Although nobody has done it yet for economics, the connection between microscopic and macroscopic dynamics is old news in physics. Over fifty years ago, and with antecedents even earlier, mathematicians and physicists discovered that by coarse-graining certain variables that describe how units of a system interact, the macroscopic dynamics of a large system could be derived from microscopic variables. The technique is called renormalization, and the wikipedia entry on renormalization group is decent enough.
To visualize how this works, it helps to picture the microscopic system as a network of nodes distributed spatially, with one or more vectors associated with each node denoting how some observable quantity flows from one node to another. In general, the magnitude and direction of these vectors may vary, but in the simplest models (such as the Ising model), there is only one vector and it has the same magnitude at each node although it varies in direction.
With nodes distributed along only one-dimension, feedback doesn't usually develop within the network. Interactions, by construction, are limited to nearest neighbors in such a network. (Doesn't this explain why long supply chains can be unstable?) The exception is for when the end of a one-dimensional chain is linked to the beginning, creating a ring. With that geometry, even a one-dimensional network can display non-transient periodicities.
But in many real networks, such as our economy, the linkages between nodes are multi-dimensional. Each link in the supply chain for a particular good is also part of multiple other supply chains for its inputs and outputs. This multi-dimensional network permits all sorts of stable and unstable feedback loops to form.
Renormalization entails combining smaller units of a system with a defined symmetry into larger units with the same symmetry. Iteration of this process eventually results in either a cancelling out or a convergence of microscopic variables. Usually, there is an "order parameter" associated with one of the microscopic variables that will end up in one of three states as the result of these iterations when it is "coarse-grained" to macroscopic scale -- perfect order, perfect randomness, or fractal (i.e., self-similar) structure at a very specific point in between. What is less well-understood is what drives the transitions from order to disorder, and especially the fractal state in between.
The dynamics of flow -- i.e., fluid dynamics -- provides some interesting clues. In fluid dynamics, physicists have used the momentum of vortices to model turbulence. Unless you already know a little physics, this may be hard to appreciate, but for every dynamic loop in space, there is an associated angular momentum, perpendicular to that loop. The sums of the angular momenta of vortices have been used in what is to date perhaps still the most successful model of turbulence in a fluid.
The formation of vortices (and so, by analogy, feedback loops) in fluids leads to all sorts of interesting dynamical patterns. In the past, it's been very difficult to study fluids such as water experimentally, so other fluids such as electron plasmas, have been studied instead. Among other interesting phenomena, one may observe "vortex crystals" form within a fluid as a turbulent flow steadies over time. These may be an example of what Ilya Prigogine calls dissipative structures. Feedback loops seems to be a key prerequisite to their formation.
At this point, we're already in deep waters, but if you've made it this far, then bear with me just a little further. From Kolmogoroff's theory, it appears that the vortex structure of turbulent flow bears similarity to the synchronized or self-organized critical structure observed in the brain during gamma wave resonance. Both the spatial and temporal structure of the vortex vectors associated with turbulent flow and the signal transduction vectors associated with gamma wave resonance obey power laws to a reasonable approximation.
Aside from the fact that such different phenomena can be approximated using similar mathematical models (not so remarkable to many physicists, really), this connection is remarkable for what it suggests about our perception of time.
Gamma waves are associated with a particular state of consciousness. A person can train to enhance the onset of gamma wave resonance. In fact, there are zen monks who have been experimentally verified to have this ability. A fact about zen meditation that might be familiar only to those who have practiced it is that it changes, at least temporarily, one's perception of time. As Csikszentmihalyi describes with respect to flow, the perception of time while in meditation may dilate or contract, but it does not remain steady.
The implication is that our perception of time is influenced by the structure of feedback loops within our brain. In particular, we lose our sense of the flow of time when these feedback loops are organized into a particular, self-similar structure that can be approximated (like turbulence) with power laws.
That's enough speculation for today. But as an aside I can't help wondering whether the measurement of time more generally cannot be said to depend on the underlying structure of feedback loops within the dynamical system studied. Doesn't the flow of time itself depend on the way in which the ticking clocks used to measure it out are linked to one other spatially? Does time flow because of an increase in turbulence?
Gravity causes convective flow. Convective flow can lead to turbulence. Thus, gravity causes time to flow?
Note that Roger Penrose has also speculated on how gravity plays a role in constituting our consciousness. Commenters more versed in the Second Law than I are welcome to rebut these speculations below!
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