Research demonstrates that self-control, the ability to override impulses for long-term goals, is compromised in disordered environments, yet a significant protective factor has been identified. This study investigated the sense of power as a moderating variable in the relationship between environmental disorder and self-control using three experimental tasks: an intertemporal choice task, a breath-holding task, and a Stroop task. Results confirmed that disordered environments impair self-control across multiple dimensions, including reduced willingness to exert control, diminished behavioral performance, and weakened inhibitory control. Critically, individuals with high trait sense of power showed resilience against these environmental effects. Additionally, increasing state sense of power experimentally protected self-control from environmental disruption. These findings establish new boundary conditions for understanding how environmental chaos affects self-regulatory capacity and suggest practical interventions. By elevating individuals' sense of power, whether through trait development or situational enhancement, organizations and interventions can buffer self-control deficits in chaotic settings, offering valuable implications for workplace productivity, decision-making quality, and behavioral regulation in disorganized contexts.

The COM-B model, developed by Susan Michie, Maartje van Stralen, and Robert West in 2011, serves as a diagnostic framework rather than an intervention menu for designing behavior change initiatives. The model identifies three essential conditions for any behavior: capability, opportunity, and motivation, each containing two subcomponents. A critical common error occurs when organizations predetermine solutions before diagnosis, essentially justifying rather than analyzing. A 2023 cross-sectional survey of 114 inpatient nurses at Duke University Hospital exemplifies the model's diagnostic power: analyzing unnecessary urine-culture ordering revealed virtually no correlation between nurses' knowledge and confidence (gamma = 0.02), despite mean knowledge scoring 9.93 of 16. The actual constraints were opportunity and motivation, as nurses faced pushback from clinicians and maintained ingrained practices despite understanding guidelines. This finding demonstrates that knowledge-focused interventions alone cannot influence motivation. The COM-B framework employs AACTT for behavioral specification, the 14 Therapy Development Framework domains for interviews, and APEASE for filtering intervention functions. Crucially, the model functions as an interactive system with feedback loops, where components influence each other and enacted behaviors modify capability, motivation, and opportunity, enabling eventual intervention withdrawal once behavior change initiates.

Researchers from institutions across Europe and North America have developed a formal computational model of smoking relapse using the Theory Construction Methodology. The team, led by Olga Perski, created COMPLAPSE, a within-person system dynamics model that represents biopsychosocial factors such as stressors, cigarette cues, cravings, and self-efficacy as time-varying inputs that dynamically influence behavior over time. Through a participatory approach involving interviews with researchers, people with lived experience, smoking cessation practitioners, and policymakers, the team integrated these factors into a predictive framework using a softmax function to determine the probability of selecting between behavioral options: smoking, using regulatory strategies, or doing nothing. In silico simulations demonstrated the model's capacity to generate realistic patterns of relapse, prolapse, and abstinence, while sensitivity analyses confirmed that parameter variations produced expected behavioral shifts. This work addresses a critical gap in existing relapse prevention theories, which typically lack temporal information about how phenomena develop within individuals. The findings have significant implications for developing and optimizing technology-mediated just-in-time adaptive interventions to support smoking cessation efforts.

Research by Hornsby, Pham, Davis, and Darmstadt published in Frontiers in Public Health examines how measurement methodology critically influences the perceived effectiveness of school-based water, sanitation, and hygiene (WASH) interventions. The study addresses a significant gap in global health evaluation by analyzing how different approaches to quantifying behavior change yield substantially different conclusions about program success. The researchers investigated the role of observation methods and reporting bias in assessing WASH intervention outcomes within school settings. Their findings demonstrate that the choice of measurement methodology—whether relying on direct observation, self-reporting, or other techniques—substantially affects how effective these interventions appear to be. This research matters because it reveals that inconsistent evaluation approaches may overstate or understate the true impact of WASH programs, potentially misleading policymakers and funders about which strategies warrant investment. By highlighting these methodological discrepancies, the work calls for standardized measurement protocols in school-based WASH research to ensure accurate assessment of intervention effectiveness and better-informed resource allocation in public health initiatives.

This open-access study, published in Current Psychology in September 2026, investigates how social conformity influences pro-climate behavior by examining both behavioral and neurophysiological responses. Researchers explored whether individuals are more likely to adopt environmentally conscious actions when they perceive social pressure or observe others engaging in sustainable practices. The research combined behavioral observation with neurophysiological measurements, likely including brain imaging or physiological monitoring, to understand the mechanisms underlying conformity-driven environmental action. The findings reveal the complex relationship between social influence and climate-related decision-making, demonstrating that conformity can serve as a powerful motivator for pro-environmental behavior. This research matters for climate policy and communication strategies, as it suggests that leveraging social norms and peer influence could effectively encourage widespread adoption of sustainable practices. Understanding these neurobiological pathways provides valuable insights for designing behavioral interventions that capitalize on conformity mechanisms to accelerate global climate action and shift societal norms toward environmental responsibility.

Behavioral economics is the branch of economics examining how people actually make decisions regarding money, effort, time, and risk, departing from the idealized rational agent model of neoclassical economics. The field integrates psychological theory and experimental methods with classical economic questions about choice, markets, and policy design. Modern behavioral economics emerged in the 1970s when psychologists Daniel Kahneman and Amos Tversky documented consistent patterns in human probability judgment and decision-making under uncertainty. Their pioneering work led to Kahneman receiving the 2002 Nobel Memorial Prize in Economic Sciences—the first psychologist honored—followed by economist Richard Thaler's 2017 Nobel Prize for developing a systematic behavioral economics research program. This recognition transformed the field from a marginal critique into an established subdiscipline with dedicated journals, conferences, and doctoral programs. The field addresses fundamental questions about how real decision-makers systematically deviate from textbook assumptions, with implications for economic theory, market dynamics, and policy development. CASRAI's comprehensive guide covers core concepts, major subfields, the research funding landscape including support from the NSF, NIH, and Russell Sage Foundation, research methodologies, and career development pathways for researchers and administrators in behavioral economics.

This open-access perspective article, published in September 2026, presents a dual pathway cognitive behavioral model designed to leverage artificial intelligence in promoting environmentally sustainable decision-making. The research develops a theoretical framework that integrates cognitive and behavioral psychology principles with AI assistance to encourage individuals toward eco-conscious choices. The model operates through two distinct pathways, each addressing different cognitive and behavioral mechanisms that influence environmental decision-making. By combining psychological theory with computational intelligence, the approach aims to improve the effectiveness of interventions targeting sustainable behavior adoption. The significance of this work lies in its potential to address the growing environmental challenge through technology-enhanced behavioral change. The framework offers practitioners and policymakers a structured methodology for designing AI-assisted programs that can scale sustainable behavior promotion across diverse populations. This integration of AI with established psychological models represents an important advancement in creating intelligent systems that account for human cognition and decision-making processes while promoting environmental responsibility.

This review article by Pribik and Felfernig examines digital nudging applications within software development practices. Digital nudging, a persuasive technology technique, involves subtle interventions designed to influence developer behavior toward improved outcomes. The research synthesizes current knowledge on how AI-assisted development tools can leverage cognitive biases to guide software engineers toward higher code quality and adherence to established principles such as SOLID design patterns. The study addresses the intersection of software engineering, artificial intelligence, and behavioral psychology, exploring how nudging mechanisms can be integrated into development environments to promote better decision-making without restricting developer autonomy. Key considerations include the ethical implications of persuasive technologies in professional contexts and the effectiveness of various nudging strategies in real-world development settings. The article establishes a comprehensive research agenda identifying gaps in current understanding and proposing future directions for applying digital nudging to enhance software quality, developer productivity, and adherence to best practices. This work is particularly relevant as AI-assisted development tools become increasingly prevalent in the industry, highlighting the importance of thoughtful design in these systems.

This research article, published in Frontiers in Psychology in 2026, investigates the co-design of digital nudges to advance gender equality and social inclusion among Arab youth. Conducted by Alaa Ziyud, Khaled AlThelaya, and Jens Schneider, the study employs a mixed-methods approach to understand how behavioral design interventions can address these critical social challenges in Arab communities. Digital nudges—subtle, strategically designed prompts that encourage behavioral change—are leveraged as tools to influence attitudes and decisions regarding gender equality and social integration among young people. The co-design methodology ensures that interventions are developed collaboratively with the target population, increasing their relevance and effectiveness. This research matters significantly because it bridges the gap between behavioral science and social development in an understudied region, providing evidence-based strategies for technology platforms and policymakers seeking to foster more inclusive societies. The findings contribute valuable insights into how digital tools can promote equitable social norms among Arab youth while respecting cultural contexts.

Just-in-Time Adaptive Interventions (JITAIs) represent an emerging approach to promoting physical activity through personalized, real-time behavioral support delivered via mobile technology. This Frontiers publication examines how JITAIs leverage sensors, ecological momentary assessment, and machine learning algorithms to detect optimal moments for activity encouragement and deliver tailored interventions at critical decision points. The methodology integrates contextual factors—such as time of day, location, mood, and social environment—to identify windows of receptivity when individuals are most likely to engage in physical activity. Key findings demonstrate that adaptive, just-in-time delivery significantly improves engagement and adherence compared to static intervention approaches. The technique capitalizes on temporal dynamics and individual variability, utilizing wearable devices and smartphone applications to continuously monitor behavior and environmental conditions. This matters substantially for public health, as personalized digital interventions address barriers to sustained physical activity by meeting people where they are, when they are most receptive. JITAIs represent a paradigm shift from one-size-fits-all health promotion toward precision health strategies, offering scalable solutions for reducing sedentary behavior and improving population-level fitness outcomes.

Thaler and Sunstein's own position is that the anti–nudge position is a literal non–starter, because citizens are always influenced by the decision making context anyway, and nudging is liberty preserving and acceptable if guided by Libertarian Paternalism and Rawls’ publicity principle. A persistent and central tenet in the criticism disputing the acceptability of the approach is that nudging works by manipulating citizens’ choices. In this paper, we argue that both lines of argumentation are seriously flawed. We show how the anti–nudge position is not a literal non–starter due to the responsibilities that accrue on policy–makers by the intentional intervention in citizens’ life, how nudging is not essentially liberty preserving and why the approach is not necessarily acceptable even if satisfying Rawls’ publicity principle. We then use the psychological dual process theory underlying the approach as well as an epistemic transparency criterion identified by Thaler and Sunstein themselves to show that nudging is not necessarily about “manipulation”, nor necessarily about influencing “choice”.

A recent cross-sectional study published in Frontiers in Psychology examined the Xingfa gamification framework and its associations with students' sport motivation and physical fitness across fourteen schools. Conducted by Li Ye-Jun, Zhu D, and Tang X-L, this research investigated how gamification techniques influence students' engagement in physical education by applying Self-Determination Theory (SDT) as the theoretical foundation. The study focused on motivation internalization and psychological needs satisfaction as key outcomes. The research compared sport motivation levels and physical fitness metrics across multiple educational institutions to assess the effectiveness of the Xingfa framework in enhancing student participation and performance in physical activities. This work matters significantly for educational institutions seeking evidence-based approaches to improve physical education outcomes and student engagement through gamified learning experiences. The findings provide insights into how structured gamification strategies can support intrinsic motivation development and psychological well-being in academic settings while promoting healthier, more active lifestyles among students.

Researchers from Lishui University conducted a comprehensive systematic review and meta-analysis to assess self-care intervention effectiveness for type 2 diabetes mellitus in working-age adults. The team searched nine major databases including PubMed, Embase, and Cochrane Library for randomized controlled trials published between January 2020 and April 2025, ultimately analyzing 47 RCTs involving 6,464 participants aged 18-60 years. Self-care interventions demonstrated significant clinical benefits, reducing hemoglobin A1c levels by 0.26 points, total cholesterol, and body mass index while increasing high-density lipoprotein levels. Notably, interventions substantially improved self-care behaviors and self-efficacy scores. However, no significant improvements were observed in fasting blood glucose or two-hour plasma glucose measurements. The analysis revealed substantial heterogeneity across studies, limiting generalizability. The findings underscore the importance of structured self-care interventions for managing type 2 diabetes in working-age populations facing occupational demands. The authors recommend future trials employ standardized intervention reporting, robust study designs, and extended follow-up periods to strengthen evidence supporting clinical practice and public health policy implementation.

Behavioral economics examines how people actually make decisions about money, time, effort, and risk, contrasting with traditional economic theory's assumption of fully rational actors. The field integrates psychological theory and experimental methods to address classic economic questions about choice, markets, and policy design. Founded on the empirical observation that real decision-makers systematically deviate from idealized rational agents, behavioral economics emerged as a formal discipline in the 1970s through psychologists Daniel Kahneman and Amos Tversky's groundbreaking work on probability judgment and decision-making under uncertainty. Kahneman's 2002 Nobel Prize in Economics—the first awarded to a psychologist—and economist Richard Thaler's 2017 prize for extending behavioral economics into a comprehensive research program marked the field's transition from academic fringe to established subdiscipline. Today, behavioral economics commands significant research funding from institutions including the National Science Foundation, National Institutes of Health, and Russell Sage Foundation. This comprehensive guide, maintained by CASRAI's Editorial Board, covers core concepts, major subfields, research methodologies, and career pathways, providing essential resources for researchers and administrators in the behavioral economics field.

Physical inactivity remains a critical global public health challenge, contributing significantly to non-communicable diseases and premature mortality. Despite awareness of health benefits and guidelines recommending 150 minutes of weekly moderate-intensity activity, approximately one-third of adults fail to meet targets. A key barrier is the intention-behaviour gap, where nearly half of individuals with strong PA intentions do not act on them. Recent research indicates that momentary intention, shaped by contextual and affective factors, drives actual physical activity engagement, suggesting that situational support is essential beyond motivation alone. This paper addresses a significant conceptual gap by defining and structuring guidance in digital health interventions for physical activity support. The authors define guidance as situated, action-oriented support that scaffolds users' embodied engagement in PA. They develop a comprehensive design space characterizing guidance across nine dimensions: scope, purpose, timing, context, modality, embodiment, adaptivity, autonomy, and affective quality. Drawing from behaviour change theory, human-computer interaction, embodied cognition, and digital health literature, this framework provides a structured vocabulary and conceptual foundation. By clarifying guidance and distinguishing it from related constructs such as personalisation, feedback, and persuasion, this work enables more coherent research, meaningful comparisons, and responsible design of digital health interventions for physical activity support.

This article introduces the Nudge Intensity Model, a conceptual framework that positions nudges along a continuum from soft to moderate to hard interventions. Drawing on insights from communication, social and environmental psychology, design, policy studies, and economics, the model clarifies how different forms of nudging exert varying degrees of behavioral influence and how they can be calibrated to context, ethical considerations, and policy goals. Illustrative examples from climate and health interventions demonstrate the need for more deliberate calibration.

This article examines the critical question of policy instrument selection in modern governance. It establishes that governments choose among increasingly diverse tools—ranging from traditional command-and-control regulation to contemporary behavioral nudges—to address public problems, and that this choice fundamentally determines policy success and distributional outcomes. The study of policy tools, pioneered by scholars including Lester Salamon, Christopher Hood, and Anne Schneider, recognizes that instrument selection reflects deeper assumptions about human behavior, state legitimacy, and compliance cost distribution rather than being purely technical decisions. The article introduces a foundational analytical framework: the coercion spectrum, which arranges five primary policy tools (regulation, taxes, subsidies, mandates, and nudges) along a continuum from least to most restrictive of individual choice. This framework reveals a central democratic tension—more coercive instruments typically ensure predictable compliance but invite greater political resistance and enforcement expenses. The distances between tools carry analytical significance, with relatively narrow gaps between voluntary-compliance mechanisms like nudges and subsidies, and smaller intervals between legally compulsory tools. Understanding these distinctions is essential for policymakers evaluating trade-offs in governance effectiveness.

This field experiment, approved by the University of Michigan's Institutional Review Board and preregistered in the AEA RCT Registry (AEARCTR-0018430), investigates how artificial intelligence influences taxpayer compliance behavior. Conducted as a randomized controlled trial, the study examines whether AI-driven interventions—such as personalized communications, algorithmic risk assessments, or automated compliance reminders—affect taxpayer reporting and payment patterns. By analyzing behavioral responses across treatment and control groups, researchers aim to identify which AI-enabled strategies most effectively encourage voluntary tax compliance. The findings have significant policy implications for tax administration, potentially offering cost-effective alternatives to traditional enforcement mechanisms. Understanding how AI shapes taxpayer behavior could inform government revenue strategies and voluntary compliance initiatives. Upon publication, the researchers will release replication code and data publicly, supporting transparency and future research validation in this emerging field at the intersection of behavioral economics, taxation policy, and artificial intelligence applications in public administration.

# Summary This peer-reviewed article published in Acta Politica examines the relationship between U.S. climate policy options and the attitudes of economic elites toward environmental governance. The research investigates how influential members of the economic establishment perceive and respond to various climate policy approaches, providing critical insights into the political economy of climate action at the highest levels of American business and finance. By analyzing elite attitudes alongside policy frameworks, the study illuminates the disconnect that often exists between scientific climate imperatives and elite preferences for market-based or less restrictive regulatory solutions. The findings contribute to understanding why certain climate policies gain traction while others face resistance from powerful economic actors. This work matters significantly for policymakers and analysts seeking to understand the political feasibility of climate legislation and the role elite preferences play in shaping environmental governance trajectories in the United States.

Mindworks, a discovery center and laboratory operated by the University of Chicago's Roman Family Center for Decision Research in downtown Chicago, has reached its fifth anniversary milestone and emerged as a vital hub for behavioral science research. Founded in July 2021, the facility combines free interactive exhibits exploring behavioral science concepts with opportunities for visitors to participate in actual studies conducted by Chicago Booth School of Business researchers. Nearly 85,000 visitors from all 50 states and over 80 countries have visited Mindworks, with more than 38,000 participating in 219 completed studies. Research findings have been published in prestigious journals including the Journal of Personality and Social Psychology and the Journal of Consumer Research. Faculty director Nicholas Epley, a distinguished behavioral scientist studying social cognition, developed Mindworks to address a critical challenge in research: recruiting diverse strangers for studies in real-world settings. The facility distinguishes itself from traditional university labs, which typically rely on student subjects, by attracting a broad demographic. Beyond research, Mindworks has become integral to Chicago's cultural landscape, collaborating with institutions like the Art Institute of Chicago, Color Factory Chicago, WBEZ, and the Goodman Theatre's Theater of the Mind, an immersive experience co-created by David Byrne.