Recent memoirs by the retired bookie Art Manteris and the storied gambler Billy Walters provide a glimpse of an industry in its fledgling form--and a preview of the DraftKings era to come. Las Vegas is no longer the seat of the sportsbook gods. In most states, it's now legal, and extremely popular, to place bets using apps or websites such as FanDuel and DraftKings. From your couch, you can wager on everything from the results of snooker championships to the color of the Gatorade poured over the victorious coach after the Super Bowl. The N.F.L., along with the other major-league American sports associations, has officially partnered with sports-betting sites, and their alliance has proved so lucrative that other industries want in on the action; last month, the Golden Globes made a deal with Polymarket, a predictions-market platform, to encourage wagering (or "trading," if you prefer) on the outcomes of its awards race.

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This paper investigates the nuanced role of biases in the decision-making processes of large language models (LLMs). While conventional research typically aims to eliminate all biases, our study reveals that not all biases are detrimental. By examining rational deviations, involving heuristic shortcuts that enhance decision-making efficiency, we highlight their potential benefits when properly balanced. We introduce the concepts of heuristic moderation and an abstention option, allowing LLMs to abstain from answering when uncertain, thereby reducing error rates and improving decision accuracy. Using our newly developed BRD (Balance Rational Deviations) dataset, our findings demonstrate that appropriately scaled bias inspection enhances model performance and aligns LLM decision-making more closely with human reasoning. This balance improves the reliability and trustworthiness of LLMs and suggests new strategies for future enhancements. Our work offers a fresh perspective on leveraging biases constructively to enhance the practical applications of LLMs, from conversational agents to decision support systems and beyond.

The Multi-Armed Bandit problem constitutes an archetypal setting for sequential decision-making, permeating multiple domains including engineering, business, and medicine. One of the hallmarks of a bandit setting is the agent's capacity to explore its environment through active intervention, which contrasts with the ability to collect passive data by estimating associational relationships between actions and payouts. The existence of unobserved confounders, namely unmeasured variables affecting both the action and the outcome variables, implies that these two data-collection modes will in general not coincide. In this paper, we show that formalizing this distinction has conceptual and algorithmic implications to the bandit setting. The current generation of bandit algorithms implicitly try to maximize rewards based on estimation of the experimental distribution, which we show is not always the best strategy to pursue. Indeed, to achieve low regret in certain realistic classes of bandit problems (namely, in the face of unobserved confounders), both experimental and observational quantities are required by the rational agent. After this realization, we propose an optimization metric (employing both experimental and observational distributions) that bandit agents should pursue, and illustrate its benefits over traditional algorithms.

Machine learning is about forecasting. Forecasts, however, obtain their usefulness only through their evaluation. Machine learning has traditionally focused on types of losses and their corresponding regret. Currently, the machine learning community regained interest in calibration. In this work, we show the conceptual equivalence of calibration and regret in evaluating forecasts. We frame the evaluation problem as a game between a forecaster, a gambler and nature. Putting intuitive restrictions on gambler and forecaster, calibration and regret naturally fall out of the framework. In addition, this game links evaluation of forecasts to randomness of outcomes. Random outcomes with respect to forecasts are equivalent to good forecasts with respect to outcomes. We call those dual aspects, calibration and regret, predictiveness and randomness, the four facets of forecast felicity.

While businesses and employees were left scrambling for public handouts, the online gambling market continued to grow and thrive. A report released in May 2021 showed that the global online gambling market could increase from $64.13 billion to $72.02 billion between 2020-21. That represented a compound annual growth rate (CAGR) of 12.3%. The report also suggested that the worldwide online gambling market might be worth $112.09 billion by 2025 with a CAGR of 12%. There's hardly any other industry that comes even close to that kind of healthy economic foresight.

Emergent artificial intelligence and machine learning technologies are affecting all areas of technology, including online gambling and gaming. AI denotes the capability of machines and electronic systems to mimic functions that resemble the cognitive activity of the human mind. These include instances of automated learning or problem-solving. Such technologies are increasingly changing all walks of life. From Google to YouTube to Netflix, every widespread service and large company employ similar services.

Most of us already know that casinos are masters at ensuring the chips are stacked firmly in their favor. However, it might come as more of a surprise to learn that many casinos are taking advantage of technological advances to ensure that unscrupulous individuals can't eat into their profits. Artificial Intelligence (AI) makes it a lot easier to predict outcomes and behavior, and online casinos have started using this to their significant advantage. AI helps casinos and betting websites manage their operations more efficiently, initiating fraud checks, clamping down on problem gamblers, and ensuring that they don't fall prey to online scammers. Below, we explore some of the ways that online casinos are using AI to their advantage in greater detail.

Since the dawn of the drone era, enterprising pilots and enthusiasts have found ways to make money off their passion for flying. Thanks to a new partnership between the Drone Racing League and DraftKings, though, gamblers can now make money off of other people's passion for flying. While DraftKings can legally operate its daily fantasy sports business in 43 states, the company stresses that betting on drone races is currently only legal in Colorado, New Hampshire, West Virginia, Tennessee and New Jersey. "The sky is now the limit for DRL fans to get skin in the game, and we're thrilled to partner with DraftKings to transform our high-speed race competition into the ultimate sport to bet on," said DRL President Rachel Jacobson in a press release. Today's announcement makes drone racing the first aerial sport people can legally bet on, and Jacobson noted to Forbes that embracing betting is part of the company's plan to scale into an "ultimately mainstream sport."