A series of slides from our Q4 2018 internal work which we think is most relevant now.
“[After the crisis], local resilience will be prized over global efficiency”
—Mark Carney, former Governor of the Bank of England, The Economist, April 16, 2020
We work in behavioural and decision sciences and define resilience as the “behavioural” property of systems and its agents of the system which are able to ensure continuity in the wake of change of parameters that alter the equilibrium of the operating parameters of the entire system. This behavioural approach is a critical element of our work in applying behavioural and decision science heuristic algorithms to derive models for real-world applications such as computational mechanism design. As seen today’s algorithms in AI and Machine Learning have proven time and time again not to perform well under uncertainty (not risk) and thus not resilient.
With the SARS CoV2 it is very evident that the vast majority and almost without exception most people and businesses spent billions of dollars trying to efficient. Efficiency was the singular goal to derive the “maximize profitability” mantra. A single event and all those systems, processes, technologies and mechanism designs have been laid threadbare as being efficient, but not resilient at the same time. The boolean property of Resiliency AND Efficiency.
Resilience by definition is the capability of the behaviour of any system to maintain its core functionality of the entire system in responding to changes beyond equilibrium threshold levels and still revert to ensure continuity.
“As defined by the pioneer of resilience, Holling’s (1973) was the amount of shock e a system can absorb without shifting into an alternate regime. Social-ecological systems exhibit thresholds that, when exceeded, result in changed system feedbacks that lead to changes in function and structure. The system is said to have undergone a regime shift (e.g., Scheffer et al. 2001, Carpenter 2003) that may be reversible, irreversible, or effectively irreversible, i.e., not reversible on time scales of interest to society. The more resilient a system, the larger the disturbance it can absorb without shifting into an alternate regime.”
While “Efficiency” is often measured as the ratio of useful output to total input, which can be expressed with the mathematical formula r=P/C, where P is the amount of useful output (“product”) produced per the amount C (“cost”) of resources consumed.
“Computing today is the “operating system” of human civilization. As computing professionals we have the awesome responsibility as the developers and maintainers of this operating system. We must recognize the trade-off between efficiency and resilience. It is time to develop the discipline of resilient algorithms.”
It is also the very same reason why “sustainability” has not yielded the expected triple bottomline of Social-Environmental-Economic returns. The key is what we believe is “Resiliency“, a portmanteau word combining Resilience & Efficiency
Why did we develop Resiliency as a Heuristic?
To Qualify & Quantify continuity? Natural ecosystems are Dynamic, work in Uncertainty & are Evolutionary by design
- Resilience – Uncertainty & Individual Agents (ex: people)
- Efficiency – Trophic Energy Transfer from producers to consumers
Why Resiliency? In nature, Ecological Systems, Efficiency ≠ Resilience & Resilience ≠ Efficiency.
The key conclusion is that;
- nature does not select for maximum efficiency, but for a balance between the two opposing poles of efficiency and resilience
- Because both are indispensable for long-term sustainability and health
1.Conversely, an excess of either attribute leads to systemic instability
2.Too much efficiency leads to brittleness, caused by too little diversity and connectivity
3.Too much resilience leads to stagnation, caused by too much diversity and connectivity
1.Human Behavior is Heuristic/Bounded Rationality/Quantumly Rational
2.Uncertainty is part of the feedforward & feedback loop
3.Resilience & Efficiency is a AND combination
4.Uncertainty as an input, all actors Absorb, Adapt and Rebound or a New Paradigm evolves
5.Efficiency is Trophic in the ecosystem
6.Sub-optimal (nature designed)
1.Based on Fixed/Engineering/Risk models
2.Models Based on Risk Models
3.Manage with an end-goal based on Efficiency
4.Designed Closed Loop Systems
5.Efficiency is specific to a unit
6.Designed for being Optimized/Maximized
Applying Efficiency (GE) vs. Resilient companies (social apps)
And to create ecosystem value “fungibility” is key and a derivative of energy transmitted from agent to agent in the ecosystem.
Fungibility is the to understanding how value is created, captured and distributed across ecosystems.
Concluding that applying behavioural, financial economics and computational models to “Resiliency” will create new opportunities as we have not imagined before, opening new vistas of ideas and thinking.
One thing is sure. The trillions of dollars being pumped into every major economy is failing to make the impact and intention because systems may be efficient but in combination fail. Current playbooks cannot tackle wicked problems and were meant to address tame problems. This is a reset 2.0 and the key will be resiliency whether in finance, computing, governance and policy or society in general.