Human and Computer Preferences at Chess

Distributional analysis of large data-sets of chess games played by humans and those played by computers shows the following differences in preferences and performance:   (1) The average error per move scales uniformly higher the more advantage is enjoyed by either side, with the effect much sharper for humans than computers;   (2) For almost any degree of advantage or disadvantage, a human player has a significant 2--3\% lower scoring expectation if it is his/her turn to move, than when the opponent is to move; the effect is nearly absent for computers.   (3) Humans prefer to drive games into positions with fewer reasonable options and earlier resolutions, even when playing as human-computer {\em freestyle\/} tandems.   The question of whether the phenomenon (1) owes more to human perception of relative value, akin to phenomena documented by Kahneman and Tversky, or to rational risk-taking in unbalanced situations, is also addressed. Other regularities of human and computer performances are described with implications for decision-agent domains outside chess.