The science of uncertainty — and the origin of conspiracy theories | Adam Kucharski

Summary

Podcast Summary: "The Science of Uncertainty — and the Origin of Conspiracy Theories" by Adam Kaczarski

Podcast Information:

• Title: TED Talks Daily
• Host: Elise Hu
• Episode: The science of uncertainty — and the origin of conspiracy theories | Adam Kaczarski
• Release Date: May 28, 2025

---

Introduction: Navigating Complexity and Trust

In this compelling episode of TED Talks Daily, mathematician Adam Kaczarski delves into the intricate relationship between uncertainty, trust in complex systems, and the emergence of conspiracy theories. Host Elise Hu sets the stage by highlighting the pervasive challenges in understanding and trusting technologies and scientific advancements in today's rapidly evolving world.

---

Understanding Complexity in Technology

Adam Kaczarski begins by illustrating the complexity inherent in modern technologies, using the example of aerodynamics:

"Aerodynamics is complex. It's difficult to understand, let alone explain in a simple, intuitive way. And yet we trust it." (03:40)

He explains how fundamental phenomena like airplane flight, heart defibrillation, and general anesthesia operate on principles that, while scientifically understood, remain non-intuitive to the general public. This complexity often necessitates a leap of faith in technologies that we use daily without fully grasping their underlying mechanisms.

---

Trusting Without Complete Understanding

Kaczarski emphasizes that trust in technology doesn't always stem from complete understanding:

"Whether we're talking about anesthesia, self-driving cars, or mathematical proofs, perhaps we don't need to fully understand something as long as the accuracy is high enough for what we need." (07:25)

He discusses the four-color theorem as a case study, where a computer-aided proof required mathematicians to trust a machine's calculations beyond manual verification. This shift from human-to-human verification to human-machine trust marks a significant change in how we validate complex information.

---

The Tension Between Prediction and Explanation

A central theme of Kaczarski's talk is the balance between prediction and explanation:

"Many data-driven areas of science increasingly focus on prediction because it's fundamentally an easier problem than explaining." (09:10)

He argues that while predictive models are invaluable, especially in fields like AI and climate science, they often come at the expense of deeper explanations. This trade-off can lead to situations where we rely on the accuracy of predictions without understanding the foundational reasons behind them.

---

Conspiracy Theories: A Human Quest for Explanation

Kaczarski transitions into the topic of conspiracy theories, linking them to the human desire for clear explanations:

"Something must be behind events; nothing is a coincidence." (12:15)

He draws parallels between scientists and conspiracy theorists, noting that both seek to explain patterns in the world. However, the key difference lies in the methodology and openness to updating beliefs based on evidence. Unlike scientists who adapt their theories with new data, conspiracy theorists often cling to their beliefs despite contradictory evidence.

---

The Role of Trust and Community in Belief Systems

Delving deeper, Kaczarski explores how trust and community play crucial roles in the persistence of conspiracy theories:

"Conspiracy theories are also about community. If people go against scientific consensus, it can make them feel like an independent thinker and part of a resistance." (13:50)

This sense of belonging and identity reinforces belief systems that oppose mainstream scientific understanding, making it challenging to address and debunk such theories effectively.

---

Bridging the Gap: Trust and Explanation in Modern Science

In his concluding remarks, Kaczarski underscores the necessity of bridging the gap between knowing and understanding:

"We're going to have to close this gap between knowing what is happening and wanting to know why it's happening." (14:55)

He advocates for improved communication and education strategies that not only present facts but also delve into the reasoning behind scientific phenomena. By enhancing transparency and fostering trust, society can better navigate the complexities of modern science and reduce the allure of unfounded conspiracy theories.

---

Conclusion: Embracing Uncertainty with Informed Trust

Adam Kaczarski's insightful talk sheds light on the delicate balance between trusting complex systems and the human need for clear explanations. As technology and science continue to advance, fostering a more profound understanding and transparent communication becomes imperative in combating misinformation and building a more informed society.

---

Notable Quotes:

• "Aerodynamics is complex. It's difficult to understand, let alone explain in a simple, intuitive way. And yet we trust it." — Adam Kaczarski (03:40)

• "Whether we're talking about anesthesia, self-driving cars, or mathematical proofs, perhaps we don't need to fully understand something as long as the accuracy is high enough for what we need." — Adam Kaczarski (07:25)

• "Many data-driven areas of science increasingly focus on prediction because it's fundamentally an easier problem than explaining." — Adam Kaczarski (09:10)

• "Something must be behind events; nothing is a coincidence." — Adam Kaczarski (12:15)

• "Conspiracy theories are also about community. If people go against scientific consensus, it can make them feel like an independent thinker and part of a resistance." — Adam Kaczarski (13:50)

• "We're going to have to close this gap between knowing what is happening and wanting to know why it's happening." — Adam Kaczarski (14:55)

---

This episode offers a thought-provoking exploration of how uncertainty and the complexity of modern science influence public trust and the proliferation of conspiracy theories. Listeners are encouraged to reflect on their own engagement with scientific information and the importance of seeking both understanding and trust in an increasingly complex world.

Transcript

[Elise Hu]

Hey TED Talks Daily listeners, it's Elise. Thank you for making this show part of your daily routine. We really appreciate it and we want to make it even better for you. So we put together a quick survey and we'd love to hear your thoughts. It's listener survey time. It only takes a few minutes, but it really helps us shape the show and get to know you, our listeners, so much better. Head to the episode description to find the link to the listener survey with we would really appreciate you doing it. Thank you so much for taking the time to help the show.

[Jane Marie]

I have this nightmare that I never finished college or that someone's going to find out that I don't have the qualifications for this job and I'm like a total fraud. Hi, I'm Jane Marie, host of the Dream, and that's a clip from my appearance on Mind if We Talk, a new podcast from BetterHelp that demystifies what therapy is really about and is here to remind us all that whatever we're going through, we're never alone. I recently sat down with host and licensed therapist Hae Soo Jo to talk about Imposter Syndrome, where I shared a bit more about my experience with feeling inadequate or not worthy of my job, motherhood, being a girlfriend, and of course, because this is therapy, we offer solutions. I'm sure a lot of you can relate to those kinds of feelings. I can't wait for you to hear it, to listen to the rest of our conversation and hear other guests explore struggles we all face in life. Listen and subscribe to Mind if We Talk Wherever you get your podcasts.

[Elise Hu]

This message is brought to you by Apple Card. Somewhere in the world, an Apple Card user is getting 3% daily cash back on the purchase of an iPhone 16 at Apple. That's not all. They also earned 2% back on the new shoes they bought using Apple Payments. Visit Apple Co Card Calculator to see how much daily cash back you can earn. Subject to credit approval. Apple Card issued by Goldman Sachs Bank USA Salt Lake City Branch terms and more at applecard.com this episode is sponsored by Monday.com let's face it, work today moves fast, but Monday.com is here to help with their work management product which enables you to reach your full potential. It's built for more than just marketing teams and it connects entire organizations to gain full visibility, make better decisions and bridge the gap between strategy and execution. The platform gives you real time insights into campaigns, content and projects so you can see what's working, shift gears when needed, and make data backed decisions without the guesswork. You'll go from being reactive to strategic plus with built in AI capabilities. Monday Work Management helps your team work smarter, not harder. Maximize your marketing impact with the first work product you and your team will love to use. Visit [e-mail] to learn more. You're listening to TED Talks Daily where we bring you new ideas and conversations to spark your curiosity every day. I'm your host Elise Hu where do conspiracy theories come from and how do they spread? For mathematician Adam Kaczarski, this question is key, especially as we navigate a world full of complex and overwhelming things, from climate and health to AI. In his talk, he shares why he finds comfort in leaning into the unknown, but asks us to consider why it's crucial that we find better ways to trust the things we cannot explain and to explain the things that we do not trust. Foreign.

[Adam Kaczarski]

It'S not easy to explain why aeroplanes stay in the sky. A common explanation is that the curved shape of the wing makes airflow faster above and slower beneath, creating lift. But this doesn't explain how planes can fly upside down. Another explanation is that the angle of the wing pushes air downwards, creating an equal and opposite upwards force. But this doesn't explain why, as the angle gets slightly steeper, planes can suddenly stall. The point is, aerodynamics is complex. It's difficult to understand, let alone explain in a simple, intuitive way. And yet we trust it. And the same is true of so many other useful technologies in our lives. The idea of heart defibrillation has been around since 1899, but researchers are still working to untangle the biology and physics that means an electric shock can reset a heart. Then there's general anesthesia. We know what combination of drugs will make a patient unconscious, but it's still not entirely clear exactly why they do. And yet you'd probably still get the operation, just like you'd still take that flight for a long time. This lack of explanation didn't really bother me. Throughout my career as a mathematician, I've worked to separate truth from fiction, whether investigating epidemics or designing new statistical methods. But the world is complicated, and that's something I'd become comfortable with. For example, if we want to know whether a new treatment is effective against a disease, we can run a clinical trial to get the answer. It won't tell us why the treatment works, but it will give us the evidence we need to take action. So I found it interesting that in other areas of life, a lack of explainability does visibly bother people. Take AI. One of the concerns about autonomous machines like self driving cars is we don't really understand why they make the decisions they do. There will be some situations where we can get an idea of why they make mistakes. Last year, a self driving car blocked off a fire truck as it was responding to an emergency in Las Vegas. The reason? The fire truck was yellow and the car had been trained to recognize red ones. But even if the car had been trained to recognize yellow fire trucks, it wouldn't go through the same thought process we do when we see an emergency vehicle. Self driving AI views the world as a series of shapes and probabilities. With sufficient training, it can convert this view into useful actions. But fundamentally, it's not seeing what we're seeing. This tension between the benefits that computers can bring and the understanding that humans have to relinquish isn't new. In 1976, two mathematicians named Kenneth Apple and Wolfgang Harken announced the first ever computer aided proof. Their discovery meant that for the first time in history, mathematicians had to accept a major theorem that they could not verify by hand. The theorem in question is what's known as the four color theorem. In short, this says if you want to fill in a map with different colors so that no two bordering countries are the same color, you'll only ever need four colors to do this. The mathematicians had found that there were too many map configurations to crunch through by hand, even if they simplify things by looking for symmetries. So they used a computer to get over the finish line. Not everyone believed the proof initially. Maybe the computer had made an error somewhere. Suddenly, mathematicians no longer had total intellectual control. They had to trust a machine. But then something curious happened. While older researchers have been skeptical, younger mathematicians took the opposite view. Why would they trust hundreds of pages of handwritten and hand checked calculations? Surely a computer would be more accurate. Whether we're talking about anesthesia, self driving cars, or mathematical proofs, perhaps we don't need to fully understand something as long as the accuracy is high enough for what we need. Let's go back to self driving cars. A common thought experiment when it comes to AI is what's known as the trolley problem. Suppose we have a heavy trolley or a big car and it's going to hit a group of people. But you have the option of pulling a lever to divert the vehicle so it hits only one person. Would you pull that lever? And would it matter whether the people are old or young? These kinds of decisions can sometimes crop up in real life with human drivers. In 2020, a car in Michigan swerved to avoid a truck and hit a young couple walking on the pavement, putting them in hospital for several months. Would AI have reacted differently? Well, it turned out that the car was also racing side by side with another vehicle at the time, and the driver didn't have a valid license. Before we get too deep into theoretical dilemmas, we should remember that humans often aren't very good drivers. If we could ensure there were far fewer accidents on our roads, would you mind being unable to explain the ones that did happen? In this complex world of ours, Maybe we should just abandon the pursuit of explanation altogether. After all, many data driven areas of science increasingly focus on prediction because it's fundamentally an easier problem than explaining. Like anesthesia, we can often make useful predictions about what something will do without fully understanding it. But explanation can sometimes really matter if we want a better world. The focus on prediction is particularly troubling in the field of justice. Increasingly, algorithms are used to decide whether to release people on bail or parole. The computer isn't deciding whether they've committed a crime. In effect, it's predicting whether they'll commit one in future. But ideally, we wouldn't just try and predict future crimes using an opaque algorithm. We'd try and prevent them. And that means understanding why people reoffend and what we can do to stop that happening. A lack of interest in explanation leaves a gap that, in this situation, creates room for injustice. But it's not the only thing that can emerge in the gap between what is happening and why it's happening. The desire for explanation can, in some cases drive people to extremes, particularly if the science behind what they're seeing is patchy or complex. Events must have a cause, goes their logic. Something or someone must be behind them. Karl Popper, who popularized the term conspiracy theory, once talked about conspiracy theories of society. Rather than events being random or unlinked, believers develop a narrative in which all of history is mapped out by shadowy influences. Nothing is a coincidence. In some ways, conspiracy theorists are similar to scientists. They want to explain the patterns they see in the world, and they want to share those explanations with others. And they'll put a lot of effort into doing so. Because I work in health and I've appeared in the media, I've ended up interacting with quite a lot of conspiracy theorists. And one of the things you'll notice if you try and debate a conspiracy theorist is they'll usually have a mountain of scientific looking data and papers ready to argue their point. The key difference, though, is that science frequently requires that we update our beliefs about the world rather than just double down on them. The point of evidence is to get us closer to the truth, not just pull us further into a theory. You can always tell quite quickly in a discussion when someone's trying to defend a position, rather than than actually discover the reality. One of the most popular conspiracy theories currently is the idea of chemtrails. This is the false claim that aeroplane vapor trails are actually a deliberate attempt to drug populations or control the weather with chemicals. Unlike the science of airplane wings, it's actually pretty straightforward to explain where vapor trails come from. Jet engines produce water vapor in their exhaust. When this hot vapor hits the very cold air outside, it freezes, creating a streak of tiny ice crystals in the sky. So why do claims like this persist? It's partly down to trust. Unless you want to brush up on thermodynamics or buy a jet engine, at some point you're going to have to take someone's word that this is how the science works. But conspiracy theories are also about community. If people go against scientific consensus, it can make them feel like an independent thinker and part of a resistance. Then there's that crucial element, the need for an explanation beyond simple coincidence. Whether we want to push the boundaries of science or push back on conspiracy theories, we need to appreciate this very human desire to explain. I've made the mistake sometimes of neglecting this in the past. I've given people an over simplistic explanation for complex process and created even more confusion than there was before. Or in a situation with limited time. I've told people it's not possible to properly untangle the complexity involved. And in doing so, I fail to acknowledge that very deep rooted need to explain. I now notice other scientists making the same mistake. They might say the evidence is clear when it isn't to a lot of people. Or they might say it's well established this is true without saying why it's true. This matters because increasingly we have to navigate a world that most of us struggle to fully understand. From climate and health to finance and AI, there often isn't a simple intuitive logic behind what we're seeing. But there are lots of catchy false explanations ready to lead us astray. As science becomes more advanced and more reliant on opaque or counterintuitive technologies, these challenges will only grow. I've got a PhD in maths and I still don't fully understand the details of every climate simulation or AI algorithm. Like many others, I've had to find other ways to evaluate published claims. I've turned to experts with good track records. I've since checked sources, I've looked for inconsistencies, and I've tried to explain as much as I can. In this changing world, we're going to have to close this gap between knowing what is happening and wanting to know why it's happening. That means finding better ways to trust the things we can't explain and better ways to explain the things that we don't trust. Thank you.

[Elise Hu]

That was Adam Kaczarski at TEDxLondon in 2025. If you're curious about TED's curation, find out [e-mail] curationguidelines and that's it for today's show. TED Talks Daily is part of the TED Audio Collective. This episode was produced and edited by our team, Martha Estefanos, Oliver Friedman, Brian Greene, Lucy Little, Alejandra Salazar and Tonsika Sarmarnivon. It was mixed by Christopher Faizy Bogan. Additional support from Emma Tobner and Daniela Ballaraizo. I'm Elise Hu. I'll be back tomorrow with a fresh idea for your feed. Thanks for listening. Support for this show comes from Capital One Banking with Capital One helps you keep more money in your wallet with no fees or minimums on checking accounts and no overdraft fees. Just ask the Capital One bank guy. It's pretty much all he talks about in a good way. He'd also tell you that this podcast is his favorite podcast too. Oh really? Thanks Capital One Bank Guy. What's in your wallet? Terms apply. See capital1.com Bank Capital One NA Member FDIC.

[Verizon Ad Voice]

When you think of hot, you might think about the tragic time you ate too much wasabi, or about getting into your car on a summer day when it feels more like stepping into a sauna. But heat can work in your favor, like during Verizon Red Hot Deal days. Get hundreds of hot deals on all your favorites like iPhone 16 Pro with Apple Intelligence, Apple Watch Series 10 and iPad. On any plan only until May 28th for new and existing customers. Offer available on MyPlan only at Verizon.

[Adam Kaczarski]

Ugh. Feels like I've been stuck in this junction for centuries.

[Jane Marie]

Actually, it might have been 113 hours per year if you're in London or or 98 hours if you're in New York. TomTom Traffic Index measures all this and more, looking at 500 cities across the globe. It gives insights on average delay time, peak hour patterns, and shows you how your city is doing in terms of traffic. It's a powerful tool for urban planners, so if you're looking to bring movement back to your city, go to tomtom.com trafficindex.