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George �鶹��Ƶ professor furthers impact of telemedicine in Ukraine

Jennifer Anzaldi — Tue, 14 Jan 2025 22:39:16 +0000

George �鶹��Ƶ professor furthers impact of telemedicine in Ukraine Jennifer Anzaldi Tue, 01/14/2025 - 17:39

Ukraine’s health care system has been hit hard amid the ongoing war. Power outages, staffing shortages, and the destruction of hospitals have added up to a drastic reduction in available care for the already-vulnerable population. In a desperate attempt to bridge the gap, Ukraine’s Ministry of Health opened the country to telehealth solutions from overseas. But will these prove to be a successful substitute for at least some necessary services, or turn out to be no better than a tech Band-Aid?

Answering that question is where Mariia Petryk, assistant professor of information systems and operations management at the Costello College of Business at �鶹��Ƶ, comes in. In her spare time, she works as volunteer director of analytics for TeleHelp Ukraine (THU).

Mariia Petryk

Founded by a cross-disciplinary group of Stanford students shortly after the war’s inception, THU was designed to succeed where other telemedicine initiatives in crisis-affected areas have failed. The founders worked tirelessly to assemble an international volunteer network comprising medical professionals, translators, interpreters and administrative “health navigators.” Aware that medical consultations were only part of the patient journey, THU’s founders sought to address the entire continuum of care.

Petryk stresses that while the project originated at Stanford, the technical team included “people from Chicago, Boston, other California schools…some very active volunteers were in Australia, South Korea, Canada and other countries.”

Petryk, herself of Ukrainian descent, was honored to lend her data science expertise to this worthy project. As analytics director, she manages a dozen or so number-crunching volunteers who measured and documented THU’s impact upon Ukraine’s displaced population during the initiative’s first full year.

As Petryk explains, “The Russian invasion created a humanitarian crisis where a lot of people were internally displaced. And when people relocate to a new place, they don’t know where to go for health care. They also are at higher risk for many issues, including mental health problems. And they don’t know where to turn to treat chronic diseases they may have.”

THU’s primary focus during its first year was delivering much-needed services to this population of war-ravaged internal exiles.

Petryk’s analytical work gave rise to a recent case study of THU published in Journal of Global Health. The paper’s other lead author was Aditya Narayan, a Stanford medical student and THU’s director of implementation and evaluation.

Their findings describe some impressive early successes. THU facilitated more than 1,200 virtual patient appointments from May 2022 to May 2023 alone. Despite often-chaotic conditions, patient attendance rates were above 70 percent for nine of the 13 months studied. As the first year wore on, the THU team found ways to prevent no-shows—for example, employing the popular texting platform Viber to communicate with patients and assigning an individual health navigator to each patient.

Even more impressively, 96 percent of patients reported that their health complaints were at least partially resolved during their visit.

The paper argues that aspects of THU’s model could be adapted for use in other humanitarian contexts. In its initial growth phase, THU had access to advanced technological infrastructure and a wide network of medical providers, by dint of its academic origins. This implies that partnerships with academia could be critical to replicating THU’s success outside Ukraine.

Petryk remains proud of THU’s impact and her role in helping define it. “Based on actual appointments and how much that amount of care would cost at a hospital, THU delivered an estimated $1 million worth of services in its first 13 months,” she says.

Looking ahead to THU’s future, she says, “I can only wish to see this ‘start-up,’ as it were, go for the IPO.”

For more information and to explore volunteering opportunities, visit THU’s website.

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Mariia Petryk

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MS in Finance students answer burning investment questions

Jennifer Anzaldi — Tue, 22 Oct 2024 17:08:48 +0000

MS in Finance students answer burning investment questions Jennifer Anzaldi Tue, 10/22/2024 - 13:08

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Derek Horstmeyer

�鶹��Ƶ Master of Science in Finance students have the rare opportunity to use their budding analytical skills to solve some of the mysteries of today’s financial markets. Moreover, their faculty-supervised research projects can land their names in the pages of The Wall Street Journal.

To date, these projects have spawned nearly 40 WSJ articles, not to mention dozens of write-ups in outlets such as MarketWatch, The Conversation, and the CFA Institute blog. They have also received television coverage on CNN and NewsNation.

SMIF officers Yashkaran Sidhu, Jonathan Pino, Matthew Rickard, Raheeg Joari, and Reema Hammad at the Chicago SMIFC conference.

As Costello College of Business finance professor Derek Horstmeyer tells it, the process started taking shape organically. “I had started writing for the CFA and The Wall Street Journal. In my talks with my students, some of them revealed they had research interests. It just kind of took off from there—I would bring it up in class and offer to supervise a research project that would last about a week.”

Student researchers are mainly recruited from Horstmeyer’s Montano Student Managed Investment Fund (SMIF) class, a hands-on exercise in security analysis and portfolio construction that serves as a capstone to the MS in finance degree.

While many student research projects originate with questions suggested by the students themselves, Horstmeyer helps the student teams devise their scope and direction.

“They might say, for example, that they’re interested in crypto. So I’ll show them previous articles on crypto and we kind of develop a new idea from there,” Horstmeyer says.

By the time they get to SMIF, students are usually comfortable getting their hands dirty with data. Finding the appropriate data-set for the question they’ve posed, however, can be a challenge. That’s another area where Horstmeyer’s support becomes critical. “The big bottleneck is they just don’t know where the data is,” he says. “We as faculty have access to these private databases. So I’ll guide them or set up the data.”

Horstmeyer also introduces the student teams to the best methods for parsing the data, while leaving students enough leeway to conduct independent follow-up analyses if needed.

Of the dozens of projects completed thus far, there are a few that, for Horstmeyer, typify the process at its best. For example, finance students Matthew Rickard and Camila Marín Builes investigated how stock-market trends fare once they have been familiarized enough to warrant a nickname. Among the eight named trends included in the study were FAANG (Facebook, Amazon, Apple, Netflix and Google), and “The Magnificent Seven” (Nvidia, Tesla, Meta, Apple, Alphabet, Amazon, and Microsoft).

“We did an extensive Google search to find the biggest named trends. Then we used the Wayback Machine to figure out when the trend was first coined, like the exact date. We did all sorts of tests to determine what kind of abnormal returns you can expect if you stick with that trend,” Horstmeyer explains.

The students found that newly christened trends experience an extended honeymoon period of about 12 months, amounting to an average of 13 percentage points in excess returns. But in their second year, trends start to go stale, underperforming the S&P 500 by an average of two points.

The Wall Street Journal published Horstmeyer’s article “Once a Hot Stock-Market Trend Has a Name, Its Best Days Are Likely Past”—published under his byline with the student researchers mentioned by name early on—in March 2024.

Horstmeyer says that participating in these projects helps students hone skills and techniques that enhance their careers and give them a competitive edge in the job market. “I get employers reaching out and saying, ‘I saw this line item on a candidate’s resume saying they did some pretty high-level data analysis for you. Is it true?’ They’re kind of surprised the student contributed to the CFA or a Wall Street Journal article.”

Melvin Jonathan Reyes Echeverria, who completed his MS in finance degree in May 2024, says, “I assisted Professor Horstmeyer with data analysis for the WSJ and the experience was amazing…I was able to leverage everything that I was taught in my MS in finance classes. It was a privilege to be published and something that was unexpected to receive at George �鶹��Ƶ.”

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The NYPD gave officers iPhones. Here’s what we learned about race and policing

Jennifer Anzaldi — Tue, 04 Jun 2024 16:50:43 +0000

The NYPD gave officers iPhones. Here’s what we learned about race and policing Jennifer Anzaldi Tue, 06/04/2024 - 12:50

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Brad Greenwood

The controversy about biased policing seems to draw endless fuel from race-based differences in public perception. Simply put, the vast majority of White citizens in the United States believe the police are doing a good job, including on issues of racial equality, while a similar percentage of Black citizens hold the opposite opinion. And while a growing number of studies have indicated persistent patterns of racial discrimination in policing, an emergent concern among scholars is that the data these papers rely on are also subject to baked-in biases, since they often derive from officers’ self-reports of their own behavior.

Brad Greenwood

Enter Brad Greenwood, professor of information systems and operations management at the Donald G. Costello College of Business at �鶹��Ƶ. One of his research interests lies in how digital technologies are bringing unprecedented transparency to police practices. For example, Greenwood’s 2022 paper documented how the introduction of body-worn cameras for the New York Police Department (NYPD) resulted in a significant reduction in abuse-of-authority complaints.

His latest work on policing is forthcoming in Proceedings of the National Academy of Sciences. Along with Gordon Burtch from Boston University and Jeremy Watson from the University of Minnesota, Greenwood examined the recent rollout of iPhones across the NYPD, which included a series of digital tools designed to replace the handwritten memo books officers previously relied on. Instead of scribbling in the physical books, which NYPD officers were required to hang onto even into retirement, officers could log their activities directly into a centralized database maintained by the NYPD. These detailed digital records shed fresh light on how cops spend their time—and attention—on the beat.

The researchers tracked data on NYPD stops and complaints in 2017 and 2018, the period when iPhones were being rolled out across precincts in New York City. A curious pattern emerged. There was an 18% increase in reported stops after a precinct received iPhones, which would be consistent with the digital tools making it easier for officers to report a citizen interaction. Further, the researchers discovered that this increase resulted in neither more arrests nor more complaints from the public. It wasn’t, therefore, that the phones were somehow causing the police to stop people more often, but rather that so-called “unproductive stops”—those leading to no further action—were being reported more often.

However, when breaking the results down across White and non-White citizens, the researchers found that unproductive stops involving non-White citizens were entirely responsible for the increase. In other words, the observed changes were based on police encounters with non-White members of the public, that would likely have gone unreported in the days of pen and paper. More specifically, after switching to the smartphone system, officers logged 22% more stops involving non-White citizens, while the number of reported stops of White citizens remained unchanged. These are statistical averages—the pattern was more marked in high-crime neighborhoods and those with a greater proportion of non-White residents.

Greenwood offered an interpretation of the finding: “The concern here is that we have an underreporting, which is concentrated in certain groups and means that we need to be cautious when interpreting prior work. On the one hand, it opens the door to bias in police interactions with civilians being worse than initially anticipated, at least based on the frequency of stops. On the other hand, it could mean that older data doesn’t accurately reflect the likelihood of an arrest once a stop occurs. And we need to be doubly cautious, because we don’t know if officers are reporting stops more frequently just because it is easier, or for some other reason.”

Greenwood cautions against making sweeping conclusions based on the study. “The only thing we know for sure is that more and deeper work is needed by scholars and policy makers to ensure transparency between law enforcement and the people they are charged to protect,” he said.

On the whole, however, the study raises the possibility that race-based disparities in policing are not only very real, but may have been underestimated thus far because of reporting gaps.

As police officers are not obligated to document all civilian interactions, their decisions regarding what—and what not—to report can be biased. The introduction of new technology, as in the case of the NYPD, can help counter such biases, but is not the only avenue worth pursuing. The researchers recommend that police departments “investigate the appropriate organizational complements (i.e., policies and procedures) necessary to uncover and eliminate such biases.”

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Cryptocurrency’s surprising transparency advantage

Marianne Klinker — Tue, 02 Jan 2024 18:18:45 +0000

Cryptocurrency’s surprising transparency advantage Marianne Klinker Tue, 01/02/2024 - 13:18

Despite the fears of regulators and skittish investors, clear and accurate signals of cryptocurrency quality may be hidden in plain sight.

As perhaps befits a product of the post-2008 economy, the cryptocurrency space has never known normalcy. In a mere 13 years, crypto went from an untried software innovation of mysterious origin to being touted as the future of investing by major movie stars in Super Bowl commercials. Soon thereafter, of course, came the “crypto winter” of 2022, which began well before, but was surely deepened by, the downfall of FTX and disgraced wunderkind Sam Bankman-Fried.

Forthcoming in Journal of Management Information Systems, a recent paper is the first to examine whether the intensity of developer engagement with a cryptocurrency could indeed be related to its quality.

The crypto backlash appears rooted in the belief that the sector is too rife with bad actors, and too technologically complicated, to be worthy of public trust. The consensus in the crypto community, however, is that accurate clues to crypto quality are hidden in plain sight. The majority of cryptocurrencies use open source platforms such as GitHub for the development of their software. Seasoned investors will routinely inspect the publicly available development history of a cryptocurrency as a basic aspect of due diligence.

Forthcoming in Journal of Management Information Systems, a recent paper co-authored by Mariia Petryk, an assistant professor of information systems at the Donald G. Costello College of Business at �鶹��Ƶ, is the first to examine whether the intensity of developer engagement with a cryptocurrency could indeed be related to its quality. (Her collaborators were Liangfei Qiu and Praveen Pathak of University of Florida.)

Mariia Petryk

“This paper is about how we evaluate reputation in a setting where traditional reputational instruments are not precise,” Petryk explains. “When we choose a doctor, we often rely on an endorsement from someone we trust. But in lieu of that, we look into the history of the doctor—education, training, etc.” By the same token (no pun intended), crypto investors could “judge quality based on the total effort developers made to write the code.”

Using GitHub data for 559 cryptocurrencies over the period August 2016 to December 2019, the researchers zeroed in on five open-source activities. They classed three under the heading of “quality enhancement.” Commits are code modifications submitted by developers and eventually added to the crypto source code. Before official adoption, proposed modifications exist as pull requests awaiting assessment. Issues are questions, bugs, or problems raised by developers or users for discussion within the open source platform.

The remaining two activities fall under “diffusion of software,” essentially a proxy for developer attention. Forks are mirrored copies of the original code attached to a developer’s account. Watches are a way for developers to “subscribe” in order to more closely follow the discussion within the community around a cryptocurrency.

Across the data-set, the researchers found that a one-standard-deviation increase in forks and watches would equate to a 0.56% price increase per month, or 6.7% per year.

As for the three “quality enhancement” metrics, increases in issues exerted upward pressure on token price, but pull requests and commits displayed the opposite effect. A one-standard-deviation increase in issues led to a 4.3 percent higher price over one year, while an equivalent increase in pull requests was linked to annual losses of five percent.

These contradictory results perhaps point to uncertainties about the length of time it would take for crypto admins to evaluate and implement proposed changes. It goes without saying that greater numbers of requests for revision would heighten such uncertainties and their possible implications for cryptocurrency quality.

Overall, these findings imply a “virtuous circle” whereby more promising cryptocurrencies attract more developer attention, which in turn produces quality improvements that are reflected in the token price. While the expansion of the developer community creates the potential for rising pull requests and commits to lower the token price, the price-positive impact of forks and watches was approximately six times stronger. As a general rule, then, intensity of developer attention and engagement could be viewed as a leading indicator of how the market values the tokens—with the very important caveat that broad statistical patterns may have little to no bearing on specific investment decisions.

Therefore, information from GitHub and other open-source platforms could be an important reference point for policymakers looking to evolve nuanced regulatory approaches. “One purpose of regulation is to create equal opportunity,” Petryk says. “Transparent mechanisms allow investors to learn about underlying assets and properties and make their own judgments. Open source platforms like GitHub are open to everyone, but not everyone thinks to use it. In terms of info disclosure, this could be an important factor.”

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�鶹��Ƶ professor contributes to groundbreaking mega-study in behavioral economics

Marianne Klinker — Wed, 09 Aug 2023 13:44:11 +0000

�鶹��Ƶ professor contributes to groundbreaking mega-study in behavioral economics Marianne Klinker Wed, 08/09/2023 - 09:44

A new "mega-study" consisting of dozens of simultaneous, independently designed experiments–shows that competitions have no automatic impact on our morality.

Academic research is tapping into the power of the crowd, thanks to an emerging paradigm known as the “mega-study.” Instead of one experimental design with a single set of parameters, mega-studies utilize a diverse array of simultaneous studies submitted by independent research teams, with a shared research question and participant pool.

In addition to efficiencies of scale, mega-studies can help scholars more quickly come to grips with the slippery issue of generalizability–i.e., the degree to which findings in one context apply to other contexts. For example, something that motivates people to do one prosocial behavior (e.g., recycling) might be different from what motivates people to do another prosocial behavior (e.g., donating to charity). Observing the full range of results across dozens of studies provides a clearer sense of how different environmental conditions may affect research outcomes.

The mega-study model is ideal for the field of behavioral economics, which explores how various factors in the world around us influence our daily decision-making. Human behavior, after all, is extremely complicated and changeable.

Einav Hart

Einav Hart, an assistant professor of management at �鶹��Ƶ School of Business, participated in the first-ever crowdsourced mega-study in behavioral economics, recently published in Proceedings of the National Academy of Sciences (PNAS). Hart’s experimental design was one of 45 selected for the mega-study. The broad research question was “Does competition erode, promote or not affect moral behavior?”

The mega-study was conducted to better understand and reconcile previous findings about the relationship between competition and moral behaviors. Previous (individual) studies have yielded mixed results. While some studies show that competition promotes moral behaviors such as trust and reciprocity, others point to a moral erosion.

In the mega-study, 18,123 online participants were randomly assigned to the 45 research designs. Notably, the 45 experiments were quite eclectic in their interpretations of “competition” and “moral behavior”. One research team, for instance, proposed a game in which participants solved puzzles either in or out of a competitive scenario, and were asked to self-report their performance. In this case, the honesty or dishonesty of their self-reported scores was used to measure “moral behavior”. Another proposed design was an online game in which an “investor” granted points to an “investee”, who could then choose whether or not to return the favor.

The pooled data from all 45 experiments showed that competitive conditions led to a minor decline in participants’ moral behavior. However, the size of the decline varied considerably from study to study. Further analysis revealed that the majority of the variance was due to the design differences among the 45 experiments.

The authors conclude that when it comes to broad research questions that admit a wide range of interpretations, there may be limitations to what a single study with a specific context–even one with a very large sample size–can reveal. Hart says, “This shows that in any individual study, you could observe very different results even with the same ‘ground truth,’ and this variance is largely dependent on how competition and moral behavior are operationalized.” In these cases, true generalizability may arise only from a greater diversity of experimental approaches, such as the mega-study and other crowdsourced research paradigms. As the PNAS paper states, “Our findings provide an argument for moving toward much larger data collections and more team science.”

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Four stop signs at intersections are one too many, suggests �鶹��Ƶ researcher

Jennifer Anzaldi — Wed, 19 Oct 2022 14:18:05 +0000

Four stop signs at intersections are one too many, suggests �鶹��Ƶ researcher Jennifer Anzaldi Wed, 10/19/2022 - 10:18

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Jiasun Li

For most drivers in the United States, obeying a stop sign upon approaching an intersection is an unavoidable annoyance. But for �鶹��Ƶ Finance Professor Jiasun Li, it’s a problem waiting to be solved.

His recent working paper proposes a simple and economical improvement: removing one stop sign from every four-way intersection. According to his calculations, this would boost not only driver safety, but environmental sustainability as well.

Li specializes in game theory, which models strategic interactions where rational agents seek – as humans generally do–to optimize outcomes for themselves. As he drove around Fairfax, Virginia, Li could not help but view four-way intersections through this academic lens.

He was struck by the suspicion that having four stop signs at an intersection was a flawed way of preventing traffic accidents. In effect, they lowered the potential cost of not stopping at the intersection, because drivers could assume that motorists from other directions, should there be any, would come to a stop. Drivers turning right, a shallower maneuver with less exposure to oncoming traffic, have the least risk to begin with and would have the greatest incentive to ignore the sign.

Li surmised that the outcome of all drivers obeying the sign fell short of a Nash equilibrium–game theory’s term for a stable set of norms that all parties are incentivized to follow.

His working paper presents mathematical models that support his intuition. Comparing the risks of collision against the gains from ignoring the sign (i.e. a smooth driving experience or conserving gasoline), he finds that a symmetric equilibrium under the current four-sign mechanism to be one in which left-turners and straight-line travellers honor the stop sign but right-turners do not.

In real life, of course, fear of getting slapped with a moving violation increases the likelihood of compliance with stop signs. But the need for law enforcement is less than ideal, Li says. If the four-way stop system were self-enforcing, expensive police resources could be directed toward serious crimes and away from preventive punishment for traffic violators.

Li’s working paper first considers what would happen if traffic laws were changed to allow right-turners to run the stop sign legally. In an intersection with four stop signs, this would indeed prevent accidents. However, many U.S. intersections have only two signs, which would place right-turners at risk of colliding with traffic coming from the left. In addition, Li argues that old habits die hard, including behind the wheel.

“If you need people to change their behavior, that’s going to be difficult,” he says.

Instead, Li recommends removing one sign–any sign–from among the four. In that case, drivers would know that running a stop sign could send them straight into the path of an oncoming car that has not been directed to stop. The universal fear would incentivize compliance without relying on the threat of police punishment. “Importantly, although the safety and incentive analysis rely on game theoretical reasoning, once implemented, a three-sign mechanism does not require any changes in behaviors for law-abiding drivers,” Li says.

Further, Li’s back-of-the-envelope math suggests that his relatively mundane change could be surprisingly impactful. Based on official statistics from various U.S. authorities, he estimates there are about one million four-way intersections with stop signs in the U.S., each one being traversed by more than 760,000 vehicles per year. That amounts to more than 760 billion (760,000 x one million) stops and starts in a year, one-fourth of which (190 billion) would be avoided in a three-sign setup.

According to Li’s calculations, assuming it takes 124.9 kilojoules of work to bring a car of average weight to a full stop, and the same amount to resume the previous level of speed, adopting a three-sign system would save a total of 118.65 trillion kJ in the U.S. per year. That equates to one billion gallons of gasoline, or about 2.7 days’ worth of national gas consumption.

Looking farther out on the technological horizon, Li speculates in the paper that “when self-driving cars finally become real, our new simpler mechanism may also economize AI computational costs.”

Li has no illusions about the resistance his recommendation may provoke. “You’re challenging people’s conventional wisdom. If there’s criticism, that’s to be expected. For example, some cops might not like this idea, because I’m taking away one of their income sources, even though there could be an overall gain to the whole society.”

This paper is only one manifestation of what Li sees as a scholarly mission to apply novel perspectives – in this case, an economics viewpoint derived from game theory – to real-world problems, especially those that have been taken for granted for so long that they are not even recognized as problems.

“Economics thinking should be applicable to a lot of different fields and improve our daily lives,” Li explains.

What seems totally natural in one context, after all, may be foreign in another. Many countries–including China, where Li was born–don’t use four-way stop intersections. “If I grew up with these stop signs, I probably wouldn’t think about it,” he says. This is an example, Li suggests, of how diverse experiences and ideas imported via immigration can enrich host nations. Far from wanting the last word on the issue, Li is hoping to inspire wide-ranging conversation about how to change environments and communities for the better.

“I would rather have the discussion ongoing and people give me ideas,” he says. “Only through debate will people understand it better.”

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Building a 'digital twin' of the real world

Jennifer Anzaldi — Mon, 26 Sep 2022 14:02:39 +0000

Building a 'digital twin' of the real world Jennifer Anzaldi Mon, 09/26/2022 - 10:02

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Jingyuan Yang

As artificial intelligence (AI) has become more adept at anticipating people’s wants and needs, predictive algorithms have been mingled with almost everything we do digitally. When we are done watching a Netflix show, they take the liberty of queueing up the next one—they finish our sentences in Outlook and Gmail.

Jingyuan Yang

In the offline world, however, the complex and multidimensional nature of many of our most pivotal decisions defies algorithmic analysis. That is, unless AI can learn to detect how real-world contingencies, such as specifics of time and place, govern our choices.

Jingyuan Yang, an assistant professor of information systems and operations management at �鶹��Ƶ School of Business, is at the forefront of AI research that aims to crack the codes of the physical world. Her results so far point toward innovative solutions for some of the biggest societal, governmental, and business challenges we face.

Several of her papers to date investigate urban bike sharing, a “last mile” extension of public transport systems designed to coax commuters out of their cars. Some early adopters of bike sharing, such as New York City and Taiwan, have seen long-term success with the model. But elsewhere, including major Chinese cities, oversaturation has led to bicycle-flooded sidewalks or, even worse, rivers and vacant lots turning into bicycle graveyards—an environmental disaster that produced friction with local politicians.

Yang discovered that in Shanghai, part of the problem was that the distribution of bikes across the city did not match demand. Some areas had far more available bikes than riders, while in others the opposite was the case. Additionally, Shanghai’s system allowed commuters to dismount wherever they chose. This dockless model made predicting rider demand even more challenging, as bikes could be located virtually anywhere.

With a team of six other researchers[1], Yang developed a data-driven model for predicting traffic flows within dockless bike-sharing systems, based on a dataset provided by leading provider Mobike. Spanning the period February 2017-March 2018, the Mobike data contained more than 957 million riding records from nearly 315,000 shared bikes.

The research team extracted flow patterns from the data by partitioning the city and “smoothing out” areas with the lowest levels of activity. The resulting grid-like “flow matrix” carved Shanghai’s bike traffic into spatio-temporal snapshots that could be studied and compared. After clustering these based on their similarities, the researchers could construct “base matrices” that provided broader, deeper points of reference for prediction than temporal or geographic cues alone. Using the base matrix, the algorithm could identify emergent patterns in a certain area as those associated with central business districts on a rainy holiday morning and forecast bike traffic in that area over the next few hours accordingly.

Yang says she was surprised by what this technique of algorithmic mapping revealed. “There are a number of surprising factors we can discover that cannot be covered by traditional model analysis. As an example, we find that there is a slight increase in bike traffic near subway stations during rain, because people want a shorter commute,” she says.

The team tested their model against six other algorithms designed for the same purpose, to see which was the most accurate at predicting actual bike traffic flow in Shanghai. Yang’s team’s solution consistently outperformed the rest on sample datasets for regular working days, rainy working days and holidays–meaning it achieved the lowest degree of prediction error. Perfect predictions are impossible, because all sorts of irregular real-world occurrences, from auto accidents to one-off public events, can cause traffic on a given day to break with the pattern. “Really odd events, we cannot capture,” Yang says. “But the base matrix lets us capture basic trends.”

For Yang, optimizing bike sharing is part of a necessary push toward environmentally sustainable options for urban living, including fewer polluting modes of transport. “All these papers are intended to help companies go a more sustainable way and help the user to tackle the last mile in an eco-friendly manner-without waste and damage to the environment.”

With some tweaks to the logic, however, the same AI-based methods can apply to a range of pressing business issues. The territory mapped by algorithms need not be geographical; researchers can also “map” a network of individuals or companies. Yang’s experiment in the field of B2B marketing is a case in point. She helped build[2] an automatic recommendation engine for marketing campaigns based on customer profiles (similar in concept to the bike-sharing base matrices) reflecting corporate affiliation as well as individual employee status. Customers from the same “region” on the grid, i.e. the same company, are treated holistically to improve recommendation quality.

“When you’re selling to a company, you’re usually dealing with a group of decision-makers who are at different buying stages," Yang explains. "To estimate buying propensity, you need to consider that they may share information. Their behavior should be considered together. Therefore, we use matrix representation to extract their shared knowledge.”

One of Yang’s current research projects focuses on predicting employee flow within networks of companies, again borrowing spatio-temporal techniques. Similar to Shanghai’s urban environment, the job-hopping professional grid has its own version of “weather”—favorable or gloomy economic conditions – that may alter the pattern.

“Based on different job positions, you can group the companies. You can aggregate company profiles and predict, collectively, how many people will leave based on the stock price,” she says.

Yang’s research suggests that by building a chessboard-like “digital twin” of the real world, spatio-temporal AI solutions can help business and society predict–and thus prevent–harmful losses such as human capital flight and damage to the natural environment.

[1] Jingjing Gu, Qiang Zhou and Yanchao Zhao (of Nanjing University of Aeronautics and Astronautics), Yanchi Lui and Hui Xiong (of Rutgers University), Fuzhen Zhuang (of Chinese Academy of Sciences)

[2] In collaboration with Chuanren Liu (of Drexel University), Mingfai Teng and Hui Xiong (of Rutgers University) and Ji Chen (Google).

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Impact Fall 2023

At the frontiers of public health

Jennifer Anzaldi — Tue, 16 Aug 2022 11:54:21 +0000

At the frontiers of public health Jennifer Anzaldi Tue, 08/16/2022 - 07:54

In This Story

Ajay Vinzé

Dean Ajay Vinzé

As we all now know from bitter experience, there’s more to proper pandemic response than pills and vaccines. Medical authorities must identify potential hot spots before widespread infection becomes manifest, through early-detection methods such as wastewater testing. With these warning signals, health departments can take preventive action – lockdowns, school closures, etc. – to stop the spread. To build the systems that can make this possible, skilled epidemiologists need to work side-by-side with data scientists who can help them crunch the numbers and predict possible outcomes of various interventions.

Long before COVID was a household word, Dr. Ajay Vinzé, now dean of �鶹��Ƶ’s business school, helped pioneer just such a collaboration with public-health officials in Maricopa County, which includes Phoenix and is the fourth-largest county in the United States by population. Vinzé calls this nearly decade-long partnership “a major part of my research and professional journey.”

According to Vinzé, the collaboration began in late 2002 with a missed flight in the Dallas airport. He found himself sitting next to another Phoenix-bound traveler, and the two struck up a conversation. At the time, Vinzé was an associate professor at W.P. Carey School of Business at Arizona State University researching artificial intelligence applications for business problem-solving, including how viruses spread on computer networks, in close collaboration with companies such as Intel and IBM. One of his ongoing research projects focused on failure analysis and failure identification (FA/FI). As Vinzé described his work, his impromptu travel companion – who, as it turned out, was Jonathan Weisbuch, then Director and chief health officer for the Maricopa Department of Public Health – listened closely, asked pertinent questions, and finally invited Vinzé to sit down with a group of leading Arizona epidemiologists.

Why would a top health officer for Arizona’s largest county want his colleagues to listen to an information systems professor? Because Weisbuc recognized that, as Vinzé says, “Computer networks are similar to human systems. Failure means not being able to detect early on that certain propagation is imminent. That’s the same thing you do in computer security.” The two fields also share basic preventive principles and best practices. “To inoculate yourself against viruses on the computer, you need to know the characteristics of the virus, how it spreads, and what happens after infection … and pertinent policy to control the same. Same thing for humans!”

Sarah Santana, Maricopa’s director of epidemiology, found common cause with Vinzé, enlisting him and his colleagues in her department’s efforts to refine post-9/11 surveillance protocols for handling the threat of bio-terrorism. Their work together ultimately led to an article in Information Systems Frontiers (co-authored by Minu Ipe and T.S. Raghu) examining the parallels between “information supply chains”, e.g. Arizona’s state-wide MEDSIS online public-health surveillance network, and e-commerce platforms such as Amazon. In both cases, so-called “information intermediaries” can play a vital role including, but not limited to, aligning incentives and reducing information asymmetry among multiple stakeholders. The paper details how Arizona’s public health department acted as intermediary, paving the way for Maricopa County to join the state-wide system instead of managing its own data independently.

Vinzé gives Santana immense credit for her foresight and openness in allowing ideas imported from business research into public-health emergency preparedness and response. In turn, Santana introduced Vinzé to contexts and questions from beyond academia that widened the aperture of his research lens.

The particularities of the human organism, after all, rarely enter into information systems research but are pivotal to the success of public-health initiatives. Vinzé observes that this comes into play when deciding on key metrics: “How do you attribute value to saving one human life?” The human element also brings a host of contingencies and complications that aren’t salient when dealing with machines.

Vinzé’s gradual coming-to-grips with these intricacies culminated in a 2012 paper for ACM Transactions on Management Information Systems (co-authored by Hina Arora and T.S. Raghu) modeling dynamic interactions between disease transmission, pharmaceutical interventions (e.g. vaccines), and policy-based interventions (e.g. school closures). “It’s a rich simulation exercise – we built the system and turned it over to the county,” Vinzé explains. Using actual characteristics from the 2009 H1N1 pandemic as a test case for various policy approaches, the mathematical model generated numerous insights for guiding public-health policymakers not just in Maricopa and the southwest United States, but perhaps worldwide. For example, the simulation showed that rolling school closures of two to four weeks were not only less effective at reducing transmission than county-wide, eight-week closures – but they also resulted in more weeks of shutdown over the length of the pandemic. Additionally, the results reinforced the researchers’ intuition that the quality of pandemic response hinges upon finding the right mix of pharmaceutical and non-pharmaceutical interventions. Years later, these data-driven principles would be put to the ultimate test during the COVID pandemic.

Over the course of his long collaboration with Santana and the Maricopa health authorities, Vinzé got the chance to view public-health challenges from virtually every angle. “We did survey research for them, sometimes experimental research, sometimes systems-building,” he says. He even served on a healthcare-focused working group as part of the planning process for Super Bowl XLII (2008), and subsequently Super Bowl XLIX (2015) both held in Glendale, Arizona. In 2012, Vinzé was recognized with a Faculty Achievement Award from Arizona State University for “defining edge research and creative work for professional application” recognizing high impact research over a ten-year period – his association with public health was acknowledged as a main driver of impact.

In hindsight, Vinzé suggests that his foray into public health spotlights and clarifies certain aspects of digital transformation. He points out that technological advancement comes in waves, producing sudden surges in computing power, processing speed and data accessibility. With each wave, public- and private-sector actors alike experience a tidal force of opportunity (for leaders) or adversity (for laggards). Collaborations between academia, business and government can help translate technological evolution into positive outcomes for the economy, communities and the wider society.

“Each wave has an implication on the workforce of that time. We’re going through this big wave as we speak, coming out of COVID. The question is, are we ready?” Vinzé says.

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