According to Rao, maps are used everywhere: geographic maps, organization charts, market evolution maps, genome maps, neural circuit maps, biome maps, sheet music, etc. In competitive situation, there are maps, maps pf maps, maps of maps of maps, etc. One can also make maps of others’ behaviors. Maps can thus be viewed as the basis of all competition. After all, a map is a simplified model of directly experienced reality, or phenomenology in the context of discourse related to the philosophy of science.
Like models, maps are efficient and useful. They reduce the cognitive load of mindful attention to phenomenology via one’s senses. Phenomenological awareness is much more expensive than listening to a model in one’s head. A good map can lower the cost of actions by orders of magnitude. But, like models, there is a hiden cost. When reality changes and catches one unaware, costly failures can occur (e.g., the spectacular failure of LTCM in 1998, or the financial crisis of 2008).
There is also a less dramatic, but more serious, cumulative cost to “map addiction”, according to Rao, i.e., an atrophy of sense-awareness. “Map blindness” turns mere known-unknowns into unknown-unknowns. Almgren and Chriss have this to say about the limitations of all model-driven strategies:
“Finally, we note that any optimal execution strategy is vulnerable to unanticipated events. If such an event occurs during the course of trading and causes a material shift in the parameters of the price dynamics, then indeed a shift in the optimal trading strategy must also occur. However, if one makes the simplifying assumption that all events are either "scheduled" or "unanticipated," then one concludes that optimal execution is always a game of static trading punctuated by shifts in trading strategy that adapt to material changes in price dynamics.”
The opportunity cost of not developing phenomenological awareness is quite high: one is effectively denied the use of tacit knowledge that has not been organized into maps (or models) in conscious awareness. German World War II military strategists refer to this particular sense-awareness as Fingerspitzengefühl, or “finger-tip feeling”. Unlike closed-loop feedback that signals where the model is wrong and how to adjust and compensate for the discrepancy, finger-tip feeling sensitizes one to the things the model does not even “know” about ( i.e., where the model is not even wrong!).
A pure map-based navigation strategy is what control theorists call open-loop strategy. One simply assumes the map is the territory, and navigate by it with eyes closed. This strategy is very cheap: a decision to not pay attention. Adding error feedback results in a closed-loop strategy, an incremental improvement that is quite a bit costlier. Now one must budget attention based on what the model assumes is important, and navigate by it with eyes wide shut. But a navigation strategy based on finger-tip feeling attempts to eliminate explicit maps from the loop altogether. By “instrumenting the phenomenology” directly, in a manner of speaking, one is finally navigating the territory not only with eyes open, but with an open mind.
In finger-top feeling based navigation, rather than budget attention based on assumed priorities, one deploys attention without importance judgment. This is a stage that precedes map-making and is vastly more expensive in terms of cognitive processing load. But this approach can achieve radical improvements in the long term. Incidentally, this is why recent advances in deep learning technology are widely considered to be significant. By instrumenting phenomenology rather than models, they can make sense of situations the model does not know about. But how does this work exactly?