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D Measuring the Physical Properties of Memes E

A Physics of Ideas

“One of the main obstacles to productivity today is the

growing problem of information overload. Information

overload results because we lack eﬀective tools for

automatically organizing information collections into

meaningful and relevant chunks.” continued >

by Nova Spivack

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Introduction

This paper provides an overview of a new approach to measuring the physical proper-

ties of ideas as they move in real-time through information spaces and populations

such as the Internet. It has applications to search, information ﬁltering, personaliza-

tion, ad targeting, knowledge discovery and text-mining, market research, trend

analysis, intelligence gathering, organizational behavior and social/cultural studies.

This article is reprinted

with permission from

the author from :

http://mindingtheplanet.net

One of the main obstacles to productivity today is the growing problem of information

overload. Information overload results because we lack eﬀective tools for automatical-

ly organizing information collections into meaningful and relevant chunks. For many

years I have been thinking about a new way to approach this problem that is based

on some ideas in classical physics. For quite some time I didn't say anything about it

because it seemed like good material for a patent, but now I've decided it would be

better to just put this in the public domain since I would rather have it be prior-art

than patented by anyone. This is fundamental and useful and everyone should beneﬁt

from it.

In this article I propose the beginning of what might be called "a physics of ideas."

My approach is based on mapping classical physics to memes that move through

information spaces over time. The key to this is to measure the momentum of ideas

as they move through space and time, and thus the momentums of documents that

contain them. This provides a means to quantify the strength and trajectory of ideas

as they move through a given corpus (and by inference, through the populations that

create and consume the documents in that corpus) — and this enables us to start

applying classical physics to empirically measure and understand the dynamics of

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ideas that are shaping our world. In other words, we can start to objectively analyze

interactions between ideas as well as the impact that various ideas have on people,

organizations and events in the "real world" and in turn the impact that those things

have back on ideas.

Ideas are perhaps the single most powerful force shaping our world today, other than

the climate. Humanity's behaviors are nothing but the results of various ideas — the

phenotype of the ideas that are actually at work in the population at a given time.

This is true for any organization, and even for individuals — ultimately much if not

all of their behavior is conditioned by their ideas. So if we can form a science of ideas

that enables us to begin to quantify and analyze their dynamics, we can start to gain

new insights into human behavior and the "hidden" forces shaping our world today.

My approach is to ﬁnd a way to map what is going on in the realm of ideas to existing

methods in classical physics — I want to make it possible to treat ideas as ideal par-

ticles in a Newtonian universe. It may then be possible to use the wealth of techniques

that physicists have developed for analyzing the dynamics of particle systems to un-

derstand the dynamics of ideas within and between individuals and groups.

BACKGROUND

But ﬁrst some background about how I came up with this idea…In 1993 I worked as

an analyst at Individual, Inc., back in the pre-Web days. In that job I was part of a

sophisticated information ﬁlter. Individual published ﬁltered personalized newsfeeds.

They aggregated content from thousands of sources and then ﬁltered it into strategic

newsfeeds tailored to the interests of their customers. You may have used Newspage

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or Heads Up, in the past. Chances are, if you did, I was your analyst. The way that the

Individual system worked was that ﬁrst a set of AI agents did a ﬁrst pass on the in-

coming content to sort it into buckets. These buckets were routed to a team of human

analysts with expertise in the relevant ﬁelds. The analysts would then go through the

articles in the buckets to prioritize them, remove duplicates or items that had come

through in previous articles as well as items that did not belong, and add in any items

that should be included.

I want to make it possible to treat ideas as

ideal particles in a Newtonian universe .

What this meant in practical terms for me as an analyst was that every night from

about 8 PM until 1 AM I had to personally read through around 1600 news articles. My

beat was emerging technology, software, broadband, online-services, multimedia and

satellite applications, so I enjoyed it (yes, I am a serious news hound!). But still it was a

challenge to keep on top of such a ﬁre hose. Not only did I have to ﬁgure out what was

important and how to prioritize it, but I also had to remember if I had ever seen and

published anything about a given subject before in the previous year. By trial and error I

evolved a solution to this problem. In summary, what I realized was that whether or not

something is relevant is much more subtle than merely keyword matching!

A good example can be found in nature — speciﬁcally frogs. Frogs have interesting

visual systems. They are tuned to focus on things that move. They are most sensitive to

size and velocity, but they also notice changes in velocity. Things that are small and that

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don't move are not of particular interest to them. Things that move in erratic ways are

most interesting. Humans are slightly more sophisticated — we notice momentum, a

measure of the "mass" or "size" of things and the way they change over time.

Every night from about 8 PM until 1 AM , I had to personally

read through around 1600 news articles .

What I realized at Individual back in 1993 was that the way I ﬁgured out what articles

to prioritize was not so diﬀerent from how a frog ﬁnds ﬂies to eat — but more so-

phisticated. I realized that I ﬁlter information according to the momentums of ideas (or

"memes") — how the various memes in the articles I was reading were moving through

space and time in the culture I lived in and the communities I was interested in.

Here is a formula that provides an overview of my heuristic for ﬁltering news articles:

Rank of item (such as an article) in a list = function of (keyword relevancy of item to

query, reputation of publisher or source, date of publication, relevancy of source to

query, 'document momentum' of item with respect to memes in corpus).

The last item, "document momentum" is the key to it all. The document momentum

is a measure of the cumulative real-time momentum of the memes that occur in the

document. This requires some explanation. The rest of this article describes how to

measure the momentum of a meme and compute the document momentum for a

document, and how this can be useful for a number of information tasks.

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