The Probability of Love: Decoding Romance Through Economics, Data, and Design

By Anshul Abbasi

What if love isn’t magic, but math? That’s the thought that first sparked this article—an idea born from quiet observation and later explored in a deep, unexpectedly profound conversation with ChatGPT. For a long time, I’ve believed that love, while emotional in its manifestation, is statistical in its origin. The people we fall in love with don’t just walk into our lives by fate; they appear within structured, economic, and social ecosystems that most of us don’t even realize we're moving through. I began reflecting on how love had shown up in my own life: school crushes, college flings, friendships that turned into something more, all happened within environments filtered by class and access. I went to a school my parents could afford, which naturally meant that I grew up around people from similar economic backgrounds. The same was true for my college and then my workplace. Whether I was aware of it or not, my romantic possibilities were always filtered through the lens of affordability, geography, exposure, and timing. In essence, economic class dictated my ecosystem—the cities I lived in, the restaurants I ate at, the public spaces I frequented, and the kind of people I could meet. This class-based ecosystem quietly but powerfully determined who I might connect with on an emotional level. It wasn’t some grand design of the universe; it was a funnel of probabilities narrowing down from the billions of people on the planet to a few hundred I’d realistically meet—and perhaps just one or two I might truly connect with.

During my discussion with ChatGPT, we started mapping this out as a model—something we called the Effective Romantic Sample, or ERS. This ERS is the actual pool of people you’re exposed to throughout your life in ways that could lead to a romantic connection. Contrary to the fantasy that we could fall in love with anyone, your ERS is shaped by several predictable variables: your economic class (E), region or geography (R), age (A), social exposure (S) through institutions like school or work, cultural capital (C) like language and lifestyle, and the simple availability of time (T) to meet and interact with others. The ERS is not the population—it’s the subset of people your life actually brings you into contact with. Even within that group, not everyone is a potential partner. That’s where we introduced the idea of the Probabilistic Affinity Score, or PAS, which helps determine the likelihood of forming a romantic connection with any individual within your ERS. PAS is calculated from a mix of factors: shared context (SC) like upbringing and worldview, physical attraction (P), emotional availability (EA), timing and vulnerability (TV)—since love often strikes when people are open and ready—and relational value (RV), meaning how well your goals and future plans align. The formula could look something like this: PASᵢ = α(SC) + β(P) + γ(EA) + δ(TV) + ε(RV). Each variable affects how likely it is that two people will emotionally click. The person with the highest PAS in your ERS is probably the one you’ll fall for—or at least try to build something with. Of course, no equation can capture love entirely, but it’s hard to deny how often we end up with people who meet these criteria.

What struck me most during this exploration was how consistently economic class acts as a silent but powerful filter. Class defines our everyday movement, whether we realize it or not. The malls we visit, the flights we take, the gyms we join, the friends we make, even the dating apps we’re drawn to—all of it is subtly curated by financial and cultural access. It’s rare for someone in a high-income group to repeatedly interact with someone from a vastly different economic background in socially equal, emotionally open ways. Even when cross-class love happens, it's often accompanied by social friction, family tension, or feelings of imbalance. For most people, love remains intra-class, not because they’re trying to keep it that way, but because that’s where the structure of their life leads them. This realization doesn’t reduce love to snobbery; it reveals how structurally filtered our experiences already are. It also explains why people tend to find partners in college, at work, or through mutual social circles—these are the places where ERS overlaps are most dense. Dating apps, far from expanding diversity, often reinforce these filters by allowing users to sort potential matches by education, income, city, and lifestyle.

Even for those who are globally mobile—people who live abroad, travel often, or work in highly cosmopolitan sectors—the pattern holds. Though the geographic exposure increases, the class filter remains intact. A high-income Indian professional working in London or Dubai is most likely to meet others from similar educational and financial backgrounds, even if they come from different countries. So yes, you’re expanding your ERS geographically, but it’s still class-consistent. That’s the real reason why you see so many NRIs marrying other NRIs, or elite MBA grads partnering with other elite grads. It’s not just preference—it’s patterning, driven by economics, exposure, and availability.

But if this is all true, then what do we do about it? Is love just a structured accident, and we’re all playing out some pre-coded probability script? I don’t think so. In fact, this understanding can be empowering. If you know that love operates within filters, you can make choices to expand your ERS and improve your PAS. You can broaden your social exposure by traveling, switching careers, volunteering, or participating in communities outside your usual routine. You can work on your emotional intelligence, self-awareness, and vulnerability to become more emotionally available to deeper, more meaningful connections. In short, while you may not control every outcome, you can create better conditions for connection—both structurally and emotionally.

The more I think about it, the more I believe that love is not a lightning bolt. It’s a weather pattern—a convergence of winds and temperatures that, under the right conditions, can form something powerful and lasting. It’s not random, but it’s still rare. That makes it no less magical. In fact, the idea that love happens despite so many filters makes it more special, not less. There’s a quiet beauty in knowing that, out of all the filtered probabilities, you found someone—and they found you back.

This theory, this article, and this reflection emerged from something deeply personal—and grew deeper through my conversation with ChatGPT, which helped me shape a framework I didn’t know I was building. I now see love as the outcome of exposure, economics, timing, and emotional readiness. It’s less fate, more filtered chance. And maybe, just maybe, that understanding can help us approach love not with fear or fantasy, but with curiosity, clarity, and courage. Because love, like anything worth finding, doesn’t just happen—it emerges from the spaces we choose to enter, and the selves we choose to grow.