Recently, much attention has been paid to the idea of viral distribution of content, but according to a study recently published in PNAS , the leading US research journal, this is far from the only way ideas, innovations and technologies can spread.
Two researchers conducted an entertaining theoretical work, tested using focus groups and concluded that the changes are spreading fast, not only because they become open to a large number of people. On the contrary, such distribution often takes place according to game rules; and the players decide whether to accept something new, only on the basis that everyone around them has already done it.
The growing popularity of web pages or gadgets is often described in the context of the “epidemic”: a social network, with a huge number of connections between people, enhancing the impact, and the adoption of something new. In it there is an intersection of interests (links) between different, in their component, groups. At the moment when the trend reaches the participant (hereinafter: node) with a huge number of connections (known personality) - its popularity explodes.
Such a development of events does indeed take place in some cases; in others, this theory is too simplified - a simple effect (“look at my new iPad!”) on an individual does not guarantee that the trend will be accepted and transmitted further along the chain.
Here is what Amin Saberi, one of the authors of the research paper says about this: “It is not only the internal qualities and values of the new technology (or other type of innovation) that make it attractive to people. A huge role is played by the environment that has already taken it. In situations where there is a benefit in making the same decisions as people around, the spread of such innovations follows the rules of the game theory, which is distinguished by much wider waves compared to viral trends or epidemics. ”
In order to show how this happens, the researchers created a theoretical scenario in which several people or nodes connected to the network took part as friends in social groups. All of them participated in the game, where in each round, each node had to decide whether to accept a new trend only on the basis of data on the current behavior of the “neighbor”.
For example, a node looks around to understand how many friends are participating in a trend of, say, Farmville. If there is no such friend, the probability that the node will start playing in Farmville is low, if all, the probability is extremely high. At the same time, the game was made in such a way that imitation of the behavior of neighbors contributed to obtaining the highest ratings, in contrast to ignoring them.
Based only on these rules, a social enclave, where each node receives complete and absolutely accurate information about what everyone else is doing, will never accept a single trend. If people could make decisions only on this basis, then none of the nodes would choose “change” as the best development strategy.
In order to correct this misunderstanding, the researchers added “noise” to this information field, as a result of which many nodes began to receive incomplete or erroneous data. Decisions made were weighed so that a node with 0 information about its neighbors would prefer to accept the trend, whatever it may be. Compare this with reality, in which a person does not care what others think or do, and he weighs the value of innovation based on some internal factors.
In the process of playing with such a structure and according to such rules, the researchers found a pattern: nodes with local connections, as opposed to “long-range” nodes (which carry epidemics), disseminate innovations much faster.
Nodes "not implanted" in the network structure and having a small number of connections (in life these are casual, trained users) transmit information several times faster than nodes with a huge number of connections, which, on the contrary, slow down the circuit.
Super-loaded nodes serve as a kind of roadblocks, because without crystal clear information about the opinions or actions of neighbors, they are still subject to more pressure from their connections ( information redundancy) Strange as it may seem, such a node should not give a damn about its own neighbors to accept a trend, or it should be surrounded by other nodes that have already accepted, without exception, this trend before it. This is the main difference between game distribution and the epidemic.
This model does not work so well in the case of individual content, where a simple “share” is often enough to cause rapid growth. On the other hand, the model perfectly explains the super-loyalty to sites that distribute content, such as Digg, Twitter, Reddit and loyalty to social “genres” and “categories”.
The authors also say that according to the game theory, decisions are also spread that influence the formation of further connections: the choice between democrats and liberals; as well as the adoption of the technological vector: the choice between Apple and Microsoft.
Dr. Saber cites the following example: “The reason I use Facebook instead of any other social network is not only its quality, but also that I have many friends who already use it.” The same thing happens, as we have already noted, with operating systems, choosing a computer, a place to relax, and so on. While each telecom operator is trying to give us the maximum reasons why we should connect to it, buns in the form of free calls or SMS can influence the decisions of entire groups of nodes to migrate from one to another. Carrying the rest.
In this game theory, networks strive to balance the adoption of new changes - this is the only thing that distinguishes theory from practice (in practice, most individuals and groups tend to extremes). But the most important thing that this model shows is that trends and innovations can spread quickly based on factors that exclude mass impact (something like “popularity / loyalty without obvious popularity / loyalty”). The rapid and powerful diffusion of innovations can occur according to other models containing more complex and subtle mechanisms of social impact, such as: “My friends advised me here ...”
PNAS via ArsTechnica