The
behavioral finance revolution has greatly expanded our understanding of deviations from efficient markets and rational expectations. Mistakes happen
because we are sloppy thinkers. However, the long list of behavioral biases
does not help us define what it means to be rational in real-life situations or
how we learn to be rational under different circumstances. Solving for rational
decision-making is difficult. My view is that the first choice is to
use quantitative tools, given that the investment world is rich with data, yet
there have been interesting attempts to find broader solutions that
incorporate other forms of thinking.
An
interesting framework for looking at problems is called ecological
rationality. The tools used for a given decision should relate to the
environment at hand. What you do as a fireman in a burning
building should be different from what you do when analyzing the cost of a
mortgage. Similarly, analyzing the potential success of a new business strategy
is different from weighing the trade-off between stocks and bonds.
Several key researchers in this important area include Gerd Gigerenzer
and his team at the Max Planck Institute for Human Development, as well as the naturalistic
decision-making expert Gary Klein. For them, context is important. They try to
solve the puzzles that Daniel Kahneman, the Nobel
Prize winner, has aptly researched. Data-rich problems lend themselves to quantitative analysis;
however, new, uncertain problems may require other approaches.
To
simplify much of this work, the focus has to be on basic coping strategies for
any decision process. This coping can be broken into three parts:
categorization, heuristics, and narrative.
The
primary strategy issue is categorizing the problem. In the case of uncertainty,
is the problem a lack of information, unreliable information, or an
inadequate understanding of the available information? For investment issues,
the problem is most likely inadequate understanding, not a lack of
information.
The
second strategic issue is determining what type of model or heuristic to be
used. In a Kahneman world, does this require fast or slow thinking? Does the
problem lend itself to a quant tool, or does it require different thinking?
The third
issue is forming the correct narrative. Decision narratives are critical for
managing and understanding actions taken. How should the problem be framed?
What is the expected answer? How do you assess whether the process was
successful?
Good
decision-making requires discipline, even when the problem is ill-defined or
ambiguous. Good solutions matter, but the process is more critical to
success.


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