Quantum computing promises transformative advances, but remains constrained by recurring misconceptions and methodological pitfalls. This paper demonstrates a fundamental incompatibility between traditional agent-based modeling (ABM) implementations and quantum optimization frameworks like Quadratic Unconstrained Binary Optimization (QUBO). Using Schelling's segregation model as a case study, we show that the standard practice of directly translating ABM state observations into QUBO formulations not only fails to deliver quantum advantage, but actively undermines computational efficiency. The fundamental issue is architectural. Traditional ABM implementations entail observing the state of the system at each iteration, systematically destroying the quantum superposition required for computational advantage. Through analysis of Schelling's segregation dynamics on lollipop networks, we demonstrate how abandoning the QUBO reduction paradigm and instead reconceptualizing the research question, from "simulate agent dynamics iteratively until convergence" to "compute minimum of agent moves required for global satisfaction", enables a faster classical solution. This structural reconceptualization yields an algorithm that exploits network symmetries obscured in traditional ABM simulations and QUBO formulations. It establishes a new lower bound which quantum approaches must outperform to achieve advantage. Our work emphasizes that progress in quantum agent-based modeling does not require forcing classical ABM implementations into quantum frameworks. Instead, it should focus on clarifying when quantum advantage is structurally possible, developing best-in-class classical baselines through problem analysis, and fundamentally reformulating research questions rather than preserving classical iterative state change observation paradigms.

Thomas C. Schelling, awarded the 2005 Nobel Memorial Prize in Economic Sciences, pointed out that ``individuals decisions (micromotives), while often personal and localized, can lead to societal outcomes (macrobehavior) that are far more complex and different from what the individuals intended.'' The current research related to large language models' (LLMs') micromotives, such as preferences or biases, assumes that users will make more appropriate decisions once LLMs are devoid of preferences or biases. Consequently, a series of studies has focused on removing bias from LLMs. In the NLP community, while there are many discussions on LLMs' micromotives, previous studies have seldom conducted a systematic examination of how LLMs may influence society's macrobehavior. In this paper, we follow the design of Schelling's model of segregation to observe the relationship between the micromotives and macrobehavior of LLMs. Our results indicate that, regardless of the level of bias in LLMs, a highly segregated society will emerge as more people follow LLMs' suggestions. We hope our discussion will spark further consideration of the fundamental assumption regarding the mitigation of LLMs' micromotives and encourage a reevaluation of how LLMs may influence users and society.

Agent-based simulation (ABS) models are potent tools for analyzing complex systems. However, understanding and validating ABS models can be a significant challenge. To address this challenge, cutting-edge data-driven techniques offer sophisticated capabilities for analyzing the outcomes of ABS models. One such technique is process mining, which encompasses a range of methods for discovering, monitoring, and enhancing processes by extracting knowledge from event logs. However, applying process mining to event logs derived from ABSs is not trivial, and deriving meaningful insights from the resulting process models adds an additional layer of complexity. Although process mining is invaluable in extracting insights from ABS models, there is a lack of comprehensive methodological guidance for its application in ABS evaluation in the research landscape. In this paper, we propose a methodology, based on the CRoss-Industry Standard Process for Data Mining (CRISP-DM) methodology, to assess ABS models using process mining techniques. We incorporate process mining techniques into the stages of the CRISP-DM methodology, facilitating the analysis of ABS model behaviors and their underlying processes. We demonstrate our methodology using an established agent-based model, Schelling model of segregation. Our results show that our proposed methodology can effectively assess ABS models through produced event logs, potentially paving the way for enhanced agent-based model validity and more insightful decision-making.

The strategic selection of resources by selfish agents is a classic research direction, with Resource Selection Games and Congestion Games as prominent examples. In these games, agents select available resources and their utility then depends on the number of agents using the same resources. This implies that there is no distinction between the agents, i.e., they are anonymous. We depart from this very general setting by proposing Resource Selection Games with heterogeneous agents that strive for joint resource usage with similar agents. So, instead of the number of other users of a given resource, our model considers agents with different types and the decisive feature is the fraction of same-type agents among the users. More precisely, similarly to Schelling Games, there is a tolerance threshold $\tau \in [0,1]$ which specifies the agents' desired minimum fraction of same-type agents on a resource. Agents strive to select resources where at least a $\tau$-fraction of those resources' users have the same type as themselves. For $\tau=1$, our model generalizes Hedonic Diversity Games with a peak at $1$. For our general model, we consider the existence and quality of equilibria and the complexity of maximizing social welfare. Additionally, we consider a bounded rationality model, where agents can only estimate the utility of a resource, since they only know the fraction of same-type agents on a given resource, but not the exact numbers. Thus, they cannot know the impact a strategy change would have on a target resource. Interestingly, we show that this type of bounded rationality yields favorable game-theoretic properties and specific equilibria closely approximate equilibria of the full knowledge setting.

Cass R. Sunstein is one of America's foremost legal scholars; he is also a big fan of science fiction authors such as Isaac Asimov and Arthur C. Clarke. Sunstein thinks that science fiction can be a useful tool to inoculate people against status quo bias--our tendency to resist anything new and unfamiliar. "If you love science fiction, you find it fun, and maybe a good little chill goes down your spine, when you think of things that hadn't been dreamt of until 1990 or 2005, and those things excite you, as well as maybe scaring you," Sunstein says in Episode 468 of the Geek's Guide to the Galaxy podcast. Sunstein's new book Averting Catastrophe lays out an approach for evaluating unpredictable threats such as asteroids, AI, climate change, and pandemics. One of the book's more science fictional ideas is that people might not need to worry so much about the well-being of future generations, an idea that Sunstein attributes to Nobel prize-winning economist Thomas Schelling.

In about A.D. 1300 the Anasazi people abandoned Long House Valley. To this day the valley, though beautiful in its way, seems touched by desolation. It runs eight miles more or less north to south, on the Navajo reservation in northern Arizona, just west of the broad Black Mesa and half an hour's drive south of Monument Valley. To the west Long House Valley is bounded by gently sloping domes of pink sandstone; to the east are low cliffs of yellow-white sedimentary rock crowned with a mist of windblown juniper. The valley floor is riverless and almost perfectly flat, a sea of blue-gray sagebrush and greasewood in sandy reddish soil carried in by wind and water. Today the valley is home to a modest Navajo farm, a few head of cattle, several electrical transmission towers, and not much else. Yet it is not hard to imagine the vibrant farming district that this once was. The Anasazi used to cultivate the valley floor and build their settlements on low hills around the valley's perimeter. Remains of their settlements are easy to see, even today. Because the soil is sandy and the wind blows hard, not much stays buried, so if you leave the highway and walk along the edge of the valley (which, by the way, you can't do without a Navajo permit), you frequently happen upon shards of Anasazi pottery, which was eggshell-perfect and luminously painted. On the site of the valley's eponymous Long House--the largest of the ancient settlements--several ancient stone walls remain standing. Last year I visited the valley with two University of Arizona archaeologists, George Gumerman and Jeffrey Dean, who between them have studied the area for fifty or more years. Every time I picked up a pottery shard, they dated it at a glance. By now they and other archaeologists know a great deal about the Anasazi of Long House Valley: approximately how many lived here, where their dwellings were, how much water was available to them for farming, and even (though here more guesswork is involved) approximately how much corn each acre of farmland produced. They have built up a whole prehistoric account of the people and their land. But they still do not know what everyone would most like to know, which is what happened to the Anasazi around A.D. 1300. "Really, we've been sort of spinning our wheels in the last eight to ten years," Gumerman told me during the drive up to the valley. "Even though we were getting more data, we haven't been able to answer that question."

What do flocks of birds, traffic jams, fads, drinking games, forest fires and residential segregation have in common? The answer could come from a new computational research method called agent-based modeling. Michael Macy, a sociologist at Cornell University, Ithaca, N.Y., is using this powerful new tool to look for elementary principles of self-organization that might shed new light on long-standing puzzles about how humans interact. A professor and chair of Cornell's Department of Sociology, Macy will speak Feb. 14 at the annual meeting of the American Association for the Advancement of Science in Denver in a symposium, "Artificial Agent Societies: A Computational Future for the Social Sciences." The Cornell sociologist begins his lecture with a flock of computer-generated birds wheeling synchronously through aerobatic maneuvers.

"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.

This text provides with an introduction to the modern approach of artificiality and simulation in social sciences. It presents the relationship between complexity and artificiality, before introducing the field of artificial societies which greatly benefited from the computer power fast increase, gifting social sciences with formalization and experimentation tools previously owned by "hard" sciences alone. It shows that as "a new way of doing social sciences", artificial societies should undoubtedly contribute to a renewed approach in the study of sociality and should play a significant part in the elaboration of original theories of social phenomena.