Class imbalance in machine learning poses a significant challenge, as skewed datasets often hinder performance on minority classes. Traditional oversampling techniques, which are commonly used to alleviate class imbalance, have several drawbacks: they treat features independently, lack similarity-based controls, limit sample diversity, and fail to manage synthetic variety effectively. To overcome these issues, we introduce AxelSMOTE, an innovative agent-based approach that views data instances as autonomous agents engaging in complex interactions. Based on Axelrod's cultural dissemination model, AxelSMOTE implements four key innovations: (1) trait-based feature grouping to preserve correlations; (2) a similarity-based probabilistic exchange mechanism for meaningful interactions; (3) Beta distribution blending for realistic interpolation; and (4) controlled diversity injection to avoid overfitting. Experiments on eight imbalanced datasets demonstrate that AxelSMOTE outperforms state-of-the-art sampling methods while maintaining computational efficiency.

As the US prepares to send astronauts back to the moon, a CIA file has resurfaced that claims to have found life there more than 25 years ago. In the 1970s and 80s, the CIA conducted experiments with individuals who claimed they could perceive information about distant objects, events, or people, a process known as'remote viewing.' The experience of remote viewer Ingo Swann was first revealed in 1998 when he explained how his psychic episode took him to the dark side of the moon, a region that always faces away from Earth and out of sight from human eyes. That's where the remote reviewer made a shocking discovery: towers, buildings, and human-like aliens working at a secret complex on the moon's surface. Disturbingly, Swann said government officials knew the aliens had a base there, and these humanoids could actually sense his presence as he viewed them with his mind from 238,000 miles away.

Former Biden administration aide Michael LaRosa claimed the White House pressured CNN to not book him after he left the White House, which CNN denied to Fox News Digital. A new book revealed that former President Joe Biden's team chose not to have the president take a cognitive test in February 2024, over concerns that taking the test itself would raise more questions about his age, The New York Times reported Sunday. Authors Tyler Pager, a reporter for The New York Times, Josh Dawsey, a reporter for the Wall Street Journal and Isaac Arnsdorf, a reporter for the Washington Post, wrote the book, titled, "2024: How Trump Retook the White House and the Democrats Lost America," which is set to be released in July. The book, one of several about the tumultuous 2024 presidential election, details that Biden's top aides debated having him complete a cognitive test to quell concerns about his age. The aides were reportedly confident Biden would pass the test.

As autonomous agents become more prevalent, understanding their collective behaviour in strategic interactions is crucial. This study investigates the emergent cooperative tendencies of systems of Large Language Model (LLM) agents in a social dilemma. Unlike previous research where LLMs output individual actions, we prompt state-of-the-art LLMs to generate complete strategies for iterated Prisoner's Dilemma. Using evolutionary game theory, we simulate populations of agents with different strategic dispositions (aggressive, cooperative, or neutral) and observe their evolutionary dynamics. Our findings reveal that different LLMs exhibit distinct biases affecting the relative success of aggressive versus cooperative strategies. This research provides insights into the potential long-term behaviour of systems of deployed LLM-based autonomous agents and highlights the importance of carefully considering the strategic environments in which they operate.

Recent advancements in Large Language Models (LLMs) have spurred a surge of interest in leveraging these models for game-theoretical simulations, where LLMs act as individual agents engaging in social interactions. This study explores the potential for LLM agents to spontaneously generate and adhere to normative strategies through natural language discourse, building upon the foundational work of Axelrod's metanorm games. Our experiments demonstrate that through dialogue, LLM agents can form complex social norms, such as metanorms-norms enforcing the punishment of those who do not punish cheating-purely through natural language interaction. The results affirm the effectiveness of using LLM agents for simulating social interactions and understanding the emergence and evolution of complex strategies and norms through natural language. Future work may extend these findings by incorporating a wider range of scenarios and agent characteristics, aiming to uncover more nuanced mechanisms behind social norm formation.

"I'm a weird person," he says, "who has a foot in each world, of model-making and of actual experiments and psychological theory building." In 2012 he and two similarly broad-minded Harvard professors, Martin Nowak and Joshua Greene, tackled a question that exercised the likes of Thomas Hobbes and Jean-Jacques Rousseau: Which is our default mode, selfishness or selflessness? Do we all have craven instincts we must restrain by force of will? Or are we basically good, even if we slip up sometimes? They collected data from 10 experiments, most of them using a standard economics scenario called a public-goods game.1 Groups of four people, either American college students or American adults participating online, were given some money. They were allowed to place some of it into a pool, which was then multiplied and distributed evenly. A participant could maximize his or her income by contributing nothing and just sharing in the gains, but people usually gave something. Despite the temptation to be selfish, most people showed selflessness. The fuzziness of psychological ideas makes them hard to test.