 Economies are large-scale systems for the production and exchange of value within society. One of the key functions of economies and economics is to figure out what might happen in the future and enable economic development by coordinating the efficient allocation of resources within that system. An economic system has to aggregate large amounts of information and figure out how to allocate available resources in an efficient manner to support its future development. The same is true for all organizations, enterprises, and individuals. They also have to figure out how to allocate both their current resources but also where to invest their resources to enable future success and growth. This can be done either in a centralized fashion or a decentralized fashion. The centralized approach involves having a large bureaucracy that monitors the economy, bringing in information from the many different industries and employing an army of economists, statisticians, and analysts of various kind to try to forecast the future and figure out a plan for the economy. Then use subsidies, taxes, and various forms of regulation to try to allocate resources according to some centralized vision. This we would call a command and control economy, as exemplified by the former communist system. But it is also a key part of how most economies are managed today by their respective national governments. This centralized approach has its advantages and disadvantages. We can look at China's current rapid development, which has to a large extent been a function of the central government's planning. But equally, this approach has its failings. It is critically dependent upon the information processing of a limited number of people who may be highly competent, but just as likely they may be incompetent. Either way, they can only process so much information, which means that our information bottlenecks as the information is centralized. Likewise, the people are making decisions about other people's resources, not their own, which can lead to a misalignment of incentives and many opportunities for corruption. With fast-paced technological and market evolution, innovation is moving to the forefront of what enterprises are required to do. At the relatively low level of change of the past, the enterprise could confine change and innovation to some small R&D department and could afford lengthy production cycles and change processes. The mass of the organization was built around a stable and predictable hierarchical structure, long production processes and product life cycles through which stable income streams could be maintained. But as the pace of change increases, this model is becoming increasingly less viable. We are living in a more and more complex and dynamic world. There are more things coming at us and they're coming at us at a faster rate, and it is not just that the pace of change is accelerating, but we also have more extreme events, the so-called black spawns that come at us out of nowhere, and we're part of nobody's plans. In that kind of world, all you can focus on is how to adapt more quickly, sense and respond more quickly to what is going on in the world. Blockchain networks and token economies are distributed, that is to say they have no centralized component. Because of this, we cannot develop the economy in the traditional top-down approach, but instead have to work with the innate, peer-to-peer market dynamics. Without centralized coordination, they rely on markets to predict the future and decide how to allocate resources and invest in response to that. It has long since been noted that markets themselves are decentralized systems for the processing of information and the distributed allocation of resources. One of the key aspects to economies and markets is as information processing systems. They aggregate all the local information that people have and use it to formulate a price that indicates something about the supply and demand of a good or service, both now and possibly in the future. Market prices are good ways of aggregating dispersed information and summarizing that information in a single key figure, the price. Futures markets are good examples of this where traders bring their knowledge into the market about some future outcome with the price then reflecting that dispersed information. These loosely coupled evolutionary type systems are long-term much more reliable than highly structured centralized systems, whether it's from biology or whether it's even engineering systems. And we are entering a world where we can have business systems that have these properties and that means that it's able to absorb and adapt to small and large changes on an ongoing basis. With highly centralized systems, you have a lot of internal fault lines that get covered up by the opacity and boundaries. They just sit and sit until things break massively and you get massive crashes. When you open the door to identifying and adjusting to your small perturbations and letting systems evolve through interaction with the community, you can actually massively reduce the probability of these mega-like corrections because you're making micro-corrections all along the way. As an example, we can think about a large financial institution or enterprise going bankrupt. The internal dysfunctionalities and stresses within the system will not be revealed for long after they happen and it will take years to wind down the operation. A token economy is a real-time economy. Think about how hard-coded financial regulation into a decentralized autonomous organization would be. Ten minutes after a bank started trading insolvent, the automatic regulation smart contracts would kick in and that bank would just immediately shut down and redistribute its assets to its creditors. That whole process of insolvency, the minute it started trading insolvent and got to the threshold, would just automatically happen and we wouldn't need agencies coming in and monitoring. With these token economies, a new form of economic development is emerging, one that is more organic and evolutionary. Key components of that are initial coin offerings as means to bootstrap the token network, prediction markets as distributed mechanisms for bringing in the best available information and predicting what will happen in the future, and advanced analytics as means of optimizing the allocation of resources on the network through big data analytics. As we will talk about in the coming video, ICOs are a critical part to these distributed networks gaining their autonomy from the traditional financial system and enabling communities to start their own networks based around their own value system. These decentralized token networks can be biologically self-sufficient. We can add inflation to a network every year with, say, 5% more tokens distributed to the network, thus creating their own value with which to fund their own development, which is being taken out of the value of the whole ecosystem. The network inflates itself to invest in itself towards creating more value, which will compensate for the inflation. So anyone can join and say, I will do marketing, or I will do all of these things to the platform that will add value to the network in the future, and if the network decides that they want to allocate value to that activity, then just by a simple vote, the network can decide to produce tokens and give them to the participant as an investment in its own development. The network is self-sufficient in a sense that it creates its own value. It mince these new tokens and distributes them as needed for future growth. In the case of Ethereum, for example, with something like a $50 billion token valuation and maybe a 10% inflation rate, Ethereum is already allocating approximately $5 billion a year in decentralized budgeting to its own development. This is an incredible degree of biological self-sufficiency that we have never seen. These token networks do not depend on anything. In this sense, they are completely autonomous code. Likewise, the blockchain exists within the context of the next-generation internet, the so-called distributed web. A key part of this is advanced analytics. When whole economies, supply chains, and enterprises are on the blockchain, the potential for analytics becomes extraordinary. One early example of this is the IBM Data Science Experience Platform that is used to analyze and visualize supply chain data from a blockchain environment. They enhance the data with info taken from weather APIs and other sources. They then train and deploy a machine learning model that predicts shipping delays. Blockchains open up data silos and expose them for running analytics over whole networks and systems. This provides new ways for us to determine the optimal allocation on a given economic network and even run simulations as to future outcomes.