Two economists are now rejecting investment models that offer sharp predictions of the future. Instead, they posit, we must accept that we are all working with imperfect knowledge.

If only the financial wizards of  Wall Street had been familiar with this theory when they embraced the complex, yet ultimately flawed economic models that led to last summer’s credit crisis. Perhaps major banks and brokerages could have avoided the massive write-downs and financial turmoil they are currently enduring.

In their latest book, Imperfect Knowledge Economics, professors Roman Frydman and Michael D. Goldberg assert that exact models of purposeful human behavior are beyond the reach of economic analysis. “Trading in financial markets cannot be reduced to mere financial engineering, even if it is based on the most recent advances in contemporary finance theory,” say the authors in an early chapter.

Frydman and Goldberg’s theory challenges some key assumptions in the current economic thinking. “On a practical level, the contemporary approaches to economics and finance are failing, and we believe they will continue to fail because they search for relationships that are either fixed over time or that change in ways that are fully pre-specified,” Frydman says.

Traditional economic methods are typically founded on the rational, self-interested behavior of individuals, and are used to predict what effect this behavior will have in the aggregate. But this approach, which is based on mechanistic human behavior, fails to take into account the individual creativity and sociopolitical changes that cannot be predicted. Likewise, more recent behavioral models are also flawed. These models may accept human irrationality, but they still attempt to form exact predictions of behavior’s effects in the aggregate. 

Change Is Inevitable

Modern economists are trained to search for “sharp predictions” through the application of scientific methods. Yet while physicists might find laws that apply consistently in the natural world, economists fail when they try to do the same, because the system they observe is constantly evolving. Economists do try to predict changes in the economic system and work these into their models, but a key point Frydman and Goldberg make is that this modeling of change itself relies on scientific methods, thus limiting its use.

For example, a model that predicts future exchange-rate movement may identify a particular macroeconomic trend that affects currency price. The trend itself could actually be built into the economic model. But the trend could also break down, leaving the model flawed. Frydman and Goldberg assert that, given this reality, it is impossible to create an exact model that will consistently work. Taking financial modeling to this level would go beyond the limits of human knowledge. “The trouble is that as we think about the world, the world itself changes,” Frydman writes. Instead, we have to accept that precise, scientific modeling of the financial system is impossible. No one can foresee how market participants and policymakers might change their decision-making processes, and hence the system.

In their book, Frydman and Goldberg offer a method for applying the lessons of imperfect-knowledge economics to the exchange-rate markets. They attempt to identify the limits of human knowledge and then examine how qualitative information can be used as a guide to currency moves. The incessant search for a correlation between the exchange rate and macroeconomic fundamentals has diminished our ability to understand what moves markets, they argue. Market participants act on the basis of different knowledge and intuitions, and therefore adopt diverse strategies in forming and revising their exchange-rate forecasts over time. No model could possibly mimic how this diversity will change as time passes.

Frydman and Goldberg’s insights illuminate one key lesson:  Too much emphasis is placed on quantitative modeling in general, and not enough on intuition and other qualitative aspects that cannot be measured. “Good investing and good forecasting are not based on a mechanical model,” Frydman says. Good forecasting is much like good entrepreneurship—it relies on personal knowledge and intuition, as well as luck in spotting profit opportunities.