Since the Arrow-Debreu-McKenzie model was introduced to economics in the 1950s, changes over time have been given less consideration in economic models. See, for example, this statement about changes in market prices by Eugene Fama. The efficient-market hypothesis implies that at any given moment an equilibrium market price exists, and (here's where the flow of time drops out of the picture) that the current price at any given moment is most likely the equilibrium price. Unless you have some information that nobody else in the market has, then you're probably making a mistake by guessing that most people are either underestimating or overestimating the value of a particular good. There are exceptions, of course, but in general economists and policymakers (including judges and lawmakers) think about (or at least did think about prior to this recession) economic equilibrium as static —- a perfect allocation of people and things that the market maintains more or less constantly.
Is there a more realistic way to model an economy? How about as an hierarchical social network? The social network must be hierarchical because smaller markets (i.e., smaller subnetworks) are usually connected to larger markets (i.e., bigger networks) such that there is nesting and overlap among them through the mechanism of cross-elasticity in demand.
Already, our model of the economy is of networks within networks. But the hierarchy goes deeper still. Each individual within an economy has his or her own neural network, which orders decisionmaking and behavior. In principle at least, we could identify subnetworks within an economy by looking to see whether particular individuals share substantially similar neural networks. Similar brain structure and dynamics are probably a physical (and biological) manifestation of consensus in belief and perception about past, present, and future within a subnetwork. Market equilibrium could be identified as the state of the system in which neural networks are matched in structure and dynamics so that the flow of goods through the economy is stable over a period of measurement. Such stability would be manifest in a market price that fluctuates only within the range of transactions costs.
From time to time, however, an individual within a subnetwork might make a new connection in his or her neural network. The new connection would arise from a new structural or dynamical pattern within that individual's neural network. Such a new pattern might be induced directly by exposure to new sensory information or statistically random variations in the individual's neural network, or indirectly from exposure to a new pattern communicated by another outside the subnetwork. In any case, the result would be a modification to the preexisting structure and dynamics of that individual's neural network.
An individual with a new pattern in mind (literally) might then attempt to reproduce the new pattern in the subnetwork of individuals who share (now similar, but not the same) neural network structure and dynamics. Through communication, the new pattern is either recognized, thereby rerouting every neural network in the subnetwork (albeit, through feedback loops, probably with some refinements), not recognized at all because too great a variation from preexisting patterns to be communicated, or rejected as inconsistent with important preexisting accepted patterns. Although a ridiculously difficult problem to solve mathematically, whether a new pattern would be accepted or rejected by the subnetwork could in principle be characterized using control theory to analyze the stability of the system including the new pattern within its preexisting environment. In practice, it's probably necessary to carry out the experiment to know.
The iteration of such a process eventually results in an increasingly sophisticated neural network shared among the subnetwork, with more and more specialized ability to recognize and explain more and more complex information over time. Interconnections among subnetworks, although difficult to achieve, might be expected to produce large returns to the individuals who are capable of matching the patterns in otherwise disconnected subnetworks.
To understand the new insights that such a model would give us in understanding economics, consider how individuals within the network of an economy include both entrepreneurs and customers. A successful entrepreneur, in practice, is one who is able to anticipate what patterns are going to be accepted by the neural networks of customers. Within this organic model of economics, capitalism is a game in which entrepreneurs conduct experiments in order to discover what patterns of behavior (i.e., what neural networks of prospective customers) will adapt by accepting a new pattern offered as an alternative to a preexisting pattern of behavior used to live with time-evolving resource constraints. Correct predictions are rewarded by the mass of individuals that adopts a new pattern of behavior. Incorrect predictions -- or inappropriately timed predictions -- generally disappear without a trace.
Not coincidentially, this game of capitalism provides a social parallel to the flow experience described by Mihalyi Csikszentmiahlyi as optimal to individual psychology. The same basic rules of the game apply both at the level of neural networks and at the level of networks of individuals within an economy. Because of how neural networks constitute economic networks, it should not surprise us to see that similar mathematical structure and dynamics apply to both. Consumer internet marketing professionals probably already know more about how neural networks work than do many neurologists!
Neoclassical theory offers zero insight into any of these dynamics.