I am most appreciative to bring to your attention an exciting new development in behavioral thinking with respect to behavioral economics and decision making by an innovative team of researchers across two countries, Marco Tagliabue (Behavioral Economics Lab, Oslo Metropolitan University, Norway) and Valeria Squattrito and Giovambattista ‘Nanni’ Presti (Kore University Behavioral Lab, Enna, Italy). In this blog, Marco, Valeria, and Nanni outline their conceptualization of a Relational Frame Theory (RFT) approach to accounting for decision making perspectives traditionally viewed as cognitive models to show how behavioral theories have untapped potential in explaining important elements of human thought processes in how we make decisions in different contexts. Nanni Presti (a past president of the Association for Contextual Behavioral Science) and colleagues have a history of thinking outside traditional behavioral boxes and applying our models and theories to novel domains. I have embedded a link to an open access full manuscript of their recent publication in Frontiers in psychology on this topic for those who wish to delve a bit deeper.Enjoy! Dr. Ian Tyndall, Department of Psychology, University of Chichester, UK.
Towards Behavior Economics Analysis: An RFT-informed model of decision-making
A baseball and a bat cost a total of $1.10. Knowing that the bat costs $1 more than the ball, how much does the ball cost? If you answered $0.10 cents, please be reassured that most other people did too (Kahneman, 2011), although the answer is wrong (the correct one being $0.05 cents). This apparently simple logic question is also referred to as a “Kahneman (and Frederick) problem”. It represents an example of how our thinking is far from flawless; even more so in a straightforward judgment task concerning price deduction.
According to Kahneman (2003), human beings process the information encompassing them and subsequently make decisions based on that information according to two processing systems. System 1 is intuitive (fast, but less precise), whereas System 2 is reflective (methodical, but slower). Thus, if you answered $0.10 cents to the problem above, it is more likely that your judgment was influenced by a System 1-like cognitive processing. Following Kahneman’s distinction, perhaps you overlooked the “more than” or you may have gone with the most “intuitive” answer. Conversely, if you answered $0.05 cents, you probably took somewhat a longer time to consider the problem and mentally calculate that x + ($1.00 + x) = $1.10, until, solving for x, we have x = $0.05.
The baseball and bat problem is one of the most famous exemplar illustrations of what the relatively new tradition of behavioral economics, which stands in sharp contrast with theories of neo-classical economics, is trying to address. Whereas the latter maintain that humans are fundamentally rational and errorless in making economic decisions, according to the behavioral economics paradigm the opposite stems from its fundamental assumptions. Thus, we are less consistent and effective whenever faced with economic decisions than we would like to admit. Beyond strictly economic decisions, errors or biases (i.e., systematic errors in information processing and decision-making) may affect the way we purchase goods, eat, and interact with other social or cultural groups and several other.
The field has been mostly neglected by behavior analysts, (but see Derek Reed’s lab here at the University of Kansas) though several applications of behavioral economics are based on behavioral principles and techniques. Both economists and psychologists have neglected the contribution that behavior analysis has already given to the field of decision-making and have turned to cognitive interpretations, while maintaining a disconnection between the way human cognition is conceptualized and solutions applied to many human issues, ranging from choice behavior concerning health, to good environmental practices, to altruistic and prosocial behavior.
Our paper (see published paper here) Models of cognition and their applications in behavioral economics: a conceptual framework for nudging derived from behavior analysis and relational frame theory (Tagliabue, Squatrito, & Presti, 2019) addresses the behavioral economics model to decision-making, finding that there is no model beyond the descriptive level. Oddly enough in our field, there seems to be no published conceptual paper that might offer a frame to understand and guide this relative new field. Our main goal was to fill this gap. Specifically, we suggest that Kahneman’s dual processing system may be interpreted by a model based on relational responding, that is able to understand, interpret and predict not only decision-making, but errors of decision-making, too. Furthermore, this model represents a continuity or rather an extension of the horizons of behavior analysis, inasmuch as it may offer a better connection to the applied field. In the traditional cognitive conceptualization of behavioral economics, we addressed a hiatus between the way the mind is modelled and the way one acts to modify behavior. Loose terms like nudging prevent the identification of specific verbal or nonverbal antecedents or consequences, thus hindering the opportunities to successfully work on independent variables. Our effort aims to reframe this vision into the contextual behavioral science approach and extend the operant vision offered by relational frame theory (RFT) to behavioral economics.
RFT takes into account the function of language in governing our arbitrary derived relational responding. In other words, are our actions in continuous interrelation with the encompassing environment, which might include our and others’ verbal behavior. Stimuli or objects can be put into relation with one another and their function may change based on the transformation of stimulus functions. For example, the value of money can be trained or derived in many ways based, for example, on the size of the coins or the color of the notes, related to the goods they can be exchanged for. Although counterintuitive at first for a pre-school young child or a novel user of Euro currency, the bigger (in size) €0.50 coin is smaller (in purchasing power) than the €1 coin. Hence, it can very quickly learnt that the bigger coin “buys” smaller gelatos, and vice versa.
We imagine talking to readers new to behavior analysis, contextual behavioral science and RFT and guiding them along a pathway. Starting from basic behavioral principles, we bring forward the distinction between verbally and nonverbally controlled human behavior, and arrive to the most recent RFT conceptualizations of arbitrary applicable relational responding, which represents the basic repertoire of what is called “cognitive” human patterns. The “bat problem” stems exactly from relationally responding to frames of “more than” in such a quick and socially reinforced way that our response is almost “automatic” and less derived.
We point out that the relational elaboration and coherence (REC) model of relational responding (Barnes- Holmes et al., 2010; Hughes & Barnes-Holmes, 2013) might account for different repertoires of relational responding: the duality of the processing systems features brief and immediate responses which represents the intuitive System 1, and extended and elaborated responding which represents the more reflective System 2. Interestingly, the difference between the two forms of relational responding does not only concern the level of complexity, probability of error, or maximization of outcomes, but latency, too. Moreover, relational responding can be quantified through a computer-based procedure called IRAP (implicit relational assessment procedure), allowing us to move beyond the faster-System 1 and slower-System 2 and measuring how much slower or faster they are. We refer to several examples in which this approach has been implemented, ranging from the purchase of functional organic products (Modica et al., 2017), to assessing religious prejudices (Watt et al., 1991). The IRAP literature documents extensively the link between overt and covert verbalizations as rule-governance and its effects on human behavior. Given its extensive usefulness in clinical settings, it might be worthwhile testing its usefulness in behavioral economics, too. In addition to this basic comparison we offered a way to look at more articulated relational responses that account for complex relational behaviors which have been recently offered in the MDML (Multi Dimensional Multi Level Model) and HMDL (Hyper Dimensional Multi Level) models (Barnes-Holmes, Barnes-Holmes, Hussey, & Luciano, 2016; Barnes-Holmes, McEnteggart & Barnes-Holmes, in press).
One way to influence the context of these relations and in which decisions are made, is resorting to nudging. The term was popularized to the broader public in a book by Thaler and Sunstein (2008). Nudging refers to the manipulation of the environment with the aim of affect users’ decisions without imposing economic incentives nor penalties. Examples of a nudge include the GPS “fastest” route to our destination when driving our car, switching the placement of chewing gum and dried fruit at the checkout counter of a store, and encouraging new potential organ donors to sign up via the national registry (BIT, 2013). Nudging stemmed from the tradition of behavioral economics and, in the cognitive informed vision of behavioral economists, are meant to replace System 1-steered decisions with other, alternative and supposedly better System 1-steered decisions.
Notwithstanding, in behavior analysis, the ways we conceptualize, measure and modify behaviors remain at the same epistemological level, whereas this is not the case in the cognitive-informed behavioral economics field. If we look closer at nudging with the operant lenses, we may be able to analyze why nudges work, improve them further, and explain why they sometime fail. Thus, nudges are regarded as a manipulation of contextual variables that we know can affect human behavior. A model informed by a contextual behavioral science vision can explain why nudges have limited effects in the short term, as per how they are currently being deployed. They lack the “maintenance” vision that is embedded in the consequence of an action. This is the subject of our next paper: stay tuned!
Barnes-Holmes, D., Barnes-Holmes, Y., Stewart, I., and Boles, S. (2010). A sketch of the implicit relational assessment procedure (IRAP) and the relational elaboration and coherence (REC) model. The Psychoogical Record, 60, 527–542. doi:10.1007/BF03395726
Barnes-Holmes P.M.D., Barnes-Holmes Y., Hussey I., & Luciano C. (2016), “Relational frame theory: finding its historical and philosophical roots and reflecting upon its future development: an introduction to part II”, in R.D. Zettle, S.C. Hayes, P.M.D. Barnes-Holmes, A. Biglan (Eds.), The Wiley handbook of contextual behavioral science, Wiley-Blackwell, West-Sussex, UK, 117-128.
Barnes-Holmes Y., McEnteggart C., & Barnes-Holmes D. (in press), “Recent Conceptual and Empirical Advances in RFT: Implications for Developing Process-Based Assessments and Interventions”, in M.E. Levin, P. Twohig, J. Krafft (Eds.), Innovations in Acceptance and Commitment Therapy, New Harbinger, Oakland, CA.
BIT – Behavioral Insights Team (2013). Applying behavioural insights to organ donation: Preliminary results from a randomised controlled trial. Retrieved from https://www.bi.team/wp-content/uploads/2015/07/Applying_Behavioural_Insights_to_Organ_Donation_report.pdf
Hughes, S. J., and Barnes-Holmes, D. (2013). A functional approach to the study of implicit cognition: the IRAP and the REC model. In S. Dymond, and B. Roche (Eds.), Advances in relational frame theory & contextual behavioural science: Research & applications (pp. 97–126). Oakland, CA: Context Press/New Harbinger.
Kahneman, D. (2003). A perspective on judgment and choice: Mapping bounded rationality. American Psychologist, 58(9), 697-720. doi:10.1037/0003-066X.58.9.697
Kahneman, D. (2011). Thinking, fast and slow. New York, NY: Farrar, Straus and Giroux.
Modica, A., Squatrito, V., Oppo, A., Presti, G., and Moderato, P. (2017). Studio della cognizione implicita sul potenziale acquisto di alimenti funzionali [Study of implicit cognition on potential purchase of functional groceries]. Poster session presented at the I° congresso italiano di confronto tra psicoterapie cognitivo-comportamentali di terza generazione. Mindfulness, Acceptance, Compassion: nuove dimensioni di relazione [1st Italian congress of conrontation between cognitive and behavioral psychotherapies of third generation. Mindfulness, Acceptance, Compassion: new dimensions of relation]. Milano, Italy.
Tagliabue M., Squatrito V. and Presti G. (2019). Models of cognition and their applications in behavioral economics: A conceptual framework for nudging derived from behavior analysis and relational frame theory. Frontiers in Psychology, 10(2418). doi:10.3389/fpsyg.2019.02418
Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness. New Haven, CT: Yale University Press.
Watt, A., Keenan, M., Barnes, D., and Cairns, E. (1991). Social categorization and stimulus equivalence. The Psychological Record, 41, 33–50. doi:10.1007/BF03395092