When Typos Evolve: What “Quasi-Species” Teach Us About Change

November 11, 2025November 19, 2025 ~ Manuel Castañón-Puga

Imagine copying a friend’s workout plan by hand and sharing it in your group chat. A few people fix a line, others tweak an exercise, and someone adds a rest day. Soon, there isn’t one “original” plan. There’s a cloud of similar versions, with one popular template in the center and numerous nearby variations. That living swarm of copies is how Manfred Eigen, John McCaskill, and Peter Schuster describe molecules that copy themselves with small errors. They call it a quasi-species: not a single perfect sequence, but a whole population clustered around a “master” one. Selection acts on the cluster, not just the one pristine original.

According to Eigen and colleagues, you can picture all possible sequences laid out in a vast “sequence space,” where distance means how many positions differ (the Hamming distance). Close neighbors are only one typo away; far ones need many changes. In high dimensions, there are countless short routes between any two points, so many ways to wander by small mistakes. That matters because copying is never perfect. A master sequence tends to be the best copier, but its mutants also copy, and together they form a stable crowd that wins because, as a group, they reproduce more effectively than rivals. Think of clans rather than isolated individuals—whoever owns the strongest “mode” of the population dominates.

There’s a catch called the error threshold. If copies are too sloppy, the crowd can’t stay clustered, and the “message” dissolves. In plain terms: the longer the sequence, the higher the bar for accuracy; if fidelity drops, the system can’t hold specific information. The authors demonstrate that selection can be viewed as hill-climbing on a landscape, where the population tends to converge toward better replicators, provided that errors are low enough to maintain group cohesion. Even cooler, evolution isn’t purely a random stumbling process. Because more successful variants become more common, new mutants tend to appear at the populated edges along “ridges” that connect good regions—so change is biased toward success. It’s like a playlist algorithm nudging you from one favorite song to another via similar tracks.

Why should you care? Because this is how ideas, habits, and even internet memes behave. Your study method, your budget, your fitness routine—each lives as a quasi-species of versions you try, keep, and share. Keep errors small in the parts that carry meaning (your essential steps), or the routine turns to noise. Allow some variation to occur at the edges so you can discover better peaks without losing the overall plot. And when you want real progress, look for ridges: minor, low-risk tweaks that connect what already works to something better. You don’t need a perfect plan; you need a stable core with room to explore. That is the everyday power of quasi-species thinking that Eigen, McCaskill, and Schuster brought to light.

Reference:
Eigen, M., McCaskill, J., & Schuster, P. (1988). Molecular quasi-species. The Journal of Physical Chemistry, 92(24), 6881–6891. https://doi.org/10.1021/j100335a010

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How Small Choices Can Lock In Big Technologies

November 10, 2025November 19, 2025 ~ Manuel Castañón-Puga

You and your friends are picking a new group chat app. Two options look similar. One person tries App A first, posts a fun sticker pack, and a couple of others join. Suddenly, homework files, inside jokes, and your weekend plans all live there. App B never really gets a chance. It feels like a tiny nudge decided everything. Economist W. Brian Arthur says that is precisely how many technologies spread in the real world. Little, almost random events can snowball into significant outcomes. The more people adopt one option, the more it improves and the more attractive it becomes—the classic “success breeds success.”

According to Arthur, this “increasing returns” loop means that early moves matter significantly. If one choice gains a slight head start, it can attract more users, draw more fixes and features, and ultimately become the standard. Another day, a different lucky break could have crowned a different winner. Think of familiar stories like popular keyboard layouts or formats that became defaults because they caught on first, not because they were perfect. The paper even illustrates this with a simple model: when gains rise with each new adopter, the process can tipt, much like a random walk that hits a wall and remains and then persists there. Once a technology enters that zone, both types of users continue to choose it, and its rivals fade out.

This has consequences you can feel. First, the outcome is difficult to predict in advance. Even if you know what people like and how good each option could become, chance can still decide which one takes over. Second, the result can be hard to undo. After a winner emerges, shifting people back takes more and more push. Third, the winner is not always the best in the long run. A slower-but-better path might lose if it misses those early breaks. Arthur contrasts this with worlds of constant or diminishing returns, where sharing is the natural end state and forecasts are easier to make. In his summary, with increasing returns, you get unpredictability, inflexibility, path dependence, and possible inefficiency; with constant or diminishing returns, you usually do not.

What should you do with this? Stay alert to early habits that can trap you. When selecting tools for study, work, or creative projects, ask: Will this choice become more effective as I use it? If so, sample more than one option before committing. Favor choices that keep doors open—ones that export, sync, or play well with others. Be cautious with hype too; expectations that “everyone will switch” can actually accelerate lock-in, even if the technology is not clearly superior. And remember, the “best” path may need extra patience and support at first. Arthur’s message is simple: small choices add up. Make them with your future self in mind.

Reference:
Arthur, W. B. (1989). Competing Technologies, Increasing Returns, and Lock-In by Historical Events. The Economic Journal, 99(394), 116. https://doi.org/10.2307/2234208

Your Immune System Is Basically a Group Chat (With Rules)

November 3, 2025November 19, 2025 ~ Manuel Castañón-Puga

Picture a busy group chat when someone drops surprising news. A few people react, others reply to those replies, and suddenly the whole thread lights up. Perelson describes our immune system in a similar way: antibodies and immune cells “talk” to each other, sending excitatory and inhibitory signals across a network first imagined by Niels Jerne. In Jerne’s simple math sketch, each kind of cell can be nudged up or down by others—like replies that either hype or hush the chat. That idea sparked decades of research to understand how such a conversation remains helpful, rather than spiraling into chaos.

To see why your body can recognize so many different “news items,” Perelson uses “shape space,” a way to picture how well antibodies fit what they’re trying to bind. Imagine a giant dartboard where every throw lands somewhere; each antibody covers a small circle around its spot, so a handful of well-placed circles can cover most of the board. With reasonable numbers, that coverage becomes impressively comprehensive: as the repertoire of different antibodies grows to around a million, almost every random target is identified. It’s a neat takeaway for everyday life: your immune system doesn’t rely on one perfect key—it keeps a messy but effective key ring so something usually fits.

However, a giant group chat can become overwhelming. Here’s the clever part: Perelson shows there’s a “phase transition,” a tipping point where connections suddenly link up so well that a signal can ripple almost everywhere. Using a simple lattice argument, he explains there’s a critical threshold for connectivity; below it, messages fade in small clusters, above it, they can sweep the network. Practically, that means your immune system is wired to catch threats, but it also needs brakes so it doesn’t overreact to every ping. In animals, estimates suggest the expressed repertoire sits on the “highly connected” side—powerful, but in need of good control.

So how does the chat stay useful? Perelson and colleagues argue for balance: stable, but not too stable. Too rigid and you miss important alerts; too twitchy and you burn out. Their shape-space models show patterns form best when activation is specific and nearby, while inhibition is broader—think close friends who nudge you, plus a quieter, system-wide “let’s chill” tone to avoid chaos. That balance also helps explain immune memory: some clones can stay elevated without constant drama from the rest of the network. For daily life, the message is clear: your body’s defenses work through diversity, smart thresholds, and healthy restraint—built to learn, remember, and react without turning every notification into an alarm.

Reference:
Perelson, A. S. (1989). Immune Network Theory. Immunological Reviews, 110(1), 5–36. https://doi.org/10.1111/j.1600-065X.1989.tb00025.x

Letting Go vs Holding On: What Your Rhythm Says About Your Mind

October 28, 2025November 19, 2025 ~ Manuel Castañón-Puga

You’re clapping along to a song with a friend. The beat speeds up. Without planning it, your hands switch from alternating claps to clapping together, just to keep up. Scientists observed the same phenomenon in simple lab tasks, where participants moved their fingers in response to a metronome. As the pace increased, one pattern transitioned into another and didn’t revert immediately, revealing two fundamental ways of moving and a natural “one-way” switch between them.

According to Kelso, this flip isn’t just about muscles; it also reveals something about intention. In the classic experiments, participants were instructed to “do not intervene”—if the movement started to change, let it. That instruction makes any change count as “spontaneous,” and yet it also acts like a mental nudge to “let go.” The result is that tiny wobbles in the rhythm grow and help trigger the switch. Kelso calls these wobbles “fluctuations,” and he argues they can reflect your intention, not just random noise. In everyday terms, choosing to let the pattern change or to hold it steady is evident in those small timing shifts.

Here’s the twist: telling yourself to “hold on” changes those wobbles. People can maintain a stable pattern for longer when they intend to, meaning the shape and size of the fluctuations adjust in accordance with their goal. That’s why Kelso says intention may be “hidden in the fluctuations.” As speed increases, those fluctuations typically swell before a switch (a hallmark of being near a change), and settling down can take longer as well. Think of cranking up the tempo on a workout track: the closer you get to your limit, the shakier it feels, and it takes a moment to steady yourself.

Why does this matter for daily life? Because the same idea links body and mind. Kelso suggests we don’t need extra knobs in the theory to explain intention; the boundary conditions—your simple rule to yourself like “let go” or “hold on”—already tune the fluctuations. Once a switch occurs, systems often don’t revert right away, much like the momentum in your habits. That’s hysteresis in action. This dance between stability and change also shows up as we learn and explore, from finding a new rhythm to the way babies discover what their actions can do. In short, tiny changes in your timing can be purposeful signals of what you mean to do—and that’s a practical reminder that setting a clear rule for yourself can gently steer your mind and your moves.

Reference:
Kelso, J. A. S. (2025). The motionable mind: How physics (dynamics) and life (movement) go(t) together—On boundary conditions and order parameter fluctuations in Coordination Dynamics. The European Physical Journal Special Topics. https://doi.org/10.1140/epjs/s11734-025-01875-7

Why “Smart” Systems Learn Like Our Group Chats

October 27, 2025November 19, 2025 ~ Manuel Castañón-Puga

Imagine your friend group chat. Some people talk a lot, others only chime in when tagged, and the vibe shifts as new friends join or old ones mute the thread. That’s a simple way to picture how many “smart” systems work: they’re networks whose connections matter and can change. Farmer calls these “connectionist” models and defines them by two things: only certain parts talk to each other at a time, and those lines of talk can strengthen, weaken, or rewire as the system runs. He also argues this idea isn’t just for neural networks—it also fits rule-based learners, immune systems, and even chemical reaction webs.

Under the hood, the common picture is a graph: dots (nodes) connected by links (who can influence whom). You can describe that picture with a matrix or a compact list, and whether the web is dense or sparse changes how you store and work with it. What makes these systems feel “alive” is that there are often three tempos at once: fast changes to the node states (what’s happening right now), slower tweaks to parameters like weights (learning), and the slowest shifts to the wiring itself (who talks to whom). That last part—rewiring—can also be a form of learning.

Take neural networks you’ve seen in AI headlines. In a feed-forward net, signals move layer by layer; in a recurrent net, outputs can circle back, which adds memory but also makes “when to stop” less noticeable. Learning can be as simple as “cells that fire together wire together” (a Hebbian principle that amplifies activity) or as guided as backpropagation, which adjusts connections to minimize error on known examples. Classifier systems look different on the surface—lots of if-this-then-that rules that post messages—but they’re still networks. Messages act like node activations, rules carry strengths, and a “bucket brigade” passes credit backward along the chain while genetic tweaks (mutations and crossovers) keep improving the rule set. Even practical details like thresholds and “support” change how many messages get through each step.

Now zoom way inside your body. Farmer and colleagues show how the immune system can also be read as a learning network. It must tell “self” from “not-self,” and that skill is learned rather than hard-wired. Beyond single cells reacting, there are interactions across types that may form a regulating web. To model this, they create an “artificial chemistry” where antibody and antigen types are encoded as strings that match more or less firmly. Then, the system learns through clonal selection and even “gene shuffling” to explore new kinds. The point isn’t fancy math—it’s the practical lesson: functional systems learn by adjusting both how strongly parts talk and which parts talk at all. Think of your own routines like that chat: prune noisy threads, boost the ones that move you forward, and don’t be afraid to rewire who—and what—you let influence you.

Reference:
Farmer, J. D. (1990). A Rosetta stone for connectionism. Physica D: Nonlinear Phenomena, 42(1–3), 153–187. https://doi.org/10.1016/0167-2789(90)90072-W