Our Science

The Science of Diversity

“The implications for all organizations is to create a work environment in which all employees are willing and able to contribute their knowledge and experience to solving the problems facing these organizations.”

—Norman Johnson PhD.

The Science of Diversity is rooted in complexity science and the study of complex adaptive systems. Over the past 4 years, we have applied these sciences into the development of a proven technology built on an Agent-Based Modeling (ABM) framework that helps organizations identify the emerging behavior resulting from the planning around "what if" scenarios.

The Science of Diversity is rooted in complexity science and the study of complex adaptive systems. Over the past 4 years, we have applied these sciences into the development of a proven technology built on an Agent-Based Modeling (ABM) framework that helps organizations identify the emerging behavior resulting from the planning around "what if" scenarios.

What is It?

Complexity Science and Complex Adaptive Systems

According to the Santa Fe Institute, Complexity Science is premised on the assumption that seemingly disparate phenomena, both natural and social, evolved and constructed, can be understood using a common conceptual framework.  The goal of the science is to better understand complex adaptive systems (CAS).  A CAS is a heterogeneous collection of interacting agents that evolves over time at the macroscopic system scale.  Any agent in the system interacts with one or more other agents in any "move" of the system according to the rules.  Some or all of the agents in the system also base their decisions about what to do in the next move based on the macroscopic state (macro state) of the system or on conditions exogenous to the system having some impact on it.  In short, a CAS is a setting where behavior is hard to predict, outcomes are not foregone conclusions and participants evolve and adapt to circumstances, influences and to each other.  Examples of complex adaptive systems include standing ovations, the stock market, ant colonies, the consumer marketplace, organizations, diversity initiatives, and even the weather.  Once a CAS is modeled and understood, it can be "tinkered" with and emerging behavior and outcomes can be observed.  Many times, unprecedented results occur that historical analysis cannot explain.  Examples of such phenomena in the consumer marketplace include the emergence of bottled water and sweet tea.

How Do You Simulate a Complex System?

Agent Based Modeling (ABM)

Agent-based modeling (ABM) is a the simulation technique that is being applied by iGlobalNetwork to solve business problems. At a high level, an ABM model is made up of agents (independent, decision-making entities) and the behavioral rules that they operate against. In the iGlobalNetwork model, consumers and media both represent agents that interact with each other in a simulated environment. Unlike traditional top-down approaches that focus exclusively on historical results, ABM models employ a bottom-up approach that gets to the "why" historical results occurred. In the iGlobalNetwork case, what employee and consumer traits, marketplace dynamics, behavioral rules and media interactions led to employees and consumers behaving the way that they did. Just like employees and consumers, ABM models can adapt and evolve, giving them emergent properties.

Complexity science in the news.

How Does a CAS Work in Organizations?

In organizations a CAS can be explained as:

According to the Santa Fe Institute, Complexity Science is premised on the assumption that seemingly disparate phenomena, both natural and social, evolved and constructed, can be understood using a common conceptual framework.  The goal of the science is to better understand complex adaptive systems (CAS).  A CAS is a heterogeneous collection of interacting agents that evolves over time at the macroscopic system scale.  Any agent in the system interacts with one or more other agents in any "move" of the system according to the rules.  Some or all of the agents in the system also base their decisions about what to do in the next move based on the macroscopic state (macro state) of the system or on conditions exogenous to the system having some impact on it.  In short, a CAS is a setting where behavior is hard to predict, outcomes are not foregone conclusions and participants evolve and adapt to circumstances, influences and to each other.  Examples of complex adaptive systems include standing ovations, the stock market, ant colonies, the consumer marketplace, organizations, diversity initiatives, and even the weather.  Once a CAS is modeled and understood, it can be "tinkered" with and emerging behavior and outcomes can be observed.  Many times, unprecedented results occur that historical analysis cannot explain.  Examples of such phenomena in the consumer marketplace include the emergence of bottled water and sweet tea.