Economic Science

Economic science — often simply called economics — is the social science that studies how people, organizations, and societies produce, distribute, and consume goods and services, and how they make decisions when resources are scarce. It seeks to understand and predict how individuals and groups behave in markets, how economies grow or decline, and how policies affect prosperity, inequality, and welfare.

Climbing the Peaks of Progress: How Small Steps (and Occasional Jumps) Help Us Improve

~ Manuel Castañón-Puga

Picture a group project that starts messy. Everyone tries tiny tweaks—renaming a file, switching slides, or fixing one chart. Each fix helps a bit, but soon you’re “stuck” on a version that’s decent, not amazing. To get something truly great, you might need a bigger change—like rebuilding the deck around a sharper story. That’s the basic idea behind how improvement often works in life: we take small steps up a hill, but sometimes we need a jump to reach a higher one. Kauffman and Levin use this simple picture to explain “adaptive walks,” their way of describing how things—organisms, ideas, even study plans—get better over time on a rugged landscape full of peaks and valleys. 

According to Kauffman and Levin, imagine every possible version of something as a point in a huge space, and give each point a “fitness” score—how well it works. Moving to a version that’s one small change away is like taking a step to a neighbor on this map. Many maps are bumpy, with lots of little peaks where nearby options are worse, so small-step climbing can end early at a “local optimum.” On totally random (uncorrelated) maps, there are tons of these local peaks, so most improvement paths are short—think a handful of steps before you top out. A greedy strategy that always picks the best neighboring step is even shorter. That’s why quick tweaks often help fast, then stall. 

Here’s the twist that matters for everyday life: progress slows as you get better because you have fewer “better neighbors” left. Early on, lots of options beat what you have, so branching out is easy; later, choices shrink and things feel stuck—Kauffman and Levin call this pattern “radiation then stasis.” One fix is to allow bigger leaps—try several changes at once. When the jump is big enough, you can escape the local peak and discover fresh ground. In that “long-jump” mode, they find a simple rule of thumb: the number of improvements grows roughly with the log of the time you keep searching. Translation: early wins come quickly, then the gaps between wins get longer, which feels slow but is normal. 

What can you do with this? When your workout, study routine, or side hustle stops improving with tiny tweaks, treat that as a signal. First, accept that short bursts of progress are followed by plateaus, which are built into the rugged nature of maps. Next, plan occasional, safer “jumps”: test a new schedule for a week, learn a different tool, or reorganize roles on your team. Expect that the better you already are, the rarer the easy wins—and that’s okay. Take small steps for steady gains, and reserve bold experiments for breaking out of ruts. That mix—patient climbing plus brilliant jumping—is how you keep finding higher peaks in school, work, and everything in between.

Reference:
Kauffman, S., & Levin, S. (1987). Towards a general theory of adaptive walks on rugged landscapes. Journal of Theoretical Biology, 128(1), 11–45. https://doi.org/10.1016/S0022-5193(87)80029-2

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This blog provides simplified educational science content, created with the assistance of both humans and AI. It may omit technical details, is provided “as is,” and does not collect personal data beyond basic anonymous analytics. For full details, please see our Privacy Notice and Disclaimer. Read About This Blog & Attribution Note for AI-Generated Content to know more about this blog project.

Leveling Up Your Choices: How Simple Rules Help You Learn Faster

~ Manuel Castañón-Puga

You’re juggling classes, a part-time job, and maybe a side hustle. Every week brings a new app to try, a study trick to test, and a different way your friends are making money online. It can feel like the world keeps changing just when you think you’ve figured it out. Holland and Miller describe this kind of world as one that’s always throwing something new at us, where smart choices come from adapting step by step rather than guessing the perfect plan in advance. In their view, useful patterns can be tested inside “artificial worlds,” like simulations, so we can watch how strategies improve over time and see which ones actually work. 

According to Holland and Miller, many real-life settings look like “complex adaptive systems.” Think of a campus or a marketplace: lots of people trying things, learning from feedback, and adjusting based on what everyone else does. There isn’t one final, perfect strategy; instead, you find local “niches” where certain habits pay off—like a study routine that works for your schedule or a pricing trick that fits your small shop. New moves by others create new niches, so improvement never really stops—more like constantly upgrading your loadout in a game than finishing a final boss. 

So how do you get better inside a shifting system? One idea is to learn the way a basic “genetic algorithm” learns: keep a bunch of simple strategies, reward the ones that perform well, and mix their best parts to create new ones. In plain terms, if Pomodoro sprints help you focus and walking meetings spark ideas, combine them into “walk, then sprint” and test again. This mixing step—called “crossover”—is powerful because it builds on what already works, instead of starting from scratch each time. Over many rounds, you bias your search toward better “building blocks,” and your average results rise without you needing perfect information or heavy math. 

Another tool they discuss is a “classifier system,” which you can picture as a bunch of if-then rules competing to guide your next move: if the library is crowded, then study in a quiet café; if a post flops, then try a shorter caption. Each rule earns “strength” when it helps you get a payoff and loses strength when it doesn’t—like a built-in scoreboard for your habits. Over time, helpful rules link up, forming smarter routines that still stay flexible, because every rule is provisional and can be replaced when the world changes. This way of learning—small rules, constant feedback, and recombining what works—makes progress feel doable even when wins are rare or delayed. It’s a reminder that you don’t need to be all-knowing to act more intelligently tomorrow than you did today.

Reference:
Holland, J. H., & Miller, J. H. (1991). Artificial Adaptive Agents in Economic Theory. The American Economic Review, Papers and Proceedings of the Hundred and Third Annual Meeting of the American Economic Association, 81(2), 365–370. https://www.jstor.org/stable/2006886

Privacy Notice & Disclaimer:
This blog provides simplified educational science content, created with the assistance of both humans and AI. It may omit technical details, is provided “as is,” and does not collect personal data beyond basic anonymous analytics. For full details, please see our Privacy Notice and Disclaimer. Read About This Blog & Attribution Note for AI-Generated Content to know more about this blog project.

How Tiny Mistakes Can Grow Cooperation

~ Manuel Castañón-Puga

You and a friend decide to study together every week. Most days, you both show up and share notes. Now and then, someone is late, or a message gets lost, and the plan derails. Do you quit, forgive, or try a new routine? Kristian Lindgren built a simple computer world to study choices like these. In his world, many “students” play a repeated cooperation game, sometimes make mistakes, and learn over generations which habits survive. The surprise is that small errors don’t just cause chaos. They can encourage communities to adopt smarter, fairer ways of cooperation. 

Here’s the setup in everyday terms. Each player follows a short rulebook, like “if they helped me last time, I help now.” These rulebooks are like tiny genomes with memory, and they can change through “mutations” such as flips, copies, or trims—simple edits that create new habits to test. Everyone plays everyone, good habits earn more “offspring,” and the game keeps going. Even a classic friendly rule like “Tit for Tat” struggles when messages glitch, averaging less than perfect because error cascades can lock partners into pointless payback. Lindgren illustrates how this occurs and how new, stricter rules emerge when mistakes are prevalent. 

What grows out of this messy mix is very relatable. Populations sit in long, calm periods, then flip fast into something new—like a group project that works for weeks and suddenly collapses when one shortcut spreads. Sometimes two different “okay-but-flawed” rules prop each other up: when they meet, they sync and recover cooperation after a slip, even though each one alone would spiral into conflict. Later, sturdier rules emerge that recall a bit more history and respond to one defection with two firm responses before returning to peace. That move blocks freeloaders and keeps the average payoff high even with noise, much like setting clear boundaries after someone flakes. 

So what can you use today? First, expect errors and design for recovery. If a friend misses once, don’t nuke the friendship; try a brief, clear consequence and then reset. Second, remember that patience plus memory beats snap reactions. Keeping track of the last couple of interactions helps you respond fairly, not just emotionally. Third, watch for sneaky patterns that benefit in the short term but ultimately erode trust; they can cause “extinctions” where good vibes vanish for everyone. Lindgren’s message is simple: cooperation is not naïve. With the right habits, it’s robust, even when life is noisy.

Reference:
Lindgren, K. (1991). Evolutionary Phenomena in Simple Dynamics. In C. G. Langton, C. Taylor, J. D. Farmer, & S. Rasmussen (Eds.), Artificial Life II, SFI Studies in the Sciences of Complexity. Santa Fe Institute. https://www.researchgate.net/publication/258883366_Evolutionary_Phenomena_in_Simple_Dynamics

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This blog provides simplified educational science content, created with the assistance of both humans and AI. It may omit technical details, is provided “as is,” and does not collect personal data beyond basic anonymous analytics. For full details, please see our Privacy Notice and Disclaimer. Read About This Blog & Attribution Note for AI-Generated Content to know more about this blog project.

Why So Much of Life Runs Through Organizations (and How That Helps You)

~ Manuel Castañón-Puga

Picture your day. Classes, a part-time job, a club meeting, maybe a shift at the cafe. Notice a pattern? Almost everything happens inside a group with rules, roles, and someone setting direction. Herbert A. Simon suggests that if a visitor from Mars looked at Earth, they’d see big “green” zones of organizations connected by thin “red” market lines—and they’d probably call this an “organizational economy,” not just a market one. The label matters because it changes what we pay attention to in real life: most people are employees, not owners, and the big question becomes how groups actually get people to work toward shared goals. 

Simon argues that classic theories love markets and contracts, but the real action is inside firms—schools, startups, nonprofits, public agencies—where people coordinate every hour. One reason firms exist is the employment deal: you agree to take direction now for tasks that can’t be fully predicted or negotiated in advance. That’s an “incomplete” contract, and it’s efficient when the future is messy. Day to day, you’re not micromanaged; you work within a “zone of acceptance” where lots of choices are fine to you but important to your boss—like which customer email to answer first or which drink to prep next—so orders can focus on results, principles, or constraints instead of step-by-step instructions. That’s why initiative matters: good work isn’t just “follow every rule,” it’s spotting decisions and moving things forward. 

So why do people try hard if a contract can’t spell everything out or pay for every extra effort? Money and promotions help, but they’re not enough on their own. Simon points to identification—the feeling of “we”—as a powerful everyday engine. When we’re taught and encouraged to care about the team, we take real pride in its wins and act for the group, not just ourselves. He links this to a broader human trait he calls “docility,” meaning teachability and responsiveness to social norms, which makes loyalty and cooperation common—even when they’re not instantly “selfish.” For you, that’s practical: choose teams where the “we” is clear, learn the local goals fast, and use simple scoreboards (quality, safety, service) to guide choices when no one is watching. That mix—some rewards, strong identity, and clear cues—explains why many organizations work surprisingly well. 

There’s one more everyday superpower of organizations: coordination. Think of “rules of the road,” or the registrar that turns campus chaos into a class schedule—standards that let everyone predict each other and get on with it. Beyond rules, groups also balance things by quantities, not just prices: low bin of cups? The system reorders; suppliers schedule production; the whole chain adjusts. Put together—authority used to set clear goals, a shared “we” that motivates effort, and simple coordination tools—organizations can specialize deeply and still run smoothly. That’s why Simon says modern economies are best seen as organizational economies, and why learning to navigate teams is a life skill as useful as any class.

Referencia:
Simon, H. A. (1991). Organizations and Markets. Journal of Economic Perspectives, 5(2), 25–44. https://doi.org/10.1257/jep.5.2.25

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This blog provides simplified educational science content, created with the assistance of both humans and AI. It may omit technical details, is provided “as is,” and does not collect personal data beyond basic anonymous analytics. For full details, please see our Privacy Notice and Disclaimer. Read About This Blog & Attribution Note for AI-Generated Content to know more about this blog project.

How Bali’s Water Temples Teach Smart Teamwork

~ Manuel Castañón-Puga

Picture your dorm’s shared kitchen. If everyone cooks at 7 p.m., the stove line explodes and dinner’s late. If nobody cleans at the same time, pests show up. The fix is simple: agree on a rhythm—stagger the cooking, sync the clean-up. Lansing and Kremer describe a real-world version of this on Bali’s terraced rice fields, where farmers face two opposite problems at once: sharing limited water and keeping crop pests down. Their solution is to coordinate when fields are wet or fallow so pests lose their home, without making every farm demand water on the same day. That balance—neither “everyone goes solo” nor “everyone moves in lockstep”—is the heart of the story. 

According to Lansing and Kremer, Bali’s farmers use “water temple” networks to plan planting like a neighborhood schedule. These temples aren’t just spiritual sites; they’re meeting points where farmer groups set calendars. One example follows two systems on the same river. Downstream subaks planted together and even delayed their start by two weeks compared with their upstream neighbors so the heaviest water demand didn’t hit at once. Pests stayed minimal that season, harvests were solid, and the shared water—though tight—stretched further because the peak didn’t collide. Think of it as staggering shower times in a crowded house so the hot water lasts. 

To see how much coordination matters, Lansing and Kremer built a computer model of two rivers, mapping 172 farmer associations and simulating rain, river flow, crop stages, water stress, and pest growth. When they compared the model with real harvests, it matched well. Then they tested different ways of coordinating. If every group planted alone, pests soared; if everyone planted the same day, water stress spiked. The sweet spot—highest yields—looked like the actual temple network scale in between. In short: the right-sized team plan beats both free-for-all and one-size-fits-all. 

Here’s the coolest part for everyday life: when the researchers let groups “copy the best neighbor” year after year, coordinated clusters popped up on their own and average yields climbed. Those networks also bounced back faster from shocks like droughts or pest bursts—because a good rhythm makes the whole system tougher, not just one farm. The authors warn that random, every-group-for-itself changes (like chasing the newest crop without syncing with neighbors) keep results uneven across the region. The takeaway for your team, club, or flatmates is simple: set a shared cadence, borrow what works nearby, and plan breaks on purpose. That’s how you get more done with less stress—and recover quicker when life throws curveballs.

Reference:
Lansing, J. S., & Kremer, J. N. (1993). Emergent Properties of Balinese Water Temple Networks: Coadaptation on a Rugged Fitness Landscape. American Anthropologist, 95(1), 97–114. https://doi.org/10.1525/aa.1993.95.1.02a00050

Privacy Notice & Disclaimer:
This blog provides simplified educational science content, created with the assistance of both humans and AI. It may omit technical details, is provided “as is,” and does not collect personal data beyond basic anonymous analytics. For full details, please see our Privacy Notice and Disclaimer. Read About This Blog & Attribution Note for AI-Generated Content to know more about this blog project.