Author: Manuel Castañón-Puga

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

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.

How Rumors Actually Travel in Your Group Chats

~ Manuel Castañón-Puga

Think of your friends as dots and the relationships between you as lines. That picture—a network—can tell us a great deal about how a message travels. Raya-Díaz and colleagues explain that we can describe who’s connected to whom with something called an adjacency matrix, which is just a grid that marks a 1 when two people are linked and 0 when they aren’t. From that grid, you can spot who knows lots of people (their “degree”) and even find “hubs,” those super-connected folks who shrink distances in a network and speed things up. In simple terms, if a rumor hits a hub, it can quickly jump to many others.

But not all groups look the same. One shape the authors use is the “barbell” network: two tightly connected friend groups, separated by a thin path—perhaps that one person who belongs to both circles. In this setup, what happens to a rumor depends a lot on the people sitting on the bridge between the two sides. If the bridging person doesn’t pass things on, one whole half may never hear the news. That’s why “betweenness centrality”—basically, how often someone sits on the shortest route between others—matters so much for real communication. The higher your betweenness, the more you act like a hallway everyone has to walk through.

The team modeled a classroom to show this in action. Each student had a few simple traits: the number of connections they had (degree), how close they were to everyone else, whether they were on those in-between routes (betweenness), and—crucially—whether they chose to cooperate by passing the message along. One student initially received the rumor; after that, its spread depended on two factors: their willingness to share and the degree of betweenness of the neighbors they spoke to. When the “bridge” students cooperated, the message flowed to both sides; when they didn’t, it stalled, even if plenty of people on each side were chatty. You’ve seen this in everyday life: a club hears about an event only if the one friend who’s in both the club and your class actually tells them.

So what can you do with this? First, notice the bridges in your world—the friend who hops between group chats, the classmate in multiple circles, the teammate who also runs student council. If you want something to spread fast (a study guide, a show, a fundraiser), talk to them early. If you want to keep something confidential, be cautious about sharing it with people who belong to different groups. And remember, spreading isn’t automatic; it’s a choice. In the authors’ simulations, flipping cooperation on or off at the bridge changed everything—proof that a single person can shape what the whole network knows. That awareness helps you share more effectively, avoid misinformation, and ensure the right people actually hear what matters.

Reference:
Raya-Díaz, K., Gaxiola-Pacheco, C., Castañón-Puga, M., Palafox, L. E., & Rosales Cisneros, R. (2018). Influence of the Betweenness Centrality to Characterize the Behavior of Communication in a Group. In Computer Science and Engineering Theory and Applications, Studies in Systems, Decision and Control (Vol. 143, pp. 89–101). https://doi.org/10.1007/978-3-319-74060-7_5

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.

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

~ 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

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.

Smarter “Maybes”: How Fuzzy Logic Helps Us Model Real Life

~ Manuel Castañón-Puga

Ever notice how most choices aren’t yes or no? You’re not just “tired” or “awake,” your shower isn’t only “hot” or “cold.” Real life lives in the in-between. Flores-Parra and colleagues present a simple way to model fuzziness, allowing non-experts to build smarter simulations in NetLogo, a free tool that many students already use. Their idea is to plug in fuzzy logic—rules that accept shades of meaning—so digital “agents” make choices more like people do.

Here’s the core idea in plain terms. A fuzzy inference system is a set of everyday IF-THEN rules that turn inputs into decisions. Think: IF the room is “a bit warm” AND my budget is “low,” THEN set the fan to “medium.” In their toolkit, you name your inputs and outputs, define what words like “low,” “medium,” and “high” mean (those are membership functions), write a few rules, and then test the results on real numbers to see the decision the system makes. There are two flavors: Type-1 (great when things are fairly clear) and Type-2 (better when there’s extra uncertainty, like noisy sensors or vague opinions). The toolkit guides you through inputs, outputs, membership functions, rules, and evaluation, so you don’t get overwhelmed by the math.

You can build these rules by hand or let data suggest them. The team shows both paths. In a classic NetLogo voting model, they add a fuzzy rule so that each patch “prefers” blue or green depending on nearby votes—much like a friend is swayed by the vibe of their group, rather than just a strict majority. Then they flip to data-driven mode: using census information from Tijuana, they cluster neighborhoods and auto-generate fuzzy rules that classify agents as Catholic or non-Catholic, then watch how people move if they don’t feel similar to neighbors. The cool part is that the toolkit exports a ready-to-use file you drop into NetLogo, so your agents can start making fuzzy decisions right away.

Why should you care? Because this is how many daily systems actually work. Apps can sort posts by “kind of relevant,” not only “relevant.” A fitness plan can adjust when your sleep is “so-so,” not purely “bad.” City planners can weigh “a little traffic” against “big safety gains.” Flores-Parra et al. point out uses in tech profiles, social and eco systems, health risk, planning, and even prices and markets. If you’re modeling anything—study habits, saving money, a club’s attendance—start with simple fuzzy rules you believe (“IF stress is high AND time is short THEN study in 25-minute sprints”), then, when you have data, let clustering refine those rules. Manual rules are easy to understand; data-mined rules can be truer to life. Mix both, test often, and remember: in real life, “a bit,” “mostly,” and “it depends” are features, not bugs.

Reference:
Flores-Parra, J. M., Castañón-Puga, M., Gaxiola-Pacheco, C., Palafox-Maestre, L. E., Rosales, R., & Tirado-Ramos, A. (2018). A Fuzzy Inference System and Data Mining Toolkit for Agent-Based Simulation in NetLogo. In Computer Science and Engineering Theory and Applications, Studies in Systems, Decision and Control (Vol. 143, pp. 127–149). https://doi.org/10.1007/978-3-319-74060-7_7

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.

From Taps to Talk: How Smart Exhibits Learn From You

~ Manuel Castañón-Puga

When you visit a museum or use an app, you’re not just a spectator—you’re part of a conversation. Rosales et al. show that the quality of that conversation depends on a few simple things: Are you actually there and paying attention? Do you get to control anything? Do you receive feedback that helps you keep going? Can you adapt what you’re doing, be creative, or even “talk” back to the system? Those ideas—presence, interactivity, control, feedback, creativity, productivity, communication, and adaptation—can be observed and visualized to create a picture of how engaged you are. The goal is to use that picture to serve you better in the moment, not after the fact.

To make this practical, the authors describe six easy-to-grasp “levels” of interaction, ranging from simply being present (Level 0) to full-on back-and-forth interactions where you create, choose, adapt, and receive instant responses (Level 5). Imagine the difference between glancing at a welcome screen and actually steering what happens next. At the high end, you can pick what you see, change the order, give input, and get tailored replies—more like a game than a poster. Thinking in levels helps designers ask: what tiny tweak would move someone up one step—add a button, a hint, a quick “nice job,” or a way to choose the next challenge?

The team tested these ideas in a Mexican science museum called “El Trompo,” watching 500 visitors try a four-screen exhibit where you control a car, plane, bike, or balloon. Their system treated each exhibit and each visitor like “agents” that sense what’s happening and adjust the experience. Think of it as a low-key guide that notices if you’re lost, bored, or excited and then nudges the content—more instructions if you’re stuck, more freedom if you’re cruising. This isn’t sci-fi; it’s built with rules that translate fuzzy, human behavior into clear decisions about what to show you next.

What’s the practical takeaway for your everyday tech life? If you’re building a school project, a club website, or a small app, aim to lift people one level at a time. Offer quick feedback so they know a tap or swipe “worked.” Offer small choices so they feel in control, such as picking the next topic or difficulty level. Let them adapt the path, not just the pace. And if you’re the user, look for tools that “listen”—ones that react when you linger, explore, or ask for more. In the study, a learning approach called a neuro-fuzzy system performed the best in recognizing the level of people, which helped the system respond more accurately. In plain terms, the tech learned to read the room and act accordingly, which made the experience smoother and more enjoyable.

Reference:
Rosales, R., Castañón-Puga, M., Lara-Rosano, F., Flores-Parra, J. M., Evans, R., Osuna-Millan, N., & Gaxiola-Pacheco, C. (2018). Modelling the interaction levels in HCI using an intelligent hybrid system with interactive agents: A case study of an interactive museum exhibition module in Mexico. Applied Sciences (Switzerland), 8(3), 1–21. https://doi.org/10.3390/app8030446

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.