Computational Science

Computational science is an interdisciplinary field that uses mathematical models, algorithms, and computer simulations to study and solve complex scientific and engineering problems. It stands at the intersection of mathematics, computer science, and domain-specific sciences (like physics, biology, chemistry, or economics).

Why Flight Cancellations Don’t Have to Wreck Your Day (and What Networks Have to Do with It)

~ Manuel Castañón-Puga

Think of a busy airport like a giant group chat: lots of people, many links, and constant messages. Parra et al. explain that systems like this are “complex” because they’re made of many simple parts that interact all the time—no single boss controls everything, but patterns still appear, a bit like ant colonies working together without a leader. That’s why a few airports seem “popular” hubs with tons of connections, while others are quieter. In a single-layer view of the European air network, some airports have over 100 direct links, so if one of those big hubs fails, the whole trip can fall apart.

Here’s the twist: life isn’t just one layer, and neither are airline routes. You might fly the same city pair with different airlines. A “multilayer” view treats each airline as its own layer. That matters because a problem in one layer (say, Airline A cancels) doesn’t kill the route if Airline B still flies it. Parra et al. show that in this multilayer setup, each layer has fewer connections than the all-in-one map, but that’s actually good for resilience—you can still switch layers to keep moving. In their example, one airline layer had 42 airports and 53 flights (20 of which were also flown by other airlines), and another had 44 airports and 55 flights (25 of which overlapped). Translation: backup options exist.

Now imagine your flight gets canceled. What happens next isn’t just luck—it can be modeled as a simple two-round “offer–counteroffer” chat between a passenger agent and an airline agent. Round one: you propose a fix; the airline accepts or rejects. Round two: the airline counters; you accept or reject. If no one agrees, you end up with a refund (the “conflict deal”). In their tests, many simulated passengers chose “fly tomorrow” over fighting it out, because it avoids the conflict outcome. In one airline layer, the average was about 27, choosing “tomorrow,” and 16 ending in conflict; in another, “tomorrow” averaged 27.6, and conflict 17.4. That sounds familiar: when travel gets messy, the practical win is often a simple reschedule.

So what’s useful for your day-to-day? First, know that big hubs really do matter—more links mean more ways through the system, but also bigger headaches if they go down. Second, check alternatives by airline, not just route; another “layer” might save your trip. Third, when a cancellation occurs, a quick and reasonable counteroffer (such as accepting next-day travel) can often work out better than digging in, because the other side is using a similar playbook and the clock is ticking. Parra et al. even note this approach can be extended to delays and connections later on, which is basically everything you care about when traveling. Understanding the network—and how simple negotiations unfold—helps you stay calm, select smart options quickly, and keep your plans on track.

Reference:
Parra, J., Gaxiola, C., & Castañón-Puga, M. (2018). Multi-layered Network Modeled with MAS and Network Theory. In Computer Science and Engineering Theory and Applications, Studies in Systems, Decision and Control (1st ed.). Springer. https://doi.org/10.1007/978-3-319-74060-7_6

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

~ 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

Smart Cities, Simple Choices: Why Your Daily Habits Matter

~ Manuel Castañón-Puga

Think of a city as a living group chat. Every person sends signals—moving to a new neighborhood, getting a job, turning on a tap—and together those signals shape what the city becomes. Marquez et al. explain that researchers use simulations to test “what if?” ideas without risking real neighborhoods or budgets, pulling info from places like population counts, job surveys, and local water utilities to see the full picture of how a city grows. This approach combines big-picture math with street-level behavior: top-down models track overall trends, while bottom-up “agents” simulate everyday choices; cellular automata provide the map, illustrating how land use changes block by block. It sounds techy, but the idea is simple: small decisions add up fast.

Sustainability is the goal that keeps the chat from turning into chaos. It means balancing what people want today with what the city needs tomorrow across society, the economy, and nature. It is not perfection; it’s a direction. In practice, this can manifest as more effective public spending. For example, judging a water project solely by who can pay misses the bigger win—fewer illnesses, more time in school or at work, and a better quality of life for everyone. When planners compare the benefits and the hidden costs—such as traffic, pollution, and even crime—they get closer to making fair, long-term choices.

The case of Ciudad Juárez shows why this matters. The city is situated in a dry region, so most of its drinking water comes from an underground aquifer, rather than a river. For years, pumping has outpaced natural recharge by roughly five to one, meaning demand keeps draining the aquifer faster than rain can refill it. The team’s model warns that if this pattern holds, the aquifer will not meet the city’s needs in about two decades. Jobs also draw people in, which increases demand for housing, services, and—yes—more water, creating a loop that planners must manage with care.

So where do you fit in? Your choices ripple. Shorter showers and fixing leaks are obvious wins in a dry city, but so is supporting policies that fund basic services, because the benefits come back to you in improved health, increased time, and greater opportunities. Paying attention to how you move (carpooling, biking, or using transit), where you live, and where you work helps keep that group chat from overheating with traffic and pollution. And when you hear about “models” or “agents,” don’t tune out. These tools exist to make everyday life smoother, not more complicated. The message from Marquez et al. is clear: when we balance people, jobs, and nature—and when our individual actions align with smart plans—the city becomes stronger for everyone.

Reference:
Marquez, B. Y., Castañon-Puga, M., Castro, J. R., & Suarez, E. D. (2010). On the Modeling of a Sustainable System for Urban Development Simulation Using Data Mining and Distributed Agencies. In G. Kou, Y. Peng, F. I. S. Ko, Y.-W. Chen, & Tomoko Tateyama (Eds.), 2nd International Conference on Software Engineering and Data Mining (1st ed., Issues 23-25 June 2010, pp. i–xvi). IEEE.

Simulating a City: How Computers Help Us Build Fair, Livable Places

~ Manuel Castañón-Puga

When a city grows, it’s not just more people and buildings. It’s traffic, jobs, rents, parks, water, and the invisible rules tying them together. Marquez et al. explain how social simulation allows us to “test-drive” city decisions on a computer, rather than in real life, where mistakes are costly. They combine three tools that fit like puzzle pieces: dynamic systems to visualize big-picture trends over time, multi-agent systems to observe how many independent “actors” (such as households, firms, or agencies) interact, and cellular automata to map those changes on a grid that resembles an actual city. Put simply, it’s top-down, bottom-up, and on-the-map thinking working together, allowing us to explore what might happen before it actually occurs (Marquez et al., 2010).

Sustainability sits at the center of this approach. The authors describe it as aiming for a good life today without wrecking tomorrow, balancing social needs, the economy, and the environment. In cities, this means tracking how population growth and migration can boost innovation and services, but also bring congestion, unemployment, or pollution if planning is done poorly. Markets set many prices and wages, yet they don’t always account for “externalities,” like dirty air or clogged roads. That’s why planners use cost–benefit thinking that values public health and well-being, not just bills and fees. An easy example is water and sanitation: the benefits include fewer illnesses and more productive days, which matter even if a simple price tag doesn’t capture them all (Marquez et al., 2010).

To illustrate this, Marquez et al. examine Ciudad Juárez. It’s a fast-growing, desert-climate city where water demand rises with population and industry. Most drinking water comes from the Bolsón del Hueco aquifer, and extraction has exceeded natural recharge several times. Residential users account for the largest share of water, with commercial, industrial, and public uses making up the remainder. The authors simulate these pressures with NetLogo and find a worrying pattern: if current trends continue, the aquifer’s supply won’t meet the city’s needs within about two decades. Growth also ties the city to larger economic cycles because it serves as a border hub, which can amplify booms and downturns (Marquez et al., 2010).

Why should young people care? Because everyday choices connect to these systems. Taking a job across town can significantly impact travel patterns. Choosing where to live changes the demand for services. Supporting smarter water use and fair public investments helps your neighborhood stay healthy and affordable. The big message is hopeful: complex doesn’t mean helpless. By simulating cities as living systems—encompassing people, money, and nature together—we can test ideas, identify side effects, and strive for a city that functions effectively in real life, not just on paper (Marquez et al., 2010).

Reference:
Marquez, B. Y., Castañón-Puga, M., & Suarez, E. D. (2010). On the Simulation of a Sustainable System Using Modeling Dynamic Systems and Distributed Agencies. In 2010 6th International Conference on Networked Computing (INC) (1st ed., pp. 1–5). IEEE.