Game theory explains why reasonable parents make vaccine choices that fuel outbreaks
When outbreaks of vaccine-preventable diseases such as measles occur despite highly effective vaccines being available, it's easy to conclude that parents who don't vaccinate their children are misguided, selfish or have fallen prey to misinformation.
As professors with expertise in vaccine policy and health economics, we argue that the decision not to vaccinate isn't simply about misinformation or hesitancy. In our view, it involves game theory, a mathematical framework that helps explain how reasonable people can make choices that collectively lead to outcomes that endanger them.
Game theory reveals that vaccine hesitancy is not a moral failure, but simply the predictable outcome of a system in which individual and collective incentives aren't properly aligned.
Game theory examines how people make decisions when their outcomes depend on what others choose. In his research on the topic, Nobel Prize-winning mathematician John Nash, portrayed in the movie 'A Beautiful Mind, showed that in many situations, individually rational choices don't automatically create the best outcome for everyone.
Vaccination decisions perfectly illustrate this principle. When a parent decides whether to vaccinate their child against measles, for instance, they weigh the small risk of vaccine side effects against the risks posed by the disease. But here's the crucial insight: The risk of disease depends on what other parents decide. If nearly everyone vaccinates, herd immunity – essentially, vaccinating enough people – will stop the disease's spread. But once herd immunity is achieved, individual parents may decide that not vaccinating is the less risky option for their kid.
In other words, because of a fundamental tension between individual choice and collective welfare, relying solely on individual choice may not achieve public health goals.
This makes vaccine decisions fundamentally different from most other health decisions. When you decide whether to take medication for high blood pressure, your outcome depends only on your choice. But with vaccines, everyone is connected.
This interconnectedness has played out dramatically in Texas, where the largest U.S. measles outbreak in a decade originated. As vaccination rates dropped in certain communities, the disease – once declared eliminated in the U.S. – returned. One county's vaccination rate fell from 96% to 81% over just five years. Considering that about 95% of people in a community must be vaccinated to achieve herd immunity, the decline created perfect conditions for the current outbreak.
This isn't coincidence; it's game theory playing out in real time. When vaccination rates are high, not vaccinating seems rational for each individual family, but when enough families make this choice, collective protection collapses.
This dynamic creates what economists call a free rider problem. When vaccination rates are high, an individual might benefit from herd immunity without accepting even the minimal vaccine risks. Game theory predicts something surprising: Even with a hypothetically perfect vaccine – faultless efficacy, zero side effects – voluntary vaccination programs will never achieve 100% coverage. Once coverage is high enough, some rational individuals will always choose to be free riders, benefiting from the herd immunity provided by others.
And when rates drop – as they have, dramatically, over the past five years – disease models predict exactly what we're seeing: the return of outbreaks.
Game theory reveals another pattern: For highly contagious diseases, vaccination rates tend to decline rapidly following safety concerns, while recovery occurs much more slowly. This, too, is a mathematical property of the system because decline and recovery have different incentive structures. When safety concerns arise, many parents get worried at the same time and stop vaccinating, causing vaccination rates to drop quickly.
But recovery is slower because it requires both rebuilding trust and overcoming the free rider problem – each parent waits for others to vaccinate first. Small changes in perception can cause large shifts in behavior. Media coverage, social networks and health messaging all influence these perceptions, potentially moving communities toward or away from these critical thresholds.
Mathematics also predicts how people's decisions about vaccination can cluster. As parents observe others' choices, local norms develop – so the more parents skip the vaccine in a community, the more others are likely to follow suit.
Game theorists refer to the resulting pockets of low vaccine uptake as susceptibility clusters. These clusters allow diseases to persist even when overall vaccination rates appear adequate. A 95% statewide or national average could mean uniform vaccine coverage, which would prevent outbreaks. Alternatively, it could mean some areas with near-100% coverage and others with dangerously low rates that enable local outbreaks.
All this means that the dramatic fall in vaccination rates was predicted by game theory – and therefore more a reflection of system vulnerability than of a moral failure of individuals. What's more, blaming parents for making selfish choices can also backfire by making them more defensive and less likely to reconsider their views.
Much more helpful would be approaches that acknowledge the tensions between individual and collective interests and that work with, rather than against, the mental calculations informing how people make decisions in interconnected systems.
Research shows that communities experiencing outbreaks respond differently to messaging that frames vaccination as a community problem versus messaging that implies moral failure. In a 2021 study of a community with falling vaccination rates, approaches that acknowledged parents' genuine concerns while emphasizing the need for community protection made parents 24% more likely to consider vaccinating, while approaches that emphasized personal responsibility or implied selfishness actually decreased their willingness to consider it.
This confirms what game theory predicts: When people feel their decision-making is under moral attack, they often become more entrenched in their positions rather than more open to change.
Understanding how people weigh vaccine risks and benefits points to better approaches to communication. For example, clearly conveying risks can help: The 1-in-500 death rate from measles far outweighs the extraordinarily rare serious vaccine side effects. That may sound obvious, but it's often missing from public discussion. Also, different communities need different approaches – high-vaccination areas need help staying on track, while low-vaccination areas need trust rebuilt.
Consistency matters tremendously. Research shows that when health experts give conflicting information or change their message, people become more suspicious and decide to hold off on vaccines. And dramatic scare tactics about disease can backfire by pushing people toward extreme positions.
Making vaccination decisions visible within communities – through community discussions and school-level reporting, where possible – can help establish positive social norms. When parents understand that vaccination protects vulnerable community members, like infants too young for vaccines or people with medical conditions, it helps bridge the gap between individual and collective interests.
Health care providers remain the most trusted source of vaccine information. When providers understand game theory dynamics, they can address parents' concerns more effectively, recognizing that for most people, hesitancy comes from weighing risks rather than opposing vaccines outright.
This article is republished from The Conversation, a nonprofit, independent news organization bringing you facts and trustworthy analysis to help you make sense of our complex world. It was written by: Y. Tony Yang, George Washington University and Avi Dor, George Washington University
Read more:
Texas records first US measles death in 10 years – a medical epidemiologist explains how to protect yourself and your community from this deadly, preventable disease
Driving the best possible bargain now isn't the best long-term strategy, according to game theory
Measles is one of the deadliest and most contagious infectious diseases – and one of the most easily preventable
The authors do not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and have disclosed no relevant affiliations beyond their academic appointment.

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Yahoo
17 hours ago
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Game theory explains why reasonable parents make vaccine choices that fuel outbreaks
When outbreaks of vaccine-preventable diseases such as measles occur despite highly effective vaccines being available, it's easy to conclude that parents who don't vaccinate their children are misguided, selfish or have fallen prey to misinformation. As professors with expertise in vaccine policy and health economics, we argue that the decision not to vaccinate isn't simply about misinformation or hesitancy. In our view, it involves game theory, a mathematical framework that helps explain how reasonable people can make choices that collectively lead to outcomes that endanger them. Game theory reveals that vaccine hesitancy is not a moral failure, but simply the predictable outcome of a system in which individual and collective incentives aren't properly aligned. Game theory examines how people make decisions when their outcomes depend on what others choose. In his research on the topic, Nobel Prize-winning mathematician John Nash, portrayed in the movie 'A Beautiful Mind, showed that in many situations, individually rational choices don't automatically create the best outcome for everyone. Vaccination decisions perfectly illustrate this principle. When a parent decides whether to vaccinate their child against measles, for instance, they weigh the small risk of vaccine side effects against the risks posed by the disease. But here's the crucial insight: The risk of disease depends on what other parents decide. If nearly everyone vaccinates, herd immunity – essentially, vaccinating enough people – will stop the disease's spread. But once herd immunity is achieved, individual parents may decide that not vaccinating is the less risky option for their kid. In other words, because of a fundamental tension between individual choice and collective welfare, relying solely on individual choice may not achieve public health goals. This makes vaccine decisions fundamentally different from most other health decisions. When you decide whether to take medication for high blood pressure, your outcome depends only on your choice. But with vaccines, everyone is connected. This interconnectedness has played out dramatically in Texas, where the largest U.S. measles outbreak in a decade originated. As vaccination rates dropped in certain communities, the disease – once declared eliminated in the U.S. – returned. One county's vaccination rate fell from 96% to 81% over just five years. Considering that about 95% of people in a community must be vaccinated to achieve herd immunity, the decline created perfect conditions for the current outbreak. This isn't coincidence; it's game theory playing out in real time. When vaccination rates are high, not vaccinating seems rational for each individual family, but when enough families make this choice, collective protection collapses. This dynamic creates what economists call a free rider problem. When vaccination rates are high, an individual might benefit from herd immunity without accepting even the minimal vaccine risks. Game theory predicts something surprising: Even with a hypothetically perfect vaccine – faultless efficacy, zero side effects – voluntary vaccination programs will never achieve 100% coverage. Once coverage is high enough, some rational individuals will always choose to be free riders, benefiting from the herd immunity provided by others. And when rates drop – as they have, dramatically, over the past five years – disease models predict exactly what we're seeing: the return of outbreaks. Game theory reveals another pattern: For highly contagious diseases, vaccination rates tend to decline rapidly following safety concerns, while recovery occurs much more slowly. This, too, is a mathematical property of the system because decline and recovery have different incentive structures. When safety concerns arise, many parents get worried at the same time and stop vaccinating, causing vaccination rates to drop quickly. But recovery is slower because it requires both rebuilding trust and overcoming the free rider problem – each parent waits for others to vaccinate first. Small changes in perception can cause large shifts in behavior. Media coverage, social networks and health messaging all influence these perceptions, potentially moving communities toward or away from these critical thresholds. Mathematics also predicts how people's decisions about vaccination can cluster. As parents observe others' choices, local norms develop – so the more parents skip the vaccine in a community, the more others are likely to follow suit. Game theorists refer to the resulting pockets of low vaccine uptake as susceptibility clusters. These clusters allow diseases to persist even when overall vaccination rates appear adequate. A 95% statewide or national average could mean uniform vaccine coverage, which would prevent outbreaks. Alternatively, it could mean some areas with near-100% coverage and others with dangerously low rates that enable local outbreaks. All this means that the dramatic fall in vaccination rates was predicted by game theory – and therefore more a reflection of system vulnerability than of a moral failure of individuals. What's more, blaming parents for making selfish choices can also backfire by making them more defensive and less likely to reconsider their views. Much more helpful would be approaches that acknowledge the tensions between individual and collective interests and that work with, rather than against, the mental calculations informing how people make decisions in interconnected systems. Research shows that communities experiencing outbreaks respond differently to messaging that frames vaccination as a community problem versus messaging that implies moral failure. In a 2021 study of a community with falling vaccination rates, approaches that acknowledged parents' genuine concerns while emphasizing the need for community protection made parents 24% more likely to consider vaccinating, while approaches that emphasized personal responsibility or implied selfishness actually decreased their willingness to consider it. This confirms what game theory predicts: When people feel their decision-making is under moral attack, they often become more entrenched in their positions rather than more open to change. Understanding how people weigh vaccine risks and benefits points to better approaches to communication. For example, clearly conveying risks can help: The 1-in-500 death rate from measles far outweighs the extraordinarily rare serious vaccine side effects. That may sound obvious, but it's often missing from public discussion. Also, different communities need different approaches – high-vaccination areas need help staying on track, while low-vaccination areas need trust rebuilt. Consistency matters tremendously. Research shows that when health experts give conflicting information or change their message, people become more suspicious and decide to hold off on vaccines. And dramatic scare tactics about disease can backfire by pushing people toward extreme positions. Making vaccination decisions visible within communities – through community discussions and school-level reporting, where possible – can help establish positive social norms. When parents understand that vaccination protects vulnerable community members, like infants too young for vaccines or people with medical conditions, it helps bridge the gap between individual and collective interests. Health care providers remain the most trusted source of vaccine information. When providers understand game theory dynamics, they can address parents' concerns more effectively, recognizing that for most people, hesitancy comes from weighing risks rather than opposing vaccines outright. This article is republished from The Conversation, a nonprofit, independent news organization bringing you facts and trustworthy analysis to help you make sense of our complex world. It was written by: Y. Tony Yang, George Washington University and Avi Dor, George Washington University Read more: Texas records first US measles death in 10 years – a medical epidemiologist explains how to protect yourself and your community from this deadly, preventable disease Driving the best possible bargain now isn't the best long-term strategy, according to game theory Measles is one of the deadliest and most contagious infectious diseases – and one of the most easily preventable The authors do not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and have disclosed no relevant affiliations beyond their academic appointment.


USA Today
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Emmy Award-winning actress on mission to show family caregivers they aren't alone
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Aduba said she always associated the term "caregiver" with medical staff. Now, she knows there are millions of caregivers across the country with no medical training, who − like her − were thrust into caregiving roles without warning once their loved one got sick. 'I didn't realize that there was this whole framework, frankly, of loved ones who were serving in this sort of invisible labor, all across the country, day in, day out, in varying ways, as caregivers to people," she said. Now, she wants other family caregivers to know they aren't alone. Aduba, known for her roles in the television series "Orange Is the New Black" and "The Residence," narrates the PBS documentary "Caregiving," which premieres June 24 at 9 p.m. EST. The film was created with executive producer and Academy Award-nominated actor Bradley Cooper and features caregivers from across the country. 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More: Chronic illness can be hard on marriage. Studies show it's worse when the wife is sick. There are a lot of family caregivers out there who don't know where to get resources, or that help exists, or about the policies advocates are trying to enact to bring more relief to caregivers. Aduba said the film does a great job at shining a light on the history of caregiving and advocacy work happening now to make things better. Aduba and her sisters didn't have time to think about anything else while they were caring for their mother, Aduba told USA TODAY. When you care for someone, she said, "your needs are second" to the patient. Her own health needs took a back seat during that time, her sleep schedule turned upside down and her free time ceased to exist. 'I would do it again," she said. "But I also know that I'm speaking from a place of immense privilege and not everybody... there are other people who are carrying way more than I on their day to day while also having to navigate caregiving at the same time.' Madeline Mitchell's role covering women and the caregiving economy at USA TODAY is supported by a partnership with Pivotal Ventures and Journalism Funding Partners. Funders do not provide editorial input. Reach Madeline at memitchell@ and @maddiemitch_ on X.

Business Insider
2 days ago
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AI isn't replacing radiologists. Instead, they're using it to tackle time-sucking administrative tasks.
Generative AI powered by large language models, such as ChatGPT, is proliferating in industries like customer service and creative content production. But healthcare has moved more cautiously. Radiology, a specialty centered on analyzing digital images and recognizing patterns, is emerging as a frontrunner for adopting new AI techniques. That's not to say AI is new to radiology. Radiology was subject to one of the most infamous AI predictions when Nobel Prize winner Geoffrey Hinton said, in 2016, that " people should stop training radiologists now." But nearly a decade later, the field's AI transformation is taking a markedly different path. Radiologists aren't being replaced, but are integrating generative AI into their workflows to tackle labor-intensive tasks that don't require clinical expertise. "Rather than being worried about AI, radiologists are hoping AI can help with workforce challenges," explained Dr. Curt Langlotz, the senior associate vice provost for research and professor of radiology at Stanford. Regulatory challenges to generative AI in radiology Hinton's notion wasn't entirely off-base. Many radiologists now have access to predictive AI models that classify images or highlight potential abnormalities. Langlotz said the rise of these tools "created an industry" of more than 100 companies that focus on AI for medical imaging. The FDA lists over 1,000 AI/ML-enabled medical devices, which can include algorithms and software, a majority of which were designed for radiology. However, the approved devices are based on more traditional machine learning techniques, not on generative AI. Ankur Sharma, the head of medical affairs for medical devices and radiology at Bayer, explained that AI tools used for radiology are categorized within computer-aided detection software, which helps analyze and interpret medical images. Examples include triage, detection, and characterization. Each tool must meet regulatory standards, which include studies to determine detection accuracy and false positive rate, among other metrics. This is especially challenging for generative AI technologies, which are newer and less well understood. Characterization tools, which analyze specific abnormalities and suggest what they might be, face the highest regulatory standards, as both false positives and negatives carry risks. The idea of a kind of gen AI radiologist capable of automated diagnosis, as Hinton envisioned, would be categorized as "characterization" and would have to meet a high standard of evidence. Regulation isn't the only hurdle generative AI must leap to see broader use in radiology, either. Today's best general-purpose large language models, like OpenAI's GPT4.1, are trained on trillions of tokens of data. Scaling the model in this way has led to superb results, as new LLMs consistently beat older models. Training a generative AI model for radiology at this scale is difficult, however, because the volume of training data available is much smaller. Medical organizations also lack access to compute resources sufficient to build models at the scale of the largest large language models, which cost hundreds of millions to train. "The size of the training data used to train the largest text or language model inside medicine, versus outside medicine, shows a one-hundred-times difference," said Langlotz. The largest LLMs train on databases that scrape nearly the entire internet; medical models are limited to whatever images and data an institution has access to. Generative AI's current reality in radiology These regulatory obstacles would seem to cast doubt on generative AI's usefulness in radiology, particularly in making diagnostic decisions. However, radiologists are finding the technology helpful in their workflows, as it can undertake some of their daily labor-intensive administrative tasks. For instance, Sharma said, some tools can take notes as radiologists dictate their observations of medical images, which helps with writing reports. Some large language models, he added, are "taking those reports and translating them into more patient-friendly language." Dr. Langlotz said a product that drafts reports can give radiologists a "substantial productivity advantage." He compared it to having resident trainees who draft reports for review, a resource that's often available in academic settings, but less so in radiology practices, such as a hospital's radiology department. Sharma said that generative AI could help radiologists by automating and streamlining reporting, follow-up management, and patient communication, giving radiologists time to focus more on their "reading expertise," which includes image interpretation and diagnosis of complex cases. For example, in June 2024, Bayer and Rad AI announced a collaboration to integrate generative AI reporting solutions into Bayer's Calantic Digital Solution Platform, a cloud-hosted platform for deploying AI tools in clinical settings. The collaboration aims to use Rad AI's technology to help radiologists create reports more efficiently. For example, RadAI can use generative AI transcription to generate written reports based on a radiologist's dictated findings. Applications like this face fewer regulatory hurdles because they don't directly influence diagnosis. Looking ahead, Dr. Langlotz said he foresees even greater AI adoption in the near future. "I think there will be a change in radiologists' day-to-day work in five years," he predicted.