All the Wiser: Wisdom from a System Dynamics Perspective
Seana Moran Research Fellow, Stanford Center on Adolescence Stanford University, United States
Michael Connell Principal, Institute for Knowledge Design Stanford University, United States
Short Definition
Our definition of wisdom corporates for “symptoms”
1)We are in cooperating the common good
2)Cooperating sense of fairness and distributed justice
3)Linking individual outcome to a shared fate
4)Balancing long term sustain ability with sort term gains or losses
Summary Points
Text from Wisdom Institute
Wisdom is a dynamic, situated phenomenon. We view it as an outcome of recognizing the leverage points within a system and making decisions by taking into account the implications for oneself, others, and the greater good. Wisdom occurs from the interaction of individuals’ perceptions, decisions, reflections, and actions over iterations of situations. The implications of this research involve finding and understanding these leverage points, which can be used by one “wise man” person with access and insight into the system, or which can be implemented through the distributed efforts of many people functioning under relatively simple system rules via trust and cooperation. This “All the Wiser” study uses an interactive simulation to test a model of wisdom that incorporates data from three perspectives—self, others, and system. The specific goal of this initial research project is to create, test, and pilot a minimal prototype to determine whether this approach is fruitful for understanding from a systems perspective what wisdom is, how it occurs, and how it can be cultivated. Data collected from the players’ moves in the simulated environment are analyzed with exploratory analyses, principal components analyses, and power analyses. This will enable them to determine the structure of the data, assess data and theory coherence, and determine sufficient sample size for a random-effects multilevel model.
During the fall 2008, Dr. Moran and Dr. Connell focused on simulator dynamics and data analytic strategy. This involved reviews of literature related to wisdom, group cognition, economic games, economics of energy, and educational simulators. The primary task was devising coupled equations for the simulator that determines the dynamics for game play—that is, how the players’ decisions about energy budget create a realistic cascade of effects related to world oil demand and pricing, and related to several other variables of each player’s country (i.e. gross domestic product, standard of living, pollution levels, and citizen “happiness”). In addition, Moran and Connell planned how simulator variables will affect research variables related to wisdom, reasoning, and social interaction.
Their more recent efforts include writing the technical specifications for the user interface and database (that stores information collected during simulator play), continuing to model simulator dynamics, finalizing the data analytic plan, and setting up the internet connection between the simulator and the external measure of wisdom, the Reasoning about Current Issues Test (Kitchener, King, & Wood, 2000). The primary focus was designing the appearance and content of the computer screens, which creates the simulation experience. The second focus was on testing computer models in order to explore how simulator data would interact with drive play. Although it is much more simplified than the real-world experience, the assumptions of the simulator were matched to real‐world parameters to the furthest extent.