Computational social choice is a research area at the intersection of social choice (the science of collective decision-making) and computer science, which focuses on the algorithmic analysis of problems related topreference aggregation and elicitation(Brandt etal.,2013).

Quantitative Relative Judgment Aggregation (QRJA) is a new research topic in (computational) social choice. In the QRJA model, agents provide judgments on the relative quality of different candidates, and the goal is to aggregate these judgments across allagents.

Weaknesses: I believe the results proposed in this paper are related to existing work. The techniques used are close to existing methods - at the very least a detailed comparison is in order. The paper fails to acknowledge lots of literature on representing coalitional games in a restricted manner. In fact, many techniques have been proposed for concisely representing coalitional games, and approximately solving them. This issue is covered in depth in (e.g): Chalkiadakis, Georgios, Edith Elkind, and Michael Wooldridge.

We investigate optimal decision making under imperfect recall, that is, when an agent forgets information it once held before. An example is the absentminded driver game, as well as team games in which the members have limited communication capabilities. In the framework of extensive-form games with imperfect recall, we analyze the computational complexities of finding equilibria in multiplayer settings across three different solution concepts: Nash, multiselves based on evidential decision theory (EDT), and multiselves based on causal decision theory (CDT). We are interested in both exact and approximate solution computation. As special cases, we consider (1) single-player games, (2) two-player zero-sum games and relationships to maximin values, and (3) games without exogenous stochasticity (chance nodes). We relate these problems to the complexity classes P, PPAD, PLS, $\Sigma_2^P$ , $\exists$R, and $\exists \forall$R.

Multiwinner voting captures a wide variety of settings, from parliamentary elections in democratic systems to product placement in online shopping platforms. There is a large body of work dealing with axiomatic characterizations, computational complexity, and algorithmic analysis of multiwinner voting rules. Although many challenges remain, significant progress has been made in showing existence of fair and representative outcomes as well as efficient algorithmic solutions for many commonly studied settings. However, much of this work focuses on single-shot elections, even though in numerous real-world settings elections are held periodically and repeatedly. Hence, it is imperative to extend the study of multiwinner voting to temporal settings. Recently, there have been several efforts to address this challenge. However, these works are difficult to compare, as they model multi-period voting in very different ways. We propose a unified framework for studying temporal fairness in this domain, drawing connections with various existing bodies of work, and consolidating them within a general framework. We also identify gaps in existing literature, outline multiple opportunities for future work, and put forward a vision for the future of multiwinner voting in temporal settings.

It's been a whirlwind few months in the world of large language models (LLMs), better known to most people as chatbots. Since the release of ChatGPT by OpenAI in Nov. 2022, we've seen billions upon billions of dollars being poured into the development and implementation of generative AIs such as Google's Bard and Microsoft's Bing chatbots--and it's easy to see why. Chatbots like ChatGPT or image generators like DALL-E and Midjourney can feel like magic. With the right prompts, you can get it to do things you wouldn't have imagined a few years ago like craft late night monologue-ready jokes and creating award-winning pieces of "art." It's no surprise that since the public launch of ChatGPT, tech companies have been working to cash in on this modern-day gold rush.

I get more emails than I can handle. Every day, I watch as the unread messages in my inbox pile up despite my best efforts. It's not because I spend too much time responding to the emails that matter -- I spend too much time reading the ones that don't. There are newsletters, brand-outreach spam, press releases, recruiters, social-media updates -- emails that are almost never urgent but still make me feel obliged to check to make sure I'm not missing anything important. Even carefully deployed filters go only so far to keep the ever-growing volume of unwanted emails at bay.

I didn't write those words, though they're quite passable as this story's first two sentences. It's true: I'd love to find new ways to streamline my workflow and produce engaging content – and I was eager to try OpenAI's new chatbot, called ChatGPT. The chatbot was released to the public in late November, and within 5 days, it gained over 1 million users. In the few weeks since its launch, the bot has been used for everything from writing code to therapy. Elon Musk, who co-founded OpenAI but resigned from the company in 2018, called ChatGPT "scary good."

Vincent Conitzer expects much to be the same when he returns to Carnegie Mellon University this coming fall. It will still be the best place in the world for computer science and the technical expertise will still be unmatched. Many of the colleagues, professors and even his Ph.D. advisor will also still be around. But don't be surprised if the renowned artificial intelligence researcher and ethicist appears lost in the corridors and hallways of the Gates and Hillman Centers. When Conitzer was finishing his graduate work in computer science in 2006, he spent his time in Wean Hall.