This document discusses the potential for behaviorally informed policies and some challenges that have limited their impact so far. It notes that while behavioral research has revolutionized economic theory, its effects on policy have been more modest. It argues that behavioral research needs to merge with complexity research to account for real-world complexity and the diversity of human behaviors. By incorporating insights about bounded rationality, heuristics, and interactions between agents, behavioral economics could help design more robust policies that nudge people in positive ways without oversimplifying human decision-making.

1. Behaviourally informed policies
Some quick and scattered notes
Luigi Marengo
Dept. of Management, LUISS University, Roma, lmarengo@luiss.it
OECD, 19th May 2014
Luigi Marengo Behaviourally informed policies

2. Summary
experimental, behavioural (and neuro?) approaches had a
tremendous impact on economic theory in the last 30
years: a true behavioural revolution (and counter-reform as
well)
also their impact in economic policies and mechanisms
design is potentially enormous, but for the time being these
high expectations have not been fully met (with some
notable exception)
I will argue that this is to a large extent due to the
counter-reform
. . . and that behavioural research must merge with
complexity research (rather than with neoclassical
economics) in order to fully develop its potential
Luigi Marengo Behaviourally informed policies

3. The experimental/behavioral revolution in economics
areas of research:
information processing: “heuristics and biases” (with a
strong emphasis on biases, Kahneman and Tversky)
motivation: social preferences, norms, reciprocity
methodology:
laboratory and, more recently, field experiments
Luigi Marengo Behaviourally informed policies

4. Main findings on humans:
Limited information processing capabilities (bounded
rationality, mental accounting)
Limited self control (hyperbolic discounting)
Limited capability to assess costs and benefits of actions
and consequences (overconfidence, neglect of
overestimation of probabilities of some events)
They are affected by frames (vs. a ”pure” notion of
rationality)
They are affected by the social context (vs. ”a-social” homo
œconomicus)
They have multiple motivations (self interest, reciprocity,
altruism, fairness, morality, etc.)
Luigi Marengo Behaviourally informed policies

5. Implications for policy:
We have a broader (and multi-faceted) picture of human
agents: policies should be evaluated not referring to the
homo œconomicus model but to ”real” humans
Policies can help overcome biases: de-biasing through
policies and through law, nudges
Luigi Marengo Behaviourally informed policies

6. Some well known problems and open issues:
In heuristics and biases research:
External validity: lab experiments de-contextualize
decisions as much as possible, but for real life policies
context is important → field experiments are a possible
solution (but how generalizable?)
Testing policies or testing mechanisms? (Philip Zimbardo’s
broken window experiment)
In nudges research:
Economic policies or “psychologically ergonomic” designs?
Economics or social psychology?
Libertarian paternalism: the policy maker and behavioural
economist know what is good for you (are we sure? are we
manipulating? what about conflicting interests?)
Luigi Marengo Behaviourally informed policies

7. Unexplored connections with complexity and ABM
variety and diversity of behaviours across different agents
(K-T’s biases are found for the majority of experimental
subjects, but a non negligible minority behaves differently)
. . . and in the same agent across time and contexts (the
same agent can behave selfishly, altruistically, spitefully,
etc.)
the heuristics view: humans have evolved simple heuristics
that make us smart in a complex and changing world
(Gigerenzer). Simple heuristics, routines, rules of thumb
may be suboptimal in every specific decision, but can be
the best solution across a wide range (Heiner)
decisions in real life are seldom as simple as in lab (and
sometimes also field) experiments: interdependencies,
conflict, multiplicity of goals and criteria are involved
Luigi Marengo Behaviourally informed policies

8. Connecting behavioural, complexity and ABM
research
designing policies and mechanisms which are robust with
respect to a variety of behaviours
take into account learning and adaptation: policies and
mechanisms induce agents to modify their behaviours, but
also their frames, representations, etc. (e.g. persistent
crowding out effects of incentives)
take into account conflicts and strategic behaviours
take into account interdependencies: policies involve
complex bundles of decisions, how bundles are built
determines the degrees of complexity, manipulability and
coordination
Luigi Marengo Behaviourally informed policies

9. The end, for the time being
THANK YOU!
Luigi Marengo Behaviourally informed policies