Nobel prize winning economist George Akerlof identified why economists have lost sight of the real economy:  they are too concerned with the purity of their mathematical models.  To make their models solvable, economists make assumptions that don’t accurately reflect how the real economy works.  Furthermore, this focus on math, what he calls “Hard”, over non-mathematical approaches, what he calls “Soft”, also means there are many relevant topics that economists simply cannot address because they cannot be expressed as mathematical equations.  Financial crises are an example of this.

Information asymmetry is too.  As Professor Akerlof notes, the focus on math means his famous “Lemon Problem” paper could never be published in a peer reviewed economics journal today.  Since the Information Matrix includes the “Lemon Problem”, it too could never be published in a peer reviewed economics journal.  And this in turn explains why the economics profession still does not know where financial crises come from, how to prevent them or how to end them.

Of course, this post wouldn’t be fun if I didn’t use the Information Matrix to skewer the economic profession’s focus on math.

Information Matrix

A quick look shows three quadrants fall under the “Soft” approach (information asymmetry is captured in the Lemon Problem and the Antique Dealer Problem quadrants; Blind Betting is the quadrant the economics profession hasn’t discovered yet).  The only quadrant that falls under the “Hard” approach is the Perfect Information quadrant.

Since the other three quadrants exist, is there any reason to think models assuming Perfect Information reflect the real economy?

No!

Regular readers know the Information Matrix explains the design of the global financial system.

The intent of the financial system’s design is to move the vast majority of investments from Wall Street’s preferred Blind Betting quadrant into the Perfect Information quadrant.

Moving investments into the Perfect Information quadrant makes sense on a number of levels.  First, the Perfect Information quadrant is where the theories developed under standard economics about how the market optimally allocates resources work best.  Second, it allowed investors to Trust, but Verify the story Wall Street tells them.

Keep in mind, behavioral economics’ observation people like a good story.  While completely understandable when using the “Soft” approach to economics, this observation must be ignored by the “Hard” approach.  Why?  Think of how hard it would be to express a good story as a mathematical equation.

Behavioral economics also observes it is in our DNA to trust.  This love of a good story and willingness to trust operates in both the Perfect Information and Blind Betting quadrant.  The key difference between the two quadrants is in the Perfect Information quadrant the story can be verified and in the Blind Betting quadrant the absence of the necessary information means the story cannot be verified.

Whether the story can be verified or not results in a vastly different response when Wall Street’s valuation story is called into doubt.  In the Perfect Information quadrant, the story can be verified and the doubt dismissed.  In the Blind Betting quadrant, the story cannot be verified.  Not only is the doubt not dismissed, but the logical follow-up question arises:  is the investment worth anything?  This too cannot be verified.  Investors recognize this and, as behavioral economics suggests, naturally panic.  The result is a “run” to get their money back.

Behavioral economics allows us to understand the classic financial crisis panic.  The Information Matrix framework shows why the financial crises always start in the Blind Betting quadrant.  Together they explain why if you want to minimize the chances of another financial crisis, you minimize the size of the Blind Betting quadrant.

Of course, the economic profession’s current focus on the “Hard” approach and its insistence on reducing everything to a mathematical equation makes it impossible for the profession to ever understand this.