Despite a substantial, nearly universal need, it is still too hard to preserve and share group memories. Science tells us memories are temporal and subject to change, shaped by language and imagery, and introducing group dynamics just adds to their complexity.
Consumers want their group experience preserved and shared the way they remember it. Each person’s experience is unique and also involves their friends, and their friends’ memories. The differences between them may be subtle, but they’re significant.
Groups vary in size from a handful to thousands, have varied levels of engagement and are often not collocated.
Each group member has their own stories and images that represent their group memories.
Group members may be emotionally connected to some--but not all--other group members. Even in the smallest of groups, not every person matters to everyone else to the same degree.
None of the systems used to preserve stories are well-suited to these challenges. Group memories are difficult because a group is, by definition, a complex system of interconnected parts. People and their relationships—one to another, some to many, but few to all, and each one to the group—define complexity. Navigating these complexities and their non-linear, unpredictable behavior requires techniques drawn from an abstract field of applied mathematics called complex systems theory, which is focused on analyzing organized, but unpredictable behaviors of complex systems found in nature.
Complex systems are composed of interconnected parts that as a whole exhibit one or more properties that are not obvious based on the properties of the individual parts. Some of the characteristics of complex systems include:
Self-Organization: In complex systems, change occurs naturally and automatically. This change is accomplished by the elements that make up the system responding to feedback from the system’s environment in order to increase efficiency and effectiveness.
Non-Linearity: Linear change is where a sequence of events affect each other in the order in which they appear. In contrast, in non-linear change, elements changed by previous elements can also affect the elements that preceded them.
Order/Chaos Dynamism: In system development, with strong knowledge of the early stages, it can be fairly easy to predict a range of possibilities for the next stage. But farther down the development sequence, it’s far more difficult to predict based only on knowledge of the first stage. Even when knowledge of the system is extensive, and even though there may be a logical flow from stage to stage, predicting developments farther down the sequence can be increasingly difficult. This uncertainty of predictability is called "chaos".
Emergent Properties: The unpredictability inherent in the natural evolution of complex systems can yield results that are totally unpredictable based solely on knowledge of original conditions. Such unpredictable results are called emergent properties. Emergent properties are still a logical result, just not a predictable one.
Out of Complexity Theory comes a framework called fractal analysis which is used to study complex patterns such as tumor growth, gene expression, forest fire progression, economic trends, and cellular differentiation in space and time. This somewhat arcane scientific domain provides mathematical validation to our perspective. In complex adaptive systems, networks of interacting individuals create an operating history that weaves together a story. Once that narrative coalesces, knowledge of it by the components of the system can influence their individual behavior.
Thus, through fractal analysis, the recall of group memories by group members can be viewed as an evolving dynamical system with group properties that emerge from the interactions among the participants, and between the participants and the group. A solution for navigating these group memory complexities demands scientifically rigorous approach not found in existing memory preservation systems.