http://blogs.zdnet.com/Hinchcliffe/?p=961&tag=nl.e539
Here are the laws in summary:
Amara's law:
We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run.
Beckstroms law:
The value of a network equals the net value of each user’s transactions conducted through that network, valued from the perspective of each user, and summed for all.
Dunbar’s Number:
150 the number of social relationships that the average person can effectively maintain before overload occurs.
Gilder’s Law:
Network bandwidth triples every 18 months... thus raw communication capability is improving faster than computing power is increasing also means the cost of communication, no matter how complex, is collapsing rapidly towards zero.
Goodharts Law:
Once a social or economic indicator or other surrogate measure is made a target for the purpose of conducting social or economic policy, then it will lose the information content that would qualify it to play such a role.
Hawthorne Effect:
Shows you tend to manage what you measure and goes hand in hand with Goodharts, it states - Subjects in a setting will improve aspects of their behavior being experimentally measured simply in response to the fact that they are being studied.
Hotelling’s Law:
This is a product rule that says it’s natural and rational for businesses to make their products as similar as possible. Even in the age of mass customisation and differentiation this shows the importance of determining what should follow this law and what shouldn't and instead be customised.
Jakob’s Law:
Users spend most of their time on other sites, and so you must be there too. Take a look at the ways you should push your solution rather than pull them to a site.
http://web2.socialcomputingjournal.com/building_modern_web_apps_better_a_have_deep_competency_in_w.htm
Kurtosis Risk:
Kurtosis Risk occurs whenever observations are occur at high frequency at high frequency at normal distribution end tails.
Long Tail:
The Long Tail or long tail is a retailing concept describing the niche strategy of selling a large number of unique items in relatively small quantities – usually in addition to selling fewer popular items in large quantities. The concept was popularised by Chris Anderson in an October 2004 Wired magazine article, in which he mentioned Amazon.com and Netflix as examples of businesses applying this strategy.
Metcalfe's law:
Metcalfe's law states that the value of a network is proportional to the square of the number of connected nodes of the system. Precisely its the triangle number n(n-1)/2 where n is the number of nodes this is proportional to n squared asymptotically. (for large n). See Reed’s Law.
Moore's Law:
Computing power doubles every eighteen months(see Gilder’s Law). However the speed and capacity of storage and communication networks has greatly exceeded Moore’s Law for quite some time now and consequently is driving dislocation in many traditional computing models where processing power used to dominate. Not only is this making distributed computing more attractive and making new techniques possible, the fact that computing hasn’t kept up with the network ensures that the network will continue to dominate as a growth engine in terms of it’s overall ability to deliver real business value. The fundamental (and slightly oversimplified) implication is that business models tied to computing can’t compete with business models that are tied to communication, which as we’ll see with Reed’s Law, is significant.
Network Effect:
Is the effect that one user of a good or service has on the value of that product to other people. When network effect is present, the value of a product or service increases as more people use it.
Enterprise software is now experiencing the network effect of viral, Web 2.0 platforms, and Dion's seen numerous recurring instances of platforms such as MediaWiki pushing larger, established enterprise installations (Sharepoint!!) to the sides of their organizations through simple patterns such as network effects by default.
Pareto Principle:
80/20 Roughly 80% of the effects come from 20% of the causes.
Any complex system, including social ones, and increasingly social and community dynamics are seen as falling under this rule in numerous ways.
Principle Of Least Power:
Given a choice among computer languages, classes of which range from descriptive to procedural, the less procedural, more descriptive the language one chooses, the more one can do with the data stored in that language.
In practice, it has considerable implications for the knowledge economy. Data that is trapped in complex or obscure formats can’t be searched or otherwise processed, and the success of the Web as a content medium has largely been responsible for giving this principle its credibility. HTML and SQL are good examples of this.
Principle Of Least Effort:
Postulates that animals, people, even well designed machines will naturally choose the path of least resistance or "effort".
An information seeking client will tend to use the most convenient search method, in the least exacting mode available. Information seeking behavior stops as soon as minimally acceptable results are found. This theory holds true regardless of the user's proficiency as a searcher, or their level of subject expertise. Also this theory takes into account the user’s previous information seeking experience. The user will use the tools that are most familiar and easy to use that find results. The principle of least effort is known as a “deterministic description of human behavior.”[1] The principle of least effort applies not only in the library context, but also to any information seeking activity. For example, one might consult a generalist co-worker down the hall rather than a specialist in another building, so long as the generalist's answers were within the threshold of acceptability.
Reed’s Law:
Reed's law is the assertion of David P. Reed that the utility of large networks, particularly social networks, can scale exponentially with the size of the network.
The reason for this is that the number of possible sub-groups of network participants is 2^N - N - 1 \, where N is the number of participants. This grows much more rapidly than either
* the number of participants, N, or
* the number of possible pair connections, \frac{N(N-1)}{2} (which follows Metcalfe's law)
so that even if the utility of groups available to be joined is very small on a peer-group basis, eventually the network effect of potential group membership can dominate the overall economics of the system.
Reflexivity (The Social Theory)
In effect action and reaction can feed off each other to create a positive feedback loop that eventually changes the action and reaction and the actors and reactors particularly their expectations and objectives! Explaining the tendency of social systems to move away from equilibrium and often towards extremes (flame wars, viral feedback loops, rapid information propagation, etc including inadvertently falling in love!
Sarnoff’s Law:
Another way to value the network, Sarnoff’s Law, created to describe older one-way networks such as broadcast medium like TV or radio, says “the value of a broadcast network is proportional to the number of viewers.” A more modern version of this is Beckstrom’s Law.
Taleb distribution:
The Taleb distribution is a probability distribution where there is a high likelihood of a small gain combined with a small probability of a very large loss, which would more than outweighs any gain. Taleb distribution is interesting to the social economy for the same reasons as Kurtosis Risk: It can make social business options such as crowdsourcing and other open business models more balanced when looked at in this light because it can appropriately highlight the lack of predictability and identify unacceptable risk profiles.
Thomas Theorem:
The Thomas Theorem identifies a trend that is particularly common within social worlds that any definition of the situation will influence the present. The theorem stresses social situations including in family, business, or other life as fundamental to the role of the participant when mentally creating a social world “in which subjective impressions can be projected on to life and thereby become real to projectors.” This is how social worlds, on a network or otherwise, can take on a life of their own. The reputation and standing of individuals as they perceived on networks, is often very different from what they have in real life. This has implications for HR, promotions, rewards, and motivations in enterprise social computing environments.
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