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Understanding Regulatory Episteme as How Regulators See the World

I. Introduction

The real world is complex, and mostly beyond human comprehension. Even the simplest phenomena like birds flying in geometrically-shaped flocks, or termites coordinating to build castles matching the best of human architecture perplexes us, exposing the limitations of our cognition. And yet, we have evolved mechanisms to make sense of this complexity by interpreting reality through conceptual lenses. This blog explores how that same cognitive tendency plays out in the world of regulation: how the lenses that regulators use to understand the world shape what they see, what they miss, and how well they work together. It argues that when regulatory institutions develop divergent worldviews, governance fragments, learning stalls, and policy coherence suffers.

The tasks that regulators perform require them to not only make rules and ensure compliance, but also analyse, interpret and keep track of reality in some way or the other. Every regulatory agency, whether it’s a financial regulator, a competition authority, or an environmental regulator, operates with a certain lens (also see this) to understand reality, identify the issues it is dealing with, and filter out the plethora of extra irrelevant information that could be noise. This underlying lens shapes what the agency notices, what it prioritizes as a risk or harm, and how it decides what counts as evidence. One of the phrases used to comprehensively denote it is ‘regulatory episteme’, building on Foucault’s notion of the episteme, loosely understood as the set of assumptions, rules, and practices that govern how knowledge is organized and validated in a given situation (see this, this, and this). This way of knowing, the regulatory episteme, runs deep and quietly in the background and is built up over years by legal mandates, economic theories, past experiences, and even the professional background of its staff. Similar to a grammar that guides a language and its structure/meaning, regulatory episteme in a regulatory institution is mostly implicit, but it determines how the regulator functions and decides.

Crucially, the regulatory episteme of a regulator influences what evidence is considered credible. A competition authority might lean heavily on economic data and complex market models to decide if a merger harms consumers, reflecting an episteme that values quantitative, effects-based analysis. In contrast, a consumer protection agency might give more weight to individual complaints and testimonies, driven by an episteme attuned to personal harm and fairness. Each regulator operates inside its own cognitive universe, employing theoretical and methodological lenses that prioritises some signals loud and clear while filtering out others, all according to its ingrained worldview

II. Fragmentation: When Institutions See Differently

An issue arises when different institutions, like regulators, tribunals and ministries develop different epistemic grammars and thus see the facts differently in matters where they need to cooperate or collaborate. In a perfect world, all arms of government would harmoniously interpret facts and risks in a complementary manner. In reality, however, each institution’s unique episteme can lead them to talk past each other. This phenomenon is what we can call fragmentation in governance, where a common perspective is fractured into siloed ways of thinking.

In practice, fragmentation is a lot more common than we might think. For example, one regulator views a new technology as an exciting innovation to be fostered (perhaps because its episteme is pro-market and innovation-friendly), while another regulator or ministry sees the same technology primarily as a source of risk that needs strict control (perhaps because its episteme emphasises caution and safety). They might both be looking at the same data, but their internal ways of knowing lead them to opposite conclusions. The result is a lack of alignment, or worse, open contradiction in how policies are made or enforced. When epistemic fragmentation happens between institutions, several issues tend to follow.

Inconsistent decisions: Each agency makes decisions that make sense within its own worldview, but these decisions may conflict with those of other bodies. For example, two regulators jointly overseeing the same digital markets, say, a competition authority and a sectoral telecom regulator, might look at the same platform’s conduct very differently. The competition authority, trained on effects-based economic analysis, might find no harm to consumer welfare, while the telecom regulator, working from a public interest and access framework, flags the same conduct as undermining fair access. Since both have legitimate authority over the same domain, their contradictory conclusions leave businesses uncertain about which standard governs. Such inconsistencies can confuse the stakeholders about what the rules really are, leading to lesser confidence and investments in the market.

Coordination failures: Effective governance often requires multiple institutions to work together (consider how food safety involves agricultural departments, health regulators, and trade authorities). While different agencies naturally operate under distinct mandates and priorities, coordination becomes difficult when their underlying frameworks for identifying problems and evaluating evidence diverge too sharply. In such situations, institutions may interpret the same policy issue through incompatible lenses, turning coordination exercises into debates over first principles rather than collaborative problem-solving.

Reduced learning: Perhaps the most hidden cost is that each institution ends up stuck in its own echo chamber. When agencies have incompatible epistemes, they struggle to learn from each other’s experiences. A regulator might dismiss valuable insights from another body simply because those insights don’t fit its established way of thinking. The government as a whole loses out on cross-pollination of ideas and the chance to improve through shared wisdom.

The Missing Feedback Loops and A Learning Deficit

One critical reason regulators struggle to correct course or harmonize their approaches is that they often lack strong feedback loops. In well-functioning systems, feedback loops help systems learn from experience: you get timely information about the effects of your actions and adjust accordingly. For many regulatory institutions, these loops are weak or broken.

Often, the impacts of regulation can be diffuse and delayed, spread across many players and over time. Regulators might not hear about unintended consequences until they’ve piled up, or they might only see anecdotal evidence. Without direct market feedback, an agency can only guess whether, say, a price control actually protected consumers or if it backfired by causing shortages.

Feedback from other institutions is also limited. Regulators don’t always have channels for sincere peer review or coordination where they reflect on each other’s outcomes. Instead of a culture of mutual learning, different agencies often operate in silos. At best, they might exchange formal comments or meet in inter-agency committees that often gloss over deep differences. Rarely is there a systematic loop where one body’s successes or failures inform another’s strategy in real time.

Even internal feedback within a regulatory agency can be problematic. Bureaucracies are not always known for self-critique. Frontline staff implement rules and might sense something is or isn’t working, but that information can get lost on its way up the chain. The agency’s leadership may be more attuned to political signals like pressure from elected officials or media criticism than to ground-level data.

When feedback loops are missing, regulators tend to move from one task to the next without learning. They issue a new rule, and instead of checking back in a year to rigorously evaluate its impact, they are already busy writing the next rule. Over time, this learning deficit means agencies stick with approaches they’ve always used, not necessarily because they’re the best, but because there’s no clear signal to change. In the worst case, a regulator might mistake silence for success (“no one is complaining, so it must be working”), when in fact the problems are simply unseen or unreported.

Institutions as Complex Adaptive Systems

How do we make sense of these learning challenges and inertias? It helps to view regulatory institutions as complex adaptive systems, a term borrowed from complexity science to describe systems, like ecosystems or economies, where many parts interact and the system evolves over time. A regulatory agency, too, is a living system of people, rules, routines, and knowledge. It adapts to its environment, but often slowly and in unpredictable ways. One feature of complex systems is that they can get locked into habits. In a regulatory context, this often means path dependency. Once an agency has established a certain procedure or mindset, it tends to stick to it. Each decision sets a precedent for the next; each crisis response becomes the template for future responses. Over years and decades, a regulator can accumulate a heavy load of traditions and customs under ‘the way we do things’. While this creates stability, this also causes inertia. Changing course is hard. Just as a large ship doesn’t turn on a dime, a regulatory institution doesn’t instantly overhaul how it thinks or operates at the first sign of trouble. It usually takes strong currents or shocks (like a major scandal or a new law) to prompt significant change, and even then, the change may be incremental.

Understanding regulators as complex adaptive systems also means recognizing that they evolve with their environment, but not always in sync with it. They respond to political pressures,

economic events, and public sentiment, but with a lag and often through the lens of their entrenched episteme. They learn, but often in a single-loop fashion, adjusting tactics without questioning core assumptions. Truly adaptive systems engage in double-loop learning (rethinking underlying goals and paradigms), but that’s a tall order for institutions that are built to be steady and rule-bound.

All this suggests that thoughtful reform is needed to support better institutional learning and adaptation. If we want regulators to be more agile and coherent in a changing world, we have to design changes in incentive structures and processes that encourage them to periodically question their own assumptions, seek outside input, and experiment in small ways. This could mean setting up dedicated units to analyze regulatory outcomes and report candid findings. It could mean creating more forums where different agencies regularly coordinate and align their views, essentially forcing some cross-pollination of epistemes. It certainly means building in stronger feedback loops like sunset provisions that force review of old regulations, stakeholder feedback mechanisms, data analytics that alert regulators to emerging issues in real time, and so on. The goal of reform isn’t to make regulators chase every trend, but to make them learning organizations that slowly but surely adjust course based on evidence and shared knowledge, rather than remaining on autopilot.

A Glimpse Ahead: India’s Competition Regulation

These concepts of regulatory episteme and fragmentation aren’t just theoretical and actively play out in real institutions every day. In our next post, we’ll see how this unfolds in the context of India’s competition governance. India has a young competition regulator, the Competition Commission of India (CCI), and an appellate tribunal for competition cases, the National Company Law Appellate Tribunal (NCLAT). In principle, both bodies deal with the same competition issues, but observers have noted an epistemic divergence between them. In simpler terms, the CCI and the NCLAT have developed somewhat different ways of looking at competition problems.

For instance, the CCI often prides itself on an economics-driven approach involving looking at market data, consumer harm, and the effects of business conduct with a fine-tooth comb of modern competition theory. The NCLAT, being a judicial appellate body, might emphasize legal interpretation, procedural fairness, and the sufficiency of evidence on record. These differing lenses can lead to tensions: decisions of the CCI have sometimes been overturned or modified by

the NCLAT, not just over factual disagreements, but seemingly because the two institutions weighed the issues through different frameworks. From the outside, it can seem like the two are not always on the same page about what competitive harm looks like or how to balance innovation against regulation. This is a prime example of a fragmented regulatory episteme in action.

When key institutions in a policy domain don’t share a common understanding, it can create uncertainty and inefficiency. Businesses under scrutiny might play one institution against the other, or they simply face prolonged uncertainty as a case ping-pongs between differing viewpoints. Moreover, the opportunity for learning for the tribunal and regulator to enrich each other’s perspectives can be lost amid turf issues or institutional pride. In the next part of this blog post, we will delve into this CCI vs. NCLAT dynamic. We’ll examine how their divergent episteme might have influenced competition law outcomes in India, and what that tells us about the broader need for coherence and feedback in regulatory systems.

Often, the impacts of regulation can be diffuse and delayed, spread across many players and over time. Regulators might not hear about unintended consequences until they’ve piled up, or they might only see anecdotal evidence. Without direct market feedback, an agency can only guess whether, say, a price control actually protected consumers or if it backfired by causing shortages.

Feedback from other institutions is also limited. Regulators don’t always have channels for sincere peer review or coordination where they reflect on each other’s outcomes. Instead of a culture of mutual learning, different agencies often operate in silos. At best, they might exchange formal comments or meet in inter-agency committees that often gloss over deep differences. Rarely is there a systematic loop where one body’s successes or failures inform another’s strategy in real time.

Even internal feedback within a regulatory agency can be problematic. Bureaucracies are not always known for self-critique. Frontline staff implement rules and might sense something is or isn’t working, but that information can get lost on its way up the chain. The agency’s leadership may be more attuned to political signals like pressure from elected officials or media criticism than to ground-level data.

When feedback loops are missing, regulators tend to move from one task to the next without learning. They issue a new rule, and instead of checking back in a year to rigorously evaluate its impact, they are already busy writing the next rule. Over time, this learning deficit means agencies stick with approaches they’ve always used, not necessarily because they’re the best, but because there’s no clear signal to change. In the worst case, a regulator might mistake silence for success (“no one is complaining, so it must be working”), when in fact the problems are simply unseen or unreported.

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