Author: Manuel Castañón-Puga

Finding Your Way Indoors: How Your Phone Can Tell Which Room You’re In

~ Manuel Castañón-Puga

GPS is great outside, but it struggles to function effectively inside buildings. Walls block signals, elevators and people move around, and accuracy drops. Castañón-Puga et al. describe a simple idea that works indoors using what most places already have: Wi-Fi. Instead of trying to pin your exact coordinates, the phone figures out which “zone” you’re in—like a specific room in a museum or a corner of a classroom floor—by listening to the strength of nearby Wi-Fi access points. Think of it like a scent trail: your phone “sniffs” the signal from at least three routers and compares that pattern to what it has learned before.

To make this work, someone first collects example readings in each zone. This is called fingerprinting, and it builds a radio map of the place. In open areas where zones are far apart, three access points are usually enough. When zones are close together—such as two rooms side by side—adding a fourth access point helps the phone distinguish between them. Real life is messy, though. Signals bounce off walls, people walk by, and routers get moved. The authors tackle this issue with two tools: a clustering step that groups similar signal patterns, and fuzzy logic, which states, “this looks mostly like Zone 2, but a bit like Zone 1,” providing a more realistic assessment than a hard yes/no. If the app receives unusual readings—perhaps one router drops out—it can disregard those faulty samples and try again, ensuring the final guess remains reliable.

There are two phases. The heavy work is done offline: setting up the routers, walking around to collect Wi-Fi readings, and training the model. After that, the app’s online phase is fast. When you open the app in the building, it listens once, compares the data to the learned patterns, and returns the zone in milliseconds. The team tested this in places where young people actually go—an interactive science museum, a university floor with numerous devices, and a typical house. Even with noise from crowds and gadgets, the zone-by-zone approach achieved high accuracy once the training utilized the optimal number of access points and sufficient sample data.

What about battery life? During everyday use, the quick “where am I?” check is lightweight—far less demanding than streaming video. Most of the power cost is associated with the one-time training walks, which you won’t do as a visitor. That makes zone-level indoor location practical for things you’d actually use: museum guides that change as you step into a new exhibit, campus apps that auto-open the right classroom materials, or smart-home helpers that switch lights and music when you move between rooms. The big takeaway is that you don’t need special hardware to get helpful indoor location. With the Wi-Fi that’s already around you, a handful of example scans, and a model that embraces uncertainty rather than fighting it, your phone can figure out the right room—and do it quickly and quietly in the background.

Reference:
Castañón-Puga, M., Salazar, A., Aguilar, L., Gaxiola-Pacheco, C., & Licea, G. (2015). A Novel Hybrid Intelligent Indoor Location Method for Mobile Devices by Zones Using Wi-Fi Signals. In Sensors (Vol. 15, Issue 12, pp. 30142–30164). https://doi.org/10.3390/s151229791

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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 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

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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.

Learning That Sticks: Turning Study Time into Real-World Skills

~ Manuel Castañón-Puga

School isn’t just about passing classes; it’s about building skills you can actually use. Ahumada-Tello and colleagues explain a simple idea: focus on three kinds of abilities—tools you use every day (such as writing or using apps), how you work with people, and how you connect ideas to solve larger problems. These are called instrumental, interpersonal, and systemic competences, and they’re the targets your study time should aim at.

They also describe how one university maps your journey in four stages: a base of essentials, hands-on technical learning with a professional practice module, training for the job you want, and finally, graduate studies if you choose. Think of it like leveling up in a game, each stage unlocking new challenges and perks. The plan behind all this includes clear policies, a shared learning philosophy, core components, and a purpose: to help you achieve a well-rounded education that translates to real-life success.

What actually helps you grow? The authors identify five key “agents” that surround you: teaching, research and development, management, university culture, and extracurricular activities. In everyday terms, that means great classes, opportunities to try new ideas, support that keeps things running smoothly, a culture that values learning, and clubs or projects that allow you to practice. If you tap into all five—show up in class, join a project, talk to mentors, get involved—you build the kind of skills employers and communities care about.

There’s also a smart way to track progress: regular check-ins. In their model, learning is assessed about every 80 hours; when your understanding reaches a high bar, new knowledge begins to emerge—like when a tough topic finally “clicks” and you can teach it to someone else. In their results, only about 15% reached that level, which serves as a reminder to be consistent and utilize every resource available to you—teachers, peers, campus culture, and activities—to push beyond the basics. Make your hours count, aim for mastery, and turn your study time into skills that open doors.

Reference:
Ahumada-Tello, E., Castañón-Puga, M., Magdaleno-palencia, J. S., & Villegas-Izaguirre, J. M. (2010). Knowledge Society a Multi-agent model for Adaptive Learning. 3rd World Congress on Social Simulation.

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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.

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.

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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.

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.

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.