As humans we sometimes invest poorly, accept software updates without reading the fine print, choose the wrong business partners, commit heroic acts, are optimistic when we should be cautious and pessimistic when we should be sanguine. We occasionally buy things we don’t really need, eat fatty foods and some of us even still smoke. We fail to systematically pursue our direct self-interest (erring at times on the side of altruistic cooperation), don’t save enough for old age, confuse the nominal and real value of money, believe an overheated economy will never crash and when it does, lose confidence for too long that things will ever be right again. It’s easy to see why neoclassical economists have preferred a pared down paradigm of human motivation largely free of human psychology. Human rationality is bounded, as some politely put it. The good news may be that, although people do many things they regret, they often do so in predictable ways.
Enter behavioural economics. The incorporation of behavioural, social and cognitive dimensions into economic thinking has grown in recent times as economists strive to improve their models, forecasts and policies. Much of the impetus for this trend can traced back to the ground-breaking (and Nobel Prize-winning) work of cognitive decision-making theorists such as Herbert Simon, Daniel Kahneman and Amos Tversky in the second half of the last century. That cognitive psychologists should receive the Nobel Prize in Economics is perhaps emblematic of this relatively new phase in economic thinking. In the intervening years, demonstrating the ways in which we humans are not always good utility maximizers has proven to be a rich vein of exploration.
“Behavioural Insights and Public Policy: Lessons from around the world”, recently published by the OECD, demonstrates the extent to which the floodgates have opened, allowing cognitive and behavioural psychology as well as other social sciences to take their place at the policy table. Behaviourally informed policy, or Behavioural Insights (BI), makes use of insights from these disciplines while applying inductive scientific methodology to policy making and implementation. In other words, experimentation. In a context in which governments are seeking more efficient outcomes without resorting to additional rules and sanctions, these developments seem timely.
For a long time, policy makers have concentrated on what they want citizens to do, less on how citizens actually behave. The place for insights into human behaviour has been slim and often considered to be outside the purview of policy makers. After all, the blunt instruments of fines and enforcement were always there to ensure compliance to rules and policies. They still are, but today, an increasing number of policy makers are willing to consider a more nuanced approach; one in which likely human behaviours, and insights based on experimental data, inform the policy making process. It’s not so revolutionary. In product markets, scenarios of human behaviour play an essential role in design. When designers get it right, the result is a better fit between user and product, along with a slew of additional benefits such as ease of use, increased productivity, greater satisfaction and, in marketing terms, heightened preference. Just like products, policies are intended to elicit a human response. To ignore behavioural insights is to pass by some important opportunities to tap into existing human motivations. As any black belt will tell you, brute strength will only get you so far. The experienced judoka uses her opponent’s own energy, channelling it to achieve the desired result more efficiently. Policies that strive to capture citizen self-motivation tend to enjoy similar efficiency.
Take the example of tax compliance. Efforts to increase compliance typically consist of threats and interest penalties in addition to audits and enforcement. But the behavioural approach might begin with an open-ended question such as ‘Can the way in which we communicate with non-payers influence their willingness to comply?’ This in turn could be tested by drafting a number of different messages reflecting relevant behavioural insights, sending them out to the targeted population and measuring the results. Indeed, the government of Ontario, Canada, wanted to reduce the number of employers filing their tax returns late. Using findings from behavioural science, the government modified the standard collection letter to include concrete details of where, how, and when to file one’s overdue annual return. The outcome of the intervention was a 4.2 and 6.1 percentage point increase in tax filing relative to the unmodified letter in 2014 and 2015, respectively. In other words, a behaviourally informed “light touch” (in this case, a single paragraph added to an existing letter) outperformed costlier and more heavy-handed approaches in a significant number of cases.
The OECD’s Behavioural Insights group, partnering with the London School of Economics, the European Nudging Network (TEN) and Harvard University spin-off and non-profit ideas42, has collected 129 cases from 14 countries in North and South America, Europe, Asia and Oceania. Case studies are drawn from a number of sectors, including financial products, energy, environment, health and safety, tax, public service delivery and more. The cases offer a glimpse into a wide variety of policy issues and suggest the versatility and power of the behavioural approach. It’s a fascinating snapshot of BI as it enters the mainstream and provides a richly documented “idea book” that will surely spark new thinking.
Yet, according to the authors, if behavioural insights are to realise their full potential, standards must be set in order to gain the trust of public bodies and offset the perception of potential ethical issues. Experimentation and the use of academic findings are fundamental to behavioural practitioners in public policy. Scientific credibility, in turn, depends on reliable data and statistically significant samples that can stand up to public scrutiny and scale up as needed. Despite these challenges—and as Behavioural Insights and Public Policy shows—the behavioural revolution that began more than forty years ago is today making a measurable difference in policy effectiveness around the world. It suggests that a behaviourally-oriented policy approach might be one of the most rational choices a government can make.
Behavioural economics challenges orthodox economics theory and its foundational assumptions regarding human behaviour, its institutional underpinnings, its poor prediction power, and its intrinsic non-falsifiability. In orthodox theory, economic agents are assumed to be fully rational and completely informed. It’s not that they do know everything, but that they can know everything and there are means to learn – epistemology – and they know how to make the best choices for themselves (even if only probabilistically, and even if the choice (sic!) of the precise foundations of the theory of probability that underpins expected utility maximisation is colourfully ad hoc).
Individuals are assumed to have underlying orders of preference for all the alternatives which are knowable, although the means of getting to know them is never specified. These rational preferences are often represented by a utility function, which is assumed to be well-behaved. The “non-satiation” assumption promises that the satiation point will never be reached, at least in the economic domain. Thus, the individuals are always in a state where “more is better”.
Behavioural economics originated, almost fully developed, during the 1950s, and can be classified into at least two streams – Classical and Modern. We would argue that Classical behavioural economics (CBE), pioneered by Herbert Simon (1953), presents a more radical break with the tradition than Modern behavioural economics (MBE) originating in work by Ward Edwards (1954), respectively. The two streams have different methodological, epistemological and philosophical aspects.
First, MBE assumes economic agents maximising utility with respect to an underlying preference order – to which “an increasingly realistic psychological underpinning” is attributed. The “realistic psychological underpinning”, however, is not itself based on any computational foundation, in contrast to Classical behavioural economics, in which the cognitive psychology of choice was intrinsically constrained by a machine model of computation. CBE assumes no underlying preference order. An economic agent’s decision-making behaviour, at any level and against the backdrop of every kind of institutional setting, is subject to bounded rationality and exhibits “satisficing” behaviour – a word Herbert Simon coined from “satisfy” and “suffice” to describe a strategy for reaching a decision the decider finds adequate, even if it’s not optimal in theory. Put another way, MBE remains within the orthodox neoclassical framework of optimisation under constraints; CBE is best understood in terms of decision.
Second, MBE concerns the behaviour of agents and institutions in or near equilibrium; CBE investigates disequilibrium or non-equilibrium phenomena.
Third, MBE accepts mathematical analysis of (uncountably) infinite events or iterations, infinite horizon optimisation problems and probabilities defined over s-algebras and arbitrary measure spaces; CBE only exemplifies cases which contain finitely large search spaces and constrained by finite-time horizons.
There is no doubting the success of MBE. You could characterise it as a massive magnet which attracts different resources, new tools and ways of explanations. In fact you could almost claim that MBE has already become a new mainstream economics, as a consequence of it playing the role of a revised approach of orthodox economics rather than an alternative approach. CBE on the other hand, is developed on completely different grounds from MBE.
MBE is fostered by orthodox economic theory, game theory, mathematical finance theory and recursive methods (Not, however, recursive in the rigorous sense of recursion theory, which forms a key foundation in the development of classical behavioural economics), experimental economics and neuroeconomics, computational economics and subjective probability theory. It preserves the doctrine of utility maximisation and does not go beyond it or discard it (the consumer tries to get the most value possible from the smallest amount of money). Though the behavioural models do consider more realistic psychological or social effects, economic agents are still assumed to be optimising agents, whatever the objective functions may be. In other words, MBE is still within the ambit of the neoclassical theories, or is in some sense only an extension of traditional theory, replacing and repairing the aspects which proved to be contradictory.
CBE is based fundamentally on a model of computation – hence, computable economics – computational complexity theory, nonlinear dynamics and algorithmic probability theory. Unlike MBE, CBE does not try to endow the economic agent with a preference order which can be represented by utility functions; nor do equilibria or optimisation play any role in the activation of behavioural decision-making by CBE agents.
Classical behavioural economics exploits the powerful notion of “bounded rationality” proposed by Simon in 1953. Simon’s definition of bounded rationality encapsulates different notions, such as limited attention, limited cognitive capacity of computation sequential decision-making, and satisficing. For Simon, it is not evident and admissible to assume that human beings are able to exhaust all the information and make the “best” choice out of it. To put it simply: Simon took the limits of human cognition into account and devised mathematical means of describing the roles of memory, experience and intuition in solving problems. His agents do not think in terms of infinite horizon optimisations (nobody in their right mind would!) rather they try to make good decisions for only the near future, but with long-term targets in mind.
“Behavioural economics: Classical and modern”, Ying-Fang Kao & K. Vela Velupillai, (2013): Behavioural economics: Classical and modern, The European Journal of the History of Economic Thought, DOI: 10.1080/09672567.2013.792366
 Whether there are ‘unknowable alternatives’ is never clearly specified – especially when the set of alternatives has the cardinality of the continuum, implying the invoking of some form of the axiom of choice, even in the routine implementation of an optimisation exercise. As a result, ‘the means of getting to know them’ cannot be specified in any constructive way.
 See, however, the caveat about probability in the first paragraph.