How Various “Irrationalities” Proven to be Rational 🧠

Bin Li, Independent Researcher, a Visiting Scholar of Unversity of North Carolina at
Chapel Hill

The rise of behavioral economics signals mainstream economists are finally recognizing the significance of various “irrationalities” – biases, anomalies, and departures from pure rationality that other schools have long emphasized. But what if these “irrationalities” could actually be proven as forms of rationality itself? The author presents a fascinating perspective on how this could be accomplished.

At the heart of the argument is the premise that human thinking operates much like a computational process, following discrete “instructions” (operations like add, multiply, compare, etc.) to manipulate information over time. Unlike neoclassical assumptions of pure deductive reasoning toward optimality, real-world thinking employs various instructional combinations suited to urgency and bounded rationality.

Just as physical production uses different resource combinations based on comparative advantages, Li argues our thought processes strategically combine different “instructions” based on their relative speed vs. accuracy tradeoffs for the situation. Purely deductive reasoning may yield quality results, but is often too time/resource intensive for pressing deadlines. Quicker intuitive “instructions” like induction, association, or imitation produce less reliable but more timely conclusions.

Three Curious Takeaways:

1) Human cognition does not rely on a single mode of pure rationality, but flexibly combines different “computational instructions” adaptively based on situational constraints.

2) So-called “irrational” tendencies like heuristics and biases are actually manifestations of these instructional tradeoffs for expediency under bounded rationality.

3) By modeling thought as computation, these “irrationalities” can be re-interpreted and proven as rational adaptations – thereby preserving rational choice theory while enriching its psychological realism.

For those intrigued by potential synergies between behavioral science and computer/information science, this piece offers a uniquely computational metaphor for understanding rationality’s nuances. It posits a unified framework where “rational” and “irrational” processes are simply different cognitive algorithms optimized for human reality.

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