We have written extensively on this issue and this short blog is a small reminder. Optimal solutions are designed to maximize efficiency, performance, or utility under specific assumptions and constraints. However, this precision often makes them brittle in the face of uncertainty, variability, or unanticipated disruptions. Here’s why:
1. Overfitting to Idealized Conditions
Optimal solutions are finely tuned to work best in a narrowly defined environment. When real-world conditions deviate—even slightly—performance collapses.
- Example:
- A machine learning model optimized for a specific dataset (e.g., lab conditions) fails when deployed in the messy, noisy real world.
- A supply chain optimized for cost efficiency becomes fragile during geopolitical disruptions (e.g., a blocked shipping route).
2. Sensitivity to Initial Assumptions
Optimality relies on precise assumptions about inputs, parameters, or boundaries. Small errors or changes can cascade into catastrophic failures.
- Example:
- In finance, portfolio strategies optimized for historical market returns collapse during black swan events (e.g., the 2008 crash).
- Aircraft control systems optimized for calm weather may fail in extreme turbulence.
3. Trade-Offs Between Efficiency and Robustness
Optimization often strips away redundancy, adaptability, or buffers—features that confer resilience.
- Example:
- Biological systems (e.g., human physiology) are suboptimal but robust due to redundancy (e.g., two kidneys).
- A “just-in-time” manufacturing system, optimized for cost, lacks inventory buffers and collapses during a parts shortage.
4. Complex Systems Have Interdependencies
Optimal solutions in one part of a system can destabilize others, creating unforeseen vulnerabilities.
- Example:
- A power grid optimized for peak efficiency becomes prone to cascading failures (e.g., the 2003 Northeast blackout).
- Social policies optimized for economic growth might exacerbate inequality, leading to political instability.
5. Nonlinear Dynamics and Chaos
In systems with feedback loops or chaotic behavior, tiny perturbations can derail “optimal” trajectories.
- Example:
- Weather prediction models fail beyond a few days due to chaos theory (the “butterfly effect”).
- Traffic flow algorithms optimized for current conditions break down during unexpected congestion.
6. Ignoring Black Swans and Tail Risks
Optimal solutions often assume normal distributions of risk, underestimating rare but catastrophic events.
- Example:
- Flood defenses built to withstand “100-year floods” fail during unprecedented storms driven by climate change.
- Economic models optimized for stable growth collapse during pandemics or wars.
7. Human Behavior Defies Optimization
Optimal solutions often assume rational actors, but humans are unpredictable, emotional, or irrational.
- Example:
- Game-theoretic strategies (e.g., Nash equilibria) fail when players act altruistically or spitefully.
- Marketing campaigns optimized for “average” consumers alienate niche demographics.
Why Robust Solutions Are Often Suboptimal
Robustness requires flexibility, redundancy, and adaptability—features that inherently sacrifice peak efficiency:
- Biological systems: Evolve for survival, not perfection (e.g., immune systems).
- Engineered systems: Airplanes are overbuilt to handle stresses beyond “normal” conditions.
- Economic policies: Diversified portfolios and social safety nets buffer against shocks.
When to Prioritize Optimality vs. Robustness
| Optimality | Robustness |
|---|---|
| Stable, predictable environments | Volatile, uncertain environments |
| Short-term goals | Long-term sustainability |
| Idealised, Isolated systems | Realistic, Interconnected systems |
In conclusion, optimal solutions are fragile because they’re exquisitely calibrated for a specific, idealized world. In reality, variability, chaos, and human unpredictability dominate. As Nassim Taleb warns in Antifragile: “Optimization is the enemy of resilience.” To thrive in complex systems, prioritize adaptability over perfection, and resilience over efficiency.
Low complexity over optimality is the good choice.
Artificial Intuition 
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