One of the most important things in economy is to make predictions about the future. This does not only hold for speculating at the stock market, but also is day to day business for fiscal politics, company development, trend scouts, entrepreneurship, education plans, research funding, climate observation and hundreds of other fields. Some even state, that making predictions on the basis of experiences is the key factor for higher intelligence and complex cognitive abilities in the human brain.

The problem with guessing the future is, that there are too many factors to keep them all in mind. Some are forgotten easily, some are newly discovered, some are controversial. So in the world single entities emerged, busy with keeping track of all factors relevant to one topic at all times, thus creating highly specialized expert systems. Such entities (individuals, groups, etc.) are very important, but can easily fall out. If an expert of a special field dies, it takes a lot of non-experts to keep the network of factors alive. And maybe they won't succeed, and the knowledge/intuition is gone.

In order to not overload this entity, it would be good, to have a digitalized version of this relation network. Not only for causal chains, but also for positive and negative correlations between factors. A wiki would be a great way, to realize such a system. Every article is an event. This article lists all correlations with other events, and it lists all the events that lead to it, that were necessary for it and which events it is necessary for. All these relations have to be supported by research studies/articles. By not restricting the field of events, several networks will spring up at the same time, and maybe get connected after some time.

The gain would be, to not only have a good overview of complex chains of reaction, but also an index of all the research data supporting them. Remote effects could be much better understood or discovered, by revealing complex chains not seen before. And confusions between causal chains and correlations could be more easily uncovered. I think it would be worth a shot, because I have not seen anything like this so far, and a wiki would be a great tool for realizing this with a large base of users to contribute.