How important is building collective intelligence to better manage systemic risks?

Author

Marc Gordon

Scott Williams

Source(s)
United Nations Office for Disaster Risk Reduction

This is the sixth in a series of eight articles co-authored by Marc Gordon (@Marc4D_risk), UNDRR and Scott Williams (@Scott42195), building off the chapter on ‘Systemic Risk, the Sendai Framework and the 2030 Agenda’ included in the Global Assessment Report on Disaster Risk Reduction 2019. These articles explore the systemic nature of risk made visible by the COVID-19 global pandemic, what needs to change and how we can make the paradigm shift from managing disasters to managing risks. 

“Never doubt that a small group of thoughtful, committed people can change the world. Indeed it is the only thing that ever has.”

Margaret Mead

Risk is a human construct. It is created in language and meaning to describe the felt or feared volatility and uncertainty of human life. In other words, it describes the experience of complexity and of complex systemic effects. Humans in many societies have become accustomed and attached to the illusion of control that the construct of risk has given us. But as the COVID-19 pandemic develops, it becomes clear that the effects of interdependent, globally connected systems and vulnerabilities may be beyond accurate human measurement or effective management. We must acknowledge the limits of that illusion and the limits of present systems of governance and organization of human knowledge.

This requires a new paradigm for understanding and living with uncertainty and complexity. One that activates the power of human, social and contextual intelligence, and where possible, leverages it through appropriately designed artificial intelligence. This is at the core of systemic risk governance.

GAR2019 graphic

Developing the capability for contextual understanding and decision-making is a far more effective way of dealing with uncertainty and complexity than the present reliance on extrinsic frames of reference and categorical technical expertise, siloed into disciplines. In part, such capability is built using a lifelong learning approach to grow an aware, internalized ability to notice the relevance of context and the role of self, and in doing so, to recognize and anticipate interdependencies and nonlinear effects. That is demonstrably not wide-spread across populations affected by the COVID-19 pandemic.

Human decision-making is emotional, not rational. It is thus more successfully activated by mental models based on meaning attached to values and beliefs. Over time, the use of narrative and meaning to negotiate the changing relationship between identity and context has proven to be an effective mechanism to build resilience and to enable rapid sensing, understanding and sensemaking. In this way, collective intelligence becomes possible as an essential precondition for collective responsibility. Collaboration with and through that intelligence holds the key to building systemic resilience to challenging, complex and dynamic risk events such as the COVID-19 pandemic.

Collective intelligence

'Collective intelligence' is the powerful combination of human intelligence, artificial or machine intelligence and processing capacity.

Building resilience is necessary to reduce risks and prevent disasters, and when necessary, adequately respond. Resilience requires:

  • Planning and preparation based on assessments to avoid or minimize risk creation and reduce the existing stock of risk;
  • The development of capacity to restore functions in the face of disruptions; and,
  • The capacity to adapt and change after a shock.

By addressing these complex systems challenges, every individual, organization or group involved in resilience building could thrive by tapping into a “bigger mind” through collective intelligence. This could be by drawing on the brain power of other people with diverse cultural experience, age, education or occupation and gender, combined with the processing power of machines.

While needed for processing big data about the functioning of complex systems, machine learning and artificial intelligence do not help people to solve more complex coordination and governance problems - like physical distancing - that need trust between people. They cannot decide on how people want to live human lives, for example in densely populated cities. This is a complex human dynamic problem, solvable only by humans making decisions and taking action.

Truly global collective intelligence is a long way short of being able to solve global problems. It is now important to assemble new combinations of tools that can help the world think and act at pace, as well as at the scale commensurate with the complex problems we are currently facing, including the COVID-19 pandemic and the climate and ecological crises.

In too many fields, the most important data and knowledge remain flawed, fragmented or closed. They lack the context and organization required for them to be accessible and useful for decisions. As yet, no-one has the means or capacity to bring them all together into a universal, pluralistic data ecosystem, let alone into a dynamic three-dimensional topographical map of risk through time. 

The critical interdependence among human health and well-being, ecology and technology is highly complex. The complexity lies both in the dynamic nature of connections and in responses in time and space. To effectively manage and govern a complex risk event like the COVID-19 pandemic, we need an improved understanding of human–ecological–technological system interactions. This is starting to be achieved in some fields through the application of new types of sophisticated multi-layered computer modelling.

Thanks to this revolution in systems modelling, it is now possible to begin modelling the interlinkages and interdependencies among the economic (values), societal (health, welfare and productivity) and environmental impacts of decisions and investments driven by the live interactions between weather, Earth crust shifts, soils, land, and ocean ecology and human activity. Geodata at many scales support this approach to better understand the interactive nature of the drivers of risk and for long-term risk reduction. But its practical application remains limited for complex, systemic risk events. As evidenced by the COVID-19 pandemic, this needs to change, and change quickly.

Technology-based solutions to coordination problems need to be combined with human-based solutions, made by or involving humans for solutions at a human scale. Unlike machines, which need to operate with probabilities, humans – within a social network of trust – can make decisions under radical uncertainty by attaching values to decisions. This ability in healthy human beings is due to emotional responses to highly complex decision situations. In such situations there are no solutions from purely calculative and value-free accounting or analysis of costs and benefits.

Under conditions of extreme, systemic risk - such as the COVID-19 pandemic - humans can (and should) decide on changing deeply embedded values that define higher level rules, and shape attitude, choices and behaviour.

We are now living a critical time calling for fundamental reflections on the impacts and consequences of individual and collective choices, and the accountability for those impacts and consequences. Otherwise, societies may continue to create financial and economic wealth at the expense of human health and the declining ecological life support functions in a positive spiralling feedback loop. This will further create systemic risks with cascading effects making overarching economic, ecological and social systems increasingly susceptible to collapse.

The next article (#7 of 8) in this series discusses the challenges and opportunities of generating relational information to inform a systemic perspective. It explores how to help decision makers, including government officials, to be more sensitive to interdependencies and the dynamic nature of risks and to ultimately improve whole-of-society outcomes during and after complex systemic risk events, like the COVID-19 pandemic.

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