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Hedge fund managers who have won cash prizes at poker tournaments apparently outperform those that have not by approximately 1% to 5% a year in investment returns. This is according to research by Yan Lu, Sandra Mortal and Sugata Ray. Furthermore, those hedge fund managers who place higher or win larger tournaments with larger prizes tend to exhibit further improvements in investment return. This is not just anecdotal data, 220 hedge fund managers have seen success in poker tournaments, creating a potentially meaningful data set.
The researchers suggest a few reasons for the link between investing and poker. Specific skills in poker may carry over to investing. One skill is patience. This can be measured in portfolio turnover, and hedge fund managers who succeed in poker tend to hold onto portfolio positions longer.
Another is avoiding the disposition effect. The disposition effect can cause you to hold onto losing investments for too long, while quickly selling winners. Skilled poker investors are less likely to do this. Furthermore, the ability to read people can be useful both in poker and investing. The researchers also find that those investors who focus on fundamental stock selection have greater links to skill in poker, than those who invest with a purely quantitative approach.
From there, poker spread up the Mississippi River and throughout the country, thanks in part to its popularity among crews of riverboats transporting goods via that great waterway. Soldiers in both the North and South played poker during the Civil War, and it became a staple of Wild West saloons in frontier settlements in the 1870s and 1880s. In 1871 the game was introduced to Europe after Queen Victoria heard the U.S. minister to Great Britain explaining the game to members of her court and asked him for the rules.
To decide under uncertainty, whether in finance, poker or another area, you first have to estimate the probabilities of different events and scenarios. That means first having an open mind and considering the different scenarios that are likely to occur. Next, you need to make an estimate of which outcomes are more likely than others.
Second, a lively dialogue ensues during poker planning, and estimators are called upon by their peers to justify their estimates. Researchers have found that this improves estimate accuracy, especially on items with a lot of uncertainty as we find on most software projects.
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An artificial intelligence program developed by Carnegie Mellon University in collaboration with Facebook AI has defeated leading professionals in six-player No-Limit Texas Hold'em poker, the world's most popular form of poker.
The AI, called Pluribus, defeated poker professional Darren Elias, who holds the record for most World Poker Tour titles; and Chris "Jesus" Ferguson, winner of six World Series of Poker events. Each pro separately played 5,000 hands of poker against five copies of Pluribus.
"Pluribus achieved superhuman performance at multiplayer poker, which is a recognized milestone in artificial intelligence and in game theory that has been open for decades," said Tuomas Sandholm, Angel Jordan Professor of Computer Science, who developed Pluribus with Noam Brown, who is finishing his Ph.D. in Carnegie Mellon's Computer Science Department as a research scientist at Facebook AI. "Thus far, superhuman AI milestones in strategic reasoning have been limited to two-party competition. The ability to beat five other players in such a complicated game opens up new opportunities to use AI to solve a wide variety of real-world problems."
"It was incredibly fascinating getting to play against the poker bot and seeing some of the strategies it chose" Gagliano said. "There were several plays that humans simply are not making at all, especially relating to its bet sizing. Bots/AI are an important part in the evolution of poker, and it was amazing to have first-hand experience in this large step toward the future."
Sandholm has led a research team studying computer poker for more than 16 years. He and Brown earlier developed Libratus, which two years ago decisively beat four poker pros playing a combined 120,000 hands of Heads-Up No-Limit Texas Hold'em, a two-player version of the game.
Games such as chess and Go have long served as milestones for AI research. In these games, all of the players know the status of the playing board and all of the pieces. But poker is a bigger challenge because it is an incomplete information game: players can't be certain which cards are in play and opponents can and will bluff. That makes it both a tougher AI challenge and more relevant to many real-world problems involving multiple parties and missing information.
Pluribus first computes a "blueprint" strategy by playing six copies of itself, which is sufficient for the first round of betting. From that point on, Pluribus does a more detailed search of possible moves in a finer-grained abstraction of game. It looks ahead several moves as it does so, but does not require looking ahead all the way to the end of the game, which would be computationally prohibitive. Limited-lookahead search is a standard approach in perfect-information games, but is extremely challenging in imperfect-information games. A new limited-lookahead search algorithm is the main breakthrough that enabled Pluribus to achieve superhuman multiplayer poker.
Though poker is an incredibly complicated game, Pluribus made efficient use of computation. AIs that have achieved recent milestones in games have used large numbers of servers and/or farms of GPUs; Libratus used around 15 million core hours to develop its strategies and, during live game play, used 1,400 CPU cores. Pluribus computed its blueprint strategy in eight days using only 12,400 core hours and used just 28 cores during live play.
Sandholm has founded two companies, Strategic Machine Inc. and Strategy Robot Inc., that have exclusively licensed strategic reasoning technologies developed in his Carnegie Mellon laboratory over the last 16 years. Strategic Machine applies the technologies to poker, gaming, business and medicine, while Strategy Robot applies them to defense and intelligence. Pluribus builds on and incorporates large parts of that technology and code. It also includes poker-specific code, written as a collaboration between Carnegie Mellon and Facebook for the current study, that will not be applied to defense applications. For any other type of usage, the parties have agreed that they can use the additional code as they wish.
Welcome to the home page of the University of Alberta Computer Poker Research Group. We are working on creating computer programs that play poker better than any human being, as a testbed for doing good science. There are many core artificial intelligence and computer science problems that need to be solved to make an excellent player, and games like poker are a fun and controllable way to examine these problems. A longer explanation of why we're looking at poker can be found in Darse Billing's M.Sc. dissertation (Billings, 1995).
We have a project manager that is adamant about planning poker. I have found that this adds stress to everyone on the team, and the times are always wrong. We have different skill levels on the team and expected to complete a task within the given time period. We estimate as a team and then take the average. I find that this adds stress to the team, and decreases motivation/morale.
This is the wrong question to ask. Planning poker is a tool. Asking if planning poker is bad for software development teams is like asking if a screwdriver is bad for plumbers. If the tool fits the job then it's a good tool, if not, then it's not.
Another thing to add is that planning poker is a consensus-based estimation techniques. That means that when you get vastly different estimates, the team members discuss it until they reach a number that they all agree with. They don't just estimate as a team and then take the average. An average completely destroys the discussion that needs to take place in order to reach consensus and also destroys the spread of the different estimations people provided. If a senior says 1 day, and a junior says 5 days, getting back (5 + 1) /2 = 3 days is not a good estimate because the senior will finish early while the junior will probably be late. Then what happens if the junior works on the task? The project manager will pressure them to finish the work in 3 days even though they thought it will take 5 (and that was probably wrong too and most likely they will finish in 7 days).
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