Most of the older models assume homogeneity and a fixed landscape. There was nothing that would balance exploration versus exploitation within a model. It assumed an equal level of risk aversion in consumer marketplaces, or assumed there wasn't a wide difference in adoption of technologies depending on social structure. The key is all of the implicit and explicit assumptions built into the model. You might expect that a prediction would have degrees of probability and also be focused as much on preventing you from being surprised about new developments.
So, a lot has changed in our ability to model situations since 1989 when the unified space plan was made.
*We no longer assume a Game-Theory style environment of homogeneous actors. This means that they are culturally, genetically, and epigenetically different.
*For more on genetics versus epigenetics in intelligence, see:
As we will see later, IQ doesn't grasp the full range of intelligent capabilities inherent in the exploration v. exploitation dynamic, e.g. resourcefulness. It also doesn't take into account cognitive biases that plague smart people:
http://thesciencepundit.blogspot.com/20 ... cians.html
(The above should also make it obvious that AGI researchers don't really want to make a "human" intelligence per se, they want to improve on the design. The diversity in thinking and intelligence between humans is a grain of sand compared to the beach of AGI. The same thing extends to synthetic biology and other developing sciences.)
*Game theory has now been expanded to include information advantages over time, however most attempts at modeling complex systems like Systems Dynamics have been doing that for decades
*A simple old-school model would be CARVER, which while very useful, isn't going to suit all of our potential needs
*System Dynamics thinking has stacks and flows, and kind of breaks down when modeling large complex systems. To fix that, modern Complex Systems thinking thinks in branches, with probabilities assigned to each given potential future.
*We now model the trade-off of exploration versus exploitation. Most under-grad decision making models assume a landscape in which things will stay relatively the same, you don't have a compounding bonus over time by exploring the landscape. Of course most real-world situations operate in a landscape that is moving very rapidly. This has brought a glut of business books touting the importance of innovation, without clearly defining the environment from which innovation comes from. I define innovation as the ability to explore, and then move into the exploitation area successfully. Most of these business books also ignore the "fast-follower advantage", you can call it imitation, or simply standing on the shoulders of giants:
http://articles.businessinsider.com/201 ... ilure-rate
In fact, a 1993 paper by Peter N. Golder and Gerard J. Tellis had a much more accurate description of what happens to startup companies entering new markets.  In their analysis Golder and Tellis found almost half of the market pioneers (First Movers) in their sample of 500 brands in 50 product categories failed. Even worse, the survivors’ mean market share was lower than found in other studies. Further, their study shows early market leaders (Fast Followers) have much greater long-term success; those in their sample entered the market an average of thirteen years later than the pioneers. What’s directly relevant from their work is a hierarchy showing what being first actually means for startups entering new or resegmented markets:
Innovator First to develop or patent an idea
Product Pioneer First to have a working model
First Mover First to sell the product 47% failure rate
Fast Follower Entered early but not first 8% failure rate
You must also consider that the costs of innovation are much less when you're imitating, 25% or more in most cases. The pioneers generally don't end up with a big marketshare. But innovation, whether or not it is a form of imitation, is usually a much better way to dealing with things versus relying on being smarter, having more capital or being harder working. With global competition it's guaranteed that there is someone already working on your problem and has more of all 3 factors.
On the subject on working hard, remember the productivity experiments so you don't burn out:
http://www.lostgarden.com/2008/09/rules ... ation.html
Getting back to innovation, Astro Teller did a good talk on how he structures his team to get innovation at Google's X Lab:
He does a lot of counter intuitive things here, the first thing is: What is the story? Any good exploitable product has a story, you have to obsess over your story. The second is, they show him 10 other bad ideas they found a long the way. This isn't a waste of time, it's to show that they have explored a lot of territory. After that they will do things like throw out the first 6 months of code, so that they aren't working on bad assumptions from the start of the project. This also gives you the ability to ruthlessly prune bad ideas. Assumptions, whether implicit or explicit in your model, will tend to cripple it over time.
The CIA Tradecraft manual makes this point loud and clear:
https://www.cia.gov/library/center-for- ... -apr09.pdf
So the next thing Astro has them do is list the tools they used to make the innovation, and have them improve on it. If you have a toolkit, improve your tools.
*This sort of exploration and improvement creates an exponential curve of growth, see Richard Hammond's You And Your Research:
Now for the matter of drive. You observe that most great scientists have tremendous drive. I worked for ten years with John Tukey at Bell Labs. He had tremendous drive. One day about three or four years after I joined, I discovered that John Tukey was slightly younger than I was. John was a genius and I clearly was not. Well I went storming into Bode’s office and said, “How can anybody my age know as much as John Tukey does?” He leaned back in his chair, put his hands behind his head, grinned slightly, and said, “You would be surprised Hamming, how much you would know if you worked as hard as he did that many years.” I simply slunk out of the office!
What Bode was saying was this: “Knowledge and productivity are like compound interest.” Given two people of approximately the same ability and one person who works ten percent more than the other, the latter will more than twice outproduce the former. The more you know, the more you learn; the more you learn, the more you can do; the more you can do, the more the opportunity – it is very much like compound interest. I don’t want to give you a rate, but it is a very high rate. Given two people with exactly the same ability, the one person who manages day in and day out to get in one more hour of thinking will be tremendously more productive over a lifetime. I took Bode’s remark to heart; I spent a good deal more of my time for some years trying to work a bit harder and I found, in fact, I could get more work done. I don’t like to say it in front of my wife, but I did sort of neglect her sometimes; I needed to study. You have to neglect things if you intend to get what you want done. There’s no question about this.
*An important concept none of these models cover is Resourcefulness, which is a talent every good entrepreneur has to have. The Prometheus Society, a high IQ society that accepts 1 out of 30,000 people, versus MENSA's loose 1 out of 50, has some very good articles about this:
The 2 basic premises, which I will first attempt to substantiate and will then use in furthering my argument, are:
1) Innate intelligence is unmeasurable; only overtly manifested ability can be evaluated—making intelligence tests, in many ways, akin to achievement tests.
2) The definition of “intelligence,” or of what component abilities should be most emphasized on an exam, is socially determined and thus may change in accordance with the needs of an era or culture.
The “classical” education highly valued by the erstwhile British aristocracy emphasized linguistic skills almost exclusively; youths were taught predominantly literature, philosophy, ancient Greek and history. In a more technologically oriented age, some of the most respected scholars of that time might have seemed profoundly inept or “one sided;” conversely, many modern scientists—esteemed for their acute mathematical and spatial reasoning—might have been judged “ill fit for higher learning” or “unintelligent” by an educational system which focused entirely on literary accomplishment.
In our own era, the ability to reason analytically is deemed vital to the advancement of technology and, thus, is stressed on many intelligence tests. Because all information is communicated through the verbal modes of speech and writing, vocabulary and linguistic reasoning are considered important indicators of “mental capacity.” The ability to “rote memorize”—vital to the retention of knowledge in a pre-literate era and important in the learning of complicated ecclesiastical rituals in the medieval period—has been de-emphasized; moreover, the focus on technologically useful forms of thinking has reduced the value placed on linguistic aptitude in isolation.
Thus, the mental abilities deserving emphasis—the criteria for assessing intelligence—may change with the times; men of extreme but one sided talent, deemed “brilliant” in one era, might be considered unremarkable in another. In evaluating intelligence, we measure how well an individual has assimilated the knowledge valued by his culture, how well he has learned to reason in conformity with the current styles of thinking, and how well he can adapt (on a cognitive level) to the conventions of his time.
“Resourcefulness” and the ability to think “broadly” (or “divergently”), to foresee how numerous factors might interact and to envision multiple possible solutions to any given problem, take priority. In an era when computers perform more and more of technology’s “analytical” work and when increasing numbers of people assume managerial roles, the incisive and narrowly-focused reasoning which considers data sequentially and ignores all ostensibly extraneous information may be superseded by the ability to consider heterogeneous pieces of information simultaneously.
The body of modern knowledge is enormous—too huge for one individual to master—even 5 lifetimes; continual advancement, especially in the technologies, assures that every man will always be “slightly ignorant” (even regarding the developments in his own specialty) and that, inevitably, he will often need to consult references for an explanation of new discoveries. The efficient use of such reference sources, necessary for adaptation to an ever-changing society, is of vital practical importance; gaining access to the facts of interest, when (abundant) information is stored in a complex manner, is facilitated by a divergent type of thinking called “resourcefulness.” This “resourcefulness,” as a key determinant of success in the modern world, may be a valid criterion by which to evaluate adult intelligence.
*Part of the reason Start-Up incubators in Silicon Valley give such good terms is because they have no idea who is going to be a winning bet, so they take lots of small ones and hope one pays off. After the initial stages, they aren't investing in an idea as much as the founders:
Like real world resourcefulness, conversational resourcefulness often means doing things you don’t want to. Chasing down all the implications of what’s said to you can sometimes lead to uncomfortable conclusions. The best word to describe the failure to do so is probably “denial,” though that seems a bit too narrow. A better way to describe the situation would be to say that the unsuccessful founders had the sort of conservatism that comes from weakness. They traversed idea space as gingerly as a very old person traverses the physical world. 
The unsuccessful founders weren’t stupid. Intellectually they were as capable as the successful founders of following all the implications of what one said to them. They just weren’t eager to
There are great startup ideas lying around unexploited right under our noses. One reason we don’t see them is a phenomenon I call schlep blindness. Schlep was originally a Yiddish word but has passed into general use in the US. It means a tedious, unpleasant task.
No one likes schleps, but hackers especially dislike them. Most hackers who start startups wish they could do it by just writing some clever software, putting it on a server somewhere, and watching the money roll in—without ever having to talk to users, or negotiate with other companies, or deal with other people’s broken code. Maybe that’s possible, but I haven’t seen it.
One of the many things we do at Y Combinator is teach hackers about the inevitability of schleps. No, you can’t start a startup by just writing code. I remember going through this realization myself. There was a point in 1995 when I was still trying to convince myself I could start a company by just writing code. But I soon learned from experience that schleps are not merely inevitable, but pretty much what business consists of. A company is defined by the schleps it will undertake. And schleps should be dealt with the same way you’d deal with a cold swimming pool: just jump in. Which is not to say you should seek out unpleasant work per se, but that you should never shrink from it if it’s on the path to something great.
The most dangerous thing about our dislike of schleps is that much of it is unconscious. Your unconscious won’t even let you see ideas that involve painful schleps. That’s schlep blindness.
How do you overcome schlep blindness? Frankly, the most valuable antidote to schlep blindness is probably ignorance. Most successful founders would probably say that if they’d known when they were starting their company about the obstacles they’d have to overcome, they might never have started it. Maybe that’s one reason the most successful startups of all so often have young founders.
In practice the founders grow with the problems. But no one seems able to foresee that, not even older, more experienced founders. So the reason younger founders have an advantage is that they make two mistakes that cancel each other out. They don’t know how much they can grow, but they also don’t know how much they’ll need to. Older founders only make the first mistake.
Ignorance can’t solve everything though. Some ideas so obviously entail alarming schleps that anyone can see them. How do you see ideas like that? The trick I recommend is to take yourself out of the picture. Instead of asking “what problem should I solve?” ask “what problem do I wish someone else would solve for me?” If someone who had to process payments before Stripe had tried asking that, Stripe would have been one of the first things they wished for.
What would someone who was the opposite of hapless be like? They’d be relentlessly resourceful. Not merely relentless. That’s not enough to make things go your way except in a few mostly uninteresting domains. In any interesting domain, the difficulties will be novel. Which means you can’t simply plow through them, because you don’t know initially how hard they are; you don’t know whether you’re about to plow through a block of foam or granite. So you have to be resourceful. You have to keep trying new things.
Be relentlessly resourceful.
That sounds right, but is it simply a description of how to be successful in general? I don’t think so. This isn’t the recipe for success in writing or painting, for example. In that kind of work the recipe is more to be actively curious. Resourceful implies the obstacles are external, which they generally are in startups. But in writing and painting they’re mostly internal; the obstacle is your own obtuseness.
*Peter Thiel's excellent class on Start-Up's has a good way of modeling things from the position of a founder:
http://blakemasters.tumblr.com/peter-th ... 83-startup
mimesis and competition
*In my opinion, the two big ones are timing and distribution. It's been incredibly hard to get those in sync. Most ventures that need less than $1 billion can get funding, and there are lots of small payment options ready for ambitious founders today. But when you start working in a distribution system, or deal with timing, you start to see the order and chaos inherent in complex systems. When you work on those two you understand how small you really are.
*So, when you zoom out on this process, you move away from the simple individual inputs and start to view the entire, complex system that changes over time. You end up with a model where creations don't need creators because simple rules can create structured environments.
Many (all?) interesting phenomena can usefully be described as "orderly ensemble properties" and productively understood in terms of the properties and interactions of sub-phenomena ("elements").
WHOLES ARE MADE OF PARTS
Ensemble properties are permitted by but not determined by element properties
WHOLES ARE MORE THAN THE SUM OF THEIR PARTS
The behavior of ensembles is both influenced by and influences the behavior of elements
THERE IS A RECIPROCAL CAUSAL RELATIONSHIP BETWEEN PARTS AND WHOLES
Orderly ensemble properties can and do arise in the absence of blueprints, plans, or discrete organizers.
INTERESTING WHOLES CAN ARISE SIMPLY FROM INTERACTING PARTS
Prove it? Try the Game of Life
Ensemble properties may be largely unaffected by variations in the properties and behavior of elements
HOLISTIC PROPERTIES MAY APPEAR RESISTANT TO CHANGES IN PARTS
Ensemble properties may be highly sensitive to variations in the properties and behavior of elements
HOLISTIC PROPERTIES MAY SUDDENLY AND APPARENTLY MYSTERIOUSLY CHANGE
An example? See chaos suddenly emerge from order
Ensemble properties can be dramatically changed by modifying the nature of the interaction among elements
ENUMERATION OF PARTS CANNOT ACCOUNT FOR WHOLES
Ensemble properties may be dynamic for reasons entirely internal to the ensemble
CHANGE DOES NOT NECESSARILY INDICATE THE EXISTENCE OF AN OUTSIDE AGENT OR FORCE
An example? See populations fluctuate for no external reason
The same change in element property or behavior may have a small effect on ensemble order at one time and a large effect at another time
THE RELATION BETWEEN PARTS AND WHOLE MAY ITSELF CHANGE FOR A GIVEN WHOLE.
Disorderly variations in element properties or behavior may be the driving force for ensemble order
INTERESTING WHOLES CAN ARISE FROM CHAOS OR RANDOMNESS
Really? Yep, look at fractals and at The Game of Life and at The Magic Sierpinski Triangle
Deterministic systems will not explore all possible ensemble states
RANDOMNESS PLAYS AN IMPORTANT ROLE IN THE EXPLORATION OF POSSIBLE WHOLES
I also recommend watching/listening to Scott Page's lecture on Understanding Complexity. You can find it on the TCC website or thepiratebay.