I don’t remember the last time I read a privacy policy. But I’ve clicked “I agree” more times than I can count, just to check the weather, book a cab, or read an article. Somewhere along the way, clicking became consent. And choice became choreography.

We’re told we have control over our data. But most of us know that’s a performance. In practice, consent in the digital world often means accepting terms we don’t understand, under conditions we didn’t set, for outcomes we can’t see. In places like India, where data protection laws are evolving and digital literacy is uneven, the gap between legal consent and informed choice is even wider.

The language of “user choice” assumes a certain kind of user, one with time, literacy, privacy, and power. But many people in the Global South navigate digital systems through shared devices, low connectivity, or limited education. Consent isn’t always active. Sometimes, it’s coerced. Sometimes, it’s inherited : a parent clicking through a form for their child, or a worker using a phone that’s not theirs. And sometimes, it’s simply invisible. You don’t know what you’ve agreed to until something goes wrong.

What makes this more complicated is the rise of Digital Public Infrastructure. Aadhaar, UPI, DigiLocker, CoWIN, all promising efficient service delivery through data. But when data flows without clear guardrails, consent becomes less about autonomy and more about access. Don’t want to link your number? You may not get your subsidy. Don’t want to share your health data? You may not get your vaccine certificate. These aren’t free choices. They’re trade-offs, and often, non-negotiable ones.

There’s also a deeper imbalance at play. In today’s data economy, the people who give up the most data often get the least protection. The same systems that track consumer preferences with precision struggle to prevent data breaches or unauthorized surveillance. Privacy becomes a luxury, something you need the language, resources, and leverage to demand. For most users, especially from marginalised communities, the architecture of platforms is tilted against them.

So what would meaningful consent look like? It would start with simplicity. Interfaces that explain what’s being collected, and why. Systems that allow refusal without penalty. Transparency not just about data use, but about where it travels, who profits, and what rights the user has when something goes wrong. Most importantly, it would require a shift in power, from the collector to the individual. From opt-in by default to opt-out with dignity.

We often think of consent as a checkbox. But real consent is context. It’s language. It’s time. It’s the ability to say no, and still be served. Until that becomes the norm, “terms and conditions” won’t be protection. They’ll be permission.

Note: This piece was written with support from an AI tool, used as part of an iterative writing process. The ideas, structure, and reflections are my own.

I grew up thinking the internet was a given. A quiet constant that hummed in the background of everyday life. Search engines for homework, WhatsApp for family groups, YouTube for music I’d never admit to liking. It felt ordinary. But the more I studied technology policy, the more I realised how extraordinary that access really was.

In cities, we debate AI regulation and data rights. But in many parts of India, the question is more basic: who even gets to log on? For millions, the internet isn’t a utility. It’s a privilege. One filtered through caste, gender, geography, and affordability.

India has over 950 million internet users, yet access is deeply uneven. Urban areas enjoy near-universal mobile coverage, while large parts of rural India remain patchy or offline. The gender divide is stark. Men are far more likely to own phones or have individual access. Caste and class also shape who gets connected and who gets left buffering. Dominant-caste, upper-income users tend to have multiple devices, digital literacy, and stable networks. Marginalised communities often depend on shared phones, borrowed data, or public Wi-Fi. The gap isn’t just technical. It’s social.

During the pandemic, online schooling became the default, but for many girls in rural India, it marked the end of education. No phone, no class. Government services are now “digital by default,” but that assumes a baseline of literacy, connectivity, and access that many don’t have. Language is another divide. Most platforms cater to English or Hindi speakers, ignoring the linguistic complexity of a country with over 20 official languages. Access isn’t just about logging in. It’s about being able to stay, navigate, participate and be seen.

I used to think digital inclusion was about infrastructure. More towers, more smartphones, more data. But inclusion isn’t a matter of coverage. It’s a matter of design. Systems that don’t account for caste, gender, disability, literacy, or language will always end up serving the already connected.

Real inclusion looks like regional-language portals. Data-light apps. Public Wi-Fi in villages. Safe online spaces for women and queer users. And digital literacy that goes beyond “how to use a browser” to “how to protect your data, your dignity, and your voice.”

The internet in India is expanding. But so is the distance between those who shape it, and those shaped by it. Until we stop asking how many people are online, and start asking on whose terms, we’ll keep mistaking connection for inclusion.

Note: This piece was written with support from an AI tool, used as part of an iterative writing process. The ideas, structure, and reflections are my own.

Moderation on the internet often feels like a black box. A post is flagged, an account disappears, a comment is removed, all with no explanation. Platforms say they’re doing their best to “keep users safe,” but for many women, caste-marginalised, and queer users, it doesn’t feel like safety. It feels like erasure. Who decides what harm looks like, and who gets to speak?

Platforms like X (formerly Twitter), YouTube, and Instagram shape what we see, how we express ourselves, and who gets heard. They write community guidelines, train moderation algorithms, and enforce rules with little public accountability. Their power resembles that of states, but without democratic checks. These aren’t just tools. They are digital infrastructures of power. And in this governance, marginalised communities often face the most harm, not through targeted policy, but through silent omission.

A feminist lens helps us look beyond “free speech” and “content moderation” and instead ask deeper questions. Whose voices are most at risk? Whose experiences of harm are taken seriously? And who defines what counts as abuse? Feminist thought foregrounds care, context, and lived experience, things algorithms are terrible at. It also recognizes that online violence isn’t just about hateful speech, but about patterns of exclusion and visibility.

Shadowbanning of Dalit activists, removal of LGBTQ+ content under vague “sensitivity” clauses, and inaction against mass trolling aren’t isolated glitches. They reflect what platforms consider normative, and what they render optional.

Feminist governance would mean transparency in how moderation decisions are made and community participation in shaping them. It would mean investing in safety before harm happens, not offering block buttons after. It would center intersectionality, understanding that a Muslim woman’s experience of abuse isn’t the same as a cisgender man’s. And it would ask platforms to treat content moderation not as a PR task, but as a form of structural care.

Platforms say their scale makes true governance impossible. But scale is a choice. So is accountability. Inclusion isn't a feature. It's a responsibility.

Note: This piece was written with support from an AI tool, used as part of an iterative writing process. The ideas, structure, and reflections are my own.

An algorithm doesn’t know caste. Or gender. Or whether a woman in a rural village shares her phone with four others. But it learns. Not in the way people do, with context and caution, but with data. And in places like the Global South, where data often mirrors inequality, machines don’t just inherit bias. They scale it.

In theory, AI promises objectivity, systems that make decisions free from human prejudice. But in reality, algorithms are trained on historical data. And in contexts like the Global South, that data is often incomplete, exclusionary, or outright discriminatory. Whether it's credit scoring, job recommendations, or access to welfare, AI systems risk reinforcing the very inequalities they claim to overcome. Unlike human bias, algorithmic bias hides behind a façade of mathematical neutrality, making it harder to contest, question, or even see.

Take caste. In many parts of India, caste is a powerful determinant of opportunity, but rarely captured directly in data. Instead, proxies like name, geography, or language are used. When an algorithm profiles users for loan approvals or gig work ratings, it may unknowingly replicate caste-based exclusion.

Or consider facial recognition. A study found that commercial AI systems performed worst on darker-skinned women, yet many of these tools are still exported to the Global South with minimal accountability. The result is biased systems used in policing, surveillance, and identification, often targeting those already at the margins.

The danger isn’t just bad data. It’s bad assumptions. Most AI systems are built elsewhere, trained on Western datasets with little context of local norms, languages, or lived realities. When these systems are deployed in countries with weak regulatory frameworks and deep social hierarchies, the risks multiply. Bias becomes embedded in governance, invisible, automated, and difficult to reverse. And for communities historically excluded from decision-making, AI can feel like yet another opaque system making judgments without consent.

In a world run by algorithms, equity can’t be the patch. It has to be the source code.

Note: This piece was written with support from an AI tool, used as part of an iterative writing process. The ideas, structure, and reflections are my own.

Leave a comment

I’ve spent enough evening hours doom-scrolling to know that algorithms can read me better than I read myself. They know when I’m anxious, when I am hungry, when I want to laugh and they serve exactly that. Efficient. Predictive. A little too intimate. Somewhere along the way, I stopped asking who trained the machine, and started asking: when did I become the dataset?

I used to think of technology as something out there: coded, built, deployed, probably because I've spent years working with models, dashboards and tools designed to optimise. It all felt clean, logical and contained. But the more I’ve watched, read, and worked in spaces where technology meets policy, the more I’ve realised it’s anything but neutral. It decides who gets seen, who gets silenced, who gets protected, and who gets left out. This isn’t just about machines getting smarter, it’s about systems getting more powerful, and sometimes, more indifferent.

This series The Human Machine is an attempt to think through how technology shapes the social world, often in ways that aren’t immediately visible. The name The Human Machine comes from a question I keep returning to: where does the human end and the machine begin? Not in a sci-fi sense, but in the way our decisions, identities, even desires are increasingly shaped by systems we don’t fully see or control.

Each essay explores a different layer of this intersection: from the biases embedded in algorithms, to the quiet authority of platforms, to the unevenness of digital access, and the illusion of control we have over our own data.

The first looks at how algorithms, often framed as neutral, can replicate and deepen existing social hierarchies, especially in contexts like the Global South, where data is already uneven and biased. The second explores the question of platform power: what it means for private companies to govern speech, visibility, and harm, and whether feminist theory can offer a more accountable way to think about digital governance. The third turns to access, asking who the internet is really built for in India, and how digital exclusion is often shaped by caste, gender, language, and geography. The fourth interrogates the idea of digital consent, and how the architecture of apps and platforms often turns privacy into a performance, rather than a right.

1. Algorithmic Inequality: Can AI Deepen Social Hierarchies in the Global South?

2. Governing the Ungovernable: Feminist Perspectives on Platform Power

3. Internet for Whom? Unpacking India’s Persistent Digital Divide

4. Terms & Conditions Apply: Consent, Privacy, and Power in the Data Economy

I don’t have grand theories or tech manifestos. Just a growing discomfort with systems that treat people like users, and a belief that we need to ask better questions about the technologies we live inside.

Consider this my field notes from the edge of the interface.

Watch this space. Essays coming soon.

Subscribe now

Note: This piece was written with support from an AI tool, used as part of an iterative writing process. The ideas, structure, and reflections are my own.

Those with an inclination towards spirituality may appreciate that within Buddhism, a principle tenet is the Eightfold Path—a framework essential for the end of suffering. Drawing from this foundational principle, yet diverging beyond the mere numerical correspondence, we might consider economics as an analogous 'eightfold path'. This metaphorical pathway guides us towards robust economic reasoning, serving as the cornerstone for informed public policy decisions.

Just as the Eightfold Path guides spiritual enlightenment, understanding economics is crucial for shaping sound public policy and develop economic enlightenment.

1.      Think in degrees, not in binary

Have you encountered policies that attempt to solve a problem with blanket bans? Consider bans on liquor, firecrackers, and certain websites. On closer analysis, you might notice that these can also be counterproductive. For instance, banning specific websites might prompt people to use VPNs to access those sites, potentially leading them to explore even shadier sites via VPN, to their own detriment—a situation that likely wouldn't have arose as much without the ban. While considering the liquor bad scenario, for every ten people who misuse it, ninety others who use it responsibly are deprived of access. This restriction can foster a sense of lost freedom, making society less trusting of the government. It also potentially increases illegal activities, like acquiring liquor through the black market.

In most cases, bans are ineffective. They can lead to corruption, the generation of black money, and smuggling. Often, the implementation of bans is fundamentally a political maneuver aimed at appealing to specific segments of the population to appease them. It can also reflect a government's limited capacity to address issues more comprehensively.

When you look at problems in absolutes, you tend to look at solutions as absolutes.

Generally, it is preferable to opt for policies that aim to reduce rather than absolutely ban.

2.      Incentives matter

The story of the boat captains and sincere mourners is a classic example of how incentives really matter. In the 18th century, the British began transporting convicts to Australian colonies, partly to alleviate overcrowding in British prisons and also to establish a British presence in the region in response to interest from other European powers. The first fleet, consisting of 11 ships carrying about 780 convicts, arrived at Botany Bay, establishing the first European settlement in Australia, which later developed into the city of Sydney. Over the course of multiple voyages, not all convicts survived because the journey, which lasted approximately eight months, was plagued by harsh conditions such as disease, limited supplies, and cramped quarters. It was a well-known fact that the captains had no incentive to part with their precious food and medicine and often significantly neglected the prisoners. When a new rule was established, providing an incentive for every convict who arrived alive, the captains immediately became concerned with the prisoners’ well-being. This change illustrates how incentives can prompt good behaviour from otherwise indifferent individuals.

People respond to incentives, whether monetary or non-monetary. This is evident in the demand and supply curve: people are incentivized to buy more when prices decrease, and sellers are motivated to sell more when prices increase. However, it’s important to recognize that incentives do not always work as intended. A classic example is the "Rat story." To eliminate a rat infestation in the city, the government announced a reward for every rat killed. In response, some people began breeding rats to take advantage of the reward.

Another contemporary example involves cosmetic companies marketing their products as ayurvedic or herbal remedies to evade taxes. This tax initiative, originally implemented to encourage the use of more natural and organic treatments, inadvertently led to this unintended consequence.

3.      Trade can make everyone better off

This principle is a cornerstone of free-market economics and supports arguments for reducing trade barriers and promoting free trade as a means to increase economic welfare globally.

Trade is a positive sum game, when voluntary, whether be in domestic or international.

This concept is based on the idea of comparative advantage, where each party specializes in producing goods or services that they can produce most efficiently relative to others.

To understand this with an easy example:

• Focus on what you're good at: Imagine if you're great at making cookies, and your friend is great at making lemonade. If you both stick to making what you're best at, you'll probably end up with more and better cookies and lemonade.

• Trade and get more: After you both have made a lot of cookies and lemonade, you can trade some with each other. You end up enjoying both delicious cookies and refreshing lemonade, and so does your friend.

• Everyone wins: By trading, you both get to enjoy more and different things than if you had tried to make both cookies and lemonade on your own.

• More choices: Trading lets everyone have more choices—like having different snacks or toys—instead of everyone having the same thing.

• Things get better for everyone: When people or countries trade a lot, they can all end up having more things they like, which makes life better.

Now looking at this through a wider lens:

• Specialization: Each trading party focuses on producing goods or services that they are relatively more efficient at making. This specialization allows them to produce these goods at a lower opportunity cost than other goods they could produce.

• Efficiency and Increased Output: Because each party specializes, they can produce more efficiently and increase the overall quantity of goods and services available.

• Mutual Benefit: When parties trade, they each receive goods or services that are more valuable to them than what they give away. This exchange means that both sides can end up better off than they would be without trading.

• Expanded Choices: Trade expands the choices available to consumers and businesses, allowing access to a variety of goods and services not otherwise available in their own country or local market.

• Economic Growth: By opening up markets and promoting competition, trade can lead to economic growth, which potentially benefits all participants in an economy through more jobs, higher incomes, and greater economic stability.

4.      Think at the margin

"Think at the margin" is an economic concept that involves making decisions based on the additional or incremental changes to a situation rather than considering the total or overall impact. In simpler terms:

• Small Changes: Instead of looking at the big picture all the time, this idea focuses on what happens if you make just a little more or a little less of something. For example, if you already have a big bowl of popcorn but are thinking about having a bit more, "thinking at the margin" means considering whether the enjoyment of a little more popcorn is worth the extra calories at that moment.

• Weighing Additional Benefits and Costs: It's about weighing the benefits and costs of adding or subtracting a small piece from what you already have. If you're studying, and it's late, you might think about whether studying for one more hour at night will help you more than it hurts (like losing sleep).

• Making Decisions: This approach helps in making efficient decisions. For instance, a business might consider whether producing one more unit of a product will bring in more profit than the cost of making that extra unit.

• Everyday Choices: You use this concept daily, often without realizing it. When deciding if you want to watch another episode of a TV show, you're thinking at the margin if you consider whether the enjoyment of the episode is worth staying up late.

Thinking at the margin helps in understanding how best to allocate resources, like time or money, by focusing on the immediate effects of small changes.

To connect this discussion to a more practical real-world use case, consider the problem of train delays. For example, imagine a train on a specific route is consistently delayed by 10 hours. What would it take to reduce this delay to 5 hours? Proactive measures could be the key. Prioritizing repairs and maintenance of these trains, and improving operational efficiency by streamlining decision-making processes within train operation centers, can expedite responses to incidents that cause delays. Regular training and simulations for emergency responses can prepare staff to handle unexpected situations more efficiently, thus significantly reducing delay times. In this case, it’s not too far fetched to assume that the resources required to do this is not much compared to the gain. Marginal cost is less than the productivity gain.

But if you’re attempting to reduce the time further from 5hrs to 2.5hrs, this becomes significantly harder. This might involve multiple measures, such as technological improvements or deploying alternate replacement trains. In such cases, the costs might outweigh the marginal gains. Therefore, conducting a marginal cost-benefit analysis is crucial to determine if the potential improvements justify the additional expenses.

5.      There is no such thing as free lunch 

The concept of "there is no free lunch" in economics emphasizes that everything has a cost, even if it's not immediately apparent. Everything we desire is scarce, and those scarce resources have competing users.·     

• Opportunity Costs: When you choose one thing, you give up the opportunity to do something else with that time, money, or resource. For instance, spending your afternoon at a free seminar means you can't use that time to work, relax, or engage in another activity that might have its own benefits or earnings.

• Costs are Everywhere: Even if you're not paying out of pocket for something, someone else is. For example, if you get a "free" lunch at an event, the cost of that lunch is covered by the event organizers, sponsors, or reflected in the ticket price.

• Indirect Costs: Many things that seem free have indirect costs associated with them. For example, "free" apps on your phone might use your data, show you advertisements, or collect your personal information, which are all costs to you in different forms.

  For every government action or expenditure, one must ask – “Is there a better use of these resources”

6.      The law of unintended consequences

The impact of decisions can be analyzed using measures like first-order, second-order, and higher-order consequences.

First-order consequences are the immediate, predictable results of an action and typically align with the direct purpose of the decision. For example, choosing to save money by not dining out leads directly to spending less.

Second-order consequences emerge from the first-order outcomes, often being less obvious and more complex. In the dining out scenario, a second-order consequence might be that you cook more at home, leading to improved culinary skills or healthier eating habits. Alternatively, it could result in less social interaction if eating out was a major social activity for you.

Third-order consequences are the subsequent ripple effects of the second-order consequences, typically more removed and harder to predict. Continuing the same example, enhancing your cooking skills and health could reduce future healthcare costs or improve your long-term quality of life in ways you hadn’t anticipated.

Higher-order consequences extend even further from third-order effects. These outcomes are challenging to predict and may only become apparent over a long period. For instance, improved health and cooking habits could influence your family's behaviours and priorities or even inspire a career change into health and wellness fields.

A recent policy that resulted in numerous unintended consequences was the demonetization initiative. This policy led to an economic slowdown, had a negative impact on the informal sectors, and placed significant strain on the banking sector, among other effects.

7.      Growth is the best way out of Poverty

India's biggest challenge is poverty, not inequality. The core issue is whether people have enough resources to meet basic standards of living. The only way to increase a nation's real income is by boosting its real output.

Approach towards using economic growth as the primary tool to alleviate poverty in India:·    

• Poverty Alleviation through GDP growth : An increase in GDP often leads to job creation, higher wages, and better services, which can collectively lift millions out of poverty. For every additional %point increase in GDP leads to ~2Million people out of poverty

• Development: Growth isn't just about increasing national income; it also impacts human development indicators like health, education, and lifespan (captured in measures such as the Human Development Index). Moreover, higher GDP growth can provide the resources needed for environmental conservation efforts, improving sustainability. This interconnection suggests that boosting GDP growth can lead to broad-based improvements in living standards and developmental outcomes.

• Isomorphic Mimicry: Isomorphic Mimicry refers to the tendency to copy policies from economically successful countries, expecting similar results without considering local contexts. The caution here is vital: what works in one country (like Sweden) may not be effective in another (like India) due to different economic structures, cultural backgrounds, and institutional capacities. This point underlines the importance of adapting and tailoring policies to fit India's unique circumstances rather than blindly adopting foreign models.

8.      Balance between Government, Markets and Society

Each of the actors – Sarkaar, Bazaar and Samaaj have a specific role to play and their roles are hugely interconnected in creating a well-functioning state and society.

• Markets are usually good at aligning self-interest with societal interest and leads to optimal allocation of resources. For example, in a well-functioning market, companies that produce what consumers want at the lowest prices thrive, benefiting the whole economy.

• Governments have to secure property rights for markets to function. Governments may step in where markets fail, such as by providing public goods, regulating monopolies, or addressing externalities. Yet, government intervention can also lead to inefficiencies (known as government failure), especially if it becomes too bureaucratic or if interventions are poorly designed, leading to corruption or misallocation of resources.

• There are certain societal issues that neither markets nor governments might adequately address, often related to cultural norms, values, or social practices. In these cases, societal reform is needed. This might include efforts to improve social cohesion, ethical behaviour, and community engagement. For example, societal pressure can lead to changes in government policy or business practices, as seen in movements for civil rights or environmental conservation.

Balancing These Actors: The balance between these three actors is crucial. Over-reliance on any one can lead to suboptimal outcomes. For instance, excessive market freedom without adequate government regulation can lead to unchecked externalities like pollution. Overbearing government control can stifle market efficiency and innovation. A vibrant society can check both government and market failures by advocating for change or reform, providing grassroots solutions, or through direct community actions.

In exploring the 8th fold path to economic reasoning, we've navigated through essential principles that illuminate not just how economies function, but how decisions at every level—individual, organizational, and governmental—affect and are influenced by economic forces. This exploration reveals how choices, from the personal to the policy level, ripple through the world of economics, highlighting that it's as much about human stories and social frameworks as it is about spreadsheets.

Subscribe now

Crafting policy is similar to mastering an art form. It is where intuition and creativity converge with the precision of mathematics. As much as good intention is crucial, one must acknowledge the multitude of factors and scenarios at play. Although striving for absolute freedom from unintended consequences is an impractical dream, each decision requires a thorough cost-benefit analysis to ascertain the most optimal path forward.

However, beneath the analytical surface lies a crucial element of humanity: intention. The driving force behind policy-making must always be the betterment of lives. Yet, good intentions alone are not enough; policies must be judged by their real-world impact. This interplay between intention and outcome imbues policy-making with an artistic quality, transcending pure objectivity and embracing the nuanced complexities of human society.

Immediately post-independence, India faced one of the biggest famines. This crisis prompted a reliance on the United States for food aid, facilitated through the enactment of US law PL-480, consequently amplifying the US's influence over Indian policymaking. This catalysed the realisation that India needs to become food secure as a country, and that became the main motivation behind the iconic Green Revolution in India. What this example compellingly illustrates is the potential for such initiatives to yield unforeseen or unwelcome outcomes.

As a result of the Green Revolution, rice cultivation expanded in regions with limited water resources, causing water scarcity. This, in turn, prompted the implementation of the Punjab Preservation of Subsoil Water Act. Consequently, there were delays in sowing the Kharif crop, leading to concentrated stubble burning, ultimately contributing to the infamous Delhi smog. While anticipating this chain of reactions is inherently challenging, it underscores the complexity of policymaking. It highlights the necessity not only of crafting policies aligned with a desired vision but also of anticipating and mitigating unintended consequences to the greatest extent possible.

This example signals that policy making is infact a complex system, which operates within a dynamic and intricate system. Unlike simple or even complicated systems where rules can be studied and solutions derived through systematic analysis, complex systems present formidable challenges due to the multitude of variables at play. Consequently, addressing issues within complex systems often necessitates experimentation and a willingness to devise solutions without expecting perfect, exhaustive research. This adaptive approach acknowledges the inherent uncertainty and interconnectedness within complex systems, emphasizing the importance of flexibility and iterative problem-solving in policymaking.

Mental models serve as frameworks that shape how individuals perceive the world, make decisions, and solve problems. However, when it comes to policymaking, several common faults in our mental models can hinder effective decision-making.

Firstly, we often view public policy as a linear deterministic system, where distinct entities—such as Society, Government, and Markets (Samaaj, Sarkaar and Bazaar resp.)—operate independently with minimal overlap. In reality, these actors are interconnected, and their functions cannot be neatly separated. Secondly, there's a tendency to underestimate the complexity of policymaking by considering only a limited number of variables. In reality, policymaking involves numerous interconnected factors that must be taken into account. Thirdly, we sometimes fall into the trap of adopting a "best practices" mindset, assuming that what worked in one context will necessarily work in another. However, this approach fails to consider the unique circumstances and complexities of each situation. Lastly, there's a tendency to seek a single policy solution for sectoral problems, ignoring the multifaceted nature of many issues. For example, implementing a technology policy to improve traffic flow in bus lanes may overlook other contributing factors to traffic congestion that cannot be addressed solely through technological means. Recognizing and addressing these faults in our mental models is crucial for more effective and nuanced policymaking.

Hence, policy making is a complex system characterized dynamic interconnections among its components – when one-part moves, another part moves. Even with an understanding of its parts and rules, predicting outcomes remains challenging due to the intricate nature of these interdependencies. Moreover, complex systems exhibit sensitivity to initial conditions, meaning that small changes at the outset can lead to significantly different outcomes over time. This phenomenon underscores the non-linearity inherent in complex systems, where seemingly minor alterations can yield substantial effects.

Also, the concept of homeostasis is relevant in understanding how complex systems strive to maintain internal stability despite external perturbations. This self-regulating mechanism ensures that system behaviour remains within a certain range of outcomes, contributing to its persistence over time.

In essence, policy making operates within a complex and dynamic framework, where interactions among various factors lead to emergent behaviours that are often unpredictable. Recognizing these characteristics is essential for navigating the complexities inherent in policy development and implementation.

Unlike using a microscope to analyse a subject placed on a petri dish, while making policy, we are entities that are inside the dish, inside the system, and looking from the inside to make sense of it.

During the policymaking process, the collaborative forces of all three pillars—Samaaj (society), Bazaar (market), and Sarkaar (government)—are crucial. Policies that fail to account for the influences of these three entities risk encountering setbacks. There exist overarching constraints associated with each of these actors that are beyond direct control. Competition poses a constraint for Bazaar, capacity for Sarkaar, and trust for Samaaj.

To illustrate, consider the varied responses of different countries to the COVID-19 pandemic. Despite similar policies, citizens' reactions differed significantly based on the level of societal trust, whether high or low. Moreover, the actions taken by each country were contingent upon its individual capacity. In the analysis of policy, the objective is to progress from a simple understanding to a complex one and ultimately to a profound comprehension. However, this journey often leads from simple confusion to profound confusion, in a somewhat amusing twist.

While analysing policy, it’s valuable to look at public policy through the lens of applied science.

1.      To understand what the state does, and how it affects you

2.      To assess a public policy against some parameter (eg. Its stated goals)

3.      To place (state) action in context (historical, geographic, economic, demographic, political, legal, cultural, geopolitical)

During analysis, multiple levels are considered, spanning from the national and global scales to regional, local, household, familial, tribal, national, and ultimately, human levels.

To take an example, when attempting to analyze the reasons behind the high number of manufacturing plants in Chennai, several potential factors come into play: it could stem from the influence of a robust leader advocating for manufacturing, the significant presence of engineering colleges in the vicinity, national policies, and other possible determinants.

Policy is made while knowing or staying aware that the system is complex.

In conclusion, navigating the intricate landscape of policy making requires mindful consideration of several key points.

Firstly, recognizing that complex solutions pose challenges in understanding and predicting behaviour. Secondly, actively engaging with the system is necessary to influence its trajectory effectively. Thirdly, contextual relevance is paramount, emphasizing the importance of learning from others rather than blindly replicating approaches. Lastly, embracing complexity also entails acknowledging that from great complexities comes greater potential for diverse pathways to success.

Subscribe now

Subscribe now