Jed Hallam at Rock Star PR has been wrestling with how you might work out influence and struggling away with a web of influence. It’s a good effort.

And we’re going to need something relatively simple (as opposed to complicated… stay with me on this…) to make a Google Adsense-style valuation of your trusted recommendations work in the peer-to-peer domain of conversational marketing – as I batted around here.

Odd that Jed should be playing with this at a time that Loic, Mike and Robert were busily swinging their dicks (with tongues firmly in cheeks – sorry for that image) on twitter over how high they scored on Facebook Grader.

Fact is, an individual’s ‘grading’, ‘influence’, ‘fame’ or however else you wish to describe it is no longer about who gets the biggest number. It is all about relevance.

I outlined that in a chapter of The Power of The Network and which you can also find here.

I’m relevant to you if you’re reading this blog. So my influence may be reasonably high. But it only remains so if:

1. I stick to my brief – you trust me to talk social media, future of publishing, impact of the power of the network etc. If I start giving you tips about horse racing you’re going to treat them with caution – at the very least.

2. I remain consistent: If I give you a series of bum steers my value to you will rapidly reduce.

3. 2 and 3 can mix. If I start giving you horse racing tips AND I am consistently right – you’ll value this new aspect (I’ll even attract new communities of purpose as a result.

4. The result of 2 and 3 mixing can impact on those who followed me because of 1&2.

5. The value of my influence can only be measured from the receivers point of view – recommendation happens in the mind of the receiver.

6. My influence among those who are yet to connect with me, but have the potential to through meta data is somewhere between 0 and infinity.

Complex, isn’t it?

The networked world gives us the power to slice and dice and value influence how we need – essentially around adhoc communities of purpose as they form and reform in real time.

So I applaud all efforts at trying to map this. I don’t agree with Jed that we’ll never get there, we just have to understand that it is complex, rather than complicated.

Evolutionary modelling (following the simple binary yes/no, if/then, on/off rules of complex adaptive systems) for me offer the best potential.

Anyone with the expertise and the computing power fancy having a crack?

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