keutmann on Nostr: How to score trust and reputation in a Web of Trust system Trust and reputation play ...
How to score trust and reputation in a Web of Trust system
Trust and reputation play a big role in both the real world and online. Trust is something that grows over time through personal experiences. It can be good or bad, depending on how someone has acted in the past. If someone consistently shows they can be reliable and honest, we naturally start trusting them more. That trust becomes a kind of safety net, allowing us to engage with others without constantly worrying about getting hurt or deceived.
But trust doesn’t just appear—it’s built slowly, through repeated interactions and consistent behavior. And, of course, trust is completely subjective. It’s shaped by how we each see and experience the world, which means one person’s trust in someone might not match another’s.
Reputation, on the other hand, is more about how others collectively see you. It’s the sum of all those individual trust levels, but still seen through everyone’s personal perspectives. In a way, reputation feels like a community reflection of trust. Even though it might look like an objective score, it’s really built on a whole range of subjective experiences. That’s what makes the dynamic between trust and reputation so interesting—it's constantly evolving, just like our interactions with people every day.
Figuring out how to score someone with trust in a digital system is like trying to rate your friend’s cooking. Do you go with 5 stars, 10 stars, or maybe something super detailed like 0-100%? The reality is, trust is a tricky thing to measure. We all have our own way of deciding who we trust—some of it’s personal, some of it’s cultural, and none of it fits neatly into a simple rating system. What makes sense to one person might be completely different for someone else.
So, instead of trying to create a complicated scale, the best solution is to simplify: trust, neutral, or distrust. It’s like a thumbs-up or thumbs-down—you either trust someone or you don’t. No need to overthink it. This binary approach is easy to implement digitally, and it keeps things straightforward. Plus, it’s easier for the algorithms to handle. No one wants an algorithm having an existential crisis over whether 4 stars means "pretty good" or "just okay."
Once you’ve got this simple trust/distrust system in place, you can start adding more details if needed. For example, you can confirm certain facts or give 5-star ratings for products, if that’s relevant. But at its heart, the binary trust system keeps things easy to understand and manage, for both people and computers.
Reputation gets interesting because it’s all about perspective. In a decentralized system, there’s no universal score—it’s all subjective, calculated by each observer based on their view. The Web of Trust comes in by aggregating these individual perspectives, creating a broader sense of someone’s reputation.
The process is pretty simple: every time someone trusts the person in question, they get a +1. If they’re distrusted, it’s a -1. It’s really just a running tally of trust vs. distrust across the network.
When the system calculates reputation, it only looks at the degrees of connections where the first opinion—either trust or distrust—appears about the subject. The idea is that opinions from closer peers matter more, while those further out aren’t as relevant and don’t get considered in the calculation.
This keeps the reputation system streamlined and ensures it reflects trust from those who are most relevant to you. After that, it’s up to you to decide how to interpret the score. For example, if the subject has 5 trust points and 2 distrust points, you might view them as generally trustworthy but still be cautious because some people in the network have expressed doubts. The system gives you the information, but how you weigh those points and act on them is entirely your call.
This simpel scoring approach enables algorithms that automated systems can follow, leveraging human trust to make decisions. By doing so, systems can guard against misuse and information spamming while still respecting individual preferences. The prospects for the Web of Trust are immense—an untapped industry where information filtering no longer depends on centralized platforms, but instead focuses on earning the trust of individuals. In this decentralized world, trust becomes the key currency, shaping how we engage and filter the overwhelming flow of information around us.
#weboftrust #wot #reputation #dwotr
Trust and reputation play a big role in both the real world and online. Trust is something that grows over time through personal experiences. It can be good or bad, depending on how someone has acted in the past. If someone consistently shows they can be reliable and honest, we naturally start trusting them more. That trust becomes a kind of safety net, allowing us to engage with others without constantly worrying about getting hurt or deceived.
But trust doesn’t just appear—it’s built slowly, through repeated interactions and consistent behavior. And, of course, trust is completely subjective. It’s shaped by how we each see and experience the world, which means one person’s trust in someone might not match another’s.
Reputation, on the other hand, is more about how others collectively see you. It’s the sum of all those individual trust levels, but still seen through everyone’s personal perspectives. In a way, reputation feels like a community reflection of trust. Even though it might look like an objective score, it’s really built on a whole range of subjective experiences. That’s what makes the dynamic between trust and reputation so interesting—it's constantly evolving, just like our interactions with people every day.
Figuring out how to score someone with trust in a digital system is like trying to rate your friend’s cooking. Do you go with 5 stars, 10 stars, or maybe something super detailed like 0-100%? The reality is, trust is a tricky thing to measure. We all have our own way of deciding who we trust—some of it’s personal, some of it’s cultural, and none of it fits neatly into a simple rating system. What makes sense to one person might be completely different for someone else.
So, instead of trying to create a complicated scale, the best solution is to simplify: trust, neutral, or distrust. It’s like a thumbs-up or thumbs-down—you either trust someone or you don’t. No need to overthink it. This binary approach is easy to implement digitally, and it keeps things straightforward. Plus, it’s easier for the algorithms to handle. No one wants an algorithm having an existential crisis over whether 4 stars means "pretty good" or "just okay."
Once you’ve got this simple trust/distrust system in place, you can start adding more details if needed. For example, you can confirm certain facts or give 5-star ratings for products, if that’s relevant. But at its heart, the binary trust system keeps things easy to understand and manage, for both people and computers.
Reputation gets interesting because it’s all about perspective. In a decentralized system, there’s no universal score—it’s all subjective, calculated by each observer based on their view. The Web of Trust comes in by aggregating these individual perspectives, creating a broader sense of someone’s reputation.
The process is pretty simple: every time someone trusts the person in question, they get a +1. If they’re distrusted, it’s a -1. It’s really just a running tally of trust vs. distrust across the network.
When the system calculates reputation, it only looks at the degrees of connections where the first opinion—either trust or distrust—appears about the subject. The idea is that opinions from closer peers matter more, while those further out aren’t as relevant and don’t get considered in the calculation.
This keeps the reputation system streamlined and ensures it reflects trust from those who are most relevant to you. After that, it’s up to you to decide how to interpret the score. For example, if the subject has 5 trust points and 2 distrust points, you might view them as generally trustworthy but still be cautious because some people in the network have expressed doubts. The system gives you the information, but how you weigh those points and act on them is entirely your call.
This simpel scoring approach enables algorithms that automated systems can follow, leveraging human trust to make decisions. By doing so, systems can guard against misuse and information spamming while still respecting individual preferences. The prospects for the Web of Trust are immense—an untapped industry where information filtering no longer depends on centralized platforms, but instead focuses on earning the trust of individuals. In this decentralized world, trust becomes the key currency, shaping how we engage and filter the overwhelming flow of information around us.
#weboftrust #wot #reputation #dwotr