Skip to main content

Applying the ‘Wisdom of the crowd’ effect in software development

I’m currently reading Noise: A Flaw in Human Judgment, the latest book by Daniel Kahneman wo also wrote Thinking, Fast and Slow.

I’m only halfway in the book but in one chapter the authors talk about an experiment done by 2 researchers, Edward Vul and Harold Pashler where they gave a person a specific question not once but twice. The hypothesis was that the average of 2 answers was always closer to the truth than each answer independently.

And indeed they were right.

One knows more than one

Turns out that this is related to the wisdom-of-crowds-effect; if you take the average of a number of independent answers from different people it typically leads to a more correct answer.

I never heard about this effect before, but it turns out that I’m applying this principle for a long time based on something I discovered in the Righting Software book by Juval Löwy, the broadband estimation technique.

This technique allows you to estimate the implementation effort for a complex system in a fast and efficient way. The idea is that you bring a large group of people(diversity of the group is key!) together and ask them to estimate the effort to build this system.  By doing this with a large enough(think somewhere between 12 and 30 people) you are applying the wisdom-of-the-crowds effect to produce more accurate results.

UPDATE: I forgot to emphasize something when applying the wisdom-of-crowds-effect. It is really important that the estimations are done independently(!) of each other. If you allow people to align first, you end up with group-thinking and skewed estimations.

Important when applying this technique is that the outliers, values that were at least one standard deviation removed from the average, are not ignored but serve as a way to identify uncertainties. After the reasoning behind these outliers is explained a new estimation round is conducted. This process is repeated until all estimations fall within one standard deviation.

I had some very good results applying this technique but it was only when reading about the wisdom-of-crowds-effect that I started to understand the science behind it.

And now it is time to continue reading as I want to find out how I can further reduce the noise in our estimations…

More information

Noise: A Flaw in Human Judgment – Wikipedia

Wisdom of the crowd – Wikipedia

Righting Software | A Method for System and Project Design by Juval Löwy

Measuring the Crowd Within: Probabilistic Representations Within Individuals (ucsd.edu)

Labels:

Popular posts from this blog

Podman– Command execution failed with exit code 125

After updating WSL on one of the developer machines, Podman failed to work. When we took a look through Podman Desktop, we noticed that Podman had stopped running and returned the following error message: Error: Command execution failed with exit code 125 Here are the steps we tried to fix the issue: We started by running podman info to get some extra details on what could be wrong: >podman info OS: windows/amd64 provider: wsl version: 5.3.1 Cannot connect to Podman. Please verify your connection to the Linux system using `podman system connection list`, or try `podman machine init` and `podman machine start` to manage a new Linux VM Error: unable to connect to Podman socket: failed to connect: dial tcp 127.0.0.1:2655: connectex: No connection could be made because the target machine actively refused it. That makes sense as the podman VM was not running. Let’s check the VM: >podman machine list NAME         ...
Read more

Cache stampede: when our cache turned against us

While investigating some performance issues, we ran into an ASP.NET Core API that cached a fairly expensive aggregation query for 60 seconds. Under normal load, that was fine: one request rebuilds the cache, everyone else reads from it. Under peak load, dozens of requests would arrive in that same expiry window, all see a cache miss, and all fire the same expensive query in parallel. The database didn't like that. That was the moment when our caching layer stopped helping and started hurting. A burst of requests comes in at the same time, all miss the cache, and all go hammer the database or the downstream API at once. That's a cache stampede . The cache was supposed to protect our backend, and for a few hundred milliseconds it did the opposite. Why this happens IMemoryCache.GetOrCreate (and its async sibling) looks like it protects you, but it doesn't add any locking on its own. Look at the naive version: public async Task<Report> GetReportAsync(string key) ...
Read more

The role of ActivitySource in OpenTelemetry for .NET

While doing some pair programming to integrate OpenTelemetry tracing to a .NET application, we had a discussion on how to use the ActivitySource . It looks simple. You new one up, give it a name, start an activity, done. The discussion started when we added a second ActivitySource with the exact same name in a different class. This made us wonder: "Are we duplicating traces now? Is this a memory leak? Do we need a singleton?" So we decided to dig deeper. This post is what we learned… What ActivitySource actually is ActivitySource is part of System.Diagnostics , not part of the OpenTelemetry NuGet packages. Microsoft built tracing primitives directly into the BCL, and OpenTelemetry's .NET SDK simply listens to them. This is why you can add distributed tracing to a library without taking a dependency on OpenTelemetry at all. An ActivitySource is a factory for Activity objects, and an Activity is .NET's name for what OpenTelemetry calls a span.(don’t ask m...
Read more