We examine whether large language models (LLMs) can predict biased decision-making in conversational settings, and whether their predictions capture not only human cognitive biases but also how those effects change under cognitive load. In a pre-registered study (N = 1,648), participants completed six classic decision-making tasks via a chatbot with dialogues of varying complexity. Participants exhibited two well-documented cognitive biases: the Framing Effect and the Status Quo Bias. Increased dialogue complexity resulted in participants reporting higher mental demand. This increase in cognitive load selectively, but significantly, increased the effect of the biases, demonstrating the load-bias interaction. We then evaluated whether LLMs (GPT-4, GPT-5, and open-source models) could predict individual decisions given demographic information and prior dialogue. While results were mixed across choice problems, LLM predictions that incorporated dialogue context were significantly more accurate in several key scenarios. Importantly, their predictions reproduced the same bias patterns and load-bias interactions observed in humans. Across all models tested, the GPT-4 family consistently aligned with human behavior, outperforming GPT-5 and open-source models in both predictive accuracy and fidelity to human-like bias patterns. These findings advance our understanding of LLMs as tools for simulating human decision-making and inform the design of conversational agents that adapt to user biases.

Hang Yu's research examines how behavioral constraints and program delivery mechanisms shape economic outcomes in poverty settings across Sub-Saharan Africa, employing randomized field experiments and original household panel data. An evaluation of Ethiopia's Productive Safety Net Program using doubly robust difference-in-differences methodology found improvements in dietary diversity and asset accumulation, yet persistent seasonal food shortages stemmed from transfer disbursement delays during civil conflict and administrative restructuring. This highlights that program effectiveness depends critically on implementation capacity and delivery timing beyond design alone. Additional research investigates how financial sector development historically supported or constrained industrialization in Ethiopia, drawing comparative lessons from East Asian experiences to assess whether state-led financial deepening strategies can be adapted to capital-scarce Sub-Saharan African economies. Yu's work also addresses pandemic health behavior, demonstrating that Mozambican households systematically underestimated community support for social distancing during COVID-19. A randomized intervention correcting these misperceptions increased distancing by seventy percent in hardest-hit districts, revealing that behavioral interventions succeed when they address strategic uncertainty in high-risk environments. Additionally, research identifies systematic overestimation of HIV-related stigma as a barrier to testing uptake, despite declining actual stigma levels. Together, these studies underscore that institutional capacity and delivery infrastructure fundamentally determine whether external resources and behavioral interventions translate into sustained health and economic improvements.

This research article examines whether individuals accurately perceive the relationship between income and life satisfaction. The study, pre-registered at the AEA RCT Registry (AEARCTR-0018251), was conducted with institutional approval from UCLA's IRB and received funding support from UCLA's Anderson School of Management Behavioral Lab. The research investigates potential misperceptions about how much income actually contributes to overall satisfaction and well-being. By employing rigorous experimental methodology, the authors explore whether people systematically overestimate or underestimate the impact of financial resources on their happiness. These findings have significant implications for understanding human decision-making regarding financial priorities and resource allocation. The research contributes to behavioral economics literature by shedding light on the disconnect between assumed and actual satisfaction derived from income, which matters for personal financial planning, policy development, and broader questions about what truly drives human well-being beyond monetary considerations.

Researchers have developed "JITAI-Twins," digital twins of target subpopulations designed to test mobile-health intervention algorithms before real-world deployment. Using a conditional time-series diffusion model that maintains temporal consistency, the method integrates three information sources: pre-training on large observational datasets, fine-tuning on prior intervention deployments in related populations, and calibration from domain-expert input. The approach was validated across the HeartSteps series (versions 2-4), studying physical-activity suggestion interventions. Results demonstrate that JITAI-Twins reproduce target subpopulation temporal structures and between-participant variation more accurately than simpler simulators. This advancement addresses a critical challenge in mobile health: poorly designed algorithms can burden and disengage users. By enabling researchers to simulate and evaluate candidate online learning algorithms against realistic simulated users before deployment, JITAI-Twins provide a prerequisite tool for optimizing just-in-time adaptive interventions. This methodology promises to improve algorithm design decisions in mHealth interventions that deliver personalized nudges for health behaviors across hundreds of thousands of decision points.

This qualitative study examines how behavioral design features embedded in Saudi Arabia's Tawakkalna digital health application sustain public engagement beyond the COVID-19 pandemic. Researchers conducted semi-structured interviews with 24 participants including healthcare professionals, digital health strategists, and policymakers, applying Braun and Clarke's thematic analysis framework to identify patterns in behavioral design and engagement. Six key themes emerged: adaptive personalization effectively reduced notification fatigue, engagement varied significantly by age and emotional framing, ethical concerns centered on transparency and cultural alignment, system integration enhanced credibility, social and family influences amplified impact, and future designs should incorporate artificial intelligence-driven bidirectional nudges. The findings demonstrate that behavioral nudges like reminders, alerts, and tailored notifications can sustain health engagement when personalized and contextually relevant. This research matters because it provides evidence-based guidance for governments deploying digital health applications to maintain citizen engagement in preventive health behaviors during the post-pandemic era. The study underscores that effective sustained engagement requires applications to integrate adaptive, transparent, and socio-culturally attuned behavioral nudges supported by advancing AI technologies.

U.S. health systems and payers are fundamentally transforming nutrition from an operational expense to a measurable clinical intervention. Sodexo's healthcare leadership argues that food represents one of healthcare's most underutilized assets for improving patient outcomes, experience, and long-term health. This shift is driven by convergence of three forces: clinical nutrition science, digital health data including biomarkers and wearables, and behavioral design principles that influence actual food choices. Healthcare providers now recognize nutrition as personalized, measurable, and actionable—essential components of value-based care delivery. The transition reframes "food service" as "nutrition infrastructure," integrating nutrition into clinical workflows including screening, diagnosis, orders, discharge planning, and follow-up. This enables tailored interventions matched to individual risk profiles and conditions. A landmark 2019 JAMA Internal Medicine study demonstrated that patients receiving medically tailored meals experienced fewer subsequent hospitalizations. This emerging paradigm is accelerated by the burden of diet-related chronic disease, financial pressures of value-based payment models, and workforce expectations for employer-supported wellbeing. Federal policy guidance increasingly clarifies reimbursement pathways for nutrition interventions, making food-as-medicine both clinically justified and economically viable.

This resource provides a comprehensive behavioral science dictionary encompassing key concepts in behavioral economics and game theory. The entries explain psychological and economic phenomena that drive real-world decision-making, including cognitive biases, pricing strategies, and social dynamics. Topics range from anchoring effects and charm pricing to bounded rationality and altruistic punishment. The dictionary illustrates how humans deviate from purely rational economic models through mechanisms like bandwagon effects, conspicuous consumption, and costly signaling. It serves as an essential reference for understanding the behavioral foundations underlying consumer choices, market dynamics, and strategic interactions in both professional and personal contexts.

E-cigarette use among adolescents has emerged as a significant public health concern, with prevalence rates rising substantially in recent years. In Malaysia, usage among teenagers aged thirteen to seventeen increased from 9.8% in 2017 to 14.9% in 2022, with notably higher rates among males at 23.3%. These devices, which deliver nicotine and various additives through inhalation, pose considerable health risks to developing brains, affecting attention and cognition while increasing vulnerability to substance abuse. Research indicates that e-cigarette use correlates with compromised lung health, mental health deterioration, and heightened lung cancer risk, particularly among dual users. The growing normalization of e-cigarettes as safer alternatives to traditional tobacco has paradoxically attracted non-smokers to unnecessary health risks, underscoring the urgent need for evidence-based prevention and intervention strategies targeting youth populations.

Nudge: The Final Edition, authored by Nobel laureate Richard Thaler and Harvard Law Professor Cass Sunstein, represents a comprehensive 2021 revision of the groundbreaking 2008 original. This influential work synthesizes thirteen years of global implementation experience across government nudge units and institutional applications. The updated edition incorporates COVID-19 pandemic insights, expands digital choice architecture theory, and introduces the EAST framework for practical nudge design. It addresses libertarian paternalism critiques and explores "sludge"—the inverse of nudges—demonstrating how friction impedes beneficial behavior. Grounded in behavioral economics research by Kahneman and Tversky, the book fundamentally reshapes how policymakers and organizations understand decision-making environments and human behavior, establishing itself as essential contemporary policy literature.

Modern society stands at a historical crossroads which would interest, and perhaps terrify, the authors of the Declaration of Independence. Thomas Jefferson constructed a magnificent engine of choice, built to withstand the constraints of the eighteenth century. He assumed the human drive toward happiness would naturally flourish if the heavy hand of the state simply receded.

Carbon farming represents a comprehensive agricultural approach designed to sequester atmospheric carbon dioxide in soils while reducing cultivation-related emissions, simultaneously addressing climate change, soil degradation, and farmer livelihoods. An interdisciplinary survey integrates perspectives from agronomy, soil science, climate science, economics, and policy to provide unified guidance for practitioners and policymakers. The analysis establishes foundations in soil organic carbon dynamics, compares carbon farming with sustainable and regenerative agriculture paradigms, and examines best practices alongside their co-benefits and trade-offs. The study reviews measurement, reporting, and verification frameworks, digital technologies, carbon credit mechanisms, and voluntary and compliance markets. Through representative case studies, the survey synthesizes implementation models, successes, and challenges. In India, widespread adoption could offset fifty percent of transport-sector emissions, yet fewer than one percent of farmers currently participate in carbon credit programs, highlighting significant adoption barriers requiring scientific and policy innovations.

This study shows how nudge theory-based interventions, when combined with consultation, can significantly improve the awareness, knowledge, attitude, and practices of diabetes patients in Sikkim. These results indicate that such interventions can act as an effective approach to improve diabetes self-care attitude and patient’s health behaviour.

Often, Individuals try to choose the best feasible option but sometimes, they don’t succeed in doing so. They make mistakes. Also, the market exchange makes psychological factors cease to matter but many psychological factors matter even for the markets. In contrast to the traditional assumption of neoclassical economics that individuals are rational Homooeconomici, that always seek to maximize their utility and follow their “true” preferences, research in behavioural economics has demonstrated that people’s judgments and decisions are often subject to systematic biases and heuristics, and are strongly dependent on the context of the decision.

Eliminating the need for manual meal announcement, the proposed algorithm overcomes a key barrier to fully automated glucose management, reducing patient burden while maintaining reliable detection performance even in unstructured, free-living conditions. These results support the integration of the algorithm into DSSs for PBH and other populations, where timely and accurate meal detection is critical.

It is a statement of basic ethical principles and guidelines that should assist in resolving the ethical problems that surround the conduct of research with ...

This article addresses a critical gap in artificial intelligence development by advocating for the integration of behavioral science into AI research and deployment. The author argues that while technical expertise dominates the field, a deeper understanding of human behavior, psychology, and decision-making is essential for creating AI systems that effectively interact with and serve human users. By incorporating behavioral science principles, AI developers can better anticipate how people will actually use these technologies, identify potential biases, and design more ethical and effective solutions. This interdisciplinary approach would enhance AI's practical applicability and trustworthiness in real-world contexts.

Continuous lactate monitoring represents an emerging frontier in metabolic health assessment, building upon the success of continuous glucose monitoring systems. While lactate has traditionally been measured only through blood samples in clinical settings, advancing wearable sensor technology now enables real-time tracking of lactate levels in interstitial fluid. Unlike glucose monitoring alone, continuous lactate measurement offers valuable insights into energy production and utilization during exercise, stress, illness, and recovery phases. As biotechnology companies and research institutions develop increasingly reliable biosensor systems, this complementary monitoring approach promises to provide a more comprehensive understanding of metabolic health. Though still in development stages, early evidence suggests these minimally invasive sensors can accurately track lactate fluctuations throughout the day, potentially revolutionizing how clinicians and patients manage metabolic conditions.

Integration of social media (SM) with mobile health (mHealth) platforms presents significant potential to address the persistent challenge of disengagement in digital health interventions among young people. However, empirical evidence supporting this combined approach remains limited, as SM and mHealth are often examined in isolation. This systematic review aimed to identify and synthesise the determinants influencing the participation in SM and mHealth interventions for behaviour change among young people aged 14 to 35 years.

Health app onboarding differs fundamentally from fintech onboarding in one critical way: trust. In fintech, users trust the platform (bank, payment app) and your job is to minimize friction. In health, users are skeptical. They're sharing intimate medical information and betting their health on your platform's ability to connect them with qualified doctors.This means health app onboarding must build trust early. Users need to see that you have real doctors, that their data is secure, and that they'll get a response quickly. Before they'll fill a health profile, they need confidence the system works.

Gamification has done some remarkable things. It taught hundreds of millions of people a second language (or at least to feel guilty about not practicing one). It made saving money feel like leveling up a character. It turned going to the gym into a competitive sport, complete with leaderboards and achievement badges. It has, arguably, made a lot of people brush their teeth longer than they otherwise would.So why, after more than a decade of serious investment and serious effort, do health apps still struggle to move the needle on the things that actually matter?