As artificial intelligence (AI) continues to transform industries and reshape traditional work models, professionals who are equipped with the right skills and mindset are better positioned to thrive in this new era.
Neal Chuenprateep MPAc ’15, one of the first human resources hires at OpenAI and a graduate of the UCI Paul Merage School of Business, offers students valuable insights into how his education prepared him for the fast-evolving technology landscape and provides a glimpse into how AI will likely affect the workforce of the future.
AI in the Workforce
Insights from Neal Chuenprateep ‘15, HR at OpenAI
From the Merage School to OpenAI
Chuenprateep graduated from the Merage School in 2015 as part of the second cohort of the Masters of Professional Accountancy Program. “The program was still very new and fresh at the time, but it was
also filled with valuable learning experiences,” he says. “The Merage School taught me the fundamentals while always keeping an open eye for innovation and whatever was on the horizon.”
After earning his master’s degree, Chuenprateep began a financial advisory career at Deloitte as a business valuation consultant. There he honed his skills in data analysis and client relations with hands-on experience working with spreadsheets and SQL.
His transition from Deloitte to tech giant Google was pivotal. At Google, Chuenprateep shifted into people operations, a role where he learned more about human
resource processes, specifically the operational side of people processes, documentation, and workforce planning. “It was during this time that I began to notice the potential for AI to revolutionize many of the tasks that had previously been done manually. A lot of the work I did back in 2017 could be significantly streamlined by AI now,” he says.
When asked how the Merage School prepared him for his latest HR leadership role at OpenAI, Chuenprateep emphasizes the supportive yet structured environment of the school. “I was a scrappy upstart type of student at first, but Merage helped me become
comfortable with all of their structure and support,” he says. This blend of support and adaptability to emerging trends and technologies was crucial as he stepped into the rapidly shifting world of AI-driven innovation.
The Future of Work: Human and AI Collaboration
In his new leadership position at OpenAI—a company at the forefront of artificial intelligence development—Chuenprateep is well positioned to discuss the future of work, and he is optimistic about the potential for AI in the workplace. “The future of work is really more like humans working alongside AI, which allows humans to focus on the highest-value / strategic projects,” he says.
Rather than envisioning a future where AI takes over all tasks, Chuenprateep sees AI handling the more routine, baseline tasks, freeing
time for workers to engage in higherlevel thinking and creative problemsolving. “If you look into the history of how industrial and technological advancements have impacted humans—for example with the development of the internet—we never stopped working,” he says. “The introduction of AI, much like
previous technological leaps, will lead to new opportunities rather than simply displacing workers.”
New Challenges and Opportunities For AI-Integration
Chuenprateep notes AI’s increasing proficiency in certain technical tasks such as coding. “If you’re looking for a coding partner for Python or SQL, quite frankly AI is already better (and patient) than a lot of humans,” he says. However, he also highlights the limitations of current AI models, particularly in more creative or subjective fields, where human involvement is still essential. “If you ask AI to do something like write a humor or sports article—or a script for a podcast, for example— it isn’t very good right now,” he says. “That’s the uncanny valley
effect in which, in the creative field, there is something about the human experience and touch that is essential. I don’t see AI thriving in those areas quite yet.” This perspective aligns with the growing consensus that AI, while powerful, has boundaries when it comes to creativity and emotional intelligence. But Chuenprateep is confident AI will continue to improve rapidly, particularly in operational and administrative tasks. In HR, for instance, he envisions AI playing a significant role in operations / lifecycle management and other routine processes. However, he stresses the importance of accuracy and trust in these systems. “In HR, if you get 99 out of 100 payroll statements right, it’s a bad day, so we’re looking for AI solutions that get it right every single time.”
Sound Advice: Embrace AI and Work-Life Balance
Chuenprateep offers practical advice for fellow Merage alumni and anyone looking to navigate the evolving landscape of AI-driven work. His first piece of advice: “Lean in and embrace it,” he says. “As AI tools become more ubiquitous and more advanced, those who learn to integrate and leverage them will have a significant advantage in their careers. Make sure you try to integrate and familiarize yourself with these AI tools ASAP because they will only get better.”
His second piece of advice focuses on maintaining work-life balance, something AI can assist with, he
“Lean in and embrace it. As AI tools become more ubiquitous and more advanced, those who learn to integrate and leverage them will have a significant advantage in their careers. Make sure you try to integrate and familiarize yourself with these AI tools ASAP because they will only get better.”
believes. “By automating repetitive tasks, AI can provide professionals with more time for creativity, strategy, and leisure, which are critical components of long-term career satisfaction and success.”
The Role of Education in the AI Revolution
Chuenprateep reflects on the role that educational institutions like the Merage School will play to
prepare the next generation for a workforce increasingly shaped by AI. He praises the school for its forward-thinking approach and emphasis on integrating AI into the curriculum. “The Merage School is really embracing AI and training their students to integrate AI whenever possible,” he says. “By fostering familiarity with AI tools and emphasizing innovation, the Merage School is equipping its students with the skills they need to succeed in an AI-enhanced workforce.”
As AI continues to develop and reshape industries, leaders like Neal Chuenprateep serve as valuable guides to navigate this transformation. His career, shaped in part by the education and experiences he gained at the UCI Merage School of Business, is a testament to the importance of staying adaptable, embracing innovation, and preparing for the future of work where humans and AI collaborate seamlessly. //
The Creativity of Humans and AI
Merage Professor Introduces a Groundbreaking ‘Random Walk’ Model to Boost Creativity
In the evolving innovation landscape, creativity stands at the core of progress across disciplines, ranging from science and engineering to the arts. Professors Vidyanand Choudhary of the UCI Merage School of Business and Shai Vardi of Purdue University’s Daniels School of Business have crafted a pioneering approach to understanding and enhancing creativity. Their new model, “A Random Walk Modeling Framework for Boosting the Creativity of Humans and AI,” draws inspiration from computer science, physics, and neuroscience. This novel framework provides insights into how creative ideas are generated, with the potential to help AI and humans boost their creative output.
Combining Semantic Networks and Brownian Motion
The collaboration between Choudhary and Vardi stemmed from a shared interest in creativity and their backgrounds in graph theory, a mathematical structure used to model relationships between objects. At a conference last year, the two scholars discussed how creativity, often viewed as abstract and difficult to quantify, could be examined through the lens of computer science. This conversation led them to look at existing literature on creativity, particularly about how neuroscience and psychology have studied the human brain as a connected graph of ideas.
“In neuroscience, the brain is thought of as a network of nodes that talk to each other,” Choudhary says. “Similarly, psychologists have mapped creativity through semantic networks, where words and ideas are linked in meaningful ways.” For example, when someone hears the word “phone,” their brain might immediately associate it with the word “communication.”
This connection forms a semantic network, illustrating how ideas are related to one another.
The duo realized they could combine this notion of semantic networks with a concept from physics known as Brownian motion, which describes random movements of particles, to model creativity. “Our
research talks about creativity as a random walk,” Choudhary says. “By walking randomly through a network of ideas, we can trace paths that lead to novel and potentially creative thoughts.” This idea forms the crux
“Our research talks about creativity as a random walk,” Choudhary says. “By walking randomly through a network of ideas, we can trace paths that lead to novel and potentially creative thoughts.”
of their model: Creativity arises from moving between a network of nodes—or ideas—in unexpected ways.
How Creativity Functions as a Random Walk
The random walk model of creativity emphasizes that while humans may tend to follow familiar patterns of thought, it is often on the lesstraveled paths where creativity emerges. For instance, if one starts with the idea of a “phone,” common associations such as “communication” or “charger” may come to mind. However, jumping to less obvious ideas, like “phone” to “paper” or the more abstract “phone” to “relationships,” may cause more creative connections to arise.
According to Choudhary, this leap from idea to idea is probabilistic. Some associations are more likely than others, but those rare,
unexpected connections are often where creativity happens. “Creative ideas are just a little rarer,” he says. These rare ideas are not only novel but can also be highly useful.
Designing Interventions to Boost Creativity
Choudhary and Vardi’s work goes beyond explaining creativity. Their goal is to design interventions that can actively boost creative thinking. After they developed their abstract model, their research involved identifying techniques to enhance creativity in both humans and AI. “We want employees, artists, plumbers—everyone—to be more creative,” Choudhary says. To achieve this, they explored welldocumented creativity-boosting strategies, or interventions, and also applied the random walk concept to design new interventions. One such method involves constraining the creative process to force deeper exploration of less obvious ideas. For example, when asked to generate a verb for the noun “desk,” an individual may be
limited to using verbs that start with the same letter as the noun. This constraint prevents them from defaulting to common associations and encourages them to dig deeper for creative alternatives.
Using AI in Creative Testing
AI played a pivotal role in testing these interventions. Choudhary and Vardi implemented a well-known method called the Verb Generation Task (VGT), in which AI was asked to come up with a creative word association. The researchers introduced novel elements, such as asking AI to consider absurd ideas like “space elephant,” while creating responses that significantly boosted the creativity of the AI. “All the
answers AI came up with were way more creative,” Choudhary says. Imposing constraints and altering the context of tasks improved creativity not just in humans but also in AI systems like ChatGPT.
Testing the Model on Humans
After conceptualizing their model and testing it on AI, Choudhary and Vardi were asked to extend their experiments to humans. They discovered their new interventions and previously known interventions also worked to boost creativity. One of the methods they used was the Alternative Uses Task (AUT), where participants were asked to concoct multiple uses for a given object. Their model not only explained
why traditional creativity-boosting interventions worked but also provided a road map for developing new methods.
This four-step process—creating an abstract model, validating existing interventions, proposing new interventions, and testing the model on both AI and humans—set the stage for future research and practical applications.
Implications for Creativity in Practice
Choudhary and Vardi’s research carries profound implications for industries that rely on creative problem-solving. By providing a structured model of creativity, their work offers companies a blueprint to develop their employees’ creative skills.
“As academics and researchers, our goal is to build one brick at a time,” Choudhary says. “It’s our hope that this research can be a useful brick that provides others the foundation to facilitate useful and inventive ideas to build upon. That’s my first hope. The second one is more practical in terms of industry: Thinking about creativity in this way helps managers understand what they need to do to nurture and improve creativity beyond hiring creative people. You can look to your employee base and teach them the skills that boost creativity.”
Inspiring the Future of Creativity Research
In the broader academic context, the researchers hope their model will serve as a foundation for future studies. “If people had a model,
“As academics and researchers, our goal is to build one brick at a time. It’s our hope that this research can be a useful brick that provides others the foundation to facilitate useful and inventive ideas to build upon. That’s my first hope. The second one is more practical in terms of industry: Thinking about creativity in this way helps managers understand what they need to do to nurture and improve creativity beyond hiring creative people. You can look to your employee base and teach them the skills that boost creativity.”
they could try to improve it or apply it in ways we haven’t thought of yet,” says Choudhary. Whether in academia or industry, this model presents a powerful tool to foster creativity, offering new methods to enhance the creative capacities of both AI systems and humans.
Choudhary and Vardi’s random walk model of creativity opens exciting possibilities for future research and practical applications. As creativity continues to play a central role in driving innovation, their groundbreaking work provides a crucial framework to understand and enhance this vital human and technological capability. //
Vidyanand (“VC”) Choudhary is a professor of Information Systems and the Director of International Programs. He is an authority on competitive strategy for technology products and AI. His research interests are in the economics of information systems; business impact of AI and Machine Learning; Boosting creativity and innovation; use of recommender systems and search tools; impact of technology on corporate governance; marketing strategy and pricing of cloud and SaaS products; and pricing and product line design of information goods.
His research has been published in several top-tier journals including Management Science, Information Systems Research, MIS Quarterly, Production and Operations Management Journal and the Journal of Management Information Systems.
VC Choudhary
The Games Go On
A New Approach to Competitive Balance in Disrupted Sports Seasons
Unexpected disruptions have a way of shaking up even the most well-established systems, and the world of professional sports is no exception. When the COVID-19 pandemic threw into chaos the traditional schedules of leagues like the NFL, MLB, and NBA, it wasn’t just the games that were affected—it was the very framework that teams and rankings relied on. With fewer games and playoff uncertainty looming, the challenge became clear: how could teams maintain competitive integrity in the face of such upheaval?
This was the question that intrigued UCI Paul Merage School of Business Associate Professor John Turner and his doctoral students, Ali Hassanzadeh and Mojtaba Hosseini. Together, they saw this disruption not just as a problem, but as an opportunity explore new ways of thinking about how professional teams are ranked. They set out to create a solution that would protect the integrity of team rankings the next time broader circumstances force seasons to spontaneously change course.
Their paper, “How to Conclude a Suspended Sports League?” explores their novel approach to this problem. It was recently published in Manufacturing & Service Operations Management.
PhD Student and Basketball Fan Tackles Pandemic NBA Issue
Turner credits the inspiration for the paper to Hassanzadeh, who was deeply affected by the uncertainty surrounding sports leagues during the pandemic. “He was working on other research when he was doing his PhD with me,” Turner says. “After COVID started, he was watching the news, and they kept talking about how no one knew what was going to happen in the NBA or how to resume the league in a shortened time frame.”
Hassanzadeh is a dedicated basketball fan who has a strong background in machine learning and optimization. He began to ponder the NBA’s issue, and his curiosity led to the creation of a research project
that would eventually involve Turner and fellow PhD student Hosseini. “The impetus for the paper was to use what we know about predictive and prescriptive analytics to look at this problem,” Turner says. The result was a comprehensive study that provides a framework for concluding sports leagues under extraordinary circumstances.
Model Predicts Fair, Accurate Outcomes
Rather than focusing on logistical concerns, Turner and his doctoral students homed in on the issue of maintaining fair and accurate team rankings. “The question we asked ourselves was, ‘If we believe the most important thing is to have rankings at the end of our shortened season that are as close to what the rankings would have been if the full season was played, what would we do?’”
This focus led them to develop an optimization model that explicitly accounted for the uncertainty
inherent in sports outcomes. They specifically modeled “the uncertainty that exists in the full season ranking,” says Turner. “We compared what the ranking was of our shortened season with the suggested games in the full season as if they had played everything that was left in the remainder of the season. Then we compared the rankings of those two. Our goal was to make those rankings as close as possible.”
“The question we asked ourselves was, ‘If we believe the most important thing is to have rankings at the end of our shortened season that are as close to what the rankings would have been if the full season was played, what would we do?’”
The research involved sophisticated mathematical modeling, including the use of stochastic processes to simulate various possible outcomes. Turner is quick to point out that this approach was not about picking winners and losers but
about maintaining the integrity of the competition. “We let the model tell us what the solution should be, in a sense,” he says. “It’s not that we said, ‘We want to pick the more competitive games.’” Rather, they spent time considering how they should go about modeling the situation.
Implications for the Future
While the study was born out of the unique circumstances of the COVID-19 pandemic, Turner believes its implications extend far beyond this specific context. “You don’t have to wait around for the next pandemic to use it. There are other instances where leagues are interrupted. Player strikes are
probably the largest one,” he says. The researchers’ model could be applied to any sport or competition facing a similar dilemma, from basketball to chess tournaments. Moreover, Turner sees potential applications in other fields, albeit with some caveats. There are some general principles, he says, but he doesn’t want to go too far to suggest others do exactly what they’ve done. “A lot of work goes into finding the right tweaks that work in a specific environment.”
One of the key takeaways from the research is its focus on fairness and accuracy in rankings, which Turner believes is crucial to maintain the integrity of any competition.
John Turner is an Associate Professor of Operations and Decision Technologies. He specializes in applying rigorous optimization-based methods to real-world problems. His research interests include revenue management, large-scale optimization and decomposition methods, online advertising allocation, sports scheduling, environmental policy, and health care management. He has published in leading academic journals including Operations Research, Manufacturing & Services Operations Management, Computers & Operations Research, the INFORMS Journal on Applied Analytics (“Interfaces”), Networks, and the Journal of Interactive Marketing. He serves as an Associate Editor at Operations Research
The model they developed aims to ensure the teams that deserve to make the playoffs do so—and that home court advantages and other critical factors are preserved. “By having rankings that are similar, we’re implicitly also making sure the right teams are given the chance to compete in the playoffs,” Turner says.
Tool Maintains Integrity of Competitions
As sports leagues around the world continue to grapple with the challenges unexpected interruptions pose, Turner and his colleagues’ research offers a valuable tool for maintaining the integrity of these competitions. By focusing on predictive and prescriptive analytics, they have developed a model that not only addresses the immediate needs of a shortened season but also provides a framework for dealing with future disruptions. In a world where uncertainty is increasingly the norm, their research serves as a reminder of the importance of adaptability and innovation to ensure that, no matter what happens, the game can go on. //
John Turner
Modern Modular Housing Case Competition
When local real estate developer and owner of AI Accommodation (AIA) Frank Jao visited UCI’s Paul Merage School of Business to discuss new advancements in modular housing with Dean Ian Williamson, they developed an idea to hold a case study competition for business plans to implement the technology. “The housing is remarkable,” says Ed Coulson, director of the Merage School’s Center for Real Estate. “This new modular housing is much cheaper to construct than regular housing,
“The housing is remarkable. This new modular housing is much cheaper to construct than regular housing, but it also brings several other cost advantages, certainly in terms of the quality of the housing, given the technological features.”
but it also brings several other cost advantages, certainly in terms of the quality of the housing, given the technological features.”
Williamson and Jao came to Coulson to gauge his interest in hosting and administering the competition. “I immediately said yes, although we had never held an event quite like this” says Coulson. “With the great help of the sponsors, and Paul Merage School and CRE staff, we were able to make it happen”
The Miracle of Modular Housing
AIA recently partnered with NousLogic to develop modular housing solutions at a cost that is two to three times lower to manufacture than conventional homes and takes only two or three weeks to construct. Modular units are stackable up to four stories and
Ed Coulson and Frank Jao
feature customized exteriors and interiors. Compact furniture options are also available.
All units are equipped with smart home services, including smart secure access with integrated smart door locks and video doorbells running on different Smart City longrange wireless networks such as Amazon Sidewalk.
These modular homes have numerous potential applications. They make excellent accessory dwelling units (ADUs), which typically are built in the backyards of existing homes. They also can serve as homeless shelters, storage buildings, or additional classrooms for crowded schools.
In partnership with NousLogic’s Remote Patient Monitoring platform, these units can serve as assisted living units for residents who need help with day-to-day activities.
With all of this in mind, participants were challenged to present judges with a business plan that would address the market segment to be filled, production plans, geographic scope, projected timeline and financial viability.
Seven Competing Teams
UCI’s first Case Competition was held on May 31, 2024, featuring seven teams who presented their
business plans to a panel of judges. Students from across UCI’s campus were invited to participate in the competition.
“We had teams from the Merage School, of course, but also from engineering, social sciences and others,” Coulson says. “We even had participation from a team from the Fulbright University of Vietnam in Ho Chi Minh City.”
In addition to competing for cash
Congratulations to the event’s winners:
First Prize Team: Verano
Coach: Andrew Burt
Rohit Kulkarni
Paaras Saxena
Richa Singhal Saurabh
Ameya Thakur
Second Prize
Team: REA Investment Management
Coach: Joanne Lucas
Long Ma Paul
Timothy Cho
Christian Chai
Third Prize
Team: Smart Urban Champs
Coach: Jade Nguyen
Abigail Cooke
Brandon D’Angelo
Perla Rojas
Shohreh Bozorgmehri
Technology Prize Team: AI Merage Solutions
Coach: Trevor Portiz
Samria Farahani
Golnar Hannah
Alyssa Enna
Samir Khanna
prizes, the event allowed students to interact with top executives in Orange County, including the competition sponsors and an impressive panel of judges. “One of the judges was the head of the Orange County Transit Authority,” says Coulson. “Another was one of the top executives at the Mayo Clinic.”
An Outstanding Variety of Approaches
Each team took a slightly different angle on their pitches, Coulson explains. “A few presentations went deep on a particular site and had detailed financial underwriting, while others discussed ways of implementing the technology aspect because that was a special request from the sponsors. Still others took a more broad-based approach. The winning team even took out a survey to determine what people’s preferences would be for modular housing, and they took into account those preferences in their presentation. They identified a large number of potential sites for this project and how to implement this in a more broad-based way.”
The Winners
The winning team, Team Verano, included four MBA students from the Merage School. “Our second place winner, REA Investment Management, was a team of undergrads who are interested in real estate,” says Coulson. “Third
place, Smart Urban Champs, was another MBA group, and the technology prize winner—AI Merage Solutions—was also an MBA group.”
The winning team was awarded $5,000. Second place received $2,000. Third place won $1,000, and an additional $2,000 was awarded to the technology prize winner.
“The special part of this was the actual cash prizes and Mr. Jao’s promise to the students—not just to the winning teams but to all the teams—to engage with AI Accommodations to implement the plans they presented.”
Practical Uses for Modular Housing in Orange County
Affordable housing has become a huge challenge in Southern California. “A lot of people think modular housing is one of the ways we can alleviate this problem,” Coulson says. “Accessory dwelling units, or ADUs, are an easy application of this modular housing concept, but the students thought
bigger. We need large-scale thinking to figure out how to get more affordable housing into people’s lives.”
Outstanding Results
“Overall I think it was a success,” says Coulson. “After the event was over, Mr. Jao and Mr. Nhu—head of NousLogic—both expressed great satisfaction with how everything worked out. We’re all very pleased.” Although it’s too soon to talk about making this Case Competition an annual event, Coulson is open to the idea. “I have not heard of anything like it,” he says, “but I would welcome the opportunity to do it again. It was a really great event and the first time we’d done anything like this within the Center for Real Estate—and that was fantastic.” //
The Merage School welcomed prominent Professor of Economics N. Edward Coulson into our faculty in 2016. He teaches in the area of Economics and Public Policy and serves in the school’s Center for Real Estate as Director of Research. In this capacity, Coulson advances the real estate program’s agenda of excellence in teaching, research and professional outreach.
N. Edward Coulson
How AI Affects Product Recommendation Bias
We’ve all been there. You search for something at your favorite online store, and suddenly a product recommendation catches your eye. The price is right. The customer ratings are solid. The next thing you know you’re clicking “add to cart” and completing your purchase. Do you know the reason behind why the store suggested that specific product? Was it because it was the best available option at the best price, or was it because the profit margin for that product was higher compared to another possibly better product that they never showed you.
That’s the type of scenario Professor Vidyanand Choudhary at the UCI Paul Merage School of Business and Associate Professor Zhe Zhang at the UT Dallas Naveen Jindal School of Management explored in “Product Recommendation and Consumer Search,” published in the Journal of Management Information Systems. Their paper examines the effects of AI/machine learning-based recommendation systems on how consumers locate products and services online. “Previously, the focus has been on how consumers search for products, but with the advent of machine learning, platforms like YouTube, Netflix, TikTok, Amazon and others can now recommend products based on their knowledge about consumer preferences,” says Choudhary.
When consumers shop online for one item on Amazon, Amazon recommends a slew of other products, he says. Similarly, Netflix recommends certain shows based on a person’s viewing history. The researchers wondered, “What incentive does the platform have to actually recommend what they think you’d really want? And how does that change if the profit margin on certain products is different?”
Information Accessibility Affects Search Recommendations
According to their study, the amount of consumer information available to the platform relates to how well the platform can estimate an individual’s preferences. The more consumer information they have, the greater the company’s ability to predict what
the customer really wants. Similarly, consumers can determine the product best suited for their needs by searching for and analyzing product information.
“There is a lot of information available about cars, so consumers tend to do their own searches,” says Choudhary. “However, the amount of information available can also mean it may take significant time and effort for a consumer to figure out which car would be best for them.” However, “if a consumer is looking for a niche product such as parts for home repairs or cabinet parts it can be hard to find the right products online.” In these cases, consumers are more apt to “seek recommendations at their local Home Depot,” Choudary says. Searching for the most suitable product takes time and effort, and online stores know this. In their article, Choudhary and Zhang explain how these “search costs” factor into the equation. “That search cost, or how difficult it is for consumers to search for and analyze this information, affects the platform,” says Choudhary.
“If it’s easy for customers to find it on their own, then the platform is more disciplined in making better recommendations.” If the customer is unable to search for a product, or if the information isn’t available, the site may take advantage of this. “In other words, you have to trust their recommendation, and they factor that into their decision making.”
Profitability, Retaining Business, and Bias Levels Incentivize Platform Decisions
The platform must also take into account how often they make poor suggestions for consumers. They know if they make subpar recommendations, people will “leave their platform altogether,” Choudhary says. “They are afraid of losing your business, and that is another disciplining force.”
Another key factor in their study relates to the profitability of the recommended products. If a particular product is much more profitable to the platform, that likely skews their recommendation, he says.
“The greater the bias in the recommendation, the greater the gap between consumers’ true preferences and the product recommended to them.”
To find answers, Choudhary and Zhang considered several outcome variables. First, the bias level in the platform’s recommendation strategy; then the profits the firm earned; the consumer surplus; and finally, social welfare. “The greater the bias in the recommendation, the greater the gap between consumers’ true preferences and the product recommended to them,” says Choudhary. As consumers, we prefer “low bias.”
‘Game Theory’ Predicts Algorithmic Behaviors
The researchers’ paper employs the mathematical framework of game theory. As Choudhary explains, “It’s where you can mathematically represent what different people are trying to do and predict how things will play out based on the optimal strategy for each player.”
They used Amazon and Netflix as examples in their study, but the methodology applies to any platform that makes recommendations, Choudhary says. “Right now it’s hard to think of a website that doesn’t recommend things,” he says. He cites YouTube and TikTok, but nearly “everyone” uses algorithms to suggest content. “Game theory applies not just to economics,” he says, but to almost “anything in real life.”
Complexity of ‘Search Costs’
Of course the platform benefits from having more consumer information. The more information the platform has, the greater its profit. “In a ‘big picture’ sense, we show that recommendation systems change what we know about the impact of search costs on consumers,” says Choudhary. “Whereas previously it was believed that lower search costs are always good for consumers, we find that search costs interact with the firm’s recommendation strategy in complex ways. Reducing search costs can adversely impact consumer surplus in some cases.”
They also determined that, in some cases, sharing more personal information on a platform may enhance the consumer experience. “Consumers may
worry the platforms that collect their information will use it to their disadvantage,” says Choudhary. “However, we find this is not necessarily true. While there are cases where more consumer information increases bias, there are also cases where more consumer information actually reduces the level of bias. This part is surprising.”
Confirmation Bias and Consumers’ Online ‘Filter Bubble’
The researchers’ findings may have societal implications beyond mere policy around platforms not overcharging customers and making fair recommendations. “A lot of the stuff we see around politics and polarization is partly because of confirmation bias,” Choudhary says. “Let’s say you love cats, and the site figures out you love cats, so it only shows you posts about cats. Over time you start to think the whole
world loves cats. Nobody cares about dogs because you haven’t seen anyone post anything about their dog. This causes a distorted way of thinking; everyone’s in their own bubble.” A person’s “sense of reality” is “distorted,” and “they think this is what the world looks like, but it’s not true.”
Game Theory Results Also Apply to Dating Apps
Choudhary is applying what he learned in this study to future research. “I’ve been working on the incentives for technology adoption in healthcare settings and another project on the incentives of the makers of dating apps, and it’s the same thing,” he says. “Maybe it’s better for the dating app to string you along so you keep paying for their service. You keep dating people, but never actually find the
“Consumers may worry the platforms that collect their information will use it to their disadvantage. However, we find this is not necessarily true. While there are cases where more consumer information increases bias, there are also cases where more consumer information actually reduces the level of bias. This part is surprising.”
one.” That strategy may work for the app, but not for users and “that’s what game theory is all about.” It’s about thinking through all the different misaligned incentives and comprehending “how that creates friction.” //
Vidyanand (“VC”) Choudhary is a professor of Information Systems and the Director of International Programs. He is an authority on competitive strategy for technology products and AI. His research interests are in the economics of information systems; business impact of AI and Machine Learning; Boosting creativity and innovation; use of recommender systems and search tools; impact of technology on corporate governance; marketing strategy and pricing of cloud and SaaS products; and pricing and product line design of information goods.
His research has been published in several top-tier journals including Management Science, Information Systems Research, MIS Quarterly, Production and Operations Management Journal and the Journal of Management Information Systems.
