Over the past 30 years, behavioral and experimental economists and psychologists have
made great strides in identifying phenomena that cannot be explained by the classical
model of rational choice—anomalies in the discounting of future wealth, present bias,
loss aversion, the endowment effect, and aversion to ambiguity, for example. In response
to these findings, there has been an enormous amount of research by behavioral scientists
aimed at modeling and understanding the nature of these biases
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. However, these models, typically assuming situation-specific psychological processes,
have shed limited light on the conditions for and boundaries of the different biases,
substantially neglecting their relative importance and joint effect. Much less attention
has been paid to the investigation of the links between different biases. As a consequence
of this approach, it is not always clear which model should be used to predict behavior
in a new setting, and maybe a more general theory is needed. We believe that the field
of neuroeconomics, which has experienced a rapid growth over the past decade, can
play an important role in bridging these gaps, contributing to the building of a general
theoretical framework for judgment and decision-making behaviors.
Apparently inconsistent biases
One of the main insights from decision-making studies is that people tend to overweight
small probability events in risky one-shot decisions (Kahneman and Tversky, 1979).
This tendency can explain why, for example, people buy lottery tickets and insurance.
However, one might wonder, for instance, why in most Western Countries driving insurance
is compulsory (how many drivers would spontaneously ensure?); Why the enforcement
of safety rules at the workplace and of safe medical procedures have become social
issues of primary importance, causing massive public and private investments (Erev
et al., 2010); or why only a small share of people actually participate in lotto games
on a regular basis (Pérez and Humphreys, 2011). Answering such questions, recent experimental
studies have shown that in repeated decisions with feedback, people tend to underweight
small probability events, and behave as if “it won't happen to me” (Barron and Erev,
2003; Hertwig and Erev, 2009)
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.
We could simply conclude that people tend to overweight rare events in one-shot decisions
from description, and underweight rare events in experience-based decisions. Unfortunately,
this assertion cannot predict behavior in a situation in which decision makers are
provided with both the description of the incentive structure and feedback about their
own choices (see Lejarraga and Gonzalez, 2011). To better illustrate this problem,
consider the situation in which people can choose whether to insure against rare devastating
natural events, whose occurrence rate and effect are known (see Marchiori et al.,
2015): Will people be willing to buy insurance at, for example, the price that equals
the expected cost from the risk? If models of one-shot decisions from description
seem to give a positive answer to this question
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, models of decisions from experience suggest the opposite
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. Which theory should inform an insurance pricing policy?
Situation-specific psychological processes have also been proposed to explain other
aspects of decision-making, such as the tendency to explore new alternatives. Empirical
evidence suggests that people appear to insufficiently explore new alternatives in
some situations (the most popular example is the preference for the status quo, as
shown by Samuelson and Zeckhauser, 1988), whereas they exhibit the opposite tendency
in others (e.g., unsafe sexual behavior and use of illicit drugs; see, for example,
Bechara, 2005). Again, one could be tempted to explain these apparently contradicting
phenomena by asserting that in some settings people tend to explore insufficiently,
whereas in others they exhibit the opposite bias.
This different-biases-different-explanations approach has not spared the judgment
field. As a result, two important streams of judgment research have led to apparently
contradicting conclusions: Whereas revision-of-opinion studies hold that judgment
is affected by conservatism (e.g., Phillips and Edwards, 1966)
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, calibration studies demonstrate that judgment is affected by the opposite bias—overconfidence
(e.g., Fischhoff et al., 1977)
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. Once again, one might explain this apparent contradiction by assuming that in some
settings judgment is conservative, and overconfident in others.
Are the pairs of judgment and decision-making biases mentioned in the previous paragraphs
really inconsistent, justifying the development of specific, post-hoc theories? Or
is it possible to formulate a more general theory that provides the (sufficient) conditions
for the different biases, as well as their relative importance and joint effect? The
field of behavioral sciences lacks of such a theory (see Camerer and Loewenstein,
2004; Erev and Greiner, 2015), but some important steps in this direction have recently
been made. For example, the contribution by Erev et al.'s (1994) addresses this methodological
issue within the judgment field. This paper shows that conservatism and overconfidence
can coexist, and proposes a common theoretical explanation for the two biases—the
assertion that human judgment is affected by random errors. Along this line, Dougherty
et al.'s (1999) MINERVA-DM memory model accounts not only for the simultaneous coexistence
of conservatism and overconfidence (besides other biases affecting judgment), but
also for the effect of experience on the relative importance of these two biases.
In the decision making domain, a recent line of research has been trying to shed light
on the boundaries of some well-known phenomena documented in previous literature.
This is the case of the recent discussions about the robustness and generality of
loss aversion provided by, for example, Ert and Erev (2008, 2013) and Yechiam and
Hochman (2014)
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; the conditions for underweighting of rare events (e.g., Rakow et al., 2008); or
the construct of risk taking by Yechiam and Telpaz (2011, 2013).
In another recent line of decision-making studies, researchers have been trying to
clarify the (sufficient) conditions under which different biases are likely to occur.
Studies in this domain abandon the (subjective) value function metaphor, and demonstrate
the value of the assumption that choice behavior is driven by past experiences in
similar situation (see Erev and Haruvy, 2014)
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. This research trend includes, for example, the contributions by Erev et al. (2015),
which analyzes choice behavior in negative-sum betting games; Teodorescu and Erev
(2014), which highlights the conditions that lead to under- and over-exploration in
the context of multi-alternative repeated decision tasks with no description and limited
feedback; and by Zion et al. (2010), which analyzes financial choice behavior. Remarkably,
most of the results reported in these studies cannot be accounted for by mainstream
behavioral models (e.g., expected payoff maximization, risk aversion, loss aversion,
and the possibility effect).
The contribution by Marchiori et al. (2015) pushes further this experiential line
of decision-making research, extending Erev et al.'s (1994) conceptual analysis and
Dougherty et al.'s (1999) MINERVA-DM memory model to the decision-making domain. Marchiori
et al.'s demonstrate the predictive value of models assuming reliance on small samples
of past experience and overgeneralization (intended as the tendency to confound instances
of previously encountered tasks that are perceived as similar to the decision problem
at hand). Marchiori et al.'s model provides sufficient conditions for two pairs of
apparently contradicting phenomena documented in the judgment and decision-making
field: Over- and under-weighting of rare events, and over- and under-estimation of
low probabilities.
A change in perspective
To cope with behaviors that cannot be accounted for by the rational framework, behavioral
economists and psychologists have developed insightful theories and models of judgment
and decision-making. However, the dominant methodology has consisted in building models
that account for behavioral anomalies in specific settings. As a result, this methodological
approach has contributed to the fragmentation of the field of behavioral economics,
and produced models whose predictive power is often limited. These problems prompt
the question of what might be the candidate theoretical framework for the development
of a more general theory of choice behavior, alternative to the rationality paradigm.
The recent developments in the decision making field mentioned earlier show that the
approach emphasizing the primary role of past experience as driver of choice behavior
can overcome the methodological issues that have since long accompanied the field
of behavioral economics and—more in general—that of behavioral sciences. This is possible
as this perspective has been shown to provide a coherent and powerful framework for
judgment and decision making modeling (see, for example, Juslin et al., 2007; Nevo
and Erev, 2012).
However, that individuals make decisions based on small samples of past experiences
in similar situations has to be interpreted as an “as if” explanation, thus prompting
the question of to what extent does this abstraction correspond to the processes that
actually occur in the human brain (cf. Yechiam and Aharon, 2011). Results from neuroscience
have already been proven to be a valuable resource for the improvement of economic
models of learning and choice behavior by highlighting, for example, the physiology
of emotions that affect decision making
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, and by suggesting new and more physiologically plausible modeling techniques (Marchiori
and Warglien, 2008, 2011).
In addition, research in neuroscience could be very helpful in shedding light on the
mechanisms underlying decision-making at the individual level. Specifically, a question
of central importance for the experiential research line of decision-making is about
how individuals assess similarity between present and past experiences. Understanding
this process by means of the traditional tools of empirical behavioral investigation
appears to be a very difficult task: Compared to the number of observations typically
available in a behavioral study, individual data appear to be affected by too much
noise and the number of free parameters of a comprehensive model of individual behavior
would be too large. In this respect, we suggest that the contribution of neuroscience
will be crucial in pinpointing and clarifying the physical processes according to
which past experiences are reinforced and mapped onto newly encountered judgment and
decision making problems. Particularly relevant to this issue is the recent neuro-research
on how past experience is used to imagine future happenings and scenarios and how
analogy links between different situations are established at the brain level (see,
for example, Buckner and Carroll, 2007; Schacter et al., 2007; Boyer, 2008; Bar, 2009).
The emerging fields of genoeconomics, geno-neuroeconomics, and evolutionary neuro-social
science might help understanding to what extent individual behavior is learnt and
to what extent it is affected by genetics, and shed light on the important issue of
individual heterogeneity in choice behavior, which has not yet been exhaustively explained
by behavioral studies. Along these directions, recent studies in these emerging fields
have already tried to investigate how genetics affects individual heterogeneity in
choice behavior (Parasuraman and Jiang, 2012), how risk taking in investment portfolios
is linked to genetic variability (Cesarini et al., 2010), the heritability of aspects
of consumer judgment and choice behavior (Simonson and Sela, 2011), or how natural
selection has shaped modern human behavior (Lieberman et al., 2003).
Conflict of interest statement
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.