Here, I occasionally post scholarly content, but it is mostly my public-facing chalkboard.
Scholarly Stuff
Our communities can be visualized as systems that inform our identities, choices, and experiences in life. Certainly, these are “complex” and dynamical systems that offer novel challenges and opportunities that constantly change with us during the journey.
The human brain only consumes about 40 watts of power, but provides amazing capabilities of perception, cognition, and memory. To achieve that feat, it necessarily consolidates information and simplifies relationships between objects and among people. This leads to a number of cognitive errors that provide insights for neuro-cognitive research.
My academic interest is a better understanding of several particularly challenging aspects of cognition, especially when bounded rationality becomes a factor in perception, knowledge, or choices. The heuristics that people adopt are often problematic and seldom complete. Typically, the more vexing situations involve problems that include
- Asymmetries
- Non-linear relationships
- Randomness
When these factors occur in complex systems, they add additional unpredictability and create the conditions for chaotic or black swan behavior.
Complexity Theory
Complex systems are often best modeled as interconnected nodes in the form of networks, where each vertex represents a person acting locally with and simultaneously with others. These are dynamical systems by virtue of varying initial conditions, non-linear processes, individual learning, and interaction among agents. Consequently, these systems operate far from equilibrium, co-evolve with their environment, unpredictably exhibit black swan (N.N. Taleb) behavior, and have emergent outcomes.
Confidence is one predictor of social decisions in complex systems. People often rely on their prior (sometimes imagined) beliefs in uncertain situations and have simplistic explanations for what they experience, which mediates their choices. Our approach to modeling this process within a complex networks is to assign the quantitative metric of confidence, to explore how “false confidence” (FC) creates errors in decision-making.
“False confidence” can be treated quantitatively as the difference between perceived and objective information. The chart above goes one step further to treat each as an evolving probability of non-randomness.
Embodiment and Shared Cognition
Embodied and shared cognition underlies the premise that human intelligence is conditioned and mediated by the physical body as it interacts in its environment. It is largely what makes us situationally distinct individuals. (See Varela, F.J. Thompson, E. and Rosch, E. 2016, The Embodied Mind, for an introduction.)
Modeling and Simulation
I am exploring the use of “Utopia” software which provides the tools to conveniently implement computer models, perform simulation runs, and evaluate the resulting data. More details to follow.