{'fraction_non_alphanumeric': 0.02237380627557981, 'fraction_numerical': 0.0016371077762619372, 'mean_word_length': 4.782334384858044, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Cognition: The Black Box of Economics

In his autobiography Bertrand Russell tells us he dropped his interest in economics after half a year's study because he thought it was too simple. Max Planck dropped his involvement with economics because he thought it was too difficult. I went into economics because I'd been trained in mathematics and I thought, as Russell did, that economics looked easy. It took me several years to get from Russell's position to Planck's. Economics is inherently difficult. In this paper I will explain one path by which I came to that view. Whether one sees economics as inherently difficult or as simple depends on how one formulates economic problems. If one sets up a problem and assumes rationality of decision making, a well-defined solution normally follows. Economics here is simple: From the Problem follows the Solution. But how agents get from Problem to Solution is a black box; and whether indeed agents can arrive at the Solution cannot be guaranteed unless we look into this box. If we open this box economics suddenly becomes difficult. Once in a while as economists, we do justify our assumed connection between problem and solution. In a well-known paper, John Rust (1987) tells the story of Harold Zurcher, the superintendent of maintenance at the Madison (Wisconsin) Metropolitan Bus Company. For 20 years Zurcher scheduled bus engine replacement of a large fleet of buses—a complicated problem that required him to balance two conflicting objectives: minimizing maintenance costs versus minimizing unexpected engine failures. Rust figured out the solution to this combinatorial optimization problem by stochastic dynamic programming, and matched that optimization against Zurcher’s. He found a reasonably close fit. The point of Rust’s article was that although this was an enormously complicated problem, Harold Zurcher found the solution, and therefore at least in this case economists’ assumption that individuals find optimal solutions to complex questions is not a bad assumption. The Zurcher example leaves us with a broad question: Can the assumption that individuals find optimal solutions to economic problems be justified so that we can avoid studying the details of the decision process? In simple cases the answer is yes. In most cases however, it is no. Think of an ocean that contains all the well-defined problems that interest us in the economy, with ever more difficult problems at greater depths. Near the surface lie problems like tic-tac-toe. Below that are problems at the level of checkers, and deeper still are problems like chess and Go. We might know theoretically that a solution to Chess exists, say in mixed Nash strategy form, but we can’t guarantee that human agents would arrive at it. So the problems that are solvable the way tic-tac-toe is solvable lie within two or three inches of the surface, and an ocean of problems deeper than these cannot be guaranteed of solution. We can add to these the many problems agents face, perhaps the majority they face, that are not well specified. Zurcher’s problem lies on the boundary of what economics agents can accomplish by way of a “rational” solution. Deeper than this, economic “solutions” may not match reality or may not exist. What happens at these deeper levels? Human decision makers do not back off from a problem because it is difficult or unspecified. We might say that when problems are too complicated to afford solutions or when they are not wellspecified, agents face not a problem but a situation. They must deal with that situation; they must frame the problem, and that framing in many ways is the most important part of