Complexity Science began in 1984, at the Santa Fe Institute, where researchers from diverse fields gathered to: study how multiple, adaptable, interconnected systems behave; explain how different system variables interact to impact outcomes; and better clarify how these variables adapt to alter outcomes over time. Examples of systems studied early on, include: financial markets, ecosystems, the brain and evolutionary biology.

Complexity Science is the interdisciplinary study of complex adaptable systems that serves as the framework for "pulling it all together". Complexity Science is a means to an end. It is an analytical perspective that brings varied disparate data, market variables and business operating systems under one "lens"—enabling business decision makers to closely examine their multifaceted business environments and better understand critical interdependencies, predict market impact and outcomes over time, and determine actions and requirements key to achieving desired business results.

In business terms, systems can consist of many variables, such as: brands, consumer markets and segments, customer preferences, employees, touch points, media spending, marketing campaigns, macro economic conditions, resource variability, pricing, competitive offerings; even weather patterns that affect sales. Some examples of Complexity Science outputs include predictive and ROI models, simulators and business systems schematics.

By embracing Complexity Science as a reality of the marketplace, we can make use of powerful methodological tools to better understand the dynamics of our markets. These tools include:

• Neural network and free-form mesh models
• Systems dynamics simulation
• Agent-based Simulations
• Genetic algorithms

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