--Economic inequality is a global challenge, intensifying disparities in education, healthcare, and social stability. Traditional systems like the U.S. federal income tax reduce inequality but lack adaptability. Although models like the Saez Optimal T axation adjust dynamically, they fail to address taxpayer heterogeneity and irrational behavior . This study introduces T axAgent, a novel integration of large language models (LLMs) with agent-based modeling (ABM) to design adaptive tax policies. In our macroeconomic simulation, heterogeneous H-Agents (households) simulate real-world taxpayer behaviors while the T axAgent (government) utilizes LLMs to iteratively optimize tax rates, balancing equity and productivity. Benchmarked against Saez Optimal T axation, U.S. federal income taxes, and free markets, T axAgent achieves superior equity-efficiency tradeoffs. This research offers a novel taxation solution and a scalable, data-driven framework for fiscal policy evaluation. Economic inequality is a critical global issue with profound social, political, and economic impacts. Research highlights its detrimental effects on education, healthcare, political stability, and economic growth[1, 2, 3].

The recent wave of artificial intelligence, epitomized by large language models (LLMs), has presented opportunities and challenges for methodological innovation in political science, sparking discussions on a potential paradigm shift in the social sciences. However, how can we understand the impact of LLMs on knowledge production and paradigm transformation in the social sciences from a comprehensive perspective that integrates technology and methodology? What are LLMs' specific applications and representative innovative methods in political science research? These questions, particularly from a practical methodological standpoint, remain underexplored. This paper proposes the "Intelligent Computing Social Modeling" (ICSM) method to address these issues by clarifying the critical mechanisms of LLMs. ICSM leverages the strengths of LLMs in idea synthesis and action simulation, advancing intellectual exploration in political science through "simulated social construction" and "simulation validation." By simulating the U.S. presidential election, this study empirically demonstrates the operational pathways and methodological advantages of ICSM. By integrating traditional social science paradigms, ICSM not only enhances the quantitative paradigm's capability to apply big data to assess the impact of factors but also provides qualitative paradigms with evidence for social mechanism discovery at the individual level, offering a powerful tool that balances interpretability and predictability in social science research. The findings suggest that LLMs will drive methodological innovation in political science through integration and improvement rather than direct substitution.

The term "Artificial Intelligence" is a broad umbrella, referring to a variety of techniques applied to a range of tasks. This breadth can breed confusion. Success in using AI to identify tumors on lung x-rays, for instance, may offer no indication of whether AI can be used to accurately predict who will commit another crime or which employees will succeed, or whether these latter tasks are even appropriate candidates for the use of AI. Misleading marketing hype often clouds distinctions between different types of tasks and suggests that breakthroughs on narrow research problems are more broadly applicable than is the case. Furthermore, the nature of the risks posed by different categories of AI tasks varies, and it is crucial that we understand the distinctions.

The real world is awash with multi-agent problems that require collective action by self-interested agents, from the routing of packets across a computer network to the management of irrigation systems. Such systems have local incentives for individuals, whose behavior has an impact on the global outcome for the group. Given appropriate mechanisms describing agent interaction, groups may achieve socially beneficial outcomes, even in the face of short-term selfish incentives. In many cases, collective action problems possess an underlying graph structure, whose topology crucially determines the relationship between local decisions and emergent global effects. Such scenarios have received great attention through the lens of network games. However, this abstraction typically collapses important dimensions, such as geometry and time, relevant to the design of mechanisms promoting cooperation. In parallel work, multi-agent deep reinforcement learning has shown great promise in modelling the emergence of self-organized cooperation in complex gridworld domains. Here we apply this paradigm in graph-structured collective action problems. Using multi-agent deep reinforcement learning, we simulate an agent society for a variety of plausible mechanisms, finding clear transitions between different equilibria over time. We define analytic tools inspired by related literatures to measure the social outcomes, and use these to draw conclusions about the efficacy of different environmental interventions. Our methods have implications for mechanism design in both human and artificial agent systems.

Over the years that I have spent with startups, I've come across both genuine and fake AI products. I'll start with the ones that truly solved problems using AI. A few years ago, one of the co-founders of Liv.ai, a Bengaluru-based AI startup, met me and demonstrated their product that used natural language processing to convert speech to text in multiple Indian languages. I had always known that text to speech was easy, but converting speech to text in multiple languages was a hard problem to solve. I was a bit sceptical at first, but when I saw the product, I was quite blown away.

It will take time, but at some point every application will have its share of "AI Inside." Today, however, we're far from that point, and false advertising of AI capabilities isn't helping, something Arvind Narayanan, Associate Professor of Computer Science at Princeton, has called out as "snake oil" in a recent presentation. It's not that there aren't real, useful ways to employ AI today, he stresses, but rather that "Much of what's being sold as'AI' today is snake oil--it does not and cannot work." To help parse good from bad AI advertising, where does Narayanan believe we're making real progress in AI, and where should we myth bust? As with any new technology, aspirations to embrace it always outpace actual production usage, and AI is no different. Even so, according to a Gartner study released earlier in 2019, 59% of enterprises surveyed are using AI today and, of that 59%, they have, on average, four AI/ML projects deployed.

This is the fourth in a series of interviews with deep tech investors, taking the temperature of their particular fields at the start of 2019, and reflecting on the year gone by. The first three installments dived into quantum computing, space and biotech. What does 2019 hold for the AI field? This was my question to Azeem Azhar, a strategist, investor, product entrepreneur and analyst, with a hugely popular newsletter Exponential View. He is Senior Advisor for AI to the CTO of Accenture, and a Venture Partner with Kindred Capital.

Artificial Intelligence (AI) is set to usher in massive benefits, but the potential pitfalls must be debated before it's too late, says Mott MacDonald's Simon Harrison. The Economist has described big data as the new oil, although I gather some insurers are calling it the new asbestos. Big data will impact almost every person in the world and almost all businesses. Its potential for transformative good is extraordinary, as is its potentially destructive power if we don't pay sufficient attention to security and privacy. Artificial intelligence (AI), big data's bigger brother, is another thing entirely.

When Justice Frank H. Easterbrook was asked, in 1996, to deliver a lecture on "Property in Cyberspace", he titled his talk--"Cyberspace and the Law of the Horse". His curious choice of title was his way of calling out the foolishness of trying to formulate laws to address new technologies when general principles could, just as well, suffice. There were a number of cases, he said, that dealt with the sale of horses, and even more where the courts have been approached to address the injuries suffered by people who have been kicked by horses. But this doesn't mean that one needs to "collect these strands into a course on "the Law of the Horse". All we need to do is study how the general law of property, torts and commercial transactions applies to the horse trade.

Many experts believe that in the coming decades, artificial intelligence will change, and perhaps significantly reduce, the demand for human labor in the economy, but there remains much uncertainty about the accuracy of this claim and what to do about it. This paper identifies several ways in which the artificial intelligence community can help society to anticipate and shape such outcomes in a socially beneficial direction. First, different technical aspirations for the field of AI may be associated with different social outcomes, increasing the stakes of decisions made in the AI community. Second, the extent of researchers' efforts to apply AI to different social and economic domains will influence the distribution of cognition between humans and machines in those domains. Third, the AI community can play a key role in initiating a more nuanced and inclusive public discussion of the social and economic possibilities afforded by AI technologies. To pave the way for such dialogue, we suggest a line of research aimed at better understanding the nature, pace, and drivers of progress in AI in order to more effectively anticipate and shape AI's role in society.