The database encompasses nearly eight years’ worth of games, capturing hundreds of millions of moves by hundreds of thousands of players, who range from hobbyists to grandmasters. Their data came from, one of the world’s largest online chess servers. Given that many real-world decisions also require a satisficing approach, Salant and Spenkuch thought that studying how players move on the chess board could help tease out the dynamics of complex decision-making. Thus, players typically consider only a small subset of all possible options and pick the first one that they consider good enough-that is, the first move that they believe will produce a win-a strategy that economists call “satisficing.” The sheer number of available moves frequently makes it impossible to evaluate every option one by one (especially in speed chess, where players are limited in how long they can spend strategizing). “Even though there are famous theorems that say that at any point in the game either white or black has a winning strategy, you often cannot find those strategies because the game is so complex,” Salant says.Ĭonsequently, the standard model of decision-making, in which a person evaluates every option one by one and then selects the best alternative, does not typically apply. Yet despite this ability to objectively evaluate every move, the nature of the game often makes it hard for even expert players to discern a good move from a bad one. “He basically said, ‘Chess isn’t an interesting game,’” Salant explains, “‘because either white has a winning strategy no matter what black is doing, or black has a winning strategy no matter what white is doing, or both of them can force a draw.’”) But this was proved more than a century ago by the German mathematician Ernst Zermelo. (It may seem bizarre that the famously cerebral game can be boiled down to predefined winning and losing moves. Other moves can similarly guarantee a draw or would result in a loss. Unlike picking an insurance plan or a marketing strategy, where accurately measuring and ranking alternatives is possible only with a well-functioning crystal ball, certain chess moves can be clearly identified as part of a winning strategy: these moves will (if followed up by subsequent optimal play) guarantee a win, no matter what one’s opponent does. How Chess Players Make DecisionsĬhess has several features that make it perfect for studying byzantine decisions.įirst, the quality of each move can be objectively ranked. And counterintuitively, they show that adding a mediocre option into the mix can actually be worse than adding a bad one. They find that slowing down helps everyone, but that masters of the game benefit considerably more from extra decision time than less-expert players. Using an immense dataset of more than 200 million moves from an online chess platform, the researchers draw novel conclusions about how chess players find their way through the fog of complexity. “Complexity and chess go hand-in-hand,” says Salant. In a new study, they derive first-of-their-kind predictions about how people behave when making complex choices by using an unusual laboratory: the chess board. So he and Spenkuch recently teamed up to shed new light on the dynamics of decision-making in complicated scenarios. These questions have received limited attention from social scientists, says Yuval Salant, a professor of managerial economics and decision sciences. So what does it take to make a good choice when facing this kind of complexity? Does slowing down or having more experience help-or do these convoluted decisions simply leave everyone grasping at straws, regardless of their expertise or how long they spend pondering their options? Spenkuch, an associate professor of managerial economics and decision sciences at the Kellogg School. “There’s a different dimension to decision-making when the available alternatives are so complex that you can’t even figure out what a given option is worth to you,” says Jörg L. Selecting a marketing strategy can be similarly knotty, as every potential move opens the door to myriad reactions from customers and competitors, leading to millions of possible scenarios, any of which the decision-maker can only imperfectly foresee. Choosing a health-insurance plan, for example, requires estimating the likelihood that you’ll need a biopsy or an appendectomy-a multilayered guessing game sure to be fraught with error.
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