Polymarket and Prose: A Literary Scavenger Hunt Through Prediction Markets

Seeking an understanding of this dynamic and complex space, I turned to my favorite literary characters

A few weeks ago, as I was walking to Aldrich Hall for a full day of classes, I hit the pause button on the podcast episode to which I was listening. That morning, the hosts of The Daily were discussing Polymarket, a major player in the relatively new decentralized prediction markets space that is seemingly taking the world by storm. (I know, not that new; Polymarket has been around since 2020, and its only true peer, Kalshi, was founded in 2018. But it is undeniable that these platforms are on the rise now more than ever.) 

If you're still living under a pre-Polymarket rock, here’s the down low: Polymarket and its peers are sites where you can place a bet on anything (literally, ranging from the highly speculative “Will Jesus Christ return before 2027?” to the much more predictable “2026 Winter Olympics: Most Gold Medals.”). 

My immediate reaction to the podcast episode was one of shock. I thought that these prediction markets seemed dangerous, yet another sports betting-like rabbit hole that would certainly be wielded to prey upon the vulnerable. But when I conducted a quick poll of 63 of my HBS classmates, asking them for their opinion on Polymarket, only 56% said it was “dangerous,” with 32% saying it was “promising” or “exciting,” and the remaining group indicating neutrality. Frankly, I had expected the “dangerous” option to win by a landslide.

As I dug deeper into the prediction markets space, one of my classmates shared a news release from 2003 that described how the U.S. Defense Department had then considered incorporating a platform called Policy Analysis Market into their processes. The PAM would have used anonymous futures speculation to predict the outcome of geopolitical events; it was never approved due to Congressional backlash over betting on terrorism. 

But the fact that PAM was ever considered, and that its inspiration, the Iowa Electronic Markets, has actually seen significant success (including in predicting the 2008 presidential election outcome to an astonishingly precise degree—within 0.5% of votes), made me pause.  

 What exactly was, and is, the draw of this kind of platform? Are they addictive, predatory gambling websites, or democratizing, epistemic tools? 

The CEO of Polymarket, Shayne Coplan, argues that his site serves as an anti-misinformation aggregator; to back this up, he draws on the fact that crowdsourced opinions are often more accurate than those of single experts or analyses (James Surowiecki’s The Wisdom of Crowds, for example, argues exactly this point). I see the merits of this argument: bettors’ incentives are aligned with those of the truth-seeking public, in that the former is incentivized (by the possibility of a monetary upside) to research thoroughly and place well-informed bets. 

 Some of our classmates are even innovating in the prediction markets space. Firas Atoui (MBA ‘26) is creating a startup called Sawa, which brings prediction markets to the community level, allowing people to participate in markets with friends on a localized scale. When I asked him what he thought about the promise and dangers of prediction markets, he advocated for the regulation of insider trading (Kalshi, for its part, does ban insider trading explicitly) and “whales,” wealthy individuals who can singlehandedly skew the outcome of bets in their favor. But, he continued, sites like Sawa, Polymarket, and Kalshi are democratizing forces that allow people to “monetize their beliefs” in a “user friendly [market] for the masses.” Everyone, regardless of upbringing, education, or career, can easily understand how to use and participate in prediction markets.  

It was clear to me that opinions on prediction markets diverged wildly and that many of my classmates viewed them with excitement and interest. And if I’ve learned anything from my time at HBS, it’s that I have plenty to learn from my peers here; I knew there was something more for me to explore in the world of these markets.

Over the winter break, I had read The Wisdom of Finance, a book by HBS Professor Mihir Desai. In the book, Professor Desai makes literary and cultural analogies to explain important financial concepts like mergers and risk management. So to better grapple with my own understanding of the prediction markets industry, its benefits and risks, I turned to my favorite authors, and their novels that have been so pivotal to my personal development and edification. I identified characters who have moved me with their strongly-held convictions, and tried to understand what they would think about prediction markets. Maybe with this framing, I thought, I could better structure my arguments for or against them.  

Of course, these are just my personal reflections on these characters. Presumably, none of the characters I reference below knew about Polymarket or its peers, and I can’t feign certainty on whether their creators would align them with my interpretations.

 But take from this what you will, and I hope you walk away with not just some food for thought on Polymarket, but a couple of book recommendations to boot. Below, I’ve aligned each character with a persona regarding prediction markets, roughly arcing from most to least skeptical.

Character: Billy Watson 

Book: The Lincoln Highway by Amor Towles 

Persona: The Regulator 

 Billy is the eight-year-old younger brother of the novel’s main character, and is one of the most brilliant young minds I have encountered in a book. His attention to detail, curiosity, interest in uncovering the truth, and wise-beyond-his-years sense of calm make him deserving of the respect and adoration of those around him. But most of all, he loves rules: 

“Billy wasn’t simply an abider. He was a stickler. He made his bed and brushed his teeth without needing to be asked... and he always raised his hand in class before speaking.” (pg. 145) 

 What would Billy think about prediction markets? I think he’d advocate for regulating them in a structured, consistent fashion, seeing great potential for adventure, but also the potential for chaos and unfairness to abound without a strategic approach. Protections (which arguably ought to be table stakes) could include, for example, placing upper limits on the bets made by individuals, to prevent the market-moving behavior of “whales.”

Characters: Dodo; Theo Decker 

Book: The Heaven & Earth Grocery Store by James McBride; The Goldfinch by Donna Tartt 

Persona: The Vulnerable Innocent; The Addicted Bettor 

 Dodo is a bright, deaf boy who is unjustly held captive at an asylum, where his intelligence and compassion shine through as he forges close friendships and stays true to his morals. Throughout the novel, his treatment is symbolic of the poor treatment of the innocent, vulnerable, and pure: 

 “She loved Dodo’s generosity. He was a simple child of love, easy to satisfy, easy to give.” (pg. 109) 

A character with a very different background, but who is similarly illustrative of struggle and vulnerability, is Theo Decker. Theo is the troubled protagonist of The Goldfinch. After the death of his mother, he struggles with grief and guilt, attempting to cope with his trauma as he grows up with a largely absent father. His addiction manifests in different ways over the course of his life: through alcohol, but also through repeated, self-destructive, reckless choices he makes through the years: 

“...I get it. We can’t choose what we want and don’t want and that’s the hard lonely truth. Sometimes we want what we want even if we know it’s going to kill us. We can’t escape who we are.” (pg. 770) 

Together, Theo and Dodo’s experiences are analogous to decentralized prediction markets’ potential to prey upon the vulnerable; to be highly addictive and pattern-forming. To take advantage of spur-of-the-moment decisions, yes, but also to enable whales or, without regulation, insider traders to gain outsized benefit from individual trades. Whether they eventually rank alongside other addictive platforms and substances is yet to be seen. But in this particular regard, I don’t see them as being meaningfully different from DraftKings and other, arguably predatory, sports betting platforms. 

Character: Hassan 

Book: The Kite Runner by Khaled Hosseini 

Persona: The Moral Compass & The Democratizer 

If you’ve read this book, you know that Hassan is a character who pulls at the heartstrings. He is the sweet childhood companion and servant of the main character, Amir, and his strong moral compass serves as a throughline that endears the reader to him until the very end: 

“While I ate and complained about homework, Hassan made my bed, polished my shoes, ironed my outfit for the day, packed my books and pencils. I’d hear him singing to himself in the foyer as he ironed, singing old Hazara songs in his nasal voice.” (pg. 27) 

 For me, Hassan serves as an indicator that our very humanity is at stake when presented with prediction markets. If we can just as easily bet on the price of Bitcoin as we can on natural disasters and the number of US measles cases, what are the implications for our morality? For our ability to understand one another?  

 Maybe I’m getting soft in my mid-twenties. But I see it as a real risk to our empathy, our acknowledgment of one another’s humanity, that many of us will soon be betting on these very real, often devastating situations as if they are as trivial as the weather. 

 At the same time, Hassan is incredibly curious. He grows up unable to read, but: 

 “...despite his illiteracy, or maybe because of it, Hassan was drawn to the mystery of words, seduced by a secret world forbidden to him.” (pg. 28) 

 And in that vein, I think Hassan is a symbol of the democratizing capabilities of prediction markets. We are rapidly progressing toward a world where access to information and capital is one of few real differentiators. Can sites like Polymarket contribute to a more equal playing field by allowing people to bet on something that is easy to understand? It is this tension, between morality and democratization, that underlies my personal internal struggle around prediction markets. 

Character: Count Alexander Ilyich Rostov 

Book: A Gentleman in Moscow by Amor Towles 

Persona: The Resilient Shape-Shifter 

 The Count is the lovable main character of this Towles classic (I’ve included 3 Towles novels in this book list, so you’re not done quite yet). Confined to his Moscow hotel, under house arrest for decades, he shows incredible resilience: 

 “With so little to do and all the time in the world to do it, the Count’s peace of mind continued to be threatened by a sense of ennui—that dreaded mire of the human emotions.... But for the virtuous who have lost their way, the Fates often provide a guide.” (pg. 55) 

The lesson here? Maybe the Count would argue for making the best of the situation you are dealt. If we are moving towards a world that favors sites like Polymarket and Kalshi, that streamlines access to not just betting but betting on everything, should we make like Sheryl Sandberg and Lean In?

Applied to prediction markets: as misinformation abounds, as generative AI makes it increasingly easy to create digital clones and generate hyper-realistic videos, I’m willing to cautiously explore whether prediction markets can actually serve as a more trustworthy information base. And maybe that’s a possibility to embrace. I see a tension here between passivity and acceptance on the one hand, and empowerment and fostering trust on the other; this is a balance that the industry and the betting public will need to grapple with moving forward. 

Character: Eve Ross 

Book: Rules of Civility by Amor Towles 

Persona: The House (Who Always Wins) 

Eve is the charming, beautiful, and brave best friend of the novel’s protagonist. She is cunning and incredibly situationally aware, meaning that she often knows how to navigate the world’s challenges so that she comes out ahead:

“A giddy girl can’t tell what’s happening next.... But there weren’t going to be any surprises for Eve. No unfamiliar gambits or sly combinations. She had drawn the squares and carved the pieces.” (pg. 115) 

The lesson? The platform itself is likely to win. Polymarket’s revenue structure isn’t ultra-clear to me: from what I’ve found through the platform’s documentation and an Intercontinental Exchange press release, it charges selective transaction fees for takers, keeping an undisclosed portion as revenue and redistributing the rest to makers, and is now exploring a data monetization strategy. But its increasing usage does foreshadow near-term financial growth.

 Billy, Dodo, Theo, Hassan, the Count, and Eve have taught me untold lessons, and now, they have helped me once more as I’ve sought to frame my understanding of this industry. 

So when it comes to prediction markets, color me... reluctantly bullish? I foresee the general appetite for prediction markets becoming steadily friendlier in the coming months, as more people embrace their truth-dispensing potential and gamified appeal. 

I will personally continue to abstain, because I am unsettled by the dangers these apps pose, especially when it comes to use by teens and the vulnerable; further, I suspect that for every dependable prediction market outcome, there are many more powered by (mostly young male, according to a recent NPR report) users squandering precious time on their bets. But even though I align myself more with Hassan than the Count, and believe that this industry demands more regulation than most, I concede that it’s likely only up from here for Polymarket, Kalshi, Sawa, and the copycats and spinoffs that are certain to proliferate.

Correction: March 30, 2026

An earlier version of this article falsely stated that Polymarket receives a cut of every winning bet on the platform. This article was updated to clarify details on Polymarket’s revenue structure.

Keerthi Medicherla (MBA ’27) is originally from McLean, Virginia. She graduated from the University of Virginia with a double major in Computer Science and Global Studies in 2022. Prior to HBS, Keerthi worked as a software engineer at JPMorganChase in Seattle, Washington.