Q-Multi-Criteria Consensus Model: Synthesizing cognitive priority structures from multi-actor disagreement

=== SUMMARY ===
Imagine you are trying to build a giant, shared map for a city that doesn't exist yet. You have a group of architects who know how buildings should stand, and a crowd of future residents who know what they want to live in. The problem is, the architects and the residents often disagree on what matters most. The architects might say, "We need a strong foundation first," while the residents scream, "I want a swimming pool!" In the world of decision science, this is a classic headache. Usually, scientists use tools to rank a list of pre-made options (like choosing between three different car models). But what happens when you don't have a list of cars yet? What happens when you need to figure out what kind of cars the city even needs before you can pick one? This is the tricky "upstream" problem: figuring out the rules of the game before you start playing.

This paper introduces a new way to solve that puzzle, called the Q-Multi-Criteria Consensus Model (Q-MCCM). Think of it as a magical translator that takes two different languages—the "strategic language" of experts and the "demand language" of regular people—and blends them into a single, stable priority list. Instead of just asking people to pick their favorite option from a menu, this method asks them to sort a deck of cards representing different ideas, forcing them to decide what is most important and what is least important. By using a clever mix of card-sorting psychology and mathematical voting, the model builds a "cognitive map" that shows the group exactly where they agree and where they are truly at odds. It's not about forcing everyone to agree; it's about creating a fair system where the experts' vision of the big picture filters the public's immediate desires, resulting in a plan that is both realistic and wanted.

The Problem: When Everyone Has a Different Map

Imagine a group of people trying to design a new school curriculum. The teachers (the experts) know that learning algebra is a prerequisite for calculus, so they think algebra is the most important thing. The students (the stakeholders), however, might think learning how to code a video game is the most urgent need. If you just ask everyone to vote, the students might win, and the school ends up with a great coding class but no math foundation, leaving everyone confused later.

Traditional decision-making tools are like a referee who only counts votes on a list of finished products. They assume the list of "what matters" is already written in stone. But in real life, the list of what matters is often the thing being fought over. The author of this paper argues that we need a tool that can build that list from the disagreement, rather than just ranking items on a list that already exists.

The Solution: A Two-Track Dance

The Q-MCCM model solves this by running two separate tracks that eventually meet in the middle.

Track 1: The Architects (Experts)
First, a group of experts acts like a team of architects. They are given a deck of cards representing different topics (like "Algebra," "Coding," "History"). They don't just pick their favorites; they are forced to sort these cards into a specific shape, like a pyramid. They must put the most important items at the top, the least important at the bottom, and a few in the middle. This "forced sorting" is a technique called Q methodology. It forces people to make hard choices, revealing their true mental map of the world.

Once the experts sort their cards, the model uses a mathematical trick called Vector Propagation. Imagine the experts' sorted cards are a blueprint. The model looks at the distance between the "Best" card and the "Worst" card in their sort and automatically calculates how much weight each topic should have. It's like the model reading the experts' minds and turning their card order into a precise set of instructions, without needing them to do hundreds of tedious comparisons.

Track 2: The Residents (Stakeholders)
Next, the students or users (the stakeholders) get their own turn. They rate the topics based on what they need or want. Maybe they think coding is a 10 out of 10, while algebra is a 3. The model takes these ratings and turns them into a "demand signal."

The Magic Merge: Strategic Filtering
Here is where the magic happens. The model doesn't just add the two lists together. Instead, it uses the Architects' Blueprint to filter the Residents' Demands.

Think of it like a water pipe system. The "Demand" is the water flowing through the pipes. The "Expert Blueprint" is the size of the pipes.

• If the experts say a topic is strategically vital (a wide pipe), but the students only want it a little bit, the model amplifies the demand. The water flows strong because the pipe is wide.
• If the students scream for a topic (lots of water), but the experts say it's not a priority (a tiny, narrow pipe), the model damps it down. The water gets squeezed out.

The result is a final list of priorities that respects what people want, but only if it fits within the strategic structure the experts believe is necessary.

What the Model Found (and Didn't Find)

The author tested this idea using a computer simulation. They created a fake world with 32 different "items" (like school subjects) and 15 fake experts and 80 fake students. They programmed these fake people to have different opinions, just like real humans do.

The results were promising. The model successfully created a stable priority list.

• It worked: When they ran the simulation, the model produced a clear ranking of items.
• It was robust: They tested the list using three different mathematical methods (called TOPSIS, VIKOR, and PROMETHEE). The results were almost identical (a correlation of over 0.9), meaning the final list didn't change just because they used a different math formula.
• It found the "Strategic Consensus": The model could tell when the experts actually agreed on the big picture (the "blocks" or categories) even if they disagreed on the small details. It could also spot when there were two completely different groups of experts with different strategies, and instead of forcing them into one fake agreement, it suggested keeping their lists separate.

However, the paper is very careful about what it hasn't done yet.

• It's a simulation: All the results come from a computer-generated test. The author explicitly states that they have not yet tested this with real people in a real organization. They suggest that while the math looks good, the next step is to try it in the real world.
• It's not a magic fix for everything: The model assumes that the experts' strategic view should "gate" or filter the public's demands. The author admits that in some situations (like safety rules), you might not want to filter demands at all. In those cases, the model might need to be adjusted.
• It needs the right people: The model works best if the group of experts is diverse enough to represent all the different viewpoints. If you only pick experts who already agree with each other, the model won't find any interesting disagreements to solve.

Why This Matters

This paper offers a new way to handle the messy business of making group decisions when everyone is confused about what the goals are. Instead of just counting votes or forcing a compromise, it builds a shared understanding of the "rules of the game" first.

For a curious teenager, think of it like this: If your school club wants to plan a big event, you don't just ask everyone "What party do you want?" You first ask the club leaders, "What kind of event fits our budget and mission?" and then you ask the members, "What do you want to do?" The Q-MCCM model is the tool that takes those two answers and figures out the perfect party that fits the budget and makes the members happy. It turns a shouting match into a structured conversation, ensuring that the final plan is both smart and popular.

The author concludes that this is a powerful tool for "upstream" decision-making—figuring out what matters before you start choosing. While it's currently a very clever simulation, it suggests a future where we can build better, more agreed-upon plans for schools, companies, and governments, even when everyone starts out disagreeing.