How We Actually Get Things Done Together

October 6, 2025November 19, 2025 ~ Manuel Castañón-Puga

Think about group projects, student clubs, or even splitting chores with roommates. A popular idea says people won’t help unless someone forces them. But Elinor Ostrom shows that real life doesn’t work that way. In simple lab games where people can chip in to a shared pot, many still give something—often almost half—especially at the start. We also tend to give more when we believe others will reciprocate. That’s a big clue: trust and expectations matter.

What really boosts cooperation is talking face-to-face and being able to call out obvious free-riding. When people can look each other in the eye, they are more likely to plan, make promises, and keep them than when they only type. And when groups are allowed to nudge rule-breakers—even lightly at first—most folks stay on track. Think of a club where everyone agrees on small, fair consequences for skipping set-up duty, starting with a reminder, not a fine. That mix of conversation, plus gentle yet escalating sanctions, keeps things fair without turning the vibe hostile.

Ostrom also explains why some community rules work for years. Strong groups set clear boundaries (who’s in, who’s out), tailor rules to local realities, involve most members in making those rules, and choose their own monitors. They use light penalties first and settle disputes quickly and nearby, so misunderstandings don’t poison trust. Even bigger efforts—such as campus organizations or neighborhood projects—work better when small circles are nested within larger ones, each handling what it knows best. If you’ve ever seen a student association with committees that set their own schedules and budgets, you’ve seen this logic in action.

Here’s the practical takeaway for everyday life: start with a small, motivated core, make membership and expectations clear, co-create simple rules that feel fair, and agree on friendly, step-by-step consequences. Talk in person when you can. Keep a quick way to resolve small conflicts before they grow. And don’t always wait for outside authorities to fix things; sometimes top-down controls can actually weaken the helpful habits you’re trying to build. Begin locally, build trust, and let good norms take hold. That’s how classmates, neighbors, and teams turn “we should” into “we did.”

Reference:
Ostrom, E. (2000). Collective Action and the Evolution of Social Norms. Journal of Economic Perspectives, 14(3), 7–158. https://about.jstor.org/terms

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What Actually Makes Young People in Mexico Feel Happy?

September 8, 2025November 19, 2025 ~ Manuel Castañón-Puga

You’re on the bus after class, scrolling WhatsApp, checking a meme on Instagram, and wondering if being “happier” is about more likes, more money, or just more sleep. Romero-Gómez and colleagues examined real data from Mexico to determine what matters most, using the national well-being survey. They found something simple yet powerful: most people report being fairly happy—an average of 8.45 out of 10, with about two-thirds falling within the 8–10 range. That’s a good baseline to start from and a reminder that many everyday lives already include plenty of reasons to feel okay.

According to Romero-Gómez et al., happiness isn’t one big thing; it’s a mix of your money and health situation, your mind, and your online connections. Messaging apps like WhatsApp and Telegram are an integral part of daily life for more than half of the people surveyed, while TikTok, Twitter, and Instagram are used less frequently in this sample. Picture a normal day: you swap voice notes with your family, share a quick update in a class group, and laugh at a short video. That small stream of connection helps—but it isn’t the main driver. In their model, social networks had a positive but modest link with happiness, while socioeconomic factors (how safe you feel, how you rate public services, your health, and the economy) did much more of the heavy lifting. Psychological strain, like stress, anxiety, or feeling down, pushed happiness in the opposite direction.

So, what’s useful day-to-day? First, care about the basics you can influence. If you’re juggling a part-time job and school, planning your budget, sleeping enough, and staying on top of a health check can pay off in how you feel. That aligns with the finding that better ratings of health, the economy, safety, and public services are associated with higher happiness. Safety and services are areas where many people feel less satisfied, which is important to consider when choosing a neighborhood, commute route, or campus service to use. Second, use your feeds like tools, not traps. A couple of chats that make you feel supported can help; doom-scrolling when you’re already anxious won’t. In the numbers, psychological factors had a clear negative impact, so noticing early signs—trouble concentrating, feeling nervous, and sadness that persists—and talking to someone is not just “mental health talk”; it’s practical happiness math.

Here’s the bottom line from Romero-Gómez and colleagues: start with the pillars, then add the polish. Socioeconomic factors show the strongest positive link with happiness; social networks add a small boost; psychological strain pulls it down. In their results, the model explained about a third of what makes people feel happier, which is a lot in real life. So yes, keep the group chat alive. Also, take that free clinic appointment, pick the safer bus stop, and set a bedtime you actually keep. Small moves add up. And if you’re having a rough stretch, that’s common, too—many people report stress or anxiety at least sometimes. Getting support is not a luxury; it’s one of the fastest ways to improve your mood this week.

Reference:
Romero-Gómez, D., Ahumada-Tello, E., Evans, R., & Castañón-Puga, M. (2024). Exploring the determinants of happiness in Mexico: The interplay of social networks, psychological well-being, and socioeconomic factors. Transactions on Energy Systems and Engineering Applications, 5(2). https://doi.org/10.32397/tesea.vol5.n2.636

AI at Work: Stress, Help, or Both?

August 25, 2025November 19, 2025 ~ Manuel Castañón-Puga

You land your first job and your manager asks you to try a new AI tool. Part of you is excited; part of you worries about mistakes, pressure, or privacy. Ahumada-Tello and colleagues looked at these everyday doubts using worker data from OECD countries to see how AI actually relates to happiness at work and to the choices people make on the job. They used survey data and factor analysis to study the links between AI use, decision-making, and well-being, finding patterns across hundreds of workers.

According to the authors, the big picture is encouraging: AI, on average, does not lower happiness. It tends to support better and faster decisions, which can feel good when your to-do list is long. In the survey, most people agreed AI improves decisions and speeds them up, showing how tools can cut busywork so you focus on real problems. Picture an intern using AI to group customer messages so the team answers faster and with fewer errors—that is the kind of help many respondents reported. The study also organized AI at work into five practical areas: worries about AI, help with decisions, general use, managing AI teams, and working directly with AI systems.

The authors dig into a surprising point: worries about AI—like job security, data, or losing control—did not automatically make people less happy. In fact, for some, noticing the risks pushed them to adapt, learn new skills, and feel more in control again. At the same time, the study highlights limits and conditions. Trust and transparency matter when algorithms are involved, and some jobs still need human empathy and flexibility that tools cannot replace. Imagine a chatbot helping with routine questions while a person handles sensitive cases; that mix fits what the researchers describe.

So what should a young worker take from this? In this study, using AI, getting help from it in decisions, and integrating it into daily tasks were all linked with higher happiness at work. The drivers behind that lift are very down-to-earth: adaptability, efficiency, a sense of mastery, leadership opportunities, and clarity about what to do next. In simple terms, learn the tools, keep your human skills sharp, and ask for clear explanations when AI is used. That way you get the speed boost without losing yourself in the process.

Referencia:
Ahumada-Tello, E., Evans, R. D., Romero-Gómez, D., López-García, J., & Castañón-Puga, M. (2023). The impact of AI on the workplace: OECD AI surveys of employers and workers. 2023 IEEE Global Conference on Artificial Intelligence and Internet of Things (GCAIoT), 121–126. https://doi.org/doi:10.1109/GCAIoT61060.2023.10385121.

Smarter Choices, Better Ideas: How to Make Decisions and Innovate Like a Pro

May 26, 2025November 19, 2025 ~ Manuel Castañón-Puga

Big choices don’t just happen; they follow a simple path. First, notice that a decision needs to be made. Then gather solid info from inside and outside your world. List your options, weigh the evidence, pick one, act, and later assess the outcome. That’s the whole loop, and doing it on purpose makes success more likely. Ahumada-Tello et al. explain this step-by-step process and stress the importance of using reliable data at each stage to avoid guesswork. Today, there’s a flood of data to help you: from basic spreadsheets to tools like data mining and business intelligence. Used well, these don’t replace your judgment—they sharpen it.

Innovation isn’t only about inventing the next smartphone. It can be a new product you can touch, a new service you experience, or a better way to do the work behind the scenes. Consider a supermarket that introduces home delivery (service), a store transitioning from paper forms to digital dashboards (process), or phones that continually add features year after year (product). Changes can be small or huge. Rebranding a snack is incremental. Jumping from older mobile networks to 3G or 4G was revolutionary because it reshaped what phones could do and how the entire industry operated. Sometimes the change is to a single part (modular), like swapping a camera lens. At other times, the entire setup is re-wired (architecturally), such as the transition from film to digital cameras, which altered how every piece fits together.

So what matters most for a team’s results? In a study of tech firms in Tijuana, researchers found that clear, professional decision-making had the strongest link to better organizational performance, even more than new-product work or innovation programs. In their model: Performance = −4.876 + 0.152×New Product Development + 0.102×Innovation Management + 0.403×Decision-Making Process. That big 0.403 shows decision-making packs the biggest punch. The takeaway is simple: learn to structure choices, manage information well, and you’ll boost your outcomes—even before you launch the next big feature.

How do you put this into practice day-to-day? Start small. When you’re picking a class, a side hustle, or a project idea, run the mini-cycle: define the problem, collect relevant info, list options, rate the pros and cons, decide, act, and review what you learned. Use easy data sources—such as surveys, quick tests, or simple dashboards—to keep emotions from steering the ship. Then look for innovation sweeps you can actually do: a smoother process for your study group, a fresh service twist for your freelance gig, or a tiny product upgrade that delights people. Small, steady improvements build momentum, and when a radical opportunity appears, you’ll be ready to make a confident call backed by a clear process.

Reference:
Ahumada-Tello, E., Castañón-Puga, M., Gaxiola-Pacheco, C., & Evans, R. D. (2019). Applied decision making in design innovation management. In Studies in Systems, Decision and Control (Vol. 209). https://doi.org/10.1007/978-3-030-17985-4_5

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

May 19, 2025November 19, 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.