Big Data offers an opportunity to observe that complex systems, natural or man-made, share many universal design principles. The cell, multi-cellular organisms, ecosystems, economic systems, societies, intricate engineered systems, and the web are all evolving complex systems existing in complex environments, and all sharing common design. Complexity theory researchers often focus on studying only one design principle, mostly applied to only one real-world complex system: ironically, still reductionism. It may be insightful to look at how design principles of complex systems are related. To develop intuition about this idea, observed principles can be assembled and organized, and then the relationships between them identified. An initial list of design principles observed in complex systems with some hinted relationships follows:

Survival of the fittest is a central design principle shaping complex systems. This concept is an outcome of competition. Competition is often not fair. Rich-get-richer is a growth process where the rich, the one having many relationships, central, essential, and fit, grows faster than the poor, lonely, unfit, weak, and less-connected. Growth in complex systems is often achieved by duplication-divergence. This known biological design principle is also common in economics or on the web. For example, car models, web-based companies, and software products grow through duplication-divergence, and where innovations play also an important role. Although growth is rapid for the rich and central, rapid growth at the periphery is also observed. Another essential concept is information transfer. Information is constantly flowing, commonly compressed, decoded, and translated. Information is commonly intercepted by sensory units. Sensors transmit the state of the environment into internal processing centers. In those centers information is processed intelligently by classifiers that learn and use memory to determine an appropriate response. Often this response is simply turning on or off a switch. Sensors often implement filters and amplifiers to convert noisy information to valuable data. To compute the right response, processing centers use learning, memory, and adaptation. The ability to adapt to new environments is critical for survival of agents in complex-systems. Robustness to fluctuations and changes in the environment is required for fitness and survival. When learning is successful, responses are often automated. Automation is also used for efficient production. Efficient and sophisticated mechanisms are in place to manufacture many exact replicas. This allows the cycle of birth-life-death to continue. The birth-life-death concept is related to the observation that agents are dynamically replaced by new agents while global patterns remain. For example, proteins in a cell, water molecules in a river, cars on a highway, blood cells traveling through blood vessels, or people commuting back and forth to work into a big city. In some cases these agents circulate while in others completely replaced. Agents often travel on elaborate and efficient transportation systems that permit the transfer of resources to remote locations quickly. To move around, locomotion is required. Locomotion is the ability to travel. Economic systems rely on planes, ships and trucks to transfer goods and workers. Botanic plants lack the ability to move. This handicap is compensated with an amazing ability to use solar energy, to extract nutrients from the ground, and to reproduce effectively.

Noticeable barriers are present to protect agents from other agents and the outside. These containers or modules hide internals from exposed externals. The externals have an interface, often able to communicate with the environment and listen to it, as well as communicating with other agents, using standard protocols. The plug-and-play design principle allows reusability and generality which permits agents to work together to form higher-order agents. This design principle is related to the ability of many agents to switch between individual behaviors to pack mentality. When in a pack, agents form distinct geometrical patterns such as trees, waves, and other symmetrical shapes. Polymorphic agents in a pack behave randomly in parallel but often display amazing synchronicity. Synchronicity can be achieved through governments but often not requires them. Randomness and noise is required for emergent behavior, where it is used to overcome local minima. Randomness and noise result in a constant search for equilibrium, but the system never settles at equilibrium forever. Phase transitions happen in short time periods where a system, being in a rather stable state, go through one small change that induces many changes, turning the system into chaos or into another new stable state. Searching for a lower energy state is a design principle directly related to efficiency and energy utilization; where many processes in complex systems use energy, and where agents compete for energy resources. This goes back to the first concept of competition and survival of the fittest. Feedback loops are important dynamical structures that set the creation of complex systems in motion. The primordial metabolic soup was suggested to be made of simple enzymes forming competing feedback loops. Competition is taking action in markets, where trade is used so all are winners. Market fluctuations often display pendulum behavior shifting from side to side. Successful trade can be achieved when there is diversity and heterogeneity, and where the winners are often the innovators, or the drafters, the best listeners to innovation. Trade also results in cooperation. Cooperation can develop to symbiosis: the codependency of two separate agents on the coexistence of each other. Unidirectional symbiosis is the emergence of parasites. These agents use the success of their hosts for their own survival. Successful agents must learn then how to self-repair and fight parasites, while engaging in a game of creative evasion strategies with parasites, because parasites fight back.

As we are able to create complete independent autonomous agents, living in new self-sustaining environments, where biological systems can be more engineered, and engineered systems are becoming more complex. By understanding how design principles of complex systems are related, we should be able to better understand, control and manipulate complex systems.