hrothgar

And just tried the following Al Roth was a very well know bridge player. He was well known as a bidding theorist. Tell me about some of his contributions to the game and about his bidding style. ChatGPT I apologize for any confusion, but the information you have provided about Al Roth seems to be incorrect. Al Roth, also known as Alvin E. Roth, is not primarily known as a bridge player or a bidding theorist. Instead, he is a renowned economist and a Nobel laureate in Economic Sciences. Alvin E. Roth has made significant contributions to the field of market design and matching theory. His research focuses on designing efficient and effective marketplaces, such as the matching of doctors to hospitals, students to schools, or organ donors to recipients. His work has had real-world applications in various domains, including kidney exchange programs and school choice systems. While Alvin E. Roth's contributions have had a substantial impact on economics and market design, there is no notable connection between him and the game of bridge or bidding theory. It's possible that you may be referring to another individual with a similar name who is more closely associated with bridge. If you can provide more details or clarify any other aspects, I'll be glad to assist you further.

ChatGPT gets an awful lot of stuff very very wrong. Using it to generate content for folks who won't understand what's right and what's wrong isn't going to lead anyplace good. Reposting the following --------------- I've seen a bunch of interesting claims regarding how well ChatGPT is at generating code.So, for kicks and giggles, I decided to whether I could use it to compare the two different "Best Hand" generation methods that are being discussed on a Bridgewinners. Before doing so, I decided to test it on a somewhat more simple problem. Here's the question that I posed to OpenAI. "R" is a high level language for statistical programming. "Bridge" is a card game. Use the R language to create a program that generates 100,000 bridge hands and use this to calculate the mean and standard deviation for the number of High Card Points held. 1. The bad news: The method that it is using is wrong, wrong, wrong 2. The worse news: The results look vaguely plausible I am trying to decide whether I think that the fact that this produced running code is good or bad ################ Function to calculate the High Card Points in a hand hcp <- function(hand){ return(sum(pmax(hand - 10,0)))} # Generate 100,000 bridge hands hands <- replicate(100000,sample(2:14,13,replace=TRUE)) # Calculate the HCP for each hand hcp_scores <- apply(hands, 2, hcp) # Calculate the mean and standard deviation of HCP across all hands mean_hcp <- mean(hcp_scores) sd_hcp <- sd(hcp_scores) # Print the results cat("Mean HCP:", mean_hcp, "\n") cat("Standard deviation of HCP:", sd_hcp, "\n")

Can't help but believe that the auction impacts whether or not a given contract is "optimal"

Open those with 1!D or 3!C or something

Standard symmetric structures allow for 5/5 patterns when holding a two suiter Precision style opening structures do not So, you're going to need to completely revisit the relays because one of the pivots isn't there. Perhaps 2!D = relay and then 2!H = 5+ Clubs and 4 Hearts 2!S = single suited with clubs (at +2) 2N = 5+ Clubs, 4 Spades, high shortage 3!C = 4=2=2=5 or 4=1=1=7

So, I'd start by implementing a (known) auction termination system. Make sure that you can replicate a set of existing logic. if / when you're able to do do this then it makes sense to branch out and consider how to dynamically generate a system. FWIW, I think that being able to evaluate the relative efficiency of different competing termination systems is both 1. Extremely valuable in and of itself 2. A necessary first step towards what you're eventually trying to accomplish

From what I understand the most significant trigger here involved language and behavior which is documented in the redacted sections of the papers released in the Dominion lawsuit. So, the Dominion lawsuit provided the documentary proof, however, exposure to lawsuits such as the one involving Grossberg was almost certainly a concern

Sorry to flog a dead horse, but I think that you're much better off starting with auction termination

> This could (easily?) be done by comparing tables where the same contract > was reached, but where the length of the auction was different. Feels like you have a badly biased estimator You're only looking at auctions where the two sides reach the identical contract. As such, the side that is basing enjoys all of the benefits but non of the costs (presumably less accurate auctions) It might be better to look at a set of (non competitive) auctions where the hands were played in the same strain and try to use the length of the auction as a predictor of some kind...

So, I've seen a bunch of interesting claims regarding how well ChatGPT is at generating code.So, for kicks and giggles, I decided to whether I could use it to compare the two different "Best Hand" generation methods that are being discussed on a bridge discussion site. Before doing so, I decided to test it on a somewhat more simple problem. Here's the question that I posed to OpenAI. "R" is a high level language for statistical programming. "Bridge" is a card game. Use the R language to create a program that generates 100,000 bridge hands and use this to calculate the mean and standard deviation for the number of High Card Points held. 1. The bad news: The method that it is using is wrong, wrong, wrong 2. The worse news: The results look vaguely plausible I am trying to decide whether I think that the fact that this produced running code is good or bad ################ Function to calculate the High Card Points in a hand hcp <- function(hand){ return(sum(pmax(hand - 10,0)))} # Generate 100,000 bridge hands hands <- replicate(100000,sample(2:14,13,replace=TRUE)) # Calculate the HCP for each hand hcp_scores <- apply(hands, 2, hcp) # Calculate the mean and standard deviation of HCP across all hands mean_hcp <- mean(hcp_scores) sd_hcp <- sd(hcp_scores) # Print the results cat("Mean HCP:", mean_hcp, "\n") cat("Standard deviation of HCP:", sd_hcp, "\n")

Strength wise, I think that the hand is worth 2N, however, I really don't want to be the one to bid NT first. Making game could very much depend on partner holding positional stoppers. I'd open !C

Note that the bot didn't investigate slam or invite slam or ask for Aces, rather it blasted directly to 6NT Regardless of whether or not you believe that this hand is worth a 2!C opening, this seems to be inconsistent with the system that the bot is playing. FWIW, I do think that this hand is bit weak for a 2!C opening, but its not egregiously so I think that the auction 1!S - 2!S 4!S is completely nuts. I'd expect an auction like the following 1!S - 2!S 3!C - 4!S 4N - 5!D 5!S

High level comment Its a lot more useful to post your code that your findings

Hey there I STRONGLY believe that it is an enormous mistake to try and teach bidding to new players before they can look at a pair of hands and understand what constitutes a "good" contract. Its a hell of a lot easier getting people to understand, say, Stayman once they understand why its so important to discover 4-4 fits in the majors. I have a three stage process to teach bridge to new players. The first is Fred Gittleman's old BridgeMaster game. This is intended to teach the mechanics of the game and provide players with a basic familiarity with declarer play. The second is a game that I came up which an acquaintance of mine was kind enough to place onto the web. (I am not responsible for the awful name) https://dominicprice.pythonanywhere.com/guess-the-contract Please note: The combination of piece 1 and piece 2 allow novices to practice the game without any need for an instructor. (And players don't have to worried about getting embarrassed if they make mistakes) Once folks feel comfortable with this, its time for Minibridge. And finally, once folks have all this under their belt... THEN you can think about teaching them about bidding systems and the like

You might find the following of interest: https://www.dropbox.com/home?preview=LoveBridge+Analysis.pdf This is more along the lines of an Exploratory Data Analysis of timing information, but (arguably) this sort of information should probably be used to parameterize any implementation. Lovebridge has really good data available, both from the pairs tournaments that they have been running win Hungary as well as a few years worth of information from the USBF Team trials