Updated: Dec 15, 2020
The products and services we offer at the Lab apply concepts and tools from complexity science to better help clients address the key challenges they face, effectively reducing risk and uncertainty. We subscribe to the notion that viewing societal challenges through the lens of complexity enables us to deliver useful, evidence-driven insights that can guide policy decisions for a better tomorrow. What, then, are some examples of the types of insights we are able to provide?
In this post, I present three concrete examples that illustrate the contribution complexity science can make to policy-making. These examples, which draw on a number of recent studies, highlight applications in three specific areas: pandemic responses, economic interventions, and conflict prevention measures. For additional examples, see this paper by Dirk Helbing and colleagues (2015).
First, complexity science can help craft behavioural policy by providing a more granular understanding of the social dynamics underlying pandemics.
A pandemic is often understood through the prism of epidemiology, that is, the number of cases, the infection rate, and the deadliness of the disease. Yet, spread is tightly related to social dynamics—psychological determinants, such as fear, and resulting group behaviour. This paper by Aiello and colleagues (2020) depicts the evolution of psychological and social responses to COVID-19 using social media data.
The analysis in Figure 1 illustrates trends in online discussion forums using a natural language processing model to categorise words and sentiment. The analysis describes three phases of psychological reaction towards the pandemic: the refusal phase (denial, “they” focus, business as usual), the suspended reality phase (anger and bargaining, “I” focus, at home) and the acceptance phase (sadness and acceptance, “we” focus, beyond home). The findings afford analysts and decision-makers a more nuanced and grounded understanding of sentiment and behaviour in an affected population.
Given that policies, such as social distancing rules, primarily aim to shape human behaviour, it is important to inform decisions for and against specific policies with empirical and generalisable behavioural insights. The example reveals how granular data can be used to unpack the social dynamics of pandemics. This approach can, in turn, enable decision-makers to design more effective policies, adequately accounting for variation in individual and group dynamics across contexts.
Figure 1. The psychological and social dynamics of pandemics (Aiello et al., 2020, p. 5)
As pandemic responses move from “refusal” to “acceptance” note how emotional responses from “fear” toward “action”. Such a chart can provide insight into communicating and intervention at each stage of the pandemic as well as greater detail on what is driving particular sentiments
Second, complexity science can inform economic policies by shedding light on the disproportional importance of some actors.
Inflation rates, consumption patterns, gross domestic products and other economic trends are system-level outcomes. These outcomes have micro-level determinants, such as the characteristics and behaviour of consumers and firms, the ways in which these actors interact with one another, and the regulations that govern the system’s rules and boundaries. Complexity science allows us to analyse the underlying processes in greater detail, for instance, by unpacking network topologies.
A paper by Guerrero and Axtell (2013) analyses how employment growth relates to the topology of networks of employees, consumers, and firms. The authors show that, contra expectation, fewer than 10% of firms account for nearly 90% of all employment growth. This type of finding allows policy-makers to craft policies that are tailored to the specific kinds of firms and, correspondingly, identify stimuli with the greatest potential impact.
Given that policy-making is resource-constrained, cost-effectiveness is an important consideration, all the more so when public funds are involved. The example depicts how impact differs when one allocates the same resources across all firms, relative to when one specifically targets firms that have a disproportionate effect on employment growth. These types of targeted interventions have a significant bearing on cost-effectiveness from a policy standpoint.
Figure 2. The topology of labour flow networks in Finland (Guerrero & Axtell, 2013, p.4)
Third, complexity science enables us to explore the effects of different political decisions in highly charged environments characterized by conflict, exploring evidence-driven counterfactual scenarios.
Policy-making is subject to slow feedback loops, insofar as decision-makers typically learn about the consequences of their decisions at some point in the medium- to long-term. Slow feedback loops pose a series of more fundamental problems in contexts characterized by ongoing political violence and conflict, given the short-term sensitivity to almost any decision. Research in this domain has made strides in leveraging complexity science to systematically compare future “what-if” scenarios, i.e., comparing likely outcomes of different decision scenarios, with a view towards mitigating risk and violence.
In a paper published in the AJPS (2014), our Lab partners, Ravi Bhavnani and Karsten Donnay together with their co-authors, modelled the micro-level dynamics of conflict in the city of Jerusalem. Using an empirically-grounded agent-based model, they simulated conflict outcomes to closely approximate real-world dynamics. This allowed them to validate their model with empirical data, and then leverage the simulations to explore a range of counterfactual “what-if” scenarios – based on concrete policy proposals – for the future status of Jerusalem.
Figure 3 below shows four scenarios that draw on different proposals for redrawing political boundaries within the city of Jerusalem: the Clinton plan, the Palestinian proposal, and the situation back in 1967. Of course, such results must be used with caution given that they are conditional on specific modelling assumptions. But, as the example illustrates, by leveraging data and simulation models, complexity science can effectively support decision-making, generating relatively unbiased assessments in high-stakes contexts.
Figure 3. Policy-relevant counterfactuals (Bhavnani et al., 2014, p. 14)
What then are the benefits associated with harnessing complexity science for policy?
In this blog post, I provided three examples to briefly illustrate that complexity science can:
Provide granular insights related to individual and group behaviour to craft more effective, socially attuned policies.
Draw insights from interaction topologies and their impacts on system behaviour, to increase policy cost-effectiveness.
Explore possible futures in highly charged contexts, providing evidence-drive assessments to mitigate risk and reduce violence.
There are many other ways complexity science can be useful. We at the lab are excited about this approach because of its ability to provide more realistic models of real-world setting, its strong interdisciplinary perspective, and its agility to support decision-making. If the content above speaks to your needs, please do not hesitate to reach out to us!