Non-Stationarity of Objective Thought: A Global Finance and Geopolitics Angle

In a world of algorithmic markets, AI-guided policymaking, and ESG dashboards flashing red, we still cling to the belief that objective thought can tame complexity. Yet what if the very pursuit of objectivity, especially in dynamic systems like finance, geopolitics, or climate policy, creates instability? Welcome to the paradox of non-stationary objectivity.

The Mirage of Stability

Non-stationarity — the notion that statistical properties like mean or variance change over time — is not just a technical hurdle. It’s a philosophical challenge. When systems evolve, any attempt to “fix” their state for analysis risks misrepresenting them. As Karl Popper argued, knowledge must always remain falsifiable. But what happens when the system itself falsifies the assumptions we build upon?

In finance, central banks target inflation, assuming stable relationships between interest rates, employment, and prices. But when these targets become operationalized, they feed back into the system. Structural shifts in behavior — such as speculative investment or debt overhangs — emerge, rendering the original models obsolete. The act of measurement alters the very thing it aims to capture.

In geopolitics, the same paradox unfolds. When states assert fixed foreign policy objectives in a non-stationary global environment — shaped by climate, AI, and shifting alliances — unintended consequences proliferate. As Zygmunt Bauman warned, we live in a world of “liquid modernity,” where the structures meant to stabilize our world are themselves in flux.

ESG and the Illusion of Measurability

Nowhere is this more evident than in ESG investing. Vast ecosystems of ratings, disclosures, and benchmarks claim to measure a firm’s sustainability. But these metrics, often based on historical data, cannot keep pace with the shifting baselines of ecological and social systems. Climate risk, for example, is non-linear: Arctic permafrost, once a predictable sink, now risks becoming a source of methane emissions. ESG models trained on yesterday’s assumptions become dangerous guides.

The stakeholder capitalism narrative compounds the issue. Boards parade sustainability goals — net zero, water positivity, human rights — while relying on static KPIs that often obscure complex local dynamics, such as indigenous land dispossession or social exclusion. The tension between what can be measured and what matters has never been more acute.

AI, Objectivity, and Reinforcing Instability

AI, our new oracle of objectivity, worsens the paradox. Machine learning systems optimize based on historical correlations. But in doing so, they entrench biases and distort future outcomes. In financial markets, algorithmic trading models increasingly train on the outputs of other models — creating feedback loops that amplify volatility and obscure causality.

The veneer of objectivity in AI conceals a deeper fragility: the assumption that past data reflects future dynamics. As central banks experiment with central bank digital currencies (CBDCs) and firms automate decision-making, they risk operationalizing policies based on flawed epistemologies — a new kind of techno-hubris.

The MENA Case Study: Sand, Sovereigns, and Signals

Nowhere is this more vivid than in the Middle East and North Africa (MENA), where the mirage of stationarity collides with geopolitical flux. In Saudi Arabia, Vision 2030’s bold objectives — diversification, smart cities, and hydrogen economies — are predicated on stable oil revenues and geopolitical calm. But the region’s hydropolitics tell a different story. The Euphrates and Nile are no longer predictable flows; climate non-stationarity has rendered river regimes erratic. Meanwhile, urban megaprojects like NEOM rely on data-driven planning models that assume long-term investment stability — despite the region’s exposure to geopolitical shocks, cyberwarfare, and drone conflict.

In Egypt, the state’s debt-driven development hinges on IMF targets and investor confidence — both of which are highly sensitive to shifts in global capital flows and ratings agencies’ perceptions. Yet these perceptions themselves are informed by models that fail to account for cascading shocks: food inflation from war in Ukraine, Red Sea shipping disruptions, or climate-induced migration from Sudan.

Even the region’s sovereign wealth funds — hailed as agile allocators of capital — rely on algorithms that chase alpha in volatile markets. But their models, often trained on historical backtests, cannot adapt quickly enough to deglobalization, green protectionism, or AI-led reshoring. As MENA economies attempt to pivot toward sustainability, their very policy apparatus becomes entangled in non-stationary systems — where achieving one objective may destabilize another.

The region offers a cautionary tale: the pursuit of fixed economic goals in a volatile geopolitical climate can lead to compounding errors. In the language of complex systems, governance becomes path-dependent, trapped in reactive loops rather than proactive strategy.

A Call for Reflexive Intelligence

What is to be done?

We must abandon the fantasy of a stationary world. Instead, analysts, policymakers, and business leaders must develop reflexive intelligence — the capacity to adapt not just our tools but our mental models in the face of change. This means embracing probabilistic thinking, investing in scenario modeling, and incorporating diverse epistemologies — from Islamic finance’s risk-sharing ethos to indigenous notions of stewardship.

Above all, we must be humble. As we build AI systems, climate models, or financial regulations, we must recognize that the map is not the territory, and the future will not resemble the past.

In a non-stationary world, objectivity is not about freezing truth. It is about learning in motion.