How Markets Quietly Shape What We Want

October 8, 2025October 11, 2025 ~ Manuel Castañón-Puga

We often think our tastes and values are purely personal, but Bowles argues that everyday systems—like shops, apps, schools, and jobs—nudge them all the time. Markets don’t just set prices; they set the scene. Paying taxes for a service feels different from buying the same thing yourself. One frames you as a citizen with rights; the other as a customer engaging in a transaction. That frame alters how fair something appears and how generous we perceive it to be. In lab games, people offered less when the situation was described like a market “exchange” and more when it felt like “splitting a pie.” Even money itself can be a powerful simplifier. In older communities studied by Bohannan, certain items weren’t traded across categories. As money spread, more items became comparable, and that changed what felt OK to swap—and what a “good life” looked like.

Motivation shifts, too. When we do things for a reward, we often start liking the activity less. Psychologists such as Deci and Ryan have demonstrated that paying or punishing can crowd out pride, curiosity, and the sense of choice. Bowles reviews real-world hints of this: when people were offered cash to accept an unpopular facility in their town, support fell; paying for blood donation sometimes made willing donors less likely to give. The takeaway isn’t “money bad.” It’s subtler: clear quid-pro-quo deals push us to focus on the payoff, while choice and autonomy keep our inner drive alive. In your daily life, that might mean mixing paid gigs with passion projects, or keeping some hobbies reward-free so they stay fun.

Norms and reputations also depend on the setting. In tight communities or teams where you’ll meet again, being trustworthy pays off. In fast, anonymous markets, identity matters less, so it’s harder for reputations to grow—and easier to act only for yourself. But market life isn’t destiny. Simple tweaks—such as talking face-to-face, showing names, or building group identity—can increase cooperation. Consider how you buy and sell online: profiles, reviews, and repeat interactions make kindness and reliability more prevalent, as your behavior now follows you later.

Finally, we learn what to value from the people around us. Bowles demonstrates that culture spreads vertically (from parents), obliquely (through teachers and creators), and horizontally (among friends). Conformity isn’t always mindless; it can be a smart shortcut when learning is costly. That’s why “what everyone does” is so sticky. Markets can shift who we see, what gets praised, and which paths look successful—so the role models change, and so do we. For everyday life, the message is empowering: choose your frames and your crowds. Decide which activities you’ll keep intrinsic. Build circles where your future self will meet you again. Small design choices—how you pay, how you participate, who you follow—quietly train your preferences. Use them on purpose.

Reference:
Bowles, S. (1998). Endogenous Preferences: The Cultural Consequences of Markets and Other Economic Institutions. Journal of Economic Literature, 36(1), 75–111. http://www.jstor.org/stable/2564952

Why Your Group Chat Acts Like an Ecosystem (and What That Says About Real-World Organizations)

May 19, 2025October 10, 2025 ~ Manuel Castañón-Puga

Think of a complex adaptive system as many small players, all doing their thing at the same time, reacting to each other, and shaping the big picture without a single boss running the show. Ahumada-Tello and Castañón-Puga describe it as a decentralized network of “agents” (people, teams, apps, even companies) whose combined choices create the overall result. When these agents interact, local moves—such as a few friends changing plans—can ripple into larger patterns, like the overall vibe of a whole club or community. That’s why studying interaction (who talks to whom, and how often) often explains more than just listing everyone’s individual traits.

Four ideas make this easier to picture in daily life. First, there are many layers: your team sits inside a class, a school, a city, and so on; each level influences the others. Second, it’s nonlinear: small inputs can snowball—one rumor or meme can flip a whole plan. Third, connectivity matters: the links between people can be more important than any one person. And fourth, agents adapt: we sense what’s going on, choose, act, and adjust again, which lets the group self-organize without a central controller.

From these interactions, patterns “emerge.” No one plans every step, but simple local rules still add up to smooth outcomes—such as a supply chain functioning or a club event coming together. Good systems keep a balance: not frozen, not chaotic, but near the “edge of chaos,” where creativity and options are highest. They also benefit from variety (different viewpoints), simple rules that everyone understands, and numerous quick feedback loops where small tweaks can lead to significant improvements. Your world is full of nested systems, too—your part-time job is inside a local market that sits inside a national economy—so expecting change and adjusting fast is a winning habit.

What does this mean for organizations you’re part of—student groups, startups, workplaces? They work best when they avoid two traps: a rigid “perfect balance” that kills innovation, and total rule overload that tips into chaos. Aim for limited instability where people can experiment safely. Over-centralizing with too many procedures can suffocate initiative, so use a few clear rules, strong communication, and trust teams to adapt. As Ahumada-Tello and co-authors note, organizations thrive through emergence, self-organization, and evolution—so design your projects to learn quickly, share information widely, and let good ideas spread.

Reference:
Ahumada-Tello, E., & Castañón-Puga, M. (2019). Sistemas adaptativos complejos: un método de análisis organizacional. In I. Plascencia López & J. Ramos (Eds.), Teorías de la complejidad en las ciencias económico-administrativas: Una aproximación (1st ed.). Universidad Autónoma de Baja California.

Smart Predictions, Simple Rules: How “Fuzzy” Agents Learn the Forex Mood

November 4, 2024October 10, 2025 ~ Manuel Castañón-Puga

We all make decisions with shades of “maybe.” That’s the idea behind the system described by Hernandez-Aguila et al.: using fuzzy logic combined with a team of simple “agents” (think: virtual traders) to predict currency prices. Each agent follows clear rules and, importantly, can express doubt. This “intuitionistic” fuzzy logic allows a rule to not only indicate how much something is true, but also how much it isn’t—and how uncertain we are—so the model remains human-readable instead of a black box.

Here’s the twist that feels very real-life: the agents don’t have to act all the time. They use “specialization” thresholds to determine when market conditions resemble situations they are familiar with. If the match is weak, they sit out—just like you might skip riding a scooter on a rainy day. These thresholds coordinate the team: agents avoid trades where they’d likely do poorly, and the model only speaks up when it recognizes a strong pattern. In practice, the system ranks how strongly each input fits an agent’s rules and picks a cut-off (a depth level) that triggers action only in the most familiar scenarios.

Why bother with “fuzzy” in the first place? Because real data is messy. Instead of forcing a yes/no, fuzzy sets allow us to say “somewhat high” or “very low,” then convert many such shades into an output. Intuitionistic fuzzy sets go further by tracking non-membership and “hesitancy,” which captures doubt—useful for markets that change mood quickly. This combo keeps rules readable (“if the trend is high, then buy is high”) while acknowledging uncertainty, such as when you plan to study more when your focus feels “medium” and you’re unsure it’ll last.

Does it work? The authors tested their approach on major currency pairs and compared it with deep learning and other popular methods. Their errors (measured by mean absolute error) were in the same ballpark as those of state-of-the-art models, and using specialized agents helped performance. They also assessed the real-world impact by comparing their specialized models to a simple “buy and hold” approach over many years; their models performed better in terms of revenue. The takeaway for daily life is simple: clear, interpretable rules that know when to act—and when to pause—can rival the complexity of black boxes. Try adopting that mindset in your own decisions: define simple rules, acknowledge uncertainty, and act only when the pattern aligns.

Reference:
Hernandez-Aguila, A., Garcia-Valdez, M., Merelo-Guervos, J. J., Castanon-Puga, M., & Lopez, O. C. (2021). Using Fuzzy Inference Systems for the Creation of Forex Market Predictive Models. IEEE Access, 9, 69391–69404. https://doi.org/10.1109/ACCESS.2021.3077910

Find the Five Levers: How Small Actions Can Shape a Town

August 12, 2024October 10, 2025 ~ Manuel Castañón-Puga

Imagine your town as a giant group chat where every issue—housing, jobs, parks, beach access—keeps reacting to everything else. That’s how Sandoval et al. look at real places: as networks where problems and opportunities are linked, not isolated. When you map those links, you can identify the “bridge” issues that drive the rest, and focus your energy there instead of trying to fix everything at once. It’s a smarter way to plan because it shows how actions in one corner ripple across daily life.

They tested this in Bahía de Los Ángeles, a small coastal community with epic natural areas, a small population, and growing pressure from tourism and real estate development. Think calm waters, protected islands, and a town of only about 800 people—beautiful, but fragile. That mix brings tough choices about land, access, and conservation that affect locals and visitors alike.

To understand what really matters, the team asked residents and authorities to list what’s working, what’s not, and what they want for the future. From those answers, they built a network of 51 everyday issues—everything from water and internet to jobs and beach access—and measured how each one influences or is influenced by the others. It’s like seeing which messages in that group chat start the longest threads.

Here’s the punchline for everyday life: five issues act as power hubs that can shift the whole system—lack of long-term planning, irregular settlements, inadequate infrastructure and services, migration, and a lack of political will. If a community strengthens just those, many other problems also begin to emerge. For example, planning and political will are tightly linked; when leaders stall, planning stalls, and risky building and weak services follow. And while migration sounds “social,” it sits at a key junction, so plans that include training, local jobs, and fair rules can ease pressure elsewhere. In short, find the bridges, not just the loudest complaints, and you’ll get more change for the effort.

Reference:
Sandoval, J., Castañón-Puga, M., Gaxiola-Pacheco, C., & Suarez, E. (2017). Identifying Clusters of Complex Urban–Rural Issues as Part of Policy Making Process Using a Network Analysis Approach: A Case Study in Bahía de Los Ángeles, Mexico. Sustainability, 9(6), 1059. https://doi.org/10.3390/su9061059

Why your choices aren’t just yours (and why that’s actually useful)

March 4, 2024October 10, 2025 ~ Manuel Castañón-Puga

Suarez and Castañón-Puga argue that no person, company, or team is a “perfect agent” acting alone. We’re all shaped by our context—family, school, apps, laws, culture—and our agency comes in degrees, not all-or-nothing. They call this view “Distributed Agency,” the idea that what we do emerges from many layers around us and within us. Think of it like a song that only makes sense on a musical staff: the notes matter, but so does the staff that holds them together. In this view, behavior is best understood in context, across multiple levels, rather than in isolation.

That’s why the paper talks about “holons”—entities that are both parts and wholes. A club, a startup, a family, even you, can be more or less of an agent depending on how you’re organized and how much the bigger system channels your options. Your couple can feel like its own mini-agent with goals that sometimes differ from either partner’s; your company “agent” sits inside an industry with rules that shape what’s realistic day to day. The trick in real life is that upper layers quietly shape the “menu” of choices we see, while our smaller sub-parts (habits, moods, departments) push from below. Once you notice those pressures, decisions feel less mysterious—and more manageable.

A classic example is the prisoner’s dilemma. On paper, the “rational” move is not to cooperate. In labs, people tend to cooperate and perform better. Through the lens of Distributed Agency, that’s not a glitch—it’s a hint that a higher-level pull is at work: reputation, future selves, group norms, or simple fear of fallout. In short, an “upper” agent nudges the “lower” agents to act together. The paper even borrows a vivid metaphor from simulations: the upper level offers “sugar” (influence, rewards) to guide behavior; lower levels act when they have enough “energy” to follow through. Once you see the sugars and drains in your world—likes, grades, pay, status, time—you can redesign your environment to make the good choice the easy choice.

Here’s the everyday payoff: what’s “optimal” depends on the level you choose. A move that’s bad for your short-term self (studying tonight) can be great for your longer-term self or your team. Cultures and institutions make this visible on a large scale. The paper contrasts the U.S. and Mexico to illustrate how social norms and enforcement influence citizens’ day-to-day choices and habits. A tighter alignment at the national level can foster cooperation more frequently, while a looser alignment can lead to greater reliance on individual or family-level action. So, when you plan a goal—fitness, grades, savings—don’t just willpower it. Build a small “upper level” around you: a friend pact, shared calendar, auto-saves, public check-ins. Give your lower-level self the sugar to act, and let your higher-level plan quietly script the menu of choices you see. That’s Distributed Agency as a life hack.

Reference:
Suarez, E. D., & Castañón-Puga, M. (2013). Distributed Agency. International Journal of Agent Technologies and Systems, 5(1), 32–52. https://doi.org/10.4018/jats.2013010103