Evidence-based policy in VUCA Environments

Introduction

Markets are unpredictable in the best of times.¹ With the pandemic, climate change, an affordability crisis, an opioid crisis, new technology, increasing political polarization, inflation, and war in Europe, things feel downright chaotic.^{2 3 4 5 6}

Management consultants call this a VUCA environment (volatile, uncertain, complex, ambiguous) and have lots of advice for how companies and organizations can navigate these stormy waters (like this book). Policy makers have less guidance to work with, and a much harder job: shepherd the market itself.

In this post, I'm going to explore the challenges of evidence-based policy-making in a VUCA environment. In future posts, I'll examine how peer networks and AI are critical tools for making markets more sustainable, equitable, and resilient in a VUCA environment.

What are VUCA Environments?

VUCA stands for volatility, uncertainty, complexity, and ambiguity. It's a model for understanding and navigating the complex landscapes of markets, politics and policy. The idea came from the US Army War College to describe the post-Cold War world, and then popularized in management circles by Warren Bennis and Burt Nanus.⁷ It's now used in business, government, and academia to describe the current environment.

• V = Volatility: The situation is highly dynamic. Things are not hard to understand; they're just moving fast and unpredictably. hard to predict and ride.
• U = Uncertainty: Refers to the potential for surprising developments in various situations. It emphasizes a diminished ability to accurately foresee events due to a limited understanding and awareness of certain issues and events.
• C = Complexity: This concept involves a multitude of intertwined forces that create a complex landscape for organizations. It points to situations where issues are confounding, and there is no straightforward cause-and-effect relationship, resulting in organizational confusion.
• A = Ambiguity: This term describes situations characterized by unclear realities and a high potential for misinterpreting circumstances. It involves unclear connections between causes and effects, leading to mixed interpretations of different conditions.

Policy makers and the VUCA Environment

Not surprisingly, times of instability are when governments try to 1) steer change toward progress and 2) stabilize the status quo. Push forward and pull back. Add energy and dampen effects. Unfortunately, given the conflicting nature of these goals, the results of governement interventions in a VUCA environment are rarely what policymakers expect, often producing instead a blizzard of unintended consequences. ^{8 9 10}

System change is hard; complex systems rarely respond the way we would like. ^{11 12} And yet, government intervention is inevitable, even necessary. Without interventions, we're almost guaranteed to get periods of high instability followed by a new (and worse) equilibrium, bringing more inequality, authoritarian structures, and a lot of environmental harm. ^{13 14 15}

The Limits of Evidence-based Policy-making

Evidence-based policy making (EBP) is the gold standard of policymaking. It's about using data to make policy. Research and evidence displace what policy makers think or feel. The goal in EPB is to make decisions based on what has been proven to be true and effective, to create policies based on what has been tested in real life.

But in a VUCA environment, EPB has limits:

1. Volatility:

   • Rapid Changes: Given the rapid changes in a volatile environment, it might be difficult to gather evidence in a timely manner to inform policy decisions. An intervention is needed faster than we can gather evidence to inform it.
   • Relevance of Evidence: The rapid changes may also mean that evidence collected at one point can become irrelevant or less applicable over time.

2. Uncertainty:

   • Predictive Challenges: Uncertainty makes it hard to forecast the future accurately, which can undermine the effectiveness of policies based on current evidence, which are essentially predictions of the future.
   • Data Gaps: Uncertain environments often have gaps in the data which make it difficult to create policies based on complete evidence.

3. Complexity:

   • Multi-dimensional Issues: Complex environments often involve multi-faceted issues that are interconnected, making it challenging to design policies based on evidence from just a few variables.
   • Implementation Challenges: The implementation of policies can be challenging due to the intricate nature of complex environments.

4. Ambiguity:

   • Interpretation of Evidence: In ambiguous environments, different stakeholders might interpret the same piece of evidence in different ways, making consensus-building difficult.
   • Moral and Ethical Considerations: Ambiguity often brings forward moral and ethical dilemmas that cannot be resolved through evidence alone.

5. Resource Constraints:

   • Resource Allocation: Gathering and analyzing evidence can be resource-intensive, which might be challenging in a VUCA environment where resources might be limited or constantly shifting.

6. Technological Limitations:

   • Analytical Tools: The existing analytical tools may not be equipped to analyze the vast and complex data in a VUCA environment, limiting the effectiveness of evidence-based policy-making.

7. Cognitive Biases:

   • Cognitive Overload: Policymakers might experience cognitive overload given the vast amount of data and the complexity of the environment, which can limit their ability to make evidence-based decisions.
   • Confirmation Bias: Given the cognitive overload, policymakers might resort to confirmation bias, where they only pay attention to the evidence that confirms their pre-existing beliefs.

8. Political and Cultural Considerations:

   • Political Pressure: In a VUCA environment, people often seek safe-harbour in strong and unambiguous leadership, which is often a straight-up denial of the complexities of reality. Policymakers face pressure to adhere to the increasingly ideological stances of their political parties or groups, which can limit the use of evidence in policy-making.
   • Cultural Sensitivities: Policies based on evidence might sometimes be at odds with emerging norms and values, posing a challenge to the implementation of evidence-based policies.

Despite these limitations, evidence-based policy making remains a critical approach in navigating VUCA environments, as it can bring a degree of rigor and objectivity to the policy-making process. So, how do we overcome these challenges?

Eden, Hermann and Miller argue that using event analysis can help, but they also acknowledge that their approach is limited to policy making around shock events, like the COVID19 pandemic, and not a persistent VUCA environment, like one caused by climate change. ¹⁶ Robert Johansen from the Institue for the Future offers a more general approach to navigating a VUCA environment.¹⁷

1. Vision: Imagine (and describe) the future and your goals within it, creating a clear and inspiring long-term plan. In a volatile and unclear world, having a clear vision can help steer markets in a steady direction. It ensures that despite the changing circumstances, the core objectives remain, participants feel safe, and the value to all stakeholders remains clear.

2. Understanding: Understand that situations are always more intricate than they first appear. Try to see the underlying patterns and dynamics at play and recognize the limits of your perception. Accept that there are multiple interconnected relationships and that outcomes are more often the result of those relationship dynamics than anything you do. Understanding complexity itself is the first step towards making sense of a complex and ambiguous world.

3. Clarity: Practice articulating issues and solutions transparently and lucidly, avoiding unnecessary complexity. Clarity may involve breaking down complex situations into manageable pieces and communicating them clearly to avoid misunderstandings. In a world ridden with ambiguity, clarity becomes a valuable asset. It can help streamline processes and foster a culture of open communication where goals are clear, and everyone knows what is expected of them.

4. Agility: Agility is the art of moving quickly and easily, adapting to changes and being flexible in response to new challenges and opportunities. In volatile and uncertain situations, agility allows policymakers to respond swiftly to changing circumstances without being paralyzed by the complexity or ambiguity of the situation. Agile organizations and governments foster innovation, can quickly pivot when necessary, and are better prepared to seize emerging opportunities.

Vision, Understanding, Clarity and Agility are all important, even essential, but, for policymakers, they are not enough. Anyone working to change the system itself, whether it be the healthcare, building, energy, transportation, or education industries need a few practical tools.

Peer Networks and AI to the Rescue

The most powerful tools are in front of us, literally right in front of us, on our phones and laptops. Social networks and artificial intelligence are disrupting our world, but they can also be a force for positive change, if brought together under the right conditions.

At its best, social network technolog coupled with AI can create powerful communities of practice with more efficient and more resilient information flows, improved evidence gathering, enhanced coordination, and accelerated innovation. Overall, emerging digital platforms like GitHub and EarthNet can help organizations, industries, and governments tackle hard problems, (including collective action problems) far more effectively.

So, what are the right conditions for peer networks and AI to support positive transformation? I'll explore this question in future posts. My next post will look at the building retrofit industry. I'll show how communities of practice (a purposeful peer network) plus AI can help the industry adapt to a rapidly changing policy environment, which, in turn, makes stronger and more innovative policy possible.

References

Footnotes

1. Patzelt, F., & Klaus Pawelzik, K. (2013).An Inherent Instability of Efficient Markets. https://www.nature.com/articles/srep02784 ↩

2. Mandelbrot, B. B. (1963). The variation of certain speculative prices. J. Bus. 36, 392–417 https://www.jstor.org/stable/2350970. ↩

3. RABlythe, R.A., An Introduction To Phase Transitions In Stochastic Dynamical Systems ↩

4. Patnaik, P., Jostad, J., Dynamical Systems Theory: Introduction https://content.csbs.utah.edu/~butner/systems/DynamicalSystemsIntro.html ↩

5. American Pyschological Association (2022). Stress in America 2022 Concerned for the future, beset by inflation, APA. https://www.apa.org/news/press/releases/stress/2022/concerned-future-inflation ↩

6. IMF (2023). Global Financial Stability Report https://www.imf.org/en/Publications/GFSR/Issues/2023/04/11/global-financial-stability-report-april-2023. ↩

7. Bennis, W., & Nanus, B. (1985). Leaders: The strategies for taking charge. New York: Harper & Row. ↩

8. Merton RK (1936) The unanticipated consequences of purposive social action. American Sociological Review 1(6): 894. https://doi.org/10.2307/2084615 ↩

9. Frankel, D., Pauzner, A. (2020) Resolving Indeterminacy in Dynamic Settings: The Role of Shocks. The Quarterly Journal of Economics, Volume 115, Issue 1, Pages 285–304, https://doi.org/10.1162/003355300554746](https://doi.org/10.1162/003355300554746 ↩

10. Brenner, T., zu Jeddeloh, S. (2023). Path dependence in an evolving system: a modeling perspective. Cliometrica. https://doi.org/10.1007/s11698-023-00266-zhttps://doi.org/10.1007/s11698-023-00266-z) ↩

11. World Resources Institute (2022). What Is Systems Change? 6 Questions, Answered https://www.wri.org/insights/systems-change-how-to-top-6-questions-answered ↩

12. Florini, A., Sharma, S., LaForge, G., (2022) Governance for Systemic and Transformational Change: Redesigning Governance for the Anthropocene. 021/22 UNDP Human Development Report BACKGROUND PAPER NO. 1-2022 https://hdr.undp.org/system/files/documents/background-paper-document/2021-22hdrfloriniandotherspdf.pdf ↩

13. Wang C, Cardon PW, Liu J, Madni GR (2020). Social and economic factors responsible for environmental performance: A global analysis. PLOS ONE 15(8): e0237597. https://doi.org/10.1371/journal.pone.0237597 ↩

14. Solomonian, L., Di Ruggiero, E. The critical intersection of environmental and social justice: a commentary. Global Health 17, 30 (2021). https://doi.org/10.1186/s12992-021-00686-4 ↩

15. Neerdaels, J., Tröster, C., & Van Quaquebeke, N. (2022). It’s (a) Shame: Why Poverty Leads to Support for Authoritarianism. Personality and Social Psychology Bulletin, 0(0). https://doi.org/10.1177/01461672221141509 ↩

16. Eden, L., Hermann, C.F., Miller, S.R., (2021) Evidence-based policymaking in a VUCA world. United Nations Conference on Trade and Development. https://unctad.org/system/files/official-document/diaeia2021d3a8_en.pdf ↩

17. Johansen, R. (2010). Get there early: Sensing the future to compete in the present. Accessible Publishing Systems Pty Ltd. ↩