It’s possible that I shall make an ass of myself. But in that case one can always get out of it with a little dialectic. I have, of course, so worded my proposition as to be right either way (K.Marx, Letter to F.Engels on the Indian Mutiny)
Thursday, February 05, 2026
AI agents debate more effectively when given personalities and the ability to interrupt
In a typical online meeting, humans don't always wait politely for their turn to speak. They interrupt to express strong agreement, stay silent when they are unsure, and let their personalities shape the flow of the discussion. Yet, when Artificial Intelligence (AI) agents are programmed to debate or collaborate, they are usually forced into a rigid, round-robin structure that stifles this natural dynamic.
Researchers from The University of Electro-Communications and the National Institute of Advanced Industrial Science and Technology (AIST) have demonstrated that allowing AI agents to break these rules can actually make them smarter.
Their new study proposes a debate framework where LLM-based agents are freed from fixed speaking orders. Instead, these agents can dynamically decide to speak up, cut someone off, or remain silent based on assigned personality traits and the urgency of the moment. The team found that this human-like flexibility led to higher accuracy on complex tasks compared to standard models.
"Current multi-agent systems often feel artificial because they lack the messy, real-time dynamics of human conversation," the researchers explain. "We wanted to see if giving agents the social cues we take for granted-like the ability to interrupt or the choice to stay quiet-would improve their collective intelligence."
To test this, the team integrated the Big Five personality traits (such as openness or agreeableness) into the agents. Unlike conventional systems where an agent generates a full paragraph before the next one begins, this new framework utilizes sentence-by-sentence processing. This granular approach allows agents to "hear" the conversation in real-time and calculate an "urgency score."
If an agent's urgency score spikes-perhaps because it spots an error or has a critical insight-it can interrupt the current speaker immediately. Conversely, if an agent has nothing valuable to add, it can choose silence, preventing the discussion from being cluttered with redundant information.
The framework was evaluated using the MMLU (Massive Multitask Language Understanding) benchmark. The results were clear: the "chaotic" agents outperformed the single-LLM baseline in task accuracy.
Interestingly, the inclusion of personality traits significantly reduced unproductive silence. Because agents acted according to their specific characters-some being more dominant, others more reflective-the group reached consensus more efficiently than a group of generic, rule-bound bots.
This study suggests that the future of AI collaboration lies not in stricter controls, but in mimicking human social dynamics. By allowing agents to navigate the friction of interruptions and the nuance of silence, developers can create systems that are not only more naturalistic but also more effective at problem-solving.
The team plans to further apply this framework to creative and collaborative tasks, aiming to develop richer metrics for understanding how "digital personalities" influence group decisions.
Authors Akikazu Kimura (The University of Electro-Communications) Ken Fukuda (National Institute of Advanced Industrial Science and Technology, The University of Electro-Communications) Yasuyuki Tahara (The University of Electro-Communications) Yuichi Sei (The University of Electro-Communications)
BALTIMORE, Feb. 5, 2026 — Conventional wisdom is that waiting in a queue online or in a physical line involves a certain cost for people and organizations. Rational analysis has largely based its queue management predictions on remaining wait time, or how long someone has left to wait. Much of the planning around the design of queues is based on this factor.
But new research examines other parameters that can play important roles in determining the “cost of queuing.” They include length of the queue, service speed and the characteristics of the wait endured so far. These variables can mean the difference between someone staying in the queue or leaving it.
“Individuals in a queue will evaluate the burden of completing it by considering the number of people in line and how fast or slow the line is moving, rather than by estimating the remaining wait time or relying on subjective queue experiences,” said Luo.
The research studied 1,163 unique subjects across 31 different queue variations. Linardi, an experimental economist, noted that this project may be one of the first to measure the discomfort of waiting in line in a way that can be directly translated into dollars.
Overall, this research cuts through a lot of confusion about what affects queueing behavior, providing a rigorous baseline for the design of future queuing systems. When there is no uncertainty about queue parameters, the pain of standing in line—measured in dollars—depends reliably only on the linear combination of the length and speed of the queue ahead.
INFORMS is the world’s largest association for professionals and students in operations research (O.R.), AI, analytics, data science, and related disciplines, serving as a global authority in advancing cutting-edge practices and fostering an interdisciplinary community of innovation. INFORMS empowers its community to enhance organizational performance and drive data-driven decision-making through its journals and resources. Learn more at www.informs.org or @informs.
Experienced and Prospective Wait in Queues: A Behavioral Investigation
New white paper on emotional intelligence as a driver of organizational wellness published by the University of Phoenix College of Doctoral Studies
New analysis by research Fellow Chanell Russell explores how emotionally intelligent leadership can strengthen psychological safety, engagement and sustainable performance
University of Phoenix College of Doctoral Studies announced the publication of “Emotional Intelligence as a Foundation for Organizational Wellness,” a new white paper by Chanell Russell, a research fellow with the University’s Center for Organizational Wellness, Engagement and Belonging (CO-WEB). The paper examines how emotional intelligence functions as a critical skill in education settings and as a leadership capability in modern organizations, particularly amid rising workplace strain, complexity and change.
The white paper synthesizes research across organizational psychology, leadership studies and health administration to explore how emotionally intelligent behaviors — such as self-awareness, empathy and relational decision-making — can support healthier workplace cultures. Russell emphasizes that organizational wellness is not solely an individual responsibility, but a systemic outcome shaped by leadership norms, policies and daily practices.
“Emotional intelligence is not a ‘soft skill’ — it is a structural leadership capability that influences trust, psychological safety and long-term organizational effectiveness,” said Russell. “When leaders are equipped to recognize emotional dynamics and respond intentionally, they can reduce preventable strain and create conditions where people are more engaged, resilient and able to perform at their best.”
White paper focus: emotional intelligence and organizational wellness
The paper explores how emotionally intelligent leadership practices can support organizational wellness across sectors, with particular relevance for healthcare, human services and mission-driven organizations. Key areas of focus include:
The relationship between emotional intelligence and psychological safety in teams
How leadership behaviors influence engagement, burnout and retention
The role of emotionally intelligent decision-making in reducing preventable organizational strain
Practical leadership strategies that align wellness, performance and sustainability
Designed for scholars, practitioners and organizational leaders, the white paper bridges theory and application, offering insights that can inform leadership development, policy design and organizational strategy.
The full white paper is available on the University of Phoenix on the Research Hub.
About the author
Russell is a published author with a professional background spanning psychiatric care, applied behavior analysis (ABA) therapy and foster care case management. As a Fellow in Residence with CO-WEB, her research interests focus on organizational wellness, psychological safety and leadership strategies that support engagement while reducing preventable strain. Russell earned her B.S. and M.S. in Psychology from University of Phoenix, where she is currently pursuing a Doctor of Health Administration (CERT/D-HA).
AboutUniversity of Phoenix
University of Phoenix innovates to help working adults enhance their careers and develop skills in a rapidly changing world. Flexible schedules, relevant courses, interactive learning, skills-mapped curriculum for our bachelor’s and master’s degree programs and a Career Services for Life® commitment help students more effectively pursue career and personal aspirations while balancing their busy lives. For more information, visit phoenix.edu.
About the College of Doctoral Studies
University of Phoenix’s College of Doctoral Studies focuses on today’s challenging business and organizational needs, from addressing critical social issues to developing solutions to accelerate community building and industry growth. The College’s research program is built around the Scholar, Practitioner, Leader Model which puts students in the center of the Doctoral Education Ecosystem® with experts, resources and tools to help prepare them to be a leader in their organization, industry and community. Through this program, students and researchers work with organizations to conduct research that can be applied in the workplace in real time.
Philadelphia communities help AI computer vision get better at spotting gentrification
Drexel researchers draw on community insights to sharpen machine learning tool for identifying gentrification
Researcher from Drexel University worked with communities in Philadelphia to develop a computer vision program to help identify and monitor gentrification.
Over the last several decades urban planners and municipalities have sought to identify and better manage the socioeconomic dynamics associated with rapid development in established neighborhoods. The term “gentrification” has been lingua franca for generations of urbanites who have seen their communities change and property values, and commensurate taxes, shift in ways that can make it difficult for longtime residents to stay. But identifying its unmanaged creep can be a challenge, particularly in densely populated areas, as its visual hallmarks — such as new facades, mixes in building materials and changes in building heights — present differently in different cities and regions.
In hopes of providing a better monitoring system for those seeking to mitigate the negative effects of gentrification, researchers at Drexel University have drawn on the wisdom of community members in Philadelphia neighborhoods that have been affected by it to hone a computer vision program that can reliably identify and track gentrification throughout the city.
Drawing on information from thousands of current and historic images of the city, construction permit records, as well as the input of focus groups from three neighborhoods identified in an analysis of Census data as presenting the socioeconomic shift associated with gentrification, the researchers produced what is believed to be the first “deep mapping” machine learning program that integrates both qualitative and quantitative data to identify gentrification.
The researchers, from Drexel’s College of Engineering, recently presented their work and the gentrification identification program they created in the journal PLOS One. They point to Philadelphia’s unique and varied architecture and development patterns, housing density and the depth of knowledge from longtime residents as key to training a computer model versatile enough to discern region-specific signs of gentrification.
“While gentrification looks different depending on where it’s happening, the people who live in those areas can identify it immediately,” said Maya Mueller, a doctoral student in the College of Engineering, who led the research. “Our research is unique in that we ask residents how they identify gentrification in their neighborhood. We then attempted to teach machine learning models to learn from these cues in order to map out where gentrification is occurring.”
According to the team, a program like this could help community leaders, urban planners and researchers who are trying to protect residents from being displaced by gentrification, as well as empowering residents who are working to preserve their communities.
“We wanted to open a discussion about how gentrification is changing these neighborhoods,” Mueller said. “And through this discussion, develop models that can one day accurately measure the speed and magnitude of these changes.”
To create the program, the team connected with residents in three Philadelphia neighborhoods whose socioeconomic shift fit the profile of gentrification and that had been identified through media coverage and the researchers’ knowledge of the area. Through a series of focus groups, the team learned about the residents’ experiences with gentrification in their neighborhood, the signs of it they recognize in buildings and business corridors and their perceptions about whether and how it has changed access to places and services in the neighborhood.
With this guidance, the team created a list of 16 architectural traits and building qualities indicative of “new-build” gentrification — new construction, as opposed to refurbishing aging buildings — the type most prevalent in gentrifying areas of Philadelphia. The list included things like “boxy” buildings, homogenous design across rowhomes, bump out windows, privacy fences and contrasting mix of building materials with color differences.
“Residents of these areas know gentrification when they see it,” said Simi Hoque, PhD, a professor in the College of Engineering, who was a co-author of the research. “In our focus groups they said these buildings ‘stick out like a sore thumb.’ So, it was then our job to translate the ‘sore thumb’ into a list of traits that we could use to train our program.”
The researchers used the list to label more than 17,000 historic images of Philadelphia neighborhoods from 2009-2013, paired with more recent images of the same places, from 2017-2024, as “gentrification” or “not gentrified.”
This information enabled the team to train a neural network machine learning model, called ResNet-50, that learns by comparing subtle variations in training data to identify important characteristics or patterns that it then applies to identify similarities in new inputs.
Through the deep learning processing and the team’s manual labeling, the program extracted 1,040 data points that are visual hallmarks of new-build gentrification.
To test the program’s gentrification-spotting ability, they showed it new sets of image pairs from around the city. The program was able to correctly identify new-build gentrification in the images with 84% accuracy. To further verify the relative accuracy of the program, the team also compared its audit to permit records for new construction, which has been used as an early indicator of gentrification trends, finding a strong correlation between the two methods.
In addition to producing an accurate program, one of the team’s primary goals was to improve transparency and take perception bias out of the process to make it a more reliable tool for urban planners, municipal leaders and community advocates.
“Machine learning models are notoriously ‘black box,’ so researchers don’t fully understand why they produce the predictions they do,” Mueller said. “This means machine learning models can learn biases and incorrect ideas and then perpetuate these judgements. It’s important that we clearly define how we’re training these models, both for ethical reasons and to make these models perform better and more accurately.”
According to the researchers, as with any machine learning program, their model would be improved through additional use and exposure to more and varied training data. But it remains a potent tool for researchers seeking to accurately map gentrification trends in areas where reliable permit and development data is lacking.
“With more reliable methods and data on gentrification’s effect on the built environment, urban planners can gain insight into how certain types of development result in inequitable effects and organizations can identify neighborhoods that require protection from displacement,” Mueller said. “Although we have a way to go, our research team hopes that more concrete measurements on the degree of new development can help to address residents’ concerns. Developing this model is one step in the process of producing more useable data.”
