be sure to try a variety of scenarios (featuring a variety of stakeholders) before launching. 

The next installment will consider eight more key concepts that emerge from a review of complexity literature, followed by related research and recommendations.

References

1. “Complexity Economics: A different framework for economic thought” – a chapter from “Complexity and the Economy” (Oxford University Press, 2013), http://tuvalu.santafe.edu/~wbarthur/Papers/Comp.Econ.SFI.pdf.
2. https://www.santafe.edu/news-center/news/learn-agent-based-modeling
3. “Agent based modeling,” Scholarpedia, http://www.scholarpedia.org/article/Agent_based_modeling#Classical_mathematical_models_vs_agent-based_simulation.
4. Boston, Liu, Jacques, and Mitrani-Reiser, 2014, “Towards assessing the resilience of a community in seismic events using agent-based modeling,” Proceedings of the 10th National Conference in Earthquake Engineering, Earthquake Engineering Research Institute, Anchorage, Alaska, https://datacenterhub.org/resources/11676/download/10NCEE-000296.pdf.
5. W. Brian Arthur, “Increasing Returns,” undated, http://tuvalu.santafe.edu/~wbarthur/increasingreturns.htm.
6. Francisco Rodriguez, “An empirical test of the poverty traps hypothesis,” https://ipcig.org/pub/IPCTechnicalPaper4.pdf.
7. http://tuvalu.santafe.edu/~wbarthur/complexityeconomics.htm
8. https://journal.emergentpublications.com/
9. https://journal.emergentpublications.com/article/we-cant-get-here-from-there/
10. Sandrine Dixon-Decleve, 2020, “It’s time to emerge from our planetary emergency. Here’s a plan,” Jan. 13, https://www.weforum.org/agenda/2020/01/its-time-to-emerge-from-our-planetary-emergency/.
11. Rockstrom and Dixson-Decleve, 2020, “Emergence from emergency: The case for a holistic economic recovery plan,” March 24, https://www.euractiv.com/section/energy-environment/opinion/emergence-from-emergency-the-case-for-a-holistic-economic-recovery-plan/.
12. “Complexity Economics,” an interview with W. Brian Arthur, 2018, Santa Fe Institute, http://tuvalu.santafe.edu/~wbarthur/complexityeconomics.htm.
13. Giovanni Jona Lasinio, 2015, “Understanding Non-Equilibrium: A Challenge for the Future,” Contributions to Science, Vol. 11, No. 2, pp. 127-130, https://dialnet.unirioja.es/servlet/articulo?codigo=5717401.
14. Barbawi and Cosi, 2017, “Engendering Economic Recovery: Modeling Alternatives to Austerity in Europe,” Feminist Economics, Vol. 23, Issue 4, https://doi.org/10.1080/13545701.2017.1344775.
15. Prante, Barmucci Hein, and Truger, 2019, “Interactive Macroeconomics – A Pluralist Simulator,” Working Paper No. 117/2019, Institute for International Political Economy, Berlin, https://www.ipe-berlin.org/fileadmin/institut-ipe/Dokumente/Working_Papers/IPE_WP_117.pdf.
16. Bill Miller, Miller Value Partners, 2020, “How Markets as Complex Adaptive Systems Process Covid-19,” March 19, https://millervalue.com/markets-complex-adaptive-systems-process-covid-19/.
17. Bill Miller, Miller Value Partners, 2020, “Typical Recession and Recovery Economic Behavior Offers Great Stock Buying Opportunity,” April 13, https://www.santafe.edu/news-center/news/transmission-t-010-bill-miller-investment-strategies-in-times-of-crisis.