Prof. Dr. Tariqullah Khan

CEO, Venture Ethica, Toronto, Canada

IsDB Laureate in Islamic Economics

For decades, economic theory has struggled to cope with the modern world’s greatest challenges – from climate crises to systemic inequality. The underlying issue – most models are built for a simple, linear world. But the real world is complex.

To address this, the field of Complexity Economics emerged. But while it provides a superior diagnosis, it often falls short on the cure. To truly solve our biggest problems, we must move from a descriptive understanding to a Dynamic Prescriptive Paradigm.

1. What is Complexity Economics?

Complexity economics, pioneered by thinkers at institutions like the Santa Fe Institute, is a paradigm shift away from the traditional, Newtonian view of economics.

It rejects the idea of a stable, equilibrium-seeking system populated by perfectly rational, optimizing agents. Instead, it views the economy as a complex adaptive system (CAS), characterized by:

• Heterogeneous Agents: Individuals, firms, and governments that learn, adapt, and make mistakes.
• Non-Linearity: Small changes can have disproportionately large effects (the butterfly effect).
• Emergence: Higher-level patterns (like market crashes or technological revolutions) arise spontaneously from the low-level interactions of agents.
• Endless Novelty: The system is constantly evolving, meaning there is no fixed, knowable ‘optimal’ state to which it returns.

2. Complexity Economics is Descriptive

The core strength of complexity economics is its power to describe and diagnose. It helps us understand how a crisis emerged, why agents are behaving the way they are, and what the potential feedback loops are.

For instance, complexity models can brilliantly simulate how housing bubbles form due to herd behaviour and adaptive expectations, or how localized climate shocks cascade into global supply chain disruptions.

However, this paradigm often stops at the diagnostic stage. It tells us we are in a complex mess, and that simple solutions won’t work, but it frequently lacks a structured, repeatable methodology for prescribing effective interventions that move the system toward a desirable, sustainable state. The model explains the problem but does not hand policymakers the levers to fix it.

3. To Solve Complex Problems, We Need a Dynamic Prescriptive Paradigm

If descriptive economics is the x-ray, and complexity economics is the MRI, we are still missing the surgical plan. This is where Dynamic Prescriptive Economics (DPE) steps in.

Inspired by Wasatiyah and other behavioural values, DPE is designed to overcome three critical barriers in policy design:

1. Noise: Overwhelming policymakers with too many competing goals (e.g., the 169 targets of the 17 SDGs).
2. Tensions: The zero-sum mind-set that treats essential goals, like equity vs. efficiency or profit vs. planet, as unavoidable trade-offs.
3. Blind Spots: The failure to integrate long-term systemic wisdom (Accumulated Epistemic Rationality – AER) into short-term decision-making.

The prescriptive paradigm demands a framework that not only acknowledges complexity but designs systems to navigate it adaptively, constantly measuring the gap between current outcomes and synergistic ideals.

4. The DPE Architecture: Designing for Synergy

The DPE framework provides the necessary architecture to transition from a trade-off mind-set to a synergy-seeking Wasatiyah mind-set. Its core components form a closed-loop system:

A. Diagnosis & Measurement (The NBCs)

DPE’s first step is Dimensional Reduction. It simplifies a complex problem by focusing on the two pivotal, conflicting dimensions (D₁ and D₂), such as climate mitigation and economic stability. It then measures policy performance using Normalized Balanced Coordinates (NBCs), a scalar measure from -1 (worst) to +1 (ideal) on each axis. These coordinates map the policy’s outcome onto a Four-Quadrant Cartesian Plane:

• Quadrant I (The Ideal): High performance on both D₁ and D₂ (Synergy).
• Quadrants II & IV: High performance on one dimension but low on the other (Trade-offs).
• Quadrant III: Low performance on both (Stasis/Worst-case).

The goal is always to calculate the policy’s distance from the synergistic ideal (the top-right corner of the plane).

B. Prescription & Action (The STOs)

Once diagnosed, DPE offers three defined prescriptive levers – STOs – to shift the policy’s coordinates toward Quadrant I:

By specifying the type and scale of STOs required, DPE translates abstract policy goals into concrete, measurable actions capable of dynamically transforming complex systems.

Conclusion: The Path to Adaptive Governance

Complexity economics taught us that the world is an ever-changing system with no simple answers. The Dynamic Prescriptive Economics framework takes this knowledge and turns it into a governing principle: Adaptive Governance.

It provides a robust, measurable methodology to:

1. Quantify the cost of trade-offs using NBCs.
2. Design a path to synergy using STOs.
3. Integrate long-term wisdom using AER.

The era of static, descriptive economics is over. To manage the 21st century’s ‘wicked problems’, we need a dynamic, prescriptive framework that not only diagnoses complexity but dictates the actions needed for true, regenerative transformation. DPE offers that essential domain neutral playbook. Access the DPE framework here.