In the realm of chess there are clear rules about each piece’s ability to move. The board is static and remains stable throughout the game. Each player may only make 1 move at a time, then must wait for the move of the other. In order to reduce the risk of fatigue on player capacity, moves must be made within a certain timeframe. Proper lighting must be available in order to establish sufficient trust and clarity of the move. The feedback is proximal. Given the imposed timeframe, we can see immediately how our opponent responds to our move – though there are important sources of feedback that are distal (regarding time) as we eventually discover how our overall strategy worked in determining the outcome of the game.
Chess masters are able to recognize move patterns in very advanced fashion based on playing and rehearsing thousands of moves—they shrink the distal timeframe. They bring the future into the present. The advantage held by a chess master in shrinking the timeframe is primarily a matter of pattern recognition. This recognition is, in turn, based on experience and prolific recall of chess move patterns. It seems that the future is brought into the present through engagement of the past (retrieving and incorporating the history of previous games).
It is no surprise, then, that a more capable memory machine that could be provided with clear rules and copies of every chess move ever made in history would be able to compete and eventually surpass all human beings. When that event happened in May of 1997 when IBM’s Deep Blue defeated World Chess Champion Gary Kasparov, the world began to question whether computers would surpass humans in all games, all sports, all professions. Certainly, that has proven true in simple, clear, well-defined, stable, static, highly constrained games—in that quadrant humans are being surpassed and replaced by more capable computers and artificial intelligence.
Complicated Systems
When we move into the quadrant of Complicated, we move from very strong rules and little degree of freedom toward more generalized governing constraints and tight coupling. Serial processes must often occur in a certain order if they are to be completed. While multiple pathways are available, there is usually only one that is of optimal value and that is most likely to lead to successful. The feedback might not be proximal—we might not know if we have taken the best pathway for a while. However, the ultimate distal feedback is quite clear. We have been successful or unsuccessful. Serial, interlocking decision-nodes must be sorted through carefully by the players of these games and sports if they are to be successful.
One can imagine a relay race taking place during a track meet. One phase of this race involves tightly coupled interaction in passing the baton. In this case, not only does each runner have to run as fast as possible in the right direction—she must also ready the baton at just the right time and in the exact space designated by the sport to hand over to another runner who must be reaching a similar speed at just the right moment without leaving the baton-passing zone. These tightly coupled transitions must occur 3 times in order for the relay race to be completed and in the right order. Thus, relay races are complicated. We have all seen events in which the most talented, speedy team fails to pass the baton efficiently or even drops the baton and all is lost.
