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Posts Tagged ‘model’

Across all mediums, advertising $$$ is off 2.8%.

Of course, this is in line with the overall economy so it’s not totally surprising.  Advertising typically lags because the budgets go in so early into the spending season.

A recession in ad spending that goes well into 2009 is going to crush many an Internet company, more than a few agencies, and a lot of traditional media companies.  Oh, by crush, I mean put them out of business for good.

Unless you work on the ground in the advertising world it’s hard to understand just how devestating the recession is especially when consumption of media will always be going up.  That means there will always be more supply of advertising impressions and the cost of media businesses are NOT coming down.  With the ad spend so far down, the prices on this oversupply of inventory is highly depressed further adding pressure to a broken model.

Many people ask me what I think then is going to work with media companies… well, I’ve said it before… media companies have to SELL SOMETHING REAL, not just ads.  Not just an impression.  Sell DVDs, sell shoes, sell licenses, sell special events… anything.  The ad rates simply will not cover the costs of running these media companies.

Oh, and if we thought 08 numbers were bad, just wait for q1 2009 when we will see the real effect of ad budget cuts.

Yes, I’m being a bit DoomsDayish.  Because there’s not a lot of runway left for folks and if they aren’t finding a strategy to cope by now, game over.

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This post is my interpretation.  Other thinkers, philosophers and researchers have other (more technical) approaches regarding this subject.

Statement: There are no models that completely explain the “how or why” sufficiently complex phenomenon.

Clarifications:

Explain – Accurately represents the causes, context, behavior and consequences of a phenomenon and presents such representation in a usable form (we can apply this knowledge outside of just explaining)

Completely – 100% (or very nearly 100%) represent all cases of the phenomenon.  In particular, there are no “exceptions” nor is there simply a “rule of thumb.”

“How and Why” –  The actual behavior, make up, and structure of the phenomenon.

In other words:

All our scientific efforts produce models, not explanations.  Models help us improve our methods and provide insight into phenomenon, but they are not the “thing” and they do not explain the “thing”.  Our explanations based on models and/or the incomplete information they are always based on (computational irreducibility, uncertainty) are forever not complete and always capable of revision (inaccurate).

Math is the ultimate model language.  It is a way to describe relationships when you strip away the gnarly details of the real world.  It sometimes has beautiful results but never produces an explanation of the real world.

Computer science is inbetween the real world and math.  A great way to simulate things and build new computational models, but because it’s not made of the stuff we’re often simulate it can’t possibly be completely accurate.

Biology and other specialized disciplines tend to rely more observations than abstract models.  The result is a nearly infinite record of exception cases making conceptual models that span multiple phenomenon very difficult (well, that’s because you mostly can’t do it.)

Though I’m giving a very truncated account of everything hopefully the point is clear.  Explanations are always our judgment, our subjective synthesis of the inaccurate data we have.  This does not imply we don’t know anything.  Nor does it imply we don’t have explanations.  For simple, or relatively simple, phenomenon we have accurate explanations and good working knowledge.

Specifically, as related to this blog, economics, behaviorism, and social models are all useful models.  None of the “laws” presented in these disciplines are fullproof.  Rational Choice theory, supply and demand, matching laws….  these are good tools, but not full explanations of the how and way of behavior, media and social activity.

Proof: Is left as an exercise to the reader.

Proof Part Duex:  This is an intractable problem.  There’s no way to formally prove these statements.  They are hunches.  I do believe the proof is somehow along these lines: to determine if an explanation is complete and accurate I’d have to be able to reduce a phenomenon down somehow, which is impossible for sufficiently complex phenomenon. (think along the lines of the halting problem.  i can’t determine if a program is going to halt any more quickly than running the program and seeing if it halts….)

For a more formal treatment of scientific explanation head here.

There are many more resources and I’ll post them as I surface them.

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As I prep for a summer in Vermont to study NKS and automata, I’m starting to build research and project concepts.  My focus, as it stands now, is to some how take concepts from behaviorism (schedules of reinforcement, operants, rewards and punishers) and use automata to study them computationally.  This is not trivial nor is their any indication yet that it will be valuable.  

I’m attempting to mash the two lines of inquiry because there really isn’t an accurate nor reliable mathematical foundation in behaviorism even though the experimental and explanatory power of behaviorism is substantial and proven.

IF, and it’s a big IF, cellular automata and a computational (as opposed to a partial differential equation set) can model the concepts in behaviorism we will have a very exciting line of research to chase down.  Modeling and researching complex human behaviors (those with lots of overlapping and interacting schedules and complex environments) has been impossible experimentally and mathematically – only the most basic of behavior is possible to study and it usually has to be isolated to the point where it looses the environment it so richly interacts with.  If we can devise cellular automata capable of showing operant conditioning in ever more realistic environments, we could set up very complicated models without all the laboratory fixings….and so much more.

Note that I am not studying Social Behavior or Social Dynamics or Swarms – not in the typical “what you read on the blogs” sense.  A lot of work has been done in that area even with automata.  The study of individual behavior (how a particular individual responds and learns) needs more research.  I believe that a more thorough understanding of individual behavior will lead to stronger more robust social behavior models – as social behavior is emergent from individual behavior.

Anywho… to whet yer whistle read some fun stuff from Alastar Hewitt on mathematical reinforcement learning and CA

 

~R

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