research report on human behavior

The future of human behaviour research

Affiliations.

• 1 Department of Political Science, Ohio State University, Columbus, OH, USA. [email protected].
• 2 School of Communication and Digital Media Research Centre (DMRC), Queensland University of Technology, Brisbane, Queensland, Australia. [email protected].
• 3 Australian Research Council Centre of Excellence for Automated Decision-Making and Society (ADM+S), Melbourne, Victoria, Australia. [email protected].
• 4 Department of Neuroscience and Padova Neuroscience Center (PNC), University of Padova, Padova, Italy. [email protected].
• 5 Venetian Institute of Molecular Medicine (VIMM), Padova, Italy. [email protected].
• 6 Annenberg School for Communication and Journalism, University of Southern California, Los Angeles, CA, USA. [email protected].
• 7 Microsoft Research New York, New York, NY, USA. [email protected].
• 8 École Normale Supérieure, Paris, France. [email protected].
• 9 Department of Economics, Massachusetts Institute of Technology, Cambridge, MA, USA. [email protected].
• 10 Department of Human Behavior, Ecology, and Culture, Max Planck Institute for Evolutionary Anthropology, Leipzig, Germany. [email protected].
• 11 Department of Psychology, University of California at Berkeley, Berkeley, CA, USA. [email protected].
• 12 American University of Beirut, Beirut, Lebanon. [email protected].
• 13 Department of Global Development, College of Agriculture and Life Sciences and Cornell Atkinson Center for Sustainability, Cornell University, Ithaca, NY, USA. [email protected].
• 14 Department of Management, The Chinese University of Hong Kong, Hong Kong, Hong Kong Special Administrative Region, China. [email protected].
• 15 Center for Social and Environmental Systems Research, Social Systems Division, National Institute for Environmental Studies, Tsukuba, Japan. [email protected].
• 16 State Key Laboratory of Brain and Cognitive Sciences and Laboratory of Neuropsychology and Human Neuroscience, The University of Hong Kong, Hong Kong, Hong Kong Special Administrative Region, China. [email protected].
• 17 WHO Collaborating Centre for Infectious Disease Epidemiology and Control, School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong Special Administrative Region, China. [email protected].
• 18 Laboratory of Data Discovery for Health, Hong Kong Science and Technology Park, Hong Kong, Hong Kong Special Administrative Region, China. [email protected].
• 19 Heinz College of Information Systems and Public Policy, Carnegie Mellon University, Pittsburgh, PA, USA. [email protected].
• 20 Department of Experimental Psychology, University of Oxford, Oxford, UK. [email protected].
• 21 Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, UK. [email protected].
• 22 CORE - Copenhagen Research Centre for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark. [email protected].
• 23 Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark. [email protected].
• 24 Department of Economics, School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands. [email protected].
• 25 Complex Human Data Hub, University of Melbourne, Melbourne, Victoria, Australia. [email protected].
• 26 ODID and SAME, University of Oxford, Oxford, UK. [email protected].
• 27 School of Public Policy, Georgia Institute of Technology, Atlanta, GA, USA. [email protected].
• 28 Centre of Excellence FAIR, NHH Norwegian School of Economics, Bergen, Norway. [email protected].
• 29 GESIS - Leibniz Institute for the Social Sciences, Köln, Germany. [email protected].
• 30 RWTH Aachen University, Aachen, Germany. [email protected].
• 31 Complexity Science Hub Vienna, Vienna, Austria. [email protected].
• PMID: 35087189
• DOI: 10.1038/s41562-021-01275-6
• Anthropology / trends
• Artificial Intelligence / trends
• Behavioral Research / trends*
• Social Sciences / trends*

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Learning, the Sole Explanation of Human Behavior: Review of The Marvelous Learning Animal: What Makes Human Nature Unique

James s macdonall.

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Corresponding author.

Collection date 2016 May.

Seemingly everyone is interested in understanding the causes of human behavior. Yet many scientists and the general public embrace causes of behavior that have logical flaws. Attributing behavior to mental events, emotions, personality, or abnormal personality, typically, is committing one of a number of common errors, such as reification, circular reasoning, or nominal fallacies (Schlinger & Poling, 1998 ). An increasingly frequent error is embracing genetic explanations of behavior in the absence of an identified gene. Similarly, explaining behavior in terms of brain structure or function fails to ask what caused that brain structure or function to develop or function in a particular way.

As Arthur Staats ( 2012 ) notes in his valuable book The Marvelous Learning Animal: What Makes Human Nature Unique , unfortunately, such flawed explanations have prospered at the expense explanations based on learning mechanisms. Consequently, many behavior analysts would like to see a book that uses non-technical language to clearly delineate the limitations of explanations based on mind, brain, genes, and personality. Such a book would clearly describe how human behavior (both typical and problematic) can be understood in terms of learning principles, how myriad daily interactions from right after birth make us who we are, how the relevant behavioral research progresses, how interventions are developed based on the research, and how these interventions are subject to research demonstrating their effectiveness. The book would also describe the proper role of genetics and brain structure and function in an understanding of behavior. Perhaps no single volume can do all of these things equally well, The Marvelous Learning Animal is a useful complement to existing works with which behavior analysts may already be familiar (e.g., Schneider, 2012 ; Skinner, 1953 ).

The Great Scientific Error

Attributing causes of behavior to mind, brain, genes, personality, intelligence, abnormal personality, or genetics Staats calls the Great Scientific Error. According to Staats, learning was overlooked as a cause of behavior because early behaviorists did not develop research programs examining learning principles in complex human behavior, behavior occurring outside the laboratory under natural contingencies. Behaviorisms’ total rejection of “personality, intelligence, attitudes, interests or psychological measurement” (p. 33) exacerbated the problem in two ways. First, many in the general population rejected behavioral views because behaviorists rejected these concepts that seemed self-evidently true. Second, behaviorists did not examine the contingencies producing the behaviors subsumed under these labels. Research on reading and language shows the importance of identifying the natural contingencies in development (Hart & Risley, 1995 ; Moerk, 1990 ). Thus, Staats calls for a new learning paradigm that extends from the genetic basis of learning principles through how these learning principles function in complex human behavior. Given the methodological advances in genetics and neuroscience, Skinner, were he alive to see it, may well have agreed with this approach.

The Human Animal

Homo sapiens , according to Staats, are unique in two ways. First, humans have considerable sensitivity to a wide range of stimuli (e.g., light, sound, heat, and tactile). Within each stimulus modality, humans are not the most sensitive (e. g., many birds see better than we do). Some species can sense stimuli that humans do not sense (e.g., honey bees discriminate polarized light). However, we are the only species with very good sensitivity in many modalities. Similarly, we have a diversified motor system. True, other species have as much or more strength or fine control of specific motor systems (e.g., cats can jump further and with greater accuracy than we can jump). But we are the only species that has very good control of a wide variety of motor systems (e. g., facial muscles, hand/finger muscles, and arm and leg muscles).

Second, diverse sensory and motor systems need a brain that not only relays “messages” from sensory receptors to muscle fibers but also integrates the inputs from diverse sensory receptors along with neural results of prior experience producing complex sets of outputs to muscle fibers (what normally is called learning). It is estimated that humans have upwards of 100 billion neurons and on average several thousand synaptic connections for each neuron (Kolb, Gibb, & Robinson, 2001 ). This very large brain, interacting with our diverse sensory and motor systems, is what makes humans unique.

Child Development and the Missing Link

The Marvelous Learning Animal is informed by Staats’ own scholarly career, in which he focused on examining contingencies of naturally occurring behavior. Once Staats identified what he hypothesized were the critical contingencies, he would manipulate them to see if he could speed development and thereby demonstrate their importance. Throughout The Marvelous Learning Animal , Staats divides behavior and its development, for convenience, into three broad areas: emotion-motivation, sensory-motor, and language-cognitive. Despite these labels, the analysis is thoroughly behavioral; there are no hidden behaviors or processes. In all of these domains, Staats argues, maturation is a function of physical growth interacting with natural contingencies, which change as a child’s behavior changes. In Staats’ world view, there is no separate process of child development.

Staats rejects genetics (except for those that program for unconditioned reflexes) and epigenetics as the cause of any behavior. Much of the evidence supporting genetic and epigenetic accounts takes the form of documenting that behavioral disruption results when genetic mechanisms are perturbed. Missing from these accounts, however, is an explanation of how, in relevant disorders, changes in genes affect learning. Thus, the behavior analyst’s task is to identify how a defective gene disrupts learning. In Staats’ view, that knowledge combined with knowledge of the natural contingencies that support normal development allow a complete understanding and effective interventions to minimize or eliminate these so-called genetic or epigenetic disorders.

An example from medicine illustrates the general spirit of this approach and its benefits. Phenylketonuria is a genetic disorder that invariably kills young children with a particular defective gene. Investigators identified the defective gene, but did not stop there. They also found that the non-defective version of the gene produces enzymes necessary for metabolizing phenylalanine, an amino acid toxic to neurons at high doses. A diet with limited phenylalanine, supplemental amino acids, and other nutrients prevents phenylalanine from accumulating and killing young children (Macleod & Ney, 2010 ), even though the genetic defect remains.

Identifying the natural contingencies in development is an exciting research area for behavior analysts. The working hypothesis, of course, is that behavior putatively caused by natural selection can instead be understood by prior experiences. For example, many consider exploratory behaviors of infants to result from genetics, as this quote from Skinner (1948, reprinted 1975 ) might be taken to imply: “No one asks how to motivate a baby. A baby naturally explores everything it can get at….” (p. 144). Staats takes the view that exploratory behaviors, and by implication differences in exploratory behaviors, result from natural reinforcement, that is, changes in the environment produced by exploring as when a baby touches an object it may rattle. If natural selection is not responsible for individual differences in behavior, then it follows that these differences result from differences in learning experiences. This is not to say that there are no intraspecies differences in behavior potential. Humans, for instance, evolved genetic and brain mechanisms that are specific to language, but critically it is early experiences that result in language acquisition and language differences across individuals.

As too few behavior analysts have recognized (e.g., Bijou & Baer, 1961 ; Schlinger, 1995 ), only a detailed examination of early experiences can identify the role of environment in typical development, and by extension in atypical development. In the case of language, research suggests a clear role for early experience in language acquisition. For instance, the more children are exposed to verbal interactions, the greater their language competences’ (Hart & Risley, 1995 ; Moerk, 1990 ). This work has inspired a spate of programs to increase the number of words heard by young children with, or at risk for, language problems, with the goal of nudging language development toward a more normal developmental trajectory (e.g., Suskind & Suskind, 2015 ). It is not yet clear whether these programs adequately reproduce the natural contingencies identified in Moerk ( 1990 ) and Hart & Risley ( 1995 ), but the general approach is consistent with what Staats’ advocates: using natural contingencies as the inspiration for early intervention strategies for children who are falling behind developmental norms.

Crucial Concepts in Human Development

In explaining development, Staats assigns an important role to classical and operant conditioning, but he proposes that complex human behavior is best understood in terms of behavior repertoires and cumulative learning . These two processes, according to Staats, are unique to humans and, when combined with basic learning processes, account for all human behavior.

For Staats, behavior repertoires are complex sets of related stimulus-control relations. He gives the example of a reading repertoire that was built in a dyslexic child via 64,000 trials with a variety of stimulus-control relations involving letters, words, etc. (Staats & Butterfield, 1965 .). Staats identified a large number of these repertoires and their interrelations. Such a reading repertoire, combined with sensory-motor development, can promote a writing repertoire. The reading repertoire may combine with a repertoire for following spoken instructions to allow individuals to follow written instructions, or combined with a sensory-motor repertoire allowing individuals to write instructions. Individual behaviors can be part of several repertoires, and repertoires can be hierarchical, with bigger repertoires comprised, in part, of smaller repertoires. One important goal of behavioral research, in Staats’ view, is to identifying relations among different repertoires and how contingencies influence these repertoires and their interrelations.

Behavior repertoires result in cumulative learning. In mastery of a repertoire, behaviors learned later are acquired more quickly than previously learned behaviors. For example, children learning to print letters late in the alphabet only require one fourth the trials compared to learning to print the letter A . Additionally, mastering one repertoire can make it easier to master a subsequent repertoire. For example, a sound-imitation repertoire combined with suitable prompts produces a word-imitation repertoire that promotes faster language learning. While it may be uncontroversial among behavior analysts to claim that behavior consists of many repertoires and learning one repertoire facilitates learning others, there are few systematic research programs to identify these repertoires, their components, and the contingencies that produce them and establish and maintain their relation to other repertoires.

Staats speculates that cumulative learning influenced human cultural development. Cultural transmission of learning in effect allows one individual’s repertoire to build upon another’s. As one generation masters a repertoire the succeeding generation can master that repertoire faster and is able to expand that repertoire or beginning learning a repertoire new to the group. Staats gives the example of artistic repertoires becoming more sophisticated across generations. Unfortunately, Staats is somewhat vague on the specific mechanisms driving such changes, implying without sufficient explanation that the cumulative learning of a culture’s individual members somehow translates to intergenerational effects (Skinner, 1984 , was similarly vague in his account of cultural selection). Staats also places great emphasis on contingency-shaped behavior in his account of cultural development and, surprisingly, omits any function for rule-governed behavior.

From a behavior analytic perspective, a further limitation of Staats’ account is uncertainty regarding whether behavior repertoires and cumulative learning, as Staats invokes them, qualify as new concepts. By claiming that these phenomena are uniquely human Staats certainly suggests so, but nevertheless behavior analysts will find much that feels familiar in his use of them. For instance, Staats’ analysis of behavioral repertoires and their complex interrelationships brings to mind how reinforcers organize behavior into operants and how the resulting class of responses may not be identical to the class of reinforced responses (Catania, 2013 ). His description of cumulative learning may relate to learning sets (Harlow, 1949 ), pivotal response (Bryson, Koegel, Koegel, Openden, Smith, & Nefdt, 2007 ), and behavior cusps (Rosales-Ruiz & Baer, ( 1997 ), although Staats is silent on these possible connection. In the end, readers will be left to ponder important questions that are suggested by, but not answered in, The Marvelous Learning Animal , not the least of which concerns what sort of research program may be imagined to test Staats’ ideas.

Learning Human Nature

With the preceding as foundational knowledge, Staats addresses specific types of behavior that supposedly are explained by the Great Scientific Error. For example, intelligence tests subsume a variety of repertoires, such as naming, counting, instruction following, and imitating. Differences in intelligence test scores must therefore be interpreted as differences in acquisition of these behavioral repertoires, not differences in an internal entity called intelligence. Staats points out that intelligence test scores predict school performance not because they describe inherent ability but rather because many of the behavior repertoires required for success in school are assessed in intelligence tests. This leads naturally to the proposal for an analysis of the repertoires comprising what we call intelligent behavior, which would include research on the natural contingencies producing these repertoires and, eventually, attempts to foster development by systematically implementing those contingencies.

Behavior analysts will correctly anticipate that Staats proposes that abnormal experiences produce abnormal behaviors. His examples of problematic early childhood behaviors—including tantrums, yelling, hitting, defiance, and so forth—are familiar, as is his suggestion that how caregivers respond to these behaviors influences whether or not they continue and become more severe. These unfortunate natural contingencies produce behavioral repertories that may eventually qualify the individual for a “psychiatric” diagnosis, and once the diagnosis is in place, it elicits sympathy or fear that may only exacerbate caregiver acquiescence to problem behavior. Within the context of autism and a few other disorders, Staats’ recommendation for action is equally familiar. He prescribes clearly identifying the relevant behavior repertoires, analyzing the abnormal contingencies which produce those repertoires and exploring how these repertoires may, through cumulative learning, produce additional problem repertoires. A particular contribution of The Marvelous Learning Animal is to apply the same approach to understanding the development of dyslexia, paranoid schizophrenia, paraphilias, depression, and other problems less frequently addressed by applied behavior analysts. Staats holds steadfastly to his environmental perspective even in cases where biological damage or genetic abnormalities typically are held to cause the disorder (e.g., Down’s syndrome).

Human Evolution and Marvelous Learning

There is much more in Staats’ analysis that is worthy of consideration by behavior analysts, including his assertion that cumulative learning has been an important influence in human natural selection. As Staats notes, those in the field of human evolution are beginning to reach a similar conclusion (Diamond, 1992 ; Gould, 1977 ; Jablonka & Lamb, 2005 ), although Staats’ account is interesting for the emphasis it places on selection for verbal abilities and how verbal abilities influence selection. Critical thinking is required to examine ways in which the account deviates from those of behavior analysts (see Skinner, 1984 , in reinforcement as a mechanism of natural selection) and evolutionary biologists. In the latter case, Staats’ hardest-to-swallow view, namely that natural selection provides all humans with equal learning abilities because variation in learning ability is selected out. This notion is at odds with the widely accepted notion that natural selection is possible only when populations contain variability (Dawkins, 1976 ).

A Human Paradigm

It is refreshing to see an environment-centric alternative to the Great Scientific Error, and behavior analysts will appreciate Staats’ panache in placing learning at the center of all explanations of human behavior. They also will be interested in his conclusion that radical changes are required in the basic science of human behavior and the application of that science to clinical practice. In Staats’ view, the revised science needs to know much more about how learning and biology combine to produce behavior, which implies relying on techniques (e.g., brain imaging technology, genetic assays) to understand the interrelatedness of learning and biology. Many behavior analysts will sympathize with Staats’ proposition that the field of child development needs to be almost entirely restarted, using sophisticated observational methods required to identify the natural contingencies in development. Perhaps less intuitive, and therefore more challenging, to behavior analysts is Staats’ implication that, ultimately, the study of human behavior can only proceed with a proper study of development as he defines it. For example, an infant lies on their stomach pushes up with their arms which raises their head allowing them to see objects hidden behind other objects. If seeing a new view is reinforcing, or seeing objects previously followed by reinforcers is reinforcing, then infants will continue to push up. As they raise their head further above the surface, more items come into view. Eventually the standing infant may lean toward a favored object. They move a foot, preventing themselves from falling, bringing them closer to a reinforcing object. The first proto step has been naturally reinforced. Although, non-behavior analysts have collected data supporting aspects of this analysis, they did not include the functions of behaviors as walking developed (Adolph, Cole, Komati, Garciagurre, Badaly, Lingemanm, Chan, & Sotsky, 2012 ).

A central irony of behavior analysis is that its adherents (beginning with Skinner, e.g., 1953 ) have maintained that complex environmental relations account for the diversity of human behaviors, while their own work carefully analyzed only a limited range of interesting behaviors. The Marvelous Learning Animal challenges behavior analysts (and other readers) to imagine what a behavior science would look like if it thoroughly examined all of those interesting behaviors. In this regard, it matters little if along the way Staats commits a variety of transgressions such as failing to fully explain every concept, possibly playing fast and loose with natural selection, relying on lay terms that carry mentalistic connotations (this is, after all, a popular press book), and occasionally speaking ill of radical behaviorism.

These details should not be allowed to distract from the book’s essential challenge, which is to ask those who would advance environmental experience as the primary engine of behavior development to develop the science that is needed to test and support such an account. Staats delivers an analysis of complex human behavior that is indisputably behavioral and often consistent with a radical behavioral view. Where the analysis diverges from radical behaviorism as it has traditionally been practiced, it most often offers expansion rather than contradiction and thereby provides a stimulating basis for further inquiry.

Acknowledgments

I thank Bob Allen for his helpful comments on an earlier version of this review. All the remaining shortcomings result from my behavior.

Compliance with Ethical Standards

The preparation of this manuscript was not funded by any organization. I have no ethical conflicts in preparing this manuscript.

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Largest Quantitative Synthesis to Date Reveals What Predicts Human Behavior and How to Change It

Prof. Dolores Albarracín and her team dug through years of research on the science behind behavior change to determine the best ways to promote changes in behavior.

By Hailey Reissman

Pandemics, global warming, and rampant gun violence are all clear lessons in the need to move large groups of people to change their behavior. When a crisis hits, researchers, policymakers, health officials, and community leaders have to know how best to encourage people to change en masse and quickly. Each crisis leads to rehashing the same strategies, even those that have not worked in the past, due to the lack of definitive science of what interventions work across the board combined with well intended but erroneous intuitions. 

To produce evidence on what determines and changes behavior, Professor Dolores Albarracín and her colleagues from the Social Action Lab at the University of Pennsylvania undertook a review of all of the available meta-analyses — a synthesis of the results from multiple studies — to determine what interventions work best when trying to change people’s behavior. What results is a new classification of predictors of behavior and a novel, empirical model for understanding the different ways to change behavior by targeting either individual or social/structural factors.

A paper published today in Nature Reviews Psychology  reports that the strategies that people assume will work — like giving people accurate information or trying to change their beliefs — do not. At the same time, others like providing social support and tapping into individuals’ behavioral skills and habits as well as removing practical obstacles to behavior (e.g., providing health insurance to encourage health behaviors) can have more sizable impacts.

“Interventions targeting knowledge, general attitudes, beliefs, administrative and legal sanctions, and trustworthiness — these factors researchers and policymakers put so much weight on — are actually quite ineffective,” says Albarracín. “They have negligible effects."

Unfortunately, many policies and reports are centered around goals like increasing vaccine confidence (an attitude) or curbing misinformation. Policymakers must look at evidence to determine what factors will return the investment, Albarracín says.

Co-author Javier Granados Samayoa, the Vartan Gregorian Postdoctoral Fellow at the Annenberg Public Policy Center, has noticed researchers’ tendency to target knowledge and beliefs when creating behavior change interventions.

“There's something about it that seems so straightforward — you think x and therefore you do y . But what the literature suggests is that there are a lot of intervening processes that have to line up for people to actually act on those beliefs, so it’s not that easy,” he says.

Targeting Human Behavior

To change behaviors, intervention researchers focus on the two types of determinants of human behavior: individual and social-structural. Individual determinants encompass personal attributes, beliefs, and experiences unique to each person, while social-structural determinants encompass broader societal influences on people, like laws, norms, socioeconomic status, social support, and institutional policies.

The researchers’ review explored meta-analyses of experiments in which specific social-structural determinants or specific individual determinants were tested for their ability to change behavior. For example, a study might test how learning more about vaccination might encourage vaccination (knowledge) or how reductions in health insurance copayment charges might encourage medication adherence (access).

These meta-analyses encompassed eight individual and eight social-structural determinants — part of the original classification made by the authors.

The results from the research are presented in the following three figures, which pertain to a. all behaviors analyzed, b. only health behaviors, and c. only environmental behaviors.

The figures present interventions with individual targets on the left, and interventions with social/structural targets on the right. For each determinant, the figures show whether the effects has been shown to be negligible, small, medium or large.

Individual Determinants 

The analyses researchers conducted showed that what are often assumed to be the most effective individual determinants to target with interventions were not the most effective. Knowledge (like educating people about the pros of vaccination), general attitudes (like implicit bias training), and general skills (like programs designed to encourage people to stop smoking) had negligible effects on behavior. 

What was effective at an individual level was targeting habits (helping people to stop or start a behavior), behavioral attitudes (having people associate certain behaviors as being “good” or “bad”), and behavioral skills (having people learn how to remove obstacles to their behavior).

Social-Structural Determinants

The researchers also found that what are often assumed to be the most effective social-structural persuasive strategies were not. Legal and administrative sanctions (like requiring people to get vaccinated) and interventions to increase trustworthiness — justice or fairness within an organization or government entity — (like providing channels for voters to voice their concerns) had negligible effects on behavior. 

Norms and forms to monitor and incentivize behavior had some effects, albeit small. What was most effective was focusing on targeting access (like providing flu vaccinations at work) or social support (facilitating groups of people who help one another to meet their physical activity goals).

Granados Samayoa says that knowing which behavior change interventions work at which levels will be especially crucial in the face of growing health and environmental challenges. 

“When faced with massive problems like climate change, policy makers and other leaders have this desire to do something to change people's behavior for the better,” says Samayoa. “Our study provides valuable insights. Our research can inform future interventions and create programs that are actually effective, not just what people assume is effective. 

Albarracín is glad policymakers will have this resource now. 

“Before this study, analyses of behavior change efforts were limited to one domain, whether that was environmental science or public health. By looking at research across domains, we now have a clearer picture of how to encourage behavior change and make a difference in people’s lives,” she says.

“Our research provides a map for what might be effective even for behaviors nobody has studied. Just like masking because a critical behavior during the pandemic but we had no research on masking, a broad empirical study of intervention efficacy can guide future efforts for an array of behaviors we have not directly studied but that need to be promoted during a crisis.”

“Determinants of Behaviour and Their Efficacy As Targets of Behavioural Change Interventions” was published in Nature Reviews Psychology and authored by Dolores Albarracín, Bita Fayaz-Farkhad, and Javier Granados Samayoa. The research was funded by National Institutes of Health (NIH) grants R01MH132415, R01 AI147487, DP1 DA048570, R01 MH114847, and NSF 2031972 to Dolores Albarracín, and by the Annenberg Foundation Endowment to the Division of Communication Science at the Annenberg Public Policy Center.

About the Social Action Lab

The Social Action Lab is a group of experts and trainees in psychology, communication, and economics who seek to understand the fundamentals of social behavior and apply this knowledge to the solution of social and health problems. The lab is led by Dolores Albarracín, a Penn Integrates Knowledge Professor and Director of the Division of Communication Science in Annenberg Public Policy Center. She holds appointments in the Annenberg School for Communication, the Department of Psychology in the School of Arts & Sciences, the School of Nursing, and the Wharton School. The other two authors are Bita Fayaz-Farkhad, who is Assistant Research Professor in the Annenberg School for Communication, and Javier Granados Samayoa, the Vartan Gregorian Postdoctoral Fellow of the Annenberg Public Policy Center. 

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Challenges and opportunities for human behavior research in the coronavirus disease (covid-19) pandemic.

• 1 Department of General Psychology, University of Padova, Padua, Italy
• 2 Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy

The COVID-19 pandemic is a serious public health crisis that is causing major worldwide disruption. So far, the most widely deployed interventions have been non-pharmacological (NPI), such as various forms of social distancing, pervasive use of personal protective equipment (PPE), such as facemasks, shields, or gloves, and hand washing and disinfection of fomites. These measures will very likely continue to be mandated in the medium or even long term until an effective treatment or vaccine is found ( Leung et al., 2020 ). Even beyond that time frame, many of these public health recommendations will have become part of individual lifestyles and hence continue to be observed. Moreover, it is implausible that the disruption caused by COVID-19 will dissipate soon. Analysis of transmission dynamics suggests that the disease could persist into 2025, with prolonged or intermittent social distancing in place until 2022 ( Kissler et al., 2020 ).

Human behavior research will be profoundly impacted beyond the stagnation resulting from the closure of laboratories during government-mandated lockdowns. In this viewpoint article, we argue that disruption provides an important opportunity for accelerating structural reforms already underway to reduce waste in planning, conducting, and reporting research ( Cristea and Naudet, 2019 ). We discuss three aspects relevant to human behavior research: (1) unavoidable, extensive changes in data collection and ensuing untoward consequences; (2) the possibility of shifting research priorities to aspects relevant to the pandemic; (3) recommendations to enhance adaptation to the disruption caused by the pandemic.

Data collection is very unlikely to return to the “old” normal for the foreseeable future. For example, neuroimaging studies usually involve placing participants in the confined space of a magnetic resonance imaging scanner. Studies measuring stress hormones, electroencephalography, or psychophysiology also involve close contact to collect saliva and blood samples or to place electrodes. Behavioral studies often involve interaction with persons who administer tasks or require that various surfaces and materials be touched. One immediate solution would be conducting “socially distant” experiments, for instance, by keeping a safe distance and making participants and research personnel wear PPE. Though data collection in this way would resemble pre-COVID times, it would come with a range of unintended consequences ( Table 1 ). First, it would significantly augment costs in terms of resources, training of personnel, and time spent preparing experiments. For laboratories or researchers with scarce resources, these costs could amount to a drastic reduction in the experiments performed, with an ensuing decrease in publication output, which might further affect the capacity to attract new funding and retain researchers. Secondly, even with the use of PPE, some participants might be reluctant or anxious to expose themselves to close and unnecessary physical interaction. Participants with particular vulnerabilities, like neuroticism, social anxiety, or obsessive-compulsive traits, might find the trade-off between risks, and gains unacceptable. Thirdly, some research topics (e.g., face processing, imitation, emotional expression, dyadic interaction) or study populations (e.g., autistic spectrum, social anxiety, obsessive-compulsive) would become difficult to study with the current experimental paradigms ( Table 1 ). New paradigms can be developed, but they will need to first be assessed for reliability and validated, which will undoubtedly take time. Finally, generalized use of PPE by participants and personnel could alter the “usual” experimental setting, introducing additional biases, similarly to the experimenter effect ( Rosenthal, 1976 ).

Table 1 . Possible consequences of non-pharmacological interventions for COVID-19 on human behavior research.

Data collection could also adapt by leveraging technology, such as running experiments remotely via available platforms, like for instance Amazon's Mechanical Turk (MTurk), where any task that programmable with standard browser technology can be used ( Crump et al., 2013 ). Templates of already-programmed and easily customizable experimental tasks, such as the Stroop or Balloon Analog Risk Task, are also available on platforms like Pavlovia. Ecological momentary assessment is another feasible option, since it was conceived from the beginning for remote use, with participants logging in to fill in scales or activity journals in a naturalistic environment ( Shiffman et al., 2008 ). Increasingly affordable wearables can be used for collecting physiological data ( Javelot et al., 2014 ). Web-based research was already expanding before the pandemic, and the quality of the data collected in this way is comparable with that of laboratory studies ( Germine et al., 2012 ). Still, there are lingering issues. For instance, for some MTurk experiments, disparities have been evidenced between laboratory and online data collection ( Crump et al., 2013 ). Further clarifications about quality, such as consistency or interpretability ( Abdolkhani et al., 2020 ), are also needed for data collected using wearables.

Beyond updating data collection practices, a significant portion of human behavior research might change course to focus on the effects of the pandemic. For example, the incidence of mental disorders or of negative effects on psychological and physical well-being, particularly across populations of interest (e.g., recovered patients, caregivers, and healthcare workers), are crucial areas of inquiry. Many researchers might feel hard-pressed to not miss out on studying this critical period and embark on hastily planned and conducted studies. Multiplication and fragmentation of efforts are likely, for instance, by conducting highly overlapping surveys in widely accessible and oversampled populations (e.g., university students). Moreover, rushed planning is bound to lead to taking shortcuts and cutting corners in study design and conduct, e.g., skipping pre-registration or even ethical committee approval or using not validated measurement tools, like ad hoc surveys. Surveys using non-probability and convenience samples, especially for social and mental health problems, frequently produce biased and misleading findings, particularly for estimates of prevalence ( Pierce et al., 2020 ). A significant portion of human behavior research that re-oriented itself to study the pandemic could result in to a heap of non-reproducible, unreliable, or overlapping findings.

Human behavior studies could also aim to inform the planning and enforcement of public health responses in the pandemic. Behavioral scientists might focus on finding and testing ways to increase adherence to NPIs or to lessen the negative effects of isolation, particularly in vulnerable groups, e.g., the elderly or the chronically ill and their caretakers. Studies could also attempt to elucidate factors that make individuals uncollaborative with recommendations from public health authorities. Though all of these topics are important, important caveats must be considered. Psychology and neuroscience have been affected by a crisis in reproducibility and credibility, with several established findings proving unreliable and even non-reproducible ( Button et al., 2013 ; Open Science Collaboration, 2015 ). It is crucial to ensure that only robust and reproducible results are applied or even proposed in the context of a serious public health crisis. For instance, the possible influence of psychological factors on susceptibility to infection and potential psychological interventions to address them could be interesting topics. However, the existing literature is marked by inconsistency, heterogeneity, reverse causality, or other biases ( Falagas et al., 2010 ). Even for robust and reproducible findings, translation is doubtful, particularly when these are based on convenience samples or on simplified and largely artificial experimental contexts. For example, the scarcity of medical resources (e.g., N-95 masks, drugs, or ventilators) in a pandemic with its unavoidable ethical conundrum about allocation principles and triage might appeal to moral reasoning researchers. Even assuming, implausibly, that most of the existent research in this area is robust, translation to dramatic real-life situations and highly specialized contexts, such as intensive care, would be difficult and error-prone. Translation might not even be useful, given that comprehensive ethical guidance and decision rules to support medical professionals already exist ( Emanuel et al., 2020 ).

The COVID-19 pandemic and the corresponding global public health response pose significant and lasting difficulties for human behavior research. In many contexts, such as laboratories with limited resources and uncertain funding, challenges will lead to a reduced research output, which might have further domino effects on securing funding and retaining researchers. As a remedy, modifying data collection practices is useful but insufficient. Conversely, adaptation might require the implementation of radical changes—producing less research but of higher quality and more utility ( Cristea and Naudet, 2019 ). To this purpose, we advocate for the acceleration and generalization of proposed structural reforms (i.e., “open science”) in how research is planned, conducted, and reported ( Munafò et al., 2017 ; Cristea and Naudet, 2019 ) and summarize six key recommendations.

First, a definitive move from atomized and fragmented experimental research to large-scale collaboration should be encouraged through incentives from funders and academic institutions alike. In the current status quo, interdisciplinary research has systematically lower odds of being funded ( Bromham et al., 2016 ). Conversely, funders could favor top-down funding on topics of prominent interest and encourage large consortia with international representativity and interdisciplinarity over bottom-up funding for a select number of excellent individual investigators. Second, particularly for research focused on the pandemic, relevant priorities need to be identified before conducting studies. This can be achieved through assessing the concrete needs of the populations targeted (e.g., healthcare workers, families of victims, individuals suffering from isolation, disabilities, pre-existing physical and mental health issues, and the economically vulnerable) and subsequently conducting systematic reviews so as to avoid fragmentation and overlap. To this purpose, journals could require that some reports of primary research also include rapid reviews ( Tricco et al., 2015 ), a simplified form of systematic reviews. For instance, The Lancet journals require a “Research in context” box, which needs to be based on a systematic search. Study formats like Registered Reports, in which a study is accepted in principle after peer review of its rationale and methods ( Hardwicke and Ioannidis, 2018 ), are uniquely suited for this change. Third, methodological rigor and reproducibility in design, conduct, analysis, and reporting should move to the forefront of the human behavior research agenda ( Cristea and Naudet, 2019 ). For example, preregistration of studies ( Nosek et al., 2019 ) in a public repository should be widely employed to support transparent reporting. Registered reports ( Hardwicke and Ioannidis, 2018 ) and study protocols are formats that ensure rigorous evaluation of the experimental design and statistical analysis plan before commencing data collection, thus making sure shortcuts and methodological shortcomings are eliminated. Fourth, data and code sharing, along with the use of publicly available datasets (e.g., 1000 Functional Connectomes Project, Human Connectome Project), should become the norm. These practices allow the use of already-collected data to be maximized, including in terms of assessing reproducibility, conducting re-analyses using different methods, and exploring new hypotheses on large collections of data ( Cristea and Naudet, 2019 ). Fifth, to reduce publication bias, submission of all unpublished studies, the so-called “file drawer,” should be encouraged and supported. Reporting findings in preprints can aid this desideratum, but stronger incentives are necessary to ensure that preprints also transparently and completely report conducted research. The Preprint Review at eLife ( Elife, 2020 ), in which the journal effectively takes into review manuscripts posted on the preprint server BioRxiv, is a promising initiative in this direction. Journals could also create study formats specifically designed for publishing studies that resulted in inconclusive findings, even when caused by procedural issues, e.g., unclear manipulation checks, insufficient stimulus presentation times, or other technical errors. This would both aid transparency and help other researchers better prepare their own experiments. Sixth, peer review of both articles and preprints should be regarded as on par with the production of new research. Platforms like Publons help track reviewing activity, which could be rewarded by funders and academic institutions involved in hiring, promotion, or tenure ( Moher et al., 2018 ). Researchers who manage to publish less during the pandemic could still be compensated for the onerous activity of peer review, to the benefit of the entire community.

Of course, individual researchers cannot implement such sweeping changes on their own, without decisive action from policymakers like funding bodies, academic institutions, and journals. For instance, decisions related to hiring, promotion, or tenure of academics could reward several of the behaviors described, such as complete and transparent publication regardless of the results, availability of data and code, or contributions to peer review ( Moher et al., 2018 ). Academic institutions and funders should acknowledge the slowdown of experimental research during the pandemic and hence accelerate the move toward more “responsible indicators” that would incentivize best publication practices over productivity and citations ( Moher et al., 2018 ). Funders could encourage submissions leveraging existing datasets or developing tools for data re-use, e.g., to track multiple uses of the same dataset. Journals could stimulate data sharing by assigning priority to manuscripts sharing or re-using data and code, like re-analyses, or individual participant data meta-analyses.

Author Contributions

CG and IC contributed equally to this manuscript in terms of its conceivement and preparation. All authors contributed to the article and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

This work was carried out within the scope of the project “use-inspired basic research”, for which the Department of General Psychology of the University of Padova has been recognized as “Dipartimento di eccellenza” by the Ministry of University and Research.

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Keywords: open science, data sharing, social distancing, preprint, preregistration, coronavirus disease, neuroimaging, experimental psychology

Citation: Gentili C and Cristea IA (2020) Challenges and Opportunities for Human Behavior Research in the Coronavirus Disease (COVID-19) Pandemic. Front. Psychol. 11:1786. doi: 10.3389/fpsyg.2020.01786

Received: 29 April 2020; Accepted: 29 June 2020; Published: 10 July 2020.

Reviewed by:

Copyright © 2020 Gentili and Cristea. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Claudio Gentili, c.gentili@unipd.it

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Happiness and Prosocial Behavior: An Evaluation of the Evidence

• Lara B. Aknin Associate Professor, Simon Fraser University
• Ashley V. Whillans Assistant Professor, Harvard Business School
• Michael I. Norton Professor, Harvard Business School
• Elizabeth W. Dunn Professor, University of British Columbia

Introduction

How to interpret the evidence, well-being benefits of giving time, well-being benefits of giving money, different currencies, different contexts, when giving to others is most likely to increase well-being, how to encourage prosociality.

Humans are an extremely prosocial species. Compared to most primates, humans provide more assistance to family, friends, and strangers, even when costly. [1] Why do people devote their resources to helping others? In this chapter, we examine whether engaging in two specific types of prosocial behavior, mainly donating one’s time and money to others, promotes subjective well-being, which encompasses greater positive affect, lower negative affect, and greater life satisfaction. [2] Next, we identify the conditions under which the well-being benefits of prosociality are most likely to emerge. Finally, we briefly highlight several levers that can be used to increase prosocial behavior, thereby potentially increasing well-being.

In interpreting the evidence presented in this chapter, it is crucial to recognize that most research on generosity and happiness has substantial methodological limitations. Many of the studies we describe are correlational, and therefore causal conclusions cannot be drawn. For example, if people who give to charity report higher happiness than people who do not, it is tempting to conclude that giving to charity increases happiness. But it is also possible that happier people are more likely to give to charity (i.e. reverse causality ). Or, people who give to charity may be wealthier, and their wealth – not their charitable giving – may make them happy. Researchers typically try to deal with this problem by statistically controlling for “confounding variables,” such as wealth. This approach works reasonably well when the variable of interest (e.g., charitable giving) and any confounding variables (e.g., wealth) are measured with a high degree of precision.

In reality, however, it is often difficult to reliably measure complex constructs (like wealth) using brief, self-report surveys. Rather than reporting all of their assets and liabilities, survey respondents might be asked to report their household income, which provides a sensible—but incomplete—indicator of the broader construct of wealth. For example, if Sian and Kelly each earn $60,000/year, but Sian has six kids and no savings, and Kelly has no kids and a six-figure savings account, then Kelly is wealthier than Sian and may be able to give more money to charity. Now, let’s imagine the relationship between charitable giving and happiness was really explained entirely by wealth. Because income does not fully capture the complex concept of wealth, charitable giving might still predict happiness over and above income because the ability to give captures an aspect of financial security not captured by income. Although researchers have recognized these challenges for decades, recent work using computer simulations has demonstrated that effectively ruling out confounds is harder than many scholars have assumed. [3]

To overcome this issue, it is essential to conduct experiments in which the variable of interest can be manipulated without altering other variables. For example, using experimental methodology, researchers can give participants money and assign them at random to spend it on themselves or on others; because participants are assigned to engage in generous spending based on the flip of a coin (metaphorically speaking), they should not be any wealthier than those assigned to spend money on themselves, on average. While experiments may sometimes seem slight or artificial because they typically involve adjustments of small behaviours, this approach eliminates many pesky confounds, like wealth, that plague correlational research, thereby enabling statements about how generous behavior affects happiness.

As the example above illustrates, conducting experiments tends to be much costlier than simply asking survey questions. Therefore, researchers have traditionally relied on relatively small sample sizes when conducting experiments, particularly when the experiments attempt to alter people’s behavior in the real world. This reliance on small sample sizes not only creates a risk of failing to detect effects that are real—it also creates a high risk of finding “false positives,” effects that turn out not to be real. [4]

In order to establish replicable effects, researchers now recognize that it is important to conduct experiments with sufficiently large sample sizes. A recent meta-analysis concluded that experiments on helping and happiness should include at least 200 participants per condition. [5] This means that an experiment in which participants are randomly assigned to one of two conditions needs at least 400 participants in order to produce reliable results. Unfortunately, almost none of the experiments conducted in this area meet this criterion, although we specifically flag those that do. In fact, many studies in this area include fewer than 50 participants per condition (including some of our own). This is worrisome because samples sizes much under 50 are barely sufficient to detect that men weigh more than women (at least 46 men and 46 women are needed to reliably detect this difference, which is about half a standard deviation). [6] Thus, unless researchers are examining an effect that is genuinely large (i.e., bigger than the gender difference in weight), studies with group sizes under 50 run a high risk of being false positives. For this reason, we describe studies with group sizes below 50 as “small” throughout this chapter, and we urge readers to treat evidence from these studies as suggestive rather than conclusive.

Volunteering is defined as helping another person with no expectation of monetary compensation. [7] A great deal of correlational research shows that spending time helping others is associated with emotional benefits for the giver. Indeed, research has documented a robust link between volunteering and greater life satisfaction, positive affect, and reduced depression. In a review of 37 correlational studies with samples ranging from 15 to over 2,100, [8] adult volunteers scored significantly higher on quality of life measures compared to non-volunteers. [9]

The conclusions of this review paper have been confirmed in two more recent large-scale examinations. First, a recent synthesis of the literature including 17 longitudinal cohort studies (N=72,241) found that volunteering was linked to greater life satisfaction, greater quality of life, and lower rates of depression. [10] The majority of the studies included in this synthesis used inconsistent quality of life measures and participants were mostly women living in North America aged fifty or older. Fortunately, converging data from a large nationally representative sample of respondents living in the UK helps to overcome these limitations. In a sample of 66,343 respondents, volunteering was associated with greater well-being, as measured by the General Health Survey, a validated scale which includes several items related to general happiness. [11] In this study, the well-being benefits of volunteering emerged most strongly for individuals forty years of age or older. Collectively, these data provide compelling evidence that there is a reliable link between volunteering and various measures of subjective well-being, while also indicating the possibility of critical moderators, which is a point we return to below.

Additional research suggests that the relationship between volunteering and well-being appears to be a cross-cultural universal. Researchers analyzed data from the Gallup World Poll, a survey that comprises representative samples from over 130 countries. Across both poor and wealthy countries (N=1,073,711), there is a positive relationship between volunteer participation and well-being (see Table 4.1 for average monthly estimates of the percentage of people who volunteered time or made charitable donations in years 2009-2017 of the Gallup World Poll, and Figure 4.1 for a graphical representation of the individual-level data depicting the strength of the relationship between volunteering and well-being for the same years). [12] These results further point to the reliability of the association between volunteering and subjective well-being across diverse economic, political, and cultural settings. [13]

Of course, it is possible that demographic differences between volunteers and non-volunteers explain observed differences in well-being. [14] For example, women are more likely than men to volunteer [15] and derive greater satisfaction from communal activities. [16] Moreover, a large survey of over 2,000 people in the UK indicates volunteers are older and from higher socioeconomic backgrounds. [17] In addition, a large sample of over 5,000 responses to the English Longitudinal Study of Aging indicates that volunteers are healthier than non-volunteers. [18] It is also possible that the benefits of volunteering are driven entirely by the fact that people who volunteer are generally more socially connected than non-volunteers. [19] Stated differently, it is possible that there is no unique relationship between volunteering and well-being. Casting doubt on these possibilities, in a sample of 10,317 women and men recruited from the Wisconsin Longitudinal Study, volunteering predicted well-being above and beyond numerous demographic characteristics and participation in self-focused social activities, such as formal sports, cultural groups, or country clubs. [20] The results of these large-scale surveys suggest a robust link between volunteering and well-being that exists beyond demographics and social connectedness.

Despite the seemingly ubiquitous association between volunteering and well-being, there is very little experimental evidence showing that volunteering causally improves happiness. For instance, in a systematic review of nine experiments with a total sample of 715 participants (median number of participants per study = 54), researchers found no evidence that volunteering casually improved well-being or reduced depressive symptoms. [21] Consistent with this observation, in a more recent experimental study, 106 Canadian 10th graders were assigned to volunteer 1-2 hours per week for 10 consecutive weeks or to a wait-list control. [22] Students assigned to volunteer showed no change in positive affect, negative affect, or self-esteem as compared to the wait-list.

Similarly, the largest known experimental study in the literature to date showed no causal impact of volunteering on subjective well-being. A sample of 293 college students in Boston were randomly assigned to complete 10-12 hours of formal volunteering each week or were randomly assigned to a wait-list control group. When subjective well-being was assessed for both groups, there was no positive benefit of formal volunteering. [23] Unlike the majority of published experimental research in this area, this experiment was pre-registered and sufficiently powered to detect a small effect of volunteering on subjective well-being. Thus, this experimental study suggests that existing correlational data may have overestimated the well-being benefits of volunteering. [24]

Another possibility is that there are critical conditions predicting when and for whom volunteering promotes well-being. In a study of more than 1,000 community dwelling older adults living in the US, volunteering was linked to greater well-being for individuals who believe that other people are fundamentally good versus those higher in hostile cynicism and believe other people are selfish and greedy. [25] As described above, older individuals benefit more from formal volunteering. [26] Relatedly, individuals who score higher in depressive symptoms also report experiencing greater boosts in well-being from volunteering. In one daily diary study— which asked 100 participants to report on their mood and helping activities each day for ten days— respondents experiencing the greatest depressive symptoms reported the greatest mood benefits from helping others. [27] Individuals who score lower in agreeableness also experience greater well-being in response to volunteering. In one experimental study (N=348), participants who scored lower in agreeableness, and who were randomly assigned to spend time helping other people in their daily life (vs. a control condition), reported the greatest increases in life satisfaction over a three-week intervention study period. [28]

In summary, the research presented in this section provides evidence for a reliable association between formal volunteering and subjective well-being in large correlational surveys but reveals little evidence for a causal relationship. Given the dearth of large-scale experimental studies sufficiently powered to explore this question, more research is needed. Recent findings indicate that individuals from at-risk groups gain the greatest benefits from volunteering, suggesting that these may be the most fruitful samples for further exploration.

Spending money on others – often called prosocial spending – is associated with higher levels of well-being. Evidence for this relationship comes from various sources. For instance, individual who pay more money in taxes – thereby directing a portion of their income to fellow citizens through public goods – report greater well-being in over two decades of German panel data, even while controlling for income and a number of other predictors of happiness. [29] In addition, charitable donations appear to activate reward centers within the human brain, such as the orbital frontal cortex and ventral striatum. [30] Moreover, in a representative sample of over 600 American adults, individuals who spent more money in a typical month on others by providing gifts and donating to charity reported greater happiness. [31] Meanwhile, how much money people reported spending on themselves in a typical month was unrelated to their happiness. [32] More broadly, responses from more than one million people in 130 countries surveyed by the Gallup World Poll indicates that financial generosity – measured as whether one has donated to charity in the past month – is one of the top six predictors of life satisfaction around the world (see Table 2.1 in Chapter 2 for the latest aggregate results, while Figure 4.2 shows results based on individual data).

In contrast to the volunteering literature discussed above, the causal impact of financial generosity on happiness is supported by several small experimental studies. [33] For example, in one of the first experiments on this topic, 46 Canadian students were randomly assigned to spend a five or twenty dollar windfall on themselves or others by the end of the day. In the evening, all students were called on the phone to report their happiness levels. [34] Individuals randomly assigned to spend money on others (vs. themselves) reported significantly higher levels of happiness. Although the sample size of this initial study was very small and consisted only of university students, more recent research has provided further support for this idea. A large scale experiment using a similar design yields consistent findings with over 200 participants per condition. [35]

Several experiments support the possibility that the relationship between prosocial spending and happiness may be detectable in most humans around the globe. [36] For instance, participants in Canada (N=140), India (N=101), and Uganda (N=700) reported higher levels of happiness after reflecting on a time they spent money on others versus themselves. [37] The emotional benefits of generous spending are also detectable among individuals from rich and poor nations immediately after purchases are made. In one study, a total of 207 students from Canada and South Africa earned a small amount of money that they could use to purchase an edible treat, such as cookies or juice, available to them at a discounted price. Half the participants were told that the items they purchased were for themselves, and the other half of participants were told that the items they purchased would be donated to a sick child at a local hospital. Importantly, participants in both conditions were able to choose between whether they wanted to make a purchase (and, if so, what to buy) or take the cash for themselves. This choice provided participants with a sense of autonomy over their spending , which is important for experiencing the emotional rewards of giving (discussed in greater detail below). Immediately afterward, all participants reported their current positive affect. Converging with earlier findings, individuals who purchased items for others were happier. [38] Importantly, this finding emerged not only in Canada (where few students reported financial hardship), but also in South Africa, where more than 20% of respondents reported trouble securing food for their family in the past year.

Additional research suggests that the emotional benefits of prosocial spending are detectable even in places where people have had little to no contact with Western culture. Consider one study conducted with a small number of villagers (N=26) from a traditional society in Vanuatu, where villagers live in huts made from local materials, survive on subsistence farming, and have no running water or electricity. Villagers participated in a version of the goody-bag study, in which they earned a small sum of money that they could use to buy packaged candy, a rare treat on the island nation. Once again, half the participants were able to purchase the candy for themselves while the other half were able to purchase the candy for another villager. Consistent with previous research, villagers reported greater happiness after purchasing treats for others rather than themselves. [39]

As well as emerging around the world, the emotional rewards of giving may be detectable early in life. In one small study conducted with 20 Canadian toddlers, children were given eight edible treats and asked to share some of these treats with a puppet. Throughout the study, children’s’ facial responses were captured on film and later coded for happiness. Coders observed that toddlers showed larger smiles when giving treats away than when receiving treats themselves, [40] and this result has been replicated in a handful of subsequent studies with larger samples. [41]

Finally, the emotional rewards of prosocial spending are even detectable among recent criminal offenders. In one large, pre-registered experiment, 1295 ex-offenders were randomly assigned to purchase items for themselves or children in need before reporting their current happiness. [42] As observed in other samples, ex-offenders reported greater happiness when purchasing for others than when purchasing for themselves. Taken together, these findings point to the possibility that the well-being benefits of generous spending may be a human universal.

Financial generosity seems to lead to happiness in a variety of contexts, suggesting that it is a relatively robust effect. Studies using the goody bag paradigm demonstrate that the emotional benefits of prosocial spending emerge even when givers do not interact directly with the recipient (N=207). [43] In addition, the positive emotions that givers experience after generous spending have been detected with various assessment tools, such as self-report happiness scales and observer reports, suggesting that findings are not accidental outcomes captured on one specific measure. Indeed, in one experiment conducted with 119 Canadian university students, a research assistant unaware of a participant’s recent spending rated individuals who bought items for charity as happier than individuals who bought items for themselves. [44]

In addition to giving time and money, people can provide assistance in various other ways. For instance, holding the door open for a stranger, paying someone a compliment, caring for a sick relative, comforting a spouse, or returning a lost wallet are all small but meaningful forms of generous action. Consistent with much of the work reported above, these demonstrations of social support and kindness may promote well-being for the helper as well. [45] In one study, 104 participants randomly assigned to commit five random acts of kindness a week over a six-week period were happier than those assigned to a no-action control group, but only when all five acts were completed on one day per week (as opposed to spread out over a week). [46] More recently, researchers conducted a six-week experiment in which a sample of students, online workers, and community dwelling adults (N=473) were randomly assigned to commit acts of kindness for either other people, humanity/the world, or themselves; meanwhile, a neutral control group did not alter their behavior. [47] Both forms of prosocial – kindness directed to others and humanity/the world – led to the greatest happiness improvements overtime.

Even in the workplace, where most adults spend a substantial portion of their time, research suggests that prosocial behavior and a prosocial orientation are linked to emotional benefits for employees and overall job satisfaction. [48] For instance, in one well-powered longitudinal survey (from 1957-2004, N > 10,000), the importance participants reported placing on the opportunity to help others when selecting a job predicted their well-being almost 30 years later. [49] In a 3-week study, employees (N = 68) completed mood measures each morning and then several times during the course of each workday. Employees who engaged in prosocial behaviors (e.g., “Helped someone outside my workgroup” and “Covered for coworkers who were absent or on break”) experienced greater positive mood over time. [50] Yet, while every corporation offers personal incentives (in the form of wages and bonuses), far fewer companies offer prosocial incentives or bonuses – such as the opportunity to donate to charity, or to spend on co-workers. Although companies clearly believe that such “personal” incentives are effective, they are linked with some unfortunate consequences, including increased competition and decreased helping among employees. [51] While personal incentives clearly are effective in some situations and with some employees, it is possible that prosocial incentives may also be effective in not only improving the well-being of employees, but also their performance. Demonstrating this, in one small-scale field experiment (N = 139), bank employees randomly assigned to donate either $50 to charity reported not only greater job satisfaction but also greater happiness, compared to employees not given this opportunity or those assigned to donate only $25. [52]

Behaving generously can increase happiness—but this effect is not inevitable. Instead, research has identified several key ingredients that seem to be important for turning good deeds into good feelings. Specifically, people are more likely to derive joy from helping others when:

they feel free to choose whether or how to help.

they feel connected to the people they are helping.

they can see how their help is making a difference.

Freedom of choice. Considering the potential benefits of giving for both individuals and society, it is tempting to require at least some groups of people (such as students or the unemployed) to engage in volunteer work or other forms of helping. But making people feel that they have been forced to help others can undercut the pleasure of giving. For example, in one study, 138 American university students were asked to keep a daily diary, reporting whether and how they helped each day, as well as rating their day-to-day happiness. [53] The students felt happier on days when they provided help to someone or did something for a good cause—but only if they did so because it seemed important to them, enjoyable, and consistent with their values. When they helped because they felt it was mandatory or necessary in order to avoid disapproval, the emotional benefits of generosity evaporated.

Similarly, data from 167 American adults reveals spending money on others is associated with greater happiness among individuals who believe strongly in social justice, equality, helping and similar self-transcendent values. [54] But there is no detectable relationship between prosocial spending and happiness for individuals who do not endorse such self-transcendent values, suggesting that requiring these people to help would not improve their happiness.

The importance of free choice may help to explain a long-standing puzzle within research on volunteering: Older people tend to derive greater emotional benefits from volunteering than younger people. [55] Although a variety of factors may contribute to this age difference, scholars have argued that younger people may derive less pleasure from volunteering in part because they are more likely to see this activity as an obligation—something they have to do to gain work experience. [56]

Several small experimental studies provide supporting evidence for the idea that choice matters. In one experiment, 80 American university students made a series of decisions about how to divide a windfall of $5 between themselves and another participant. The more they gave away, the better they felt afterward. [57] However, when the opportunity to choose was removed, such that participants were forced to give a certain amount of money away, the benefits of giving disappeared entirely. And in an fMRI study with 19 participants, people exhibited greater activation in regions of the brain linked to processing rewards when they were allowed to make voluntary donations to a local food bank than when these donations were mandatory. [58] Participants in this study also reported feeling 10% more satisfied with their donation when it was voluntary rather than mandatory, even though the money was always going to a good cause.

How, then, can people be encouraged to engage in generous behavior, without undermining the emotional benefits of their generosity? Simply altering the way help is requested or framed may make a difference. In a small lab study, 104 American university students were presented with an opportunity to help out with a task and were told that they “should help out” or that “it’s entirely your choice whether to help or not.” [59] When their freedom to choose was highlighted, participants felt happier after helping compared to those who were told they should help. In a more extensive six-week study, 218 university students across both the US and South Korea were required to complete acts of kindness each week. [60] Half of them were randomly assigned to receive messages designed to support their feelings of autonomy by, for example, emphasizing that how and where they chose to help was entirely up to them. Across both cultural groups, students who received these messages showed greater improvement in happiness compared to students who engaged in acts of kindness without receiving these messages. These results were somewhat inconsistent across outcome measures, however, and like all of the findings presented in this section, this promising approach would be worthwhile to test on a larger scale.

Social connection. When engaging in generous behavior provides opportunities for positive social interactions and relationships, helping is likely to be especially beneficial for the helper. Correlational research on volunteering suggests that part of the reason volunteers are less depressed than non-volunteers is simply that volunteers attend more meetings, providing more opportunities for social integration. [61] A correlational study of spending habits points to a similar conclusion. A sample of over 1,500 Japanese students were asked whether they had spent any money on others over the summer and whether doing so had exerted any positive influence on their social relationships. Most students who spent money on others reported that this expenditure had positively influenced their relationships. [62] And these students reported greater overall happiness compared to students who had not spent money on others or had spent money on others without perceiving any positive impact on their relationships. Of course, these correlational findings are open to a variety of explanations—for example, happier people may simply be more likely to spend money on others and to perceive positive effects on their relationships.

Several small experimental studies provide at least some supporting evidence for the idea that feelings of social connection are important in turning generosity into happiness. When 80 adults were approached on a Canadian university campus and asked to reflect on a past prosocial spending experience, they felt happier if they were asked to think about spending money on a close friend or family member rather than an acquaintance. [63] Even when people give money to stranger or acquaintances, providing an opportunity for social interaction might increase the emotional benefits of giving. A small sample of twenty-four students in a lecture hall were given $10 and allowed to decide how much, if any, to share with a classmate who had not received any money. [64] The more money these students gave away, the better they felt afterward—but only if they were allowed to deliver the money in person to their classmate. When students made the same decision without having the opportunity to personally deliver the donation, those who gave away more money actually felt slightly worse.

For charities, then, an important challenge lies in making donors feel connected to causes that otherwise would feel distant or unfamiliar. To explore this idea, researchers approached 68 adults on a Canadian university campus and presented them with an opportunity to donate to a charity that provides fresh water to rural African communities. [65] Half the time, the researcher disclosed that she was personally involved with the charity and that she was helping raise money for a friend who had recently returned from working with the charity in Africa. The rest of the time, the researcher did not reveal this information. Although participants made their donations in private, without the researcher’s knowledge, they got more of an emotional boost from giving if they knew that the researcher was personally connected to the cause. [66] Because this experiment (like all the others in this section) relied on a small convenience sample, these results should be interpreted with special caution. Still, we would tentatively suggest that enhancing feelings of social connection for volunteers and donors may represent a promising avenue for increasing the emotional benefits of helping.

Seeing how you made a difference. Generous behavior may be more likely to promote happiness when helpers can easily see how their actions make a difference for others. When people look back on their past acts of kindness, they feel happier if they are asked to think about actions that were motivated by a genuine concern for others, rather than by benefits for themselves (N=299). [67] This finding aligns with research examining the health correlates of volunteering. For instance, a study examining data from over 10,000 individuals in the Wisconsin Longitudinal Study found that volunteering is associated with lower mortality risk in older adults, [68] but only when volunteering is motivated by other-oriented (as opposed to self-oriented) reasons. [69] These findings tentatively suggest that helping people see how their actions make a difference for others might enhance their long-term positive feelings about engaging in acts of kindness.

To test this idea more directly, researchers presented 120 people on a Canadian university campus with an opportunity to donate to charity. [70] Half of them were asked to donate to UNICEF. The others were asked to donate to Spread the Net. Although both UNICEF and Spread the Net are devoted to promoting children’s health, UNICEF tackles a very broad range of initiatives, potentially making it difficult for donors to envision how their dollars will make a difference. In contrast, Spread the Net offers a clear, concrete promise: For every $10 donated, they supply one bed net to protect a child from malaria. The more participants donated to Spread the Net, the better they felt afterward, whereas this emotional “return on investment” was eliminated when people gave money to UNICEF. This finding suggests that charities may be able to increase donors’ happiness by making it easier for them to envision how their help is making a concrete difference.

In fact, simply re-framing helpers’ goals to be more concrete and achievable can make giving feel more satisfying. [71] While taking a break between completing surveys, 92 American university students were asked to help recruit bone marrow donors by preparing flyers. Before completing this task, they were asked to pursue either a relatively abstract goal (providing “hope” to those in need of bone marrow donations) or a more concrete one (providing “a better chance of finding a donor”). After helping out with the fliers, individuals who had been told to pursue the more concrete goal felt happier than those presented with the more abstract goal. Thus, by prompting donors and volunteers to give with a concrete, achievable goal in mind, charities may be able to increase the emotional rewards of their contributions.

Finally, some research suggests that the benefits of having a specific prosocial impact also strengthen the link between helping and emotional benefits both at and after work. [72] Indeed, some initial evidence from a small sample (N = 33) of employees suggests that feelings of prosocial impact may in some cases lead to improved employee performance. In a two week longitudinal study, call center employees who read information about how their work made a difference in the lives of others were more successful in garnering donations than workers who read about how their work could benefit them personally, or those in a control condition. [73]

Summary and implications for policy. Research on the factors that amplify the happiness benefits of helping is limited, due to reliance on correlational designs and experiments with small convenience samples. Still, this literature provides some valuable clues: people seem most likely to derive happiness from giving experiences that provide a sense of free choice, opportunities for social connection, and a chance to see how the help has made a difference.

Policies and programs that offer all three of these ingredients may have a particularly high likelihood of providing happiness benefits for givers. For example, consider Canada’s innovative Group of 5 program, whereby any five Canadians can privately sponsor a family of refugees. Although tax dollars provide support for refugees in many countries, Canada is the only country in the world that allows ordinary citizens to take such an autonomous role in this process. After raising enough money to support a family for their first year in Canada, the sponsorship group has the opportunity to meet the family at the airport, as they first set foot in Canada. Because the sponsorship group provides help with everything from finding housing and a family doctor to getting the kids enrolled in school, there is ample opportunity to see how the family’s life is being transformed and to develop strong social relationships with them. It is also notable that no Canadian is allowed to undertake this alone; requiring people to work together in a group of five or more is likely to increase social bonds among those who want to help (as well as improving feasibility). Thus, this policy provides a model of one way in which governments can facilitate positive helping experiences for their own citizens, while addressing broader problems in the world.

Finally, while the evidence above examines the link between prosociality and happiness for the giver , it is worth asking if receiving assistance is beneficial for the recipient . To this end, a large body of research demonstrates that receiving social support, such as encouragement from close others, is typically associated with greater psychological and physical well-being. [74] However, receiving other forms of aid, such as financial support, may have detrimental consequences for the recipient because it may lead to social stigma [75] or threaten one’s self-esteem. [76] As a result, it is critical to examine when generosity is beneficial for both parties. To the best of our knowledge, research in this area is limited but early evidence suggests that two of the aforementioned ingredients – autonomy and social connection – may prove important. Highlighting the value of autonomy, one small experiment (N=124) found that both helpers and recipients experienced greater positive emotion after helpers provided autonomous help (as opposed to controlled help or no help at all). [77] Another small study demonstrates the potential value of social connection. Above we described a study in which twenty-four students could decide how much of a $10 sum to give another student in their classroom. [78] Givers were happier when they gave more money, but only when the funds were delivered in person. Interestingly, recipients also reported greater happiness from receiving more money when the funds were given in person (vs. through an intermediary). Taken together these findings provide tentative evidence that giving which facilitates autonomy and social connection may offer the greatest benefits for both parties.

Given its benefits, how can prosociality be encouraged? A large body of research suggests that prosocial behavior can be increased through various individual, organizational, and cultural factors, some of which we briefly describe below.

At the individual level , some research suggests that helpers are more likely to provide assistance when experiencing positive emotional states. [79] For instance, awe – a positive emotion felt when encountering vast and expansive stimuli, such a panoramic view of the Pacific Ocean – is associated with and leads to greater generosity. Evidence supporting this claim comes from several sources. Among a large, nationally representative sample of over 1,500 Americans, people reporting that they experience more awe in their daily lives were also more likely to generously share raffle tickets for a large cash draw with a stranger. [80] Supplementing this correlational research, an experiment conducted with 254 students suggests that awe causally increases generosity. Students randomly assigned to view an awe-inducing video of stunning nature scenes were more generous in a subsequent task than students shown an amusing or emotionally neutral film. [81] How can communities and policy makers harness this research to increase generosity? One way may be to invest in public green spaces, such as parks, trails, or beaches. Exposure to nature, especially scenes that are large and expansive, may boost kindness in light of the research discussed above.

A number of other factors have been shown to promote prosocial behavior. As just one example, some evidence suggests that people donate more money to charitable causes and campaigns when they appreciate how their assistance will help those in need. For instance, one experiment found that providing potential donors with information about how their funds would be used led to donations that were nearly double the size. [82] Therefore, information about the impact of one’s help may not only unleash the emotional benefits of giving as discussed above, it may also increase generosity. Organizations and charities can capitalize on these findings by providing clear information about their programs. Doing so allows people to see how they can effectively improve the lives of vulnerable targets, which should bolster support from potential donors.

In addition, certain large-scale or cultural factors can impact generosity as well. For instance, culture may shape the rates and forms of help provided around the world. Indeed, while generosity appears to be valued in many cultures, [83] cultural norms shape rates and forms of helping behavior. [84] In our analyses of the Gallup World Poll, it is evident that rates of volunteering and charitable giving differ dramatically depending on the cultural context. For example, rates of charitable donation within the past month range from the lowest of 7% of respondents in Myanmar to the highest of 89% in Burundi (See Table 4.1).

This chapter summarizes research on the link between prosocial behavior and happiness. While numerous large-scale surveys document a robust association between donating time and well-being (even while statistically controlling for a number of confounds), experimental evidence offers little support for a causal relationship. Meanwhile, a growing body of experimental evidence suggests that using money to benefit others leads to happiness. Future research should aim to utilize large, pre-registered experiments that identify key predictions in advance.

As research examining these questions continues, there may be opportunities for testing and harnessing the benefits of prosociality in daily life. For instance, education and health care services may adopt prosocial strategies that can be compared to current “business as usual” practices used elsewhere. This also has the advantage of building collaborations spanning academic, private, and governmental partners. The involvement of front-line service providers in both the design and execution of alternatives would do much to increase the success, policy relevance and wider application of the innovations being tested. Harnessing pro-sociality offers the prospect of managing institutions and delivering services in ways that can save resources while potentially boosting happiness for all parties. [85]

Figure 4.1. A graphical representation of the association between volunteer participation and well-being around the world.

Note: Volunteer work predicts greater life satisfaction in most countries surveyed by the Gallup World Poll (2009-2017; N=1,073,711) while controlling for several important covariates, such as age, household income, gender, food security, education, and marital status. Shading depicts the degree of association in standardized beta weights.

Figure 4.2. A graphical representation of the association between prosocial spending and well-being around the world.

Note: Donating money to charity predicts greater life satisfaction in most countries surveyed by the Gallup World Poll (2009-2017; N=1,073,711) while controlling for several important covariates, such as age, household income, gender, food security, education, and marital status. Shading depicts the degree of association in standardized beta weights.

Table 4.1. The percentage of respondents within each country who reported donating to charity or volunteering within the last month.

Note: This table presents the percentage of respondents reporting that they donated money to charity or volunteered time to an organization within the past month within each country surveyed by the Gallup World Poll, averaged across 2009-2017.

Fehr & Fischbacher, 2003; Warneken & Tomasello, 2006 ↩︎

Diener, 1999 ↩︎

Westfall & Yarkoni, 2016 ↩︎

Fraley & Vazire, 2014 ↩︎

Curry et al., 2018 ↩︎

Simmons et al., 2011 ↩︎

Tilly & Tilly, 1994 ↩︎

Wheeler, Gorey & Greenblatt ,1998 ↩︎

see also Brown & Brown, 2005; Grimm, Spring, & Dietz, 2007; Harris & Thoreson, 2005; Musick & Wilson, 2003; Oman, 2007; Wilson & Musick, 1999 ↩︎

Jenkinson et al., 2013 ↩︎

Tabassum, Mohan & Smith, 2016 ↩︎

Mimicing analyses from Aknin et al. 2013, we examined the relationship between SWB and volunteering while controlling for household income and whether respondents had lacked money to buy food in last year as well as demographic variables (age, gender, marital status, and education level). We also included dummy controls for year/wave of data collection and the specific well-being measure used. This allowed us to create a regression equation for each country, pooled over years 2009-2017, examining the relationship between volunteering and well-being at the individual level while controlling for household income, food inadequacy, age, gender, marital status, and education across various waves of the GWP and measures of well-being. These findings are shown in Figure 4.1. A nearly identical analysis was conducted for prosocial spending in Figure 4.2; the only difference is that the volunteering information was replaced with charitable donation information. ↩︎

see also Kumar et al., 2012, c.f. Fiorillo & Nappo, 2013; Haski-Leventhal, 2009 ↩︎

Bekkers, 2012 ↩︎

e.g., Willer, Wimer & Owens, 2012 ↩︎

Low, Butt, Ellis, & Smith, 2007 ↩︎

McMunn et al., 2009 ↩︎

Creaven, Healy & Howard, 2018 ↩︎

Piliavin & Siegl, 200, c.f., Creaven, Healy & Howard, 2017 ↩︎

Schreier, Schonert-Reichl, & Chen, 2013 ↩︎

Whillans et al., 2016 ↩︎

see also Ruhm, 2000; Wilkinson, 1992 ↩︎

Poulin, 2014, see Konrath et al., 2012 for similar results ↩︎

see also Van Willigen, 2000; Wheeler, Gorey & Greenblatt, 1998 ↩︎

Schacter & Margolin, 2018 ↩︎

Mongrain, Barnes, Barnhart & Zalan, 2018 ↩︎

Akay et al., 2012 ↩︎

Harbaugh, Mayr, & Bughart, 2007; Moll et al., 2006; Tankersley, Stowe, & Huettel, 2007 ↩︎

Dunn, Aknin, & Norton, 2008 ↩︎

Dunn et al., 2008 ↩︎

see Curry et al., 2018 for meta-analysis ↩︎

Whillans, Aknin, Ross, Chen, & Chen, under review ↩︎

Aknin, Barrington-Leigh et al., 2013 ↩︎

Aknin, Broesch, Hamlin, & Van de Vondervoort, 2015 ↩︎

Aknin, Hamlin & Dunn, 2012 ↩︎

Van de Vondervoort, Hamlin & Aknin, in prep ↩︎

Hanniball et al., 2018 ↩︎

see Study 3 in Aknin, Barrington-Leigh et al., 2013 ↩︎

Aknin, Fleerackers & Hamlin, 2014 ↩︎

e.g., Brown, Nesse, Vinokur & Smith, 2003; Inagaki & Oherek, 2017; Uchino, Cacioppo, & Kiecolt-Glaser, 1996 ↩︎

Lyubomirsky et al., 2005 ↩︎

Nelson and colleagues (2016) ↩︎

e.g., Grant, 2007 ↩︎

Moynihan, DeLeire, & Enami, 2015 ↩︎

Glomb, Bhave, Miner, & Wall, 2011 ↩︎

Bloom, 1999; Lazear, 1989 ↩︎

Anik, Aknin, Dunn, Norton, & Quoidbach, 2013 ↩︎

Weinstein & Ryan, 2010 ↩︎

Hill & Howell, 2014 ↩︎

Musick & Wilson, 2003 ↩︎

Harbaugh, Mayr, & Burghart, 2007; see Hubbard et al., 2016 for a conceptual replication ↩︎

Nelson et al., 2015 ↩︎

Yamaguchi et al., 2016 ↩︎

Aknin, Sandstrom, Dunn, & Norton, 2011 ↩︎

Aknin, Sandstrom, Dunn, & Norton, 2013 ↩︎

Wiwad & Aknin, 2017 ↩︎

Konrath, Fuhrel-Forbis, Lou and Brown (2012) ↩︎

see also Poulin, 2014 ↩︎

Aknin et al. (2013) ↩︎

Rudd, Aaker, & Norton, 2014 ↩︎

Grant & Sonnentag, 2010; Sonnentag & Grant 2012 ↩︎

Grant, 2008 ↩︎

e.g., Holt-Lunstad, Smith, Baker, Harris & Stephenson, 2015; Uchino et al., 1996 ↩︎

Rothstein, 1998 ↩︎

e.g., Fisher, Nadler & Whitcher-Alagna, 1982 ↩︎

Aknin, Dunn, Sandstrom, & Norton, 2013 ↩︎

see Aknin, Van de Vondervoort, & Hamlin, 2018 for summary of child and adult evidence ↩︎

Piff, Dietze, Feinberg, Stancato, & Keltner, 2015 ↩︎

Piff et al., 2015 ↩︎

Cryder et al., 2013, c.f. Aknin, Dunn, Whillans, Grant & Norton, 2013 ↩︎

Klein, Grossmann, Uskul, Kraus, & Epley, 2015 ↩︎

House et al. 2013 ↩︎

Frijers, 2013; Helliwell & Aknin, 2018 ↩︎

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• Published: 20 March 2023

A manifesto for applying behavioural science

• Michael Hallsworth   ORCID: orcid.org/0000-0002-7868-4727 1  

Nature Human Behaviour volume  7 ,  pages 310–322 ( 2023 ) Cite this article

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• Human behaviour

Recent years have seen a rapid increase in the use of behavioural science to address the priorities of public and private sector actors. There is now a vibrant ecosystem of practitioners, teams and academics building on each other’s findings across the globe. Their focus on robust evaluation means we know that this work has had an impact on important issues such as antimicrobial resistance, educational attainment and climate change. However, several critiques have also emerged; taken together, they suggest that applied behavioural science needs to evolve further over its next decade. This manifesto for the future of applied behavioural science looks at the challenges facing the field and sets out ten proposals to address them. Meeting these challenges will mean that behavioural science is better equipped to help to build policies, products and services on stronger empirical foundations—and thereby address the world’s crucial challenges.

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There has been “a remarkable increase in behavioural studies and interventions in public policy on a global scale” over the past 15 years 1 . This growth has been built on developments taking place over many preceding decades. One was the increasing empirical evidence of the importance of non-conscious drivers of behaviour. While psychologists have studied these drivers since at least as far back as the work of William James and Wilhelm Wundt in the nineteenth century, they received renewed attention from the research agenda that showed how “heuristics and biases” influence judgement and decision-making 2 . These and other studies led many psychologists to converge on dual-process theories of behaviour that proposed that rapid, intuitive and non-conscious cognitive processes sit alongside deliberative, reflective and self-aware ones 3 .

These theories challenged explanations that foregrounded the role of conscious attitudes, motivations and intentions in determining actions 4 . One result was the creation of the field of behavioural economics, which developed new explanations for why observed behaviour diverged from existing economic models 5 . For example, the concept of “mental accounting” showed how people assign money to certain purposes and—contrary to standard economic theory—are reluctant to repurpose those sums, even when they might benefit from doing so 6 .

Behavioural economics may represent only one strand of applied behavioural science, but it has attracted substantial attention. By the mid-2000s, these advances had an increasingly receptive audience among some governments and policymakers 7 . The publication of the book Nudge in 2008 responded to this demand by using the evidence mentioned earlier to create practical policy solutions (Box 1 ) 8 . Then, in 2010, the UK government set up its Behavioural Insights Team 9 . The creation of the Behavioural Insights Team is notable because it became “a paradigmatic example for the translation of behavioural insights into public policy” that acted as “a blueprint for the establishment of similar units elsewhere” 10 , 11 , 12 . Similar initiatives were adopted by many public sector bodies at the local, national and supra-national levels and by private companies large and small 1 , 11 , 13 , 14 . The Organisation for Economic Development and Cooperation has labelled this creation of more than 200 dedicated public entities a “paradigm shift” 15 that shows that applied behavioural science has “taken root in many ways across many countries around the world and across a wide range of sectors and policy areas” 16 .

This history is necessarily selective; it does not attempt to cover the full range of work in the behavioural sciences. Rather, my focus is on the main ways that approaches often grouped under the term ‘behavioural insights’ have been applied to practical issues in the public and private sectors over the past 15 years 17 (see Box 1 for definitions of these and other terms). These approaches have been adopted in both developed and developing economies, and their precise forms of implementation have varied from context to context 18 . However, a crucial point to emphasize is that they have gone far beyond the self-imposed limits of nudges, even if that label is still used (often unhelpfully) as a blanket term. Instead, a broader agenda has emerged that explores how behavioural science can be integrated into core public and private sector activities such as regulation, taxation, strategy and operations. This broader agenda is reflected in the creation of research programmes on “behavioural public policy” 19 or “behavioural public administration” 20 .

Proponents of these approaches can point to improved outcomes in many areas, including health 21 , education 22 , sustainability 23 and criminal justice 24 . Yet criticisms have emerged alongside these successes. For example, there is an ongoing debate about how publication bias may have inflated the published effect sizes of nudge interventions 25 , 26 . Other criticisms target the goals, assumptions and techniques associated with recent applications of behavioural science (Box 2 ).

This Perspective attempts to respond to these criticisms by setting out an agenda to ensure that applied behavioural science can fulfil its potential in the coming decades. It does so by offering ten proposals, as summarized in Table 1 . These proposals fall into three categories: scope (the range and scale of issues to which behavioural science is applied), methods (the techniques and resources that behavioural science deploys) and values (the principles, ideals and standards of conduct that behavioural scientists adopt). These proposals are the product of a non-systematic review of relevant literature and my experience of applying behavioural science. They are not an attempt to represent expert consensus; they aim to provoke debate as well as agreement.

Figure 1 shows how each proposal aims to address one or more of the criticisms set out in Box 2 . Figure 1 also indicates how responsibilities for implementing the proposals are allocated among four major groups in the behavioural science ecosystem: practitioners (individuals or teams who apply behavioural science findings in practical settings), the clients who commission these practitioners (for example, public or private sector organizations), academics working in the behavioural sciences (including disciplines such as anthropology, economics and sociology) and funders who support the work of these academics. These groups constitute the ‘we’ referred to in the rest of the paper, which summarizes a full-length, in-depth report available at www.bi.team .

The left side shows common criticisms made of the behavioural insights approach. The middle column presents ten proposals to improve the way behavioural science is applied. These proposals are organized into three categories (scope, methods and values), which are represented by red, blue and yellow, respectively. The arrows from the criticisms to the proposals show which of the latter attempt to address the former. The matrix on the right shows the four main groups involved with implementing the proposals: practitioners, clients, academics and funders. The dots in each column indicate that the relevant group will need to make a substantive contribution to achieving the goals of the proposal in the corresponding row.

Box 1 Glossary of main terms

Behavioural science . In its broadest sense, a discipline that uses scientific methods to generate and test theories that explain and predict the behaviour of individuals, groups and populations. This piece focuses particularly on the implications of dual-process theories of behaviour. Behavioural science is different from ‘the behavioural sciences’, which refers to a broader group of any scientific disciplines that study behaviour.

Behavioural insights . The application of findings from behavioural science to analyse and address practical issues in real-world settings, usually coupled with a rigorous evaluation of the effects of any interventions. In the current piece, this term is used interchangeably with ‘applied behavioural science’.

Behavioural economics . The application of findings from behavioural science to the field of economics to create explanations for economic behaviour that often diverge from the principles of neoclassical economic theory.

Nudge . The design of choices so that non-conscious cognitive processes lead individuals to select the option that leaves them better off, as judged by themselves. Nudges do not involve coercion or any substantial change to economic incentives, leaving people with a meaningful ability to choose a different option from the one that the choice architect intends.

Box 2 Criticisms of the behavioural insights approach

Limited impact . The approach has focused on more tractable and easy-to-measure changes at the expense of bigger impacts; it has just been tinkering around the edges of fundamental problems 29 , 50 , 172 .

Failure to reach scale . The approach promotes a model of experimentation followed by scaling, but it has not paid enough attention to how successful scaling happens—and the fact that it often does not happen 18 .

Mechanistic thinking . The approach has promoted a simple, linear and mechanistic approach to understanding behaviour that ignores second-order effects and spillovers (and employs evaluation methods that assume a move from A to B against a static background) 29 , 62 , 173 .

Flawed evidence base . The replication crisis has challenged the evidence base underpinning the behavioural insights approach, adding to existing concerns such as the duration of its interventions’ effects 79 , 174 .

Lack of precision . The approach lacks the ability to construct precise interventions and establish what works for whom, and when. Instead, it relies either on overgeneral frameworks or on disconnected lists of biases 80 , 92 , 94 .

Overconfidence . The approach can encourage overconfidence and overextrapolation from its evidence base, particularly when testing is not an option 175 .

Control paradigm . The approach is elitist and pays insufficient attention to people’s own goals and strategies; it uses concepts such as irrationality to justify attempts to control the behaviour of individuals, since they lack the means to do so themselves 176 , 177 .

Neglect of the social context . The approach has a limited, overly cognitive and individualistic view of behaviour that neglects the reality that humans are embedded in established societies and practices 125 , 178 , 179 .

Ethical concerns . The behavioural insights approach will face more ethics, transparency and privacy conundrums as it attempts more ambitious and innovative work 143 , 145 , 154 .

Homogeneity of participants and perspectives . The range of participants in behavioural science research has been narrow and unrepresentative 164 ; homogeneity in the locations and personal characteristics of behavioural scientists influences their viewpoints, practices and theories 124 , 166 .

Use behavioural science as a lens

The early phase of the behavioural insights movement was marked by scepticism about whether effects obtained in laboratories would translate to real-world settings 27 . In response, practitioners developed standard approaches that could demonstrate a clear causal link between an intervention and an outcome 28 . In practice, these approaches directed attention towards how the design of specific aspects of a policy, product or service influences discrete behaviours by actors who are considered mostly in isolation 29 .

These standard approaches are strong and have produced valuable results in many contexts around the world 20 , 30 . However, in the aggregate, they have also fostered a perspective centred on the metaphor of behavioural science as a specialist tool. This view mostly limits behavioural science to the role of fixing concrete aspects of predetermined interventions rather than aiding the consideration of broader policy goals 31 .

Over time, this view has created a self-reinforcing perception that only certain kinds of tasks are suitable for behavioural scientists 29 . Opportunities, skills and ambitions have been constricted as a result; a rebalancing is needed. Behavioural science also has much to say about pressing societal issues such as discrimination, pollution and economic mobility and the structures that produce them 32 , 33 . These ambitions have always been present in the behavioural insights movement 34 , but the factors just outlined acted against their being realized more fully 35 .

The first step towards achieving these ambitions is to replace the dominant metaphor of behavioural science as a tool. Instead, behavioural science should be understood as a lens that can be applied to any public or private issue. This change offers several advantages:

A lens metaphor shows that behavioural science can enhance the use of standard policy options (for example, revealing new ways of structuring taxes) rather than just acting as an alternative to them.

A lens metaphor conveys that the uses of behavioural science are not limited to creating new interventions. A behavioural science lens can, for example, help to reassess existing actions and understand how they may have unintended effects. It emphasizes the behavioural diagnosis of a situation or issue rather than pushing too soon to define a precise target outcome and intervention 31 .

Specifying that this lens can be applied to any action conveys the error of separating ‘behavioural’ and ‘non-behavioural’ issues: most of the goals of private and public action depend on certain behaviours happening (or not). Behavioural science should therefore be integrated into an organization’s core activities rather than acting as an optional specialist tool 36 .

It may seem odd to start with a change of metaphor, but the primary problem here is one of perception. Behavioural science itself shows us the power of framing: the metaphors we use shape the way we behave and therefore can be agents of change 37 . Metaphors are particularly important in this case because the task of broadening the use of behavioural science requires making a compelling case to decision makers 38 . The metaphor of behavioural science as a tool has established credibility and acceptance in a defined area; expanding beyond that area is the task for the next decade.

Build behavioural science into organizations

The second proposal is to broaden the scope of how behavioural science is used in organizations. Given that many dedicated behavioural science teams exist worldwide, it is understandable that much attention has been paid to the question of how they should be set up successfully. However, this focus has diverted attention from considering how to use behavioural science to shape organizations themselves 39 . We need to talk less about how to set up a dedicated behavioural science team and more about how behavioural science can be integrated into an organization’s standard processes. For example, as well as trying to ensure that a departmental budget includes provisions for behavioural science, why not use behavioural science to improve the way this budget is created (for example, are managers anchored to outdated spending assumptions) 40 ?

The overriding message here is for greater focus on the organizational changes that indirectly apply or support behavioural science principles, rather than just thinking through how the direct and overt use of behavioural science can be promoted in an organization. One advantage to this approach is that it can help organizations to address problems with scaling interventions 36 . If some of the barriers to scaling concern cognitive biases in organizations, these changes could minimize the effect of such biases 41 . Rather than starting with a behavioural science project and then trying to scale it, we could start by looking at operations at scale and understanding how they can be influenced.

It is useful to understand how this approach maps onto existing debates about how to set up a behavioural function in organizations. Doing so reveals six main scenarios, as shown in Table 2 . In the ‘baseline’ scenario, there is limited awareness of behavioural science in the organization, and its principles are not incorporated into processes. In the ‘nudged organization’, behavioural science awareness is still low, but its principles have been used to redesign processes to create better outcomes for staff or service users. In ‘proactive consultancy’, leaders may have set up a dedicated behavioural team without grafting it onto the organization’s standard processes. This lack of institutional grounding puts the team in a less resilient position, meaning that it must always search for new work. In ‘call for the experts’, an organization has concentrated behavioural expertise, but there are also prompts and resources that allow this expertise to be integrated into business as usual. Expertise is not widespread, but access to it is. Processes stimulate demand for behavioural expertise that the central team can fulfil. In ‘behavioural entrepreneurs’, there is behavioural science capacity distributed throughout the organization, through either direct capacity building or recruitment. The problem is that organizational processes do not support these individual pockets of knowledge. Finally, a ‘behaviourally enabled organization’ is one where there is knowledge of behavioural science diffused throughout the organization, which also has processes that reflect this knowledge and support its deployment.

Most discussions make it seem like the meaningful choice is between the different columns in Table 2 —how to organize dedicated behavioural science resources. Instead, the more important move is from the top row to the bottom row: moving from projects to processes, from commissions to culture. A useful way of thinking about this task is about building or upgrading the “choice infrastructure” of the organization 42 . In other words, we should place greater focus on the institutional conditions and connections that support the direct and indirect ways that behavioural science can infuse organizations.

Working out how best to build the choice infrastructure in organizations should be a major priority for applied behavioural science. Already we can see that some features will be crucial: reducing the costs of experimentation, creating a system that can learn from its actions, and developing new and better ways of using behavioural science principles to analyse the behavioural effects of organizational processes, rules, incentives, metrics and guidelines 36 .

See the system

Many important policy challenges emerge from complex adaptive systems, where change often does not happen in a linear or easily predictable way, and where coherent behaviour can emerge from interactions without top-down direction 43 . There are many examples of such systems in human societies, including cities, markets and political movements 44 . These systems can create “wicked problems”—such as the COVID-19 pandemic—where ideas of success are contested, changes are nonlinear and difficult to model, and policies have unintended consequences 45 .

This reality challenges the dominant behavioural science approach, which usually assumes stability over time, keeps a tight focus on predefined target behaviours and predicts linear effects on the basis of a predetermined theory of change 46 . The result, some argue, is a failure to understand how actors are acting and reacting in a complex system that leads policymakers to conclude they are being irrational—and then actually disrupt the system in misguided attempts to correct perceived biases or inefficiencies 47 , 48 , 49 .

These criticisms may overstate the case, but they point to a way forward. Behavioural science can be improved by using aspects of complexity thinking to offer new, credible and practical ways of addressing major policy issues. The first step is to reject crude distinctions of ‘upstream’ versus ‘downstream’ or the ‘individual frame’ versus the ‘system frame’ 50 . Instead, complex adaptive systems show that higher-level features of a system can actually emerge from the lower-level interactions of actors participating in the system 44 . When they become the governing features of the system, they then shape the lower-level behaviour until some other aspect emerges, and the fluctuations continue. An example might be the way that new coronavirus variants emerged in particular settings and then went on to change the course of the whole pandemic, requiring new overall strategic responses.

In other words, we are dealing with “cross-scale behaviours” 49 . For example, norms, rules, practices and culture itself can emerge from aggregated social interactions; these features then shape cognition and behavioural patterns in turn 51 . Recognizing cross-scale behaviours means that behavioural science could:

Identify “leverage points” where a specific shift in behaviour will produce wider system effects 52 . One option is to identify when and where tipping points are likely to occur in a system and then either nudge them to occur or not, depending on the policy goal 53 . For example, if even a subset of consumers decides to switch to a healthier version of a food product, this can have broader effects on a population’s health through the way the food system responds by restocking and product reformulation 54 .

Model the collective implications of individuals using simple heuristics to navigate a system. For example, new models show how small changes to simple heuristics that guide savings (in this case, how quickly households copy the savings behaviours of neighbours) can lead to the sudden emergence of inequalities in wealth 55 .

Find targeted changes to features of a system that create the conditions for wide-ranging shifts in behaviour to occur. For example, a core driver of social media behaviours is the ease with which information can be shared 46 . Even minor changes to this parameter can drive widespread changes—some have argued that such a change is what created the conditions leading to the Arab Spring, for example 56 .

This approach also suggests that a broader change in perspective is needed. We need to realize the flaws in launching interventions in isolation and then moving on when a narrowly defined goal has been achieved. Instead, we need to see the longer-term impact on a system of a collection of different policies with varying goals 57 . The best approach may be “system stewardship”, which focuses on creating the conditions for behaviours and indirectly steering adaptation towards overall goals 58 .

Of course, not every problem will involve a complex adaptive system; for simple issues, standard approaches to applying behavioural science work well. Behavioural scientists should therefore develop the skills to recognize the type of system that they are facing (see the system) and then choose their approach accordingly. These skills can be developed through agent-based simulations 59 , immersive technologies 60 or just basic checklists 61 .

Put randomized controlled trials in their place

Randomized controlled trials (RCTs) have been a core part of applied behavioural science, and they work well in relatively simple and stable contexts. But they can fare worse in complex adaptive systems, whose many shifting connections can make it difficult to keep a control group isolated and where a narrow focus on predetermined outcomes may neglect others that are important but difficult to predict 43 , 62 .

We can strengthen RCTs to deal better with complexity. We can try to gain a better understanding of the system interactions and anticipate how they may play out, perhaps through “dark logic” exercises that try to trace potential harms rather than just benefits 63 . For example, we might anticipate that sending parents text messages encouraging them to talk to their children about the school science curriculum may achieve this outcome at the expense of other school-supporting behaviours—as turned out to be the case 64 . Engaging the people who will implement and participate in an intervention will be a key part of this effort.

Another option is to set up RCTs to measure diffusion and contagion in networks, either by creating separate online environments or by randomizing real-world clusters, such as separate villages 65 , 66 . Finally, we can build feedback and adaptation into the design of the RCT and the intervention, allowing adjustments to changing conditions 67 , 68 . Options include using two-stage trial protocols 69 , evolutionary RCTs 70 , sequential multiple assignment randomized trials 71 and ‘bandit’ algorithms that identify high-performing interventions and allocate more people to them 72 .

Behavioural science can also be used to enhance alternative ways of measuring impacts—in particular, agent-based modelling, which tries to simulate the interactions between the different actors in a system 73 . The agents in these models are mostly assumed to be operating on rational choice principles 74 , 75 . There is therefore an opportunity to build in more evidence about the drivers of behaviour—for example, habits and social comparisons 49 .

Replication, variation and adaptation

The ‘replication crisis’ of the past decade has seen intense debate and concern about the reliability of behavioural science findings. Poor research practices were a major cause of the replication crisis; the good news is that many have improved as a result 76 , 77 . Now there are sharper incentives to preregister analysis plans, greater expectations that data and code will be freely shared, and wider acceptance of post-publication review of findings 78 .

Behavioural scientists need to secure and build on these advances to move towards a future where appropriately scoped meta-analyses of high-quality studies (including deliberate replications) are used to identify the most reliable interventions, develop an accurate sense of the likely size of their effects and avoid the weaker options. We have a responsibility to discard ideas if solid evidence now shows that they are shaky, and to offer a realistic view of what behavioural science can accomplish 18 .

That responsibility also requires us to have a hard conversation about heterogeneity in results: the complexity of human behaviour creates so much statistical noise that it is often hard to detect consistent signals and patterns 79 . The main drivers of heterogeneity are that contexts influence results and that the effect of an intervention may vary greatly between groups within a population 80 , 81 . For example, choices of how to set up experiments vary greatly between studies and researchers, in ways that often go unnoticed 82 . A recent study ran an experiment to measure the impact of these contextual factors. Participants were randomly allocated to studies designed by different research teams to test the same hypothesis. For four of the five research questions, studies actually produced effects in opposing directions. These “radically dispersed” results indicate that “idiosyncratic choices in stimulus design have a very large effect on observed results” 83 . These factors complicate the idea of replication itself: a ‘failed’ replication may not show that a finding was false but rather show how it exists under some conditions and not others 84 .

These challenges mean that applied behavioural scientists need to set a much higher bar for claiming that an effect holds true across many unspecified settings 85 . There is a growing sense that interventions should be talked about as hypotheses that were true in one place and that may need adapting to be true elsewhere 18 , 86 .

Narrative changes need to be complemented by specific proposals. The first concerns data collection: behavioural scientists should expand studies to include (and thus examine) a wider range of contexts and participants and gather richer data about them. To date, only a small minority of behavioural studies have provided enough information to see how effects vary 87 . Moreover, the gaps in data coverage may result from and create systemic issues in society: certain groups may be excluded or may have their data recorded differently from others 88 . Coordinated multi-site studies will be needed to collect enough data to explore heterogeneity systematically; crowdsourced studies offer particular promise for testing context and methods 83 . Realistically, this work is going to require a major investment in research infrastructure to set up standing panels of participants, coordinate between institutions, and reduce barriers to data collection and transfer 80 . These efforts cannot be limited to just a few countries.

Behavioural scientists also need to get better at judging how strongly an intervention’s results were linked to its context and therefore how much adaptation it needs 81 . We should use and modify frameworks from implementation science to develop such judgement 89 . Finally, we need to codify and cultivate the practical skills that successfully adapt interventions to new contexts; expertise in behavioural science should not be seen as simply knowing about concepts and findings in the abstract. It is therefore particularly valuable to learn from practitioners how they adapted specific interventions to new contexts. These accounts are starting to emerge, but they are still rare 18 , since researchers are incentivized to claim universality for their results rather than report and value contextual details 82 .

Beyond lists of biases

The heterogeneity in behavioural science findings also means that our underlying theories need to improve: we are lacking good explanations for why findings vary so much 84 . This need for better theories can be seen as part of a wider ‘theory crisis’ in psychology, which has thrown up two big concerns for behavioural science 90 , 91 .

The first stems from the fact that theories of behaviour often try to explain phenomena that are complex and wide-ranging 92 . If you are trying to show how emotion and cognition interact (for example), this involves many causes and interactions. Trying to cover this variability can produce descriptions of relationships and definitions of constructs that are abstract and imprecise 85 . The result is theories that are vague and weak, since they can be used to generate many different hypotheses—some of which may actually contradict each other 90 . That makes theories hard to disprove, and so weak theories stumble on, unimproved 93 .

The other concern is that theories can make specific predictions, but they are disconnected from each other—and from a deeper, general framework that can provide broader explanations (such as evolutionary theory) 94 . The main way this issue affects behavioural science is through heuristics and biases. Examples of individual biases are accessible, popular and how many people first encounter behavioural science. These ideas are incredibly useful, but they have often been presented as lists of standalone curiosities in a way that is incoherent, reductive and deadening. Presenting lists of biases does not help us to distinguish or organize them 95 , 96 , 97 . Such lists can also create overconfident thinking that targeting a specific bias (in isolation) will achieve a certain outcome 98 .

Perhaps most importantly, focusing on lists of biases distracts us from answering core underlying questions. When does one or another bias apply? Which are widely applicable, and which are highly specific? How does culture or life experience affect whether a bias influences behaviour or not 99 , 100 ? These are highly practical questions when one is faced with tasks such as taking an intervention to new places.

The concern for behavioural science is that it uses both these high-level frameworks (such as dual-process theories) and jumbled collections of heuristics and biases, with little in the middle to draw both levels together 94 . Recent years have seen valuable advances in connecting and systematizing theories 101 , 102 . At the same time, there are various ongoing attempts to create strong theories: “coherent and useful conceptual frameworks into which existing knowledge can be integrated” 93 (see also refs. 91 , 103 , 104 ). Naturally, such work should continue, but I think that applied behavioural science will benefit particularly from theories that are practical. By this I mean:

They fill the gap between day-to-day working hypotheses and comprehensive and systematic attempts to find universal underlying explanations.

They are based on data rather than being derived from pure theorizing 105 .

They can generate testable hypotheses, so they can be disproved 106 .

They specify the conditions under which a prediction applies or does not 85 .

They are geared towards realistic adaptation by practitioners and offer “actionable steps toward solving a problem that currently exists in a particular context in the real world” 107 .

Resource rationality may be a good example of a practical theory. It starts from the basis that people make rational use of their limited cognitive resources 108 . Given that there is a cost to thinking, people will look for solutions that balance choice quality with effort. Resource rationality can offer a “unifying framework for a wide range of successful models of seemingly unrelated phenomena and cognitive biases” that can be used to build models for how people act 108 .

A recent study has shown how these models not only can predict how people will respond to different kinds of nudges in certain contexts but also can be integrated with machine learning to create an automated method for constructing “optimal nudges” 109 . Such an approach could reveal new kinds of nudges and make creating them much more efficient. More reliable ways of developing personalized nudges are also possible. These are all highly practical benefits coming from applying a particular theory.

Predict and adjust

Hindsight bias is what happens when we feel ‘I knew it all along’, even if we did not 110 . When the results of an experiment come in, hindsight bias may mean that behavioural scientists are more likely to think that they had predicted them or quickly find ways of explaining why they occurred. Hindsight bias is a big problem because it breeds overconfidence, impedes learning, dissuades innovation and prevents us from understanding what is truly unexpected 111 , 112 .

In response, behavioural scientists should establish a standard practice of predicting the results of experiments and then receiving feedback on how their predictions performed. Hindsight bias can flourish if we do not systematically capture expectations or priors about what the results of a study will be 113 . Making predictions provides regular, clear feedback of the kind that is more likely to trigger surprise and reassessment rather than hindsight bias 114 . Establishing the average expert prediction—which may be different from the null hypothesis in an experiment—clearly reveals when results challenge the consensus 115 .

There are existing practices to build on here, such as the practice of preregistering hypotheses and trial protocols and the use of a Bayesian approach to make priors explicit. Indeed, more and more studies are explicitly integrating predictions 116 , 117 . However, barriers lie in the way of further progress. People may not welcome the ensuing challenge to their self-image, predicting may seem like one thing too many on the to-do list, and the benefits lie in the future. Some responses to these challenges are to make predicting easy by incorporating it into standard processes; minimize threats to predictors’ self-image (for example, by making and feeding back predictions anonymously) 118 ; give concrete prompts for learning and reflection, to disrupt the move from surprise to hindsight bias 119 ; and build learning from prediction within and between institutions.

Be humble, explore and enable

This proposal is made up of three connected ideas. First, behavioural scientists need to become more aware of the limits of their knowledge and to avoid fitting behaviours into pre-existing ideas around biases or irrationality. Second, they should broaden the exploratory work they conduct, in terms of gaining new types of qualitative data and recognizing how experiences vary by group and geography. Finally, they should develop new approaches to enable people to apply behavioural science themselves—and adopt new criteria for judging when these approaches are appropriate.

Humility is important because behavioural scientists (like other experts) may overconfidently rely on decontextualized principles that do not match the real-world setting for a behaviour 29 . Deeper inquiry can reveal reasonable explanations for what seem to be behavioural biases 120 . In response, those applying behavioural science should avoid using the term ‘irrationality’, which can limit attempts to understand actions in context; acknowledge that diagnoses of behaviour are provisional and incomplete (epistemic humility) 121 ; and design processes and institutions to counteract overconfidence 122 .

How do we conduct these deeper inquiries? Three areas demand particular focus in the future. First, pay greater attention to people’s goals and strategies and their own interpretations of their beliefs, feelings and behaviours 123 . Second, reach a wider range of experiences, including marginalized voices and communities, understanding how structural inequalities can lead to expectations and experiences varying greatly by group and geography 124 . Third, recognize how apparently universal cognitive processes are shaped by specific contexts, thereby unlocking new ways for behavioural science to engage with values and culture 125 , 126 . For example, one influential view of culture is that it influences action “not by providing the ultimate values toward which action is oriented but by shaping a repertoire or ‘toolkit’ of habits, skills, and styles” 127 . There are similarities here to the heuristics-and-biases toolkit perspective on behaviour: behavioural scientists could start explaining how and when certain parts of the toolkit become more or less salient.

More can and should be done to broaden ownership of behavioural science approaches. Many (but far from all) behavioural science applications have been top-down, with a choice architect enabling certain outcomes 8 , 128 . One route is to enable people to become more involved in designing interventions that affect them—and “nudge plus” 129 , “self-nudges” 130 and “boosts” 131 have been proposed as ways of doing this. Reliable criteria are needed to decide when enabling approaches may be appropriate, including whether the opportunity to use an enabling approach exists; ability and motivation; preferences; learning and setup costs; equity impacts; and effectiveness, recognizing that evidence on this point is still emerging 132 , 133 .

But these new approaches should not be seen simplistically as enabling alternatives to disempowering nudges 134 . Instead, we need to consider how far the person performing the behaviour is involved in shaping the initiative itself, as well as the level and nature of any capacity created by the intervention. People may be heavily engaged in selecting and developing a nudge intervention that nonetheless does not trigger any reflection or build any skills 135 . Alternatively, a policymaker may have paternalistically assumed that people want to build up their capacity to perform an action, when in fact they do not. This is the real choice to be made.

A final piece missing from current thinking is that enabling people can lead to a major decentring of the use of behavioural science. If more people are enabled to use behavioural science, they may decide to introduce interventions that influence others 136 . Rather than just creating self-nudges through altering their immediate environments, they may decide that wider system changes are needed instead. A range of people could be enabled to create nudges that generate positive societal change (with no central actors involved). This points towards a future where policy or product designers act less like (choice) architects and more like facilitators, brokers and partnership builders 137 .

Data science for equity

Recent years have seen growing interest in using new data science techniques to reliably analyse the heterogeneity of large datasets 138 , 139 . Machine learning is claimed to offer more sophisticated, reliable and data-driven ways of detecting meaningful patterns in datasets 140 , 141 . For example, a machine learning approach has been shown to be more effective than conventional segmentation approaches at analysing patterns of US household energy usage to reduce peak consumption 142 .

A popular idea is to use such techniques to better understand what works best for certain groups and thereby tailor an offering to them 143 . Scaling an intervention stops being about a uniform roll-out and instead becomes about presenting recipients with the aspects that are most effective for them 144 .

This vision is often presented as straightforward and obviously desirable, but it runs almost immediately into ethical quandaries and value judgements. People are unlikely to know what data have been used to target them and how; the specificity of the data involved may make manipulation more likely, since it may exploit sensitive personal vulnerabilities; and expectations of universality and non-discrimination in public services may be violated 143 , 145 .

Closely related to manipulation concerns is the fear that data science will open up new opportunities to exploit, rather than to help, the vulnerable 146 . One aspect is algorithmic bias. Models using data that reflect historical patterns of discrimination can produce results that reinforce these outcomes 147 . Since disadvantaged groups are more likely to be subject to the decisions of algorithms, there is a particular risk that inequalities will be perpetuated—although some studies argue that algorithms are actually less likely to be biased than human judgement 148 , 149 .

There is also emerging evidence that people often object to personalization. While they support some personalized services, they consistently oppose advertising that is customized on the basis of sensitive information—and they are generally against the collection of the information that personalization relies on 150 . To navigate this landscape, behavioural scientists need to examine four factors:

Who does the personalization target, and using what criteria? Many places have laws or norms to ensure equal treatment based on personal characteristics. When does personalization violate those principles?

How is the intervention constructed? To what extent do the recipients have awareness of the personalization, choice over whether it occurs, control over its level or nature, and the opportunity to give feedback on it 151 ?

When is it directed? Is it at a time when the participant is vulnerable? Would they probably regret it later, if they had time to reflect?

Why is personalization happening? Does it aim to exploit and harm or to support and protect, recognizing that those terms are often contested?

Taking these factors into account, I propose that the main opportunity is for data science to identify the ways in which an intervention or situation appears to increase inequalities, and reduce them 152 . For example, groups that are particularly likely to miss a filing requirement could be offered pre-emptive help. Algorithms can be used to better explain the causes of increased knee pain experienced in disadvantaged communities, thereby giving physicians better information to act on 153 .

I call this idea data science for equity. It addresses the ‘why’ factor by using data science to support, not exploit. ‘Data science for equity’ may seem like a platitude, but it is a very real choice: the combination of behavioural and data science is powerful and has been used to create harm in the past. Moreover, it needs to be complemented by attempts to increase agency (the ‘how’ factors), as in a recent study that showed how boosts can be used to help people to detect micro-targeting of advertising 154 , and studies that obtain more data on which uses of personalization people find acceptable.

No “view from nowhere”

The final proposal is one of the most wide-ranging, challenging and important. For the philosopher Thomas Nagel, the “view from nowhere” was an objective stance that allowed us to “transcend our particular viewpoint” 155 . Taking such a stance may not be possible for behavioural scientists. We bring certain assumptions and ways of seeing to what we do; we are always situated in, embedded in and entangled with ideas and situations 124 . We cannot assume that there is some set-aside position from which to observe the behaviour of others; no objective observation deck outside society exists 156 .

Behavioural scientists are defined by having knowledge, skills and education; many of them can use these resources to shape public and private actions. They are therefore in a privileged position, but they may not see the extent to which they hold elite positions that stop them from understanding people who think differently (for example, those who are sceptical of education) 157 . The danger is that elites place their group values and preferences on others, while thinking that they are adopting a view from nowhere 158 , 159 . This does not mean that they can never act or opine, but rather that they need to carefully understand their own positionality and those of others before doing so.

There have been repeated concerns that the field is still highly homogeneous in other ways as well. Gender, race, physical abilities, sexuality and geography also influence the viewpoints, practices and theories of behavioural scientists 160 , 161 . Only a quarter of the behavioural insights teams catalogued in a 2020 survey were based in the Global South 162 . An over-reliance on using English in cognitive science has led to the impact of language on thought being underestimated 163 . The past decade has shown how behaviours can vary greatly from culture to culture, even as psychology has tended to generalize from relatively small and unrepresentative samples 164 . Behavioural science studies often present data from Western, educated, industrialized, rich and democratic samples as more generalizable to humans as a whole 165 . So, rather than claiming that science is value-free, we need to find realistic ways of acknowledging and improving this reality 166 .

A starting point is for behavioural scientists to cultivate self-scrutiny by querying how their identities and experiences contribute to their stance on a topic. Hypothesis generation could particularly benefit from this exercise, since arguably it is closely informed by the researcher’s personal priorities and preferences 167 . Behavioural scientists could be actively reflecting on interventions in progress, including what factors are contributing to power dynamics 168 . Self-scrutiny may not be enough. We should also find more ways for people to judge researchers and decide whether they want to participate in research—going beyond consent forms. If they do participate, there are many opportunities to combine behavioural science with co-design 128 .

Finally, we should take actions to increase diversity (of several kinds) among behavioural scientists, teams, collaborations and institutions. Doing this requires addressing barriers such as the lack of professional networks connecting the Global North and Global South, and the time needed to build understanding of the tactics required to write successful grant applications from funders 169 . In many countries, much more could be done to increase the ethnic and racial diversity of the behavioural science field—for example, through support for starting and completing PhDs or through reducing the substantial racial gaps present in much public funding of research 170 , 171 .

Applied behavioural science has seen rapid growth and meaningful achievements over the past decade. Although the popularity of nudging provided its initial impetus, an ambition soon formed to apply a broader range of techniques to a wider range of goals. However, a set of credible critiques have emerged as levels of activity have grown. As Fig. 1 indicates, there are proposals that can address these critiques (and progress is already being made on some of them). When considered together, these proposals present a coherent vision for the scope, methods and values of applied behavioural science.

This vision is not limited to technical enhancements for the field; it also covers questions of epistemology, identity, politics and praxis. A common theme throughout the ten proposals is the need for self-reflective practice that is aware of how its knowledge and approaches have originated and how they are situated. In other words, a main priority for behavioural scientists is to recognize the various ways that their own behaviour is being shaped by structural, institutional, environmental and cognitive factors.

Realizing these proposals will require sustained work and experiencing the discomfort of disrupting what may have become familiar and comfortable practices. That is a particular problem because incentives for change are often weak or absent. Improving applied behavioural science has some characteristics of a social dilemma: the benefits are diffused across the field as a whole, while the costs fall on any individual party who chooses to act (or act first). Practitioners are often in competition. Academics often want to establish a distinctive research agenda. Commissioners are often rewarded for risk aversion. Impaired coordination is particularly problematic because coordination forms the basis for several necessary actions, such as the multi-site studies to measure heterogeneity.

Solving these problems will be hard. Funders need to find mechanisms that adequately reward coordination and collaboration by recognizing the true costs involved. Practitioners need to perceive the competitive advantages of adopting new practices and be able to communicate them to clients. Clients themselves need to have a realistic sense of what can be achieved but still be motivated to commit resources. Stepping back, the starting point for addressing these barriers needs to be a change in the narrative about what the field does and could do—a new set of ambitions to aim for. This manifesto aims to help to shape such a narrative.

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Acknowledgements

I thank L. Tublin for her editorial support. I also thank S. Banerjee, E. Berkman, A. Buttenheim, F. Callaway, J. Collins, J. Doctor, A. Gyani, D. Halpern, P. John, T. Marteau, M. Muthukrishna, D. Perera, D. Perrott, K. Ruggeri, R. Schmidt, D. Soman, H. Strassheim, C. Sunstein and members of the Behavioural Insights Team for their feedback on previous drafts.

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Hallsworth, M. A manifesto for applying behavioural science. Nat Hum Behav 7 , 310–322 (2023). https://doi.org/10.1038/s41562-023-01555-3

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Received : 26 September 2022

Accepted : 10 February 2023

Published : 20 March 2023

Issue Date : March 2023

DOI : https://doi.org/10.1038/s41562-023-01555-3

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