Complexity Science

Complexity science is the study of how large systems made up of many interacting parts give rise to emergent behaviors — patterns, structures, or dynamics that cannot be understood by analyzing the parts in isolation. It seeks to understand how order, adaptation, and organization emerge in natural, social, and technological systems — from ant colonies and ecosystems to economies, cities, and human societies. Complexity science studies systems that are more than the sum of their parts.

How We Actually Get Things Done Together

~ 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

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

~ 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.

Test-Drive Your City: How Simple Simulations Make Smarter Policies

~ Manuel Castañón-Puga

Cities are messy. Many people, rules, and surprises collide, which means even good intentions can backfire. Sandoval Félix and Castañón-Puga argue that decision-makers should “mock up” policies on a computer first, like trying a route in a map app before leaving home. These lightweight models allow people to explore what might happen if they build a new park, change bus routes, or tighten zoning—before affecting the real city. That kind of “anticipatory knowledge” helps avoid short-term fixes that create long-term problems.

The chapter explains why this matters: cities aren’t machines that can be tuned with one knob. They’re complex systems where small tweaks can trigger big, unexpected outcomes, because everything is connected. In complex systems, patterns “emerge” from many small actions—think of traffic waves or shopping streets that pop up on their own. This is why looking only at one piece often fails. The complexity lens focuses on interactions and probabilities, rather than rigid plans, allowing policies to account for side effects across different parts of the city.

To explore these interactions, the authors highlight agent-based models—small worlds filled with “agents” (such as households, shops, or buses) that follow simple rules. There’s no central boss; each agent has limited knowledge and reacts to its surroundings. When you run the simulation, their choices add up to city-scale patterns. A related technique, cellular automata, applies these rules to a grid, allowing nearby cells to influence each other—useful because, in cities, what’s next door often matters most. These tools don’t predict the future with certainty, but they help identify counterintuitive moves, path-dependent traps, and situations where individual wins don’t add up to a public win.

Getting started is less scary if you treat it like learning a creative skill. The authors suggest tinkering first, building simple blocks, keeping version notes, and borrowing small code “snippets” from similar models. Even sketching a flow diagram helps you stay focused and avoid accidental behaviors. Then, present the results clearly: use plain language, visuals, and connect the outputs to real-life steps, such as which rules or budgets would need to be changed. Communication guides, such as ODD/ODD+ D and the STRESS checklist, can help keep your work organized and understandable for non-experts. The point isn’t perfection—it’s making choices that are better informed, more transparent, and less likely to surprise everyone later.

In everyday terms, this chapter is an invitation to play “what if?” with the city you care about. Treat models like a safe sandbox where you can test ideas fast and see the ripple effects, not a crystal ball. When you understand that cities are living networks, you’re more likely to ask better questions, spot side effects early, and push for policies that work in the real world—not just on paper.

Reference:
Félix, J. S., & Castañón-Puga, M. (2019). From simulation to implementation: Practical advice for policy makers who want to use computer modeling as an analysis and communication tool. In Studies in Systems, Decision and Control (Vol. 209). https://doi.org/10.1007/978-3-030-17985-4_6

City Building 101: Why “Where Stuff Goes” Shapes Your Day

~ Manuel Castañón-Puga

Sandoval-Félix et al. examine a simple question with significant everyday effects: where should a city allocate homes, jobs, and roads to ensure smooth operation? They model Ensenada, Mexico, and introduce a handy idea called “Attractive Land Footprints.” Think of these as spots that are extra tempting for new factories because they’re near workers and big roads, on gentle slopes, and away from homes. These spots don’t stay put—they pop up, move, shrink, or disappear as the city changes. That constant shape-shifting is why planning rules need to keep up.

Here’s the twist: the model finds that more of these “attractive” factory zones fall in places the current rules don’t allow than in places they do. In plain terms, demand for good industrial space exceeds what the plan permits. That mismatch pushes industry to bend rules or sprawl into awkward spots, which you feel as longer commutes, clogged streets, and noisy trucks cutting through neighborhoods. The authors even see a future “attractive” corridor forming along a northeastern road—useful if the road exists and rules adapt, frustrating if not.

Density—how many people live in an area—ends up being a quiet hero. When density is low, the city spreads out, and those attractive spots are quickly consumed by other uses, especially housing. The model shows that at 10–15 people per hectare, as much as 65% of those desirable areas can be urbanized in a single year; at around 35 people per hectare (Ensenada’s current average), that drops to about 14%. Translation: Compact neighborhoods help protect space for jobs, which in turn protects your time and wallet. If density slips lower, industry tends to locate in worse places more often, and residential projects often occupy the very land that would have made commutes shorter and deliveries cheaper.

So what should young residents take from this? First, roads matter: without strong connections, even “perfect” locations won’t work, and good jobs end up farther away. Second, rules matter: if plans ignore how attractive spots really form, the city grows in messy ways you feel daily. Third, your housing choices matter too: choosing, supporting, and voting for denser, well-located neighborhoods helps keep industry near major roads and workers, not next to your bedroom window. In short, smarter density, updated rules, and better road links make everyday life—commuting, deliveries, prices—smoother for everyone. That’s the message behind the model: pay attention to where stuff goes, because it quietly shapes how you live.

Reference:
Sandoval-Félix, J., Castañón-Puga, M., & Gaxiola-Pacheco, C. G. (2021). Analyzing urban public policies of the city of Ensenada in Mexico using an attractive land footprint agent-based model. Sustainability (Switzerland), 13(2), 1–32. https://doi.org/10.3390/su13020714

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

~ 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