Final Jeopardy (Alexandra Cooper Mysteries) - Linda Fairstein [92]
Craig had been following IBM’s Jeopardy project and was especially curious about Watson’s statistically derived game strategy. He understood that language processing was a far greater challenge for the IBM team. But as a human, Craig had language down. What he didn’t have was a team of Ph.D.s to run millions of game simulations on a cluster of powerful computers. This would presumably lead to the ideal strategy for betting and picking clues at each step of the game. His interest in this was hardly idle. By winning his six games, Craig would likely qualify for Jeopardy’s Tournament of Champions in 2011. Watson’s techniques could prove invaluable. As soon as his shows had aired in mid-September (and he was free to discuss his victories), he e-mailed Ferrucci, asking for a chance to IBM and spar with Watson. Ferrucci’s response, while cordial, was noncommittal. Jeopardy, not IBM, was in charge of selecting Watson’s sparring partners.
Before going on Jeopardy, Craig had long relied on traditional strategies. He’d read books on the game, including the 1998 How to Get on Jeopardy—And Win, by Michael DuPee. He’d also gone to Google Scholar, the search engine’s repository of academic works, and downloaded papers on Final Jeopardy betting. Craig was steeped in the history and lore of the games, as well as various strategies, many of them named for players who had made them famous. One Final Jeopardy technique, Marktiple Choice, involves writing down a number of conceivable answers and then eliminating the unlikely ones. Formulated by a 2003 champion, Mark Dawson, it prods players to extend the search beyond the first response that pops into their mind. (In that sense, it’s similar to the more systematic approach used by Watson.) Then there’s the Forrest Bounce, a tactic named for a 1986 champion, Chuck Forrest, who disoriented his foes by jumping from one category to the next. “You can confuse your opponents,” said Craig, who went on to use the technique. (This irked even some viewers. On a Jeopardy online bulletin board, one North Carolinian wrote, “I could have done without Roger winning . . . I can’t stand players that hop all over the board. It drives me nuts.”)
When it came to Jeopardy’s betting models, Craig knew them cold. One standard in the Final Jeopardy repertoire is the two-thirds rule. It establishes that a second-place player with at least two-thirds the leader’s score often has a better chance to win by betting that the leader will botch the final clue (which players do almost half the time). Say the leader going into Final Jeopardy has $15,000 and the second-place player has $10,000. To ensure a tie for victory (which counts as a win for both players), the leader must bet at least $5,000. Otherwise, the number two could bet everything, reach $20,000, and win. But missing the clue, and losing that $5,000, will drop the leader into a shared victory with the second-place player—if that player bets nothing. This strategy often makes sense, Craig said, because of the statistical correlation among players. He hadn’t run nearly as many numbers as the IBM team, but he knew that if one player missed a Final Jeopardy clue, it was probably a hard one, and the chances were much higher that others would miss it as well.
Craig bolstered his Jeopardy studies with readings on evolutionary psychology and behavioral economics, including books by Dan Ariely and Daniel Kahneman. They reinforced what he already knew