(The following is excerpted from an Appendix of the Citizen’s Survival Guide, which can be downloaded free by clicking on the book cover in the right sidebar.)
What’s Wrong with Economics?
It is a question worthy of a graduate seminar in economics, but we will try to illustrate the key issues in a few pages. One of the central assumptions in general equilibrium (GE) models that are widely used in neoclassical macroeconomics today is that people are pretty much alike, or homogeneous in their preferences. Consumers, workers, savers and investors, for example, all want to maximize utility and profits. A second assumption is that these preferences remain fairly fixed over time and do not vary or adapt under different circumstances. Lastly, economic models assume the availability of requisite information and the ability of individuals to process that information accurately.
Using these three basic assumptions, economic theory employs higher-order mathematics to build very complex and powerful models that enable us to study and understand the economic world we live in. Straight (or convex curve) functions usually yield clean solutions, known in economics as optimal equilibrium solutions. A good example is the graph of supply and demand curves that intersect at the equilibrium price. However, we have found that the assumptions cited above are frequently violated by real people acting in the real world, with significant implications for the results of the models. The most serious problems result from information failures and the ways in which we rationally adapt to these failures.
Imagine a box of dried spaghetti pasta. The individual strands or sticks can bend slightly without breaking, but they are mostly rigid and straight as an arrow. They cannot be folded back on themselves or twisted into a pretzel. Now, imagine building a model of a house or some other structure, such as a globe, with these sticks. The straight lines will intersect at many different points where you can glue them together. Depending on how big your model or how small the lengths of noodles, you can create a pretty good representation of curved surfaces using only straight lines. The model looks reasonably good, we can see what the model is meant to represent, and the structure holds together well.
Now, imagine trying to build the same model with wet, cooked spaghetti. Impossible. The strands wiggle around like worms, twisting and turning and refusing to retain a fixed shape. The model collapses into a tangled, gooey mess. This crude comparison illustrates the difference between an economic model that is built with simultaneous straight line equations that yield optimal solutions—those based on assumptions of homogeneous fixed preferences that don’t change over time and that reflect perfect information—and the often sad reality of a messy world. Economic behavioralists have discovered that our preferences vary, they change over time as we receive feedback and our circumstances change, and often they are not fully informed because we lack the requisite information. People are not like dried spaghetti noodles, they are like cooked noodles – unpredictable. They are plagued by the uncertainty of their environment and are dominated by loss aversion. This creates serious limitations for modeling specific economic puzzles, especially those that relate to distributional dynamics characterized by variable preferences, feedback, and adaptability. Examples include inequality, the business cycle, networked market solutions, and information cascades. So, what are we to do?
First, let’s not throw out the baby with the bathwater. Neo-classical GE models based on higher-order mathematics are very useful and powerful tools for explaining a wide range of market phenomena. Computer technology now offers us another powerful tool with simulation modeling. The exploding processing power of computers allows us to build market models from the bottom up using individual “agents” that can behave according to any number of simple rules that can be used in combination to create complexity. Individual agents can be unique in their preferences and adapt in an instant to changing circumstances, so our model doesn’t need to be constrained by rigid, representative agents. We can simulate how these programmed agents interact, as in a market, and observe the results. In effect, we can build ourselves interactive economic worlds that mimic the real world and then observe how these models behave as we change parameters.
I believe these techniques will allow us to fill in the gaps of conventional economic theory and improve our understanding of market dynamics.* This promising new branch of economics is called agent-based computational economics. It’s not as elegant or intimidating as general equilibrium models, but it can do things GE models cannot. It is a sign of progress that hopefully in fifty years we won’t still be grappling with the same epistemological problems and policy failures.
* My own attempt to contribute to agent-based modeling resulted in a simple model called CasinoWorld.